WO2020010186A1 - Pcsk9 variants - Google Patents

Pcsk9 variants Download PDF

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Publication number
WO2020010186A1
WO2020010186A1 PCT/US2019/040477 US2019040477W WO2020010186A1 WO 2020010186 A1 WO2020010186 A1 WO 2020010186A1 US 2019040477 W US2019040477 W US 2019040477W WO 2020010186 A1 WO2020010186 A1 WO 2020010186A1
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Prior art keywords
pcsk9
loss
subject
function
cell
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PCT/US2019/040477
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French (fr)
Inventor
Derek Klarin
Scott M. DAMRAUER
Christopher J. O'donnell
Philip S. Tsao
Sekar Kathiresan
Daniel J. Rader
Peter W.F. WILSON
Themistocles L. ASSIMES
Original Assignee
Derek Klarin
Damrauer Scott M
Odonnell Christopher J
Tsao Philip S
Sekar Kathiresan
Rader Daniel J
Wilson Peter W F
Assimes Themistocles L
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Application filed by Derek Klarin, Damrauer Scott M, Odonnell Christopher J, Tsao Philip S, Sekar Kathiresan, Rader Daniel J, Wilson Peter W F, Assimes Themistocles L filed Critical Derek Klarin
Publication of WO2020010186A1 publication Critical patent/WO2020010186A1/en

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    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/113—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing
    • C12N15/1137—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing against enzymes
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    • C12Y304/00—Hydrolases acting on peptide bonds, i.e. peptidases (3.4)
    • C12Y304/21—Serine endopeptidases (3.4.21)
    • C12Y304/21061—Kexin (3.4.21.61), i.e. proprotein convertase subtilisin/kexin type 9
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    • C12N2310/00—Structure or type of the nucleic acid
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    • C12N2310/00—Structure or type of the nucleic acid
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • TC total cholesterol
  • LDL low-density lipoprotein
  • HDL high-density lipoprotein
  • TG triglycerides
  • a GWAS was perfromed, including a discovery phase in MVP and a replication phase in the Global Lipids Genetics Consortium (GLGC) (Fig. 1).
  • GLGC Global Lipids Genetics Consortium
  • an association testing was performed among 297,626 white, black, and Hispanic MVP participants with lipids stratified by ethnicity followed by a meta-analysis of results across all three groups.
  • Replication of MVP findings was conducted in one of two independent studies from the GLGC. Genome-wide lipid-associated, low-frequency missense variants were examined unique to black and Hispanic individuals.
  • Disclosed are methods for determining a subject’s risk for having or developing abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, and wherein the presence of the variant indicates the subject's reduced risk for having or abdominal aortic aneurysm.
  • the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
  • the PCSK9 loss of function variant is determined from a sample obtained from the subject.
  • the PCSK9 loss of function variant is determined by amplifying or sequencing a nucleic acid sample obtained from the subject.
  • the amplifying is performed using polymerase chain reaction (PCR).
  • the amplifying or sequencing comprises using primers having sequences complementary to PCSK9 DNA or RNA sequences.
  • primers and probes having sequences complementary to a portion of the PCSK9 nucleic acid sequence found in accession number NG_009061.1.
  • Disclosed are methods comprising: obtaining a sample from a subject; detecting whether a PCSK9 loss of function variant is present in the sample; diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and administering an effective amount of a PCSK9 inhibitor to the subject.
  • Disclosed are methods of treating a subject comprising administering a composition that inhibits the function of PCSK9 to a subject, wherein the subject has been determined to lack a loss of function mutation in PCSK9.
  • Disclosed are methods of screening for test compositions that cause a loss of function mutation in PCSK9 comprising: contacting a PCSK9 gene with a test composition; detecting the presence of a mutation in the PCSK9 gene; and determining if the mutation is a loss of function mutation, wherein the presence of a loss of function mutation in PCSK9 indicates a test composition that causes a loss of function in PCSK9.
  • Disclosed are methods of screening for therapeutic candidates for treating abdominal aortic aneurysm compositions comprising: contacting a cell lacking a loss of function mutation in PCSK9 with a test composition; and determining if the test composition inhibits PCSK9 in the cell, wherein if the test composition inhibits PCSK9 then it is a therapeutic candidate for treating abdominal aortic aneurysm.
  • vectors comprising a loss of function PCSK9 variant, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
  • Disclosed are methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, wherein the presence of a PCSK9 loss of function variant indicates that the subject is not in need of treatment for abdominal aortic aneurysm.
  • Disclosed are methods of identifying a subject in need of screening for the development of abdominal aortic aneurysm comprising determining in the subject the absence of a PCSK9 loss of function variant, wherein the absence of a a PCSK9 loss of function variant indicates a subject in need of screening for the development of abdominal aortic aneurysm.
  • CRISPR-CAS systems comprising: a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises a PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein.
  • Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one silencing agent to the cell, wherein said silencing agent silences or inhibits expression of the wild type PCSK9 in the cell.
  • RNA comprises or forms a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure, and the RNA comprising the double-stranded structure inhibits expression of PCSK9.
  • RNAs comprising a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure.
  • RNA comprising a double-stranded structure comprising a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate sequences that hybridize to each other to form said double-stranded structure, and (c) subsequently introducing the cell into a host, wherein said RNA comprising the double-stranded structure inhibits expression of the target gene in the cell in the host.
  • Figures 1A and 1B show a diagram of a GWAS Study Design a) DNA sequence variants across 3 separate ancestry groups in the Million Veteran Program were meta-analyzed using an inverse-variance weighted fixed effects meta-analysis in the discovery phase. Variants with suggestive association were then brought forward for independent replication b) DNA sequence variants with suggestive association (P ⁇ 10 4 ) in discovery were brought forward for independent replication and tested using summary statistics from either 1) the 2017 exome-array focused GLGC meta-analysis (exome chip replication) or 2) the 2013“joint meta-analysis” (joint meta-analysis replication) from the GLGC.
  • Figures 2A-2C show Genetic Variation in Million Veteran Program Participants. Histogram of rare (MAF 0.0003-0.05, a) and common (MAF 0.05-0.5, b) variants passing quality control stratified by ethnicity in the MVP. c) The number of pLoF variants passing quality control for white, black, and Hispanic participants in MVP. Each pLoF annotation (frameshift, splice donor/acceptor, stop gain) is depicted by a separate color.
  • MVP Million Veteran Program
  • pLoF predicted Loss of Function
  • SD Standard Deviations
  • LDL Low-Density Lipoprotein
  • R Pearson correlation coefficient
  • Figures 4A and 4B show PCSK9 46Leu Carrier Disease Associations and Lipid Associations with Abdominal Aortic Aneurysm a) Forest plot for a representative 33 of the 1004 disorders tested in the PCSK9 p.Arg46Leu PheWAS. Statistically significant associations are shown with an asterisk b) Association of 223 variant lipid genetic risk score with abdominal aortic aneurysm in a multivariable Mendelian randomization analysis. Odds ratios are displayed per l-standard deviation genetically increased or decreased lipid fraction. Abbreviations: HDL, High-Density Lipoprotein; LDL, Low-Density Lipoprotein.
  • Figure 5 shows a supervised ADMIXTURE analysis was performed on all MVP samples using 1000 Genomes Project reference samples as the reference panel. Following training of the ADMIXTURE model on 5 populations representing East Asia (CHB), Europe (GBR), East Africa (LWK), South American (PEL), and West Africa (YRI), individuals with at least 50% African (LWK or YRI) ancestry and self-identifying as“non-Hispanic” and“black” were assigned to a separate MVP“black” population.
  • the x-axis depicts each of the 57,332 samples assigned to this group, the Y-axis shows the percentage of each reference population per sample.
  • Figure 6 shows a supervised ADMIXTURE analysis was performed on all MVP samples using 1000 Genomes Project reference samples as the reference panel. Following training of the ADMIXTURE model on 5 populations representing East Asia (CHB), Europe (GBR), East Africa (LWK), South American (PEL), and West Africa (YRI), individuals self- identifying as“Hispanic” were assigned to a separate MVP“Hispanic” population.
  • the x-axis depicts each of the 24,743 samples assigned to this group, the Y-axis shows the percentage of each reference population per sample.
  • Figure 7 shows a plot of the Z score of association (b/SE) for 444 independent lipid exome-wide associated (P ⁇ 2.2x10-7) DNA sequence variants per trait as reported in the published GLGC 2017 exome chip analysis3 and in our MVP discovery GW AS analysis aligned to the lipid raising allele.
  • SE standard error
  • GLGC Global Lipids Genetics Consortium
  • MVP Million Veteran Program
  • HDL-C High-Density Lipoprotein Cholesterol
  • LDL-C Low-Density Lipoprotein Choleterol
  • TG Triglycerides
  • TC Total Cholesterol
  • Figure 8 shows a plot of the effect estimates (b) for 444 independent lipid exome- wide associated (P ⁇ 2.2x 10-7) DNA sequence variants per trait as reported in the published GLGC 2017 exome chip analysis3 and in our MVP discovery GW AS analysis.
  • GLGC Global Lipids Genetics Consortium
  • MVP Million Veteran Program
  • HDL-C High-Density Lipoprotein Cholesterol
  • TG Global Lipids Genetics Consortium
  • FIG. 9 shows the expected association P values versus the observed distribution of P values for LDL cholesterol, TG, TC, and HDL cholesterol association are displayed.
  • SD Standard Deviations
  • HDL-C High-Density Lipoprotein Cholesterol
  • TG Triglycerides
  • TC Total Cholesterol.
  • Figure 11 shows a flow chart for generation of summary statistics used in GCTA- COJO approximate stepwise conditional analysis.
  • the GCTA-COJO software requires GWAS summary statistics and an LD-matrix of a representative group of samples with similar genetic ancestry to those used for the GWAS.
  • summary statistics were combined from MVP (European ancestry subgroup), the GLGC 2017 exome chip analysis (predominantly European ancestry) and the GLGC 2013“joint meta-analysis” (predominantly European ancestry) via an inverse-variance weight fixed effects meta-analysis. These combined results were then used with an LD-matrix of 10,000 randomly selected European samples from the UK Biobank interim release6 for GCTA-COJO stepwise conditional analysis.
  • Figure 12 shows a plot of -loglO(P) for lipid-gene associations by chromosomal position for all genes analyzed in the TWAS. The genes nearest to the top associated variants are displayed.
  • Figure 13 shows a graph of the 655 genome-wide (P ⁇ 5x10 8 ) gene-lipid associations for each of four tissues (adipose, liver, tibial artery, and whole blood) resulting from the lipids TWAS analysis.
  • Figure 14 shows the association results of previously reported genome-wide significant loci for HDL cholesterol in the MVP lipids discovery (trans-ethnic) analysis.
  • Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g, (CETP)] if applicable.
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • SE Standard Error
  • I Insertion
  • D Deletion.
  • Figure 15 shows the association results of previously reported genome-wide significant loci for LDL cholesterol in the MVP lipids discovery (trans-ethnic) analysis.
  • Figure 16 shows the association results of previously reported genome-wide significant loci for TG in the MVP lipids discovery (trans-ethnic) analysis.
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • SE Standard Error
  • I Insertion
  • D Deletion.
  • Figure 17 shows the association results of previously reported genome-wide significant loci for TC in the MVP lipids discovery (trans-ethnic) analysis.
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • SE Standard Error
  • I Insertion
  • D Deletion.
  • Figure 18 shows the novel genome-wide significant loci for HDL in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • EAF SE Standard Error in Allele Frequency
  • Het 12 Heterogeneity I-Sqaured Statistic
  • SE Standard Error.
  • Figure 19 shows the novel genome-wide significant loci for LDL in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • EAF SE Standard Error in Allele Frequency
  • Het 12 Heterogeneity I-Sqaured Statistic
  • SE Standard Error.
  • Figure 20 shows the novel genome-wide significant loci for TG in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • EAF SE Standard Error in Allele Frequency
  • Het 12 Heterogeneity I-Squared Statistic
  • SE Standard Error.
  • Figure 21 shows the novel genome-wide significant loci for TC in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
  • EA Effect Allele
  • NEA Non-effect Allele
  • EAF Effect Allele Frequency
  • EAF SE Standard Error in Allele Frequency
  • Het 12 Heterogeneity I-Sqaured Statistic
  • SE Standard Error.
  • Figure 22 shows the 223 variants (across 223 distinct loci) used for a weighted genetic risk score. Effect estimates/P values are taken from 2017 GLGC exome array analysis.
  • Figure 23 shows the increase in variance explained as a function of the number of repeated measures in MVP non-Hispanic whites (for a fixed sample size of 171,314 MVP participants; only individuals with five or more measures were included). Variance explained was calculated using a genetic risk score of 223 previously described lipid hits with previous effect sizes.
  • Figure 24 shows examples of PCSK9 variants.
  • Figure 25 shows no significant associations were observed for ANGPTL8 p.Glnl2lTer (rs 145464906).
  • Figure 26 shows a transcriptome-wide association study (TWAS) results for HDL Cholesterol in 4 tissues.
  • TWAS transcriptome-wide association study
  • Figure 27 shows a transcriptome-wide association study (TWAS) results for LDL Cholesterol in 4 tissues.
  • Figure 28 shows a transcriptome-wide association study (TWAS) results for TG in 4 tissues.
  • TWAS transcriptome-wide association study
  • Figure 29 shows a transcriptome-wide association study (TWAS) results for TC in 4 tissues.
  • TWAS transcriptome-wide association study
  • Figure 30 shows a transcriptome-wide association study (TWAS) results in loci not identified in previous GLGC or current MVP Lipids GWAS.
  • TWAS transcriptome-wide association study
  • Figure 31 shows black-specific novel low-frequency protein-altering variants associated with lipids.
  • Exome Chip Beta/P refers to the effect estimate/P from the 2017 GLGC exome chip meta-analysis.
  • Beta/P refers to the effect estimate/P from the 2017 GLGC exome chip meta-analysis.
  • Figure 33 shows genome-wide significant pLoF variants for lipids in the MVP discovery analysis. * pLoF Confidence reflects the reported annotation by the VEP software
  • LOFTEE Plugin in which a series of filters are applied to candidate pLoF variants. Confident means the variant does not fail any filters. Not Confident means the mutation fails at least one of these filters. A full list of filters is provided at
  • each of the combinations A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D.
  • any subset or combination of these is also specifically contemplated and disclosed.
  • the sub-group of A-E, B-F, and C- E are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D.
  • This concept applies to all aspects of this application including, but not limited to, steps in methods of making and using the disclosed compositions.
  • steps in methods of making and using the disclosed compositions are if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods, and that each such combination is specifically contemplated and should be considered disclosed.
  • Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, also specifically
  • the word“comprise” and variations of the word, such as“comprising” and“comprises,” means“including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps.
  • each step comprises what is listed (unless that step includes a limiting term such as“consisting of’), meaning that each step is not intended to exclude, for example, other additives, components, integers or steps that are not listed in the step.
  • Disclosed are methods for determining a subject’s risk for having or developing abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, and wherein the presence of the variant indicates the subject's reduced risk for having or developing abdominal aortic aneurysm.
  • the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
  • the PCSK9 loss of function or damaging variant can be any of those found in Figure 24.
  • the PCSK9 loss of function or damaging variant can be any of those variants provided in the ExAc Browser (Beta) Exome Agregation Consortium as found at http://exac.broadinstitute.org/.
  • the PCSK9 loss of function variant is determined from a sample obtained from the subject.
  • the sample obtained from the subject can be, for example, blood, plasma, serum, cells, urine, mucus, spinal fluid, or sweat.
  • the PCSK9 loss of function variant is determined by amplifying or sequencing a nucleic acid sample obtained from the subject.
  • the amplifying can be performed using polymerase chain reaction (PCR).
  • the amplifying or sequencing comprises using primers having sequences complementary to PCSK9 DNA or RNA sequences.
  • primers and probes having sequences complementary to a portion of the PCSK9 nucleic acid sequence found in accession number NG_009061.1
  • PCSK9 loss of function variant is present in the biological sample by contacting the biological sample with an anti- PCSK9 loss of function variant antibody or antigen binding fragment thereof and detecting binding between the PCSK9 loss of function variant and the antibody, or fragment thereof.
  • the PCSK9 loss of function variant has the mutation
  • Disclosed are methods of detecting one or more PCSK9 loss of function or damaging variants in a subject comprising: obtaining a biological sample from a subject; detecting whether one or more PCSK9 loss of function or damaging variants are present in the biological sample by performing whole genome or whole exome sequencing. After detecting the presence of a variant the effect of these variant on function of the protein or expression of the protein can be predicted. pLOFs can lead to truncation of a protein, splice site problems, or frameshifts.
  • Disclosed are methods comprising: obtaining a sample from a subject; detecting whether a PCSK9 loss of function variant is present in the sample; diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and administering an effective amount of a PCSK9 inhibitor to the subject.
  • the sample can be, but is not limited to, blood, plasma, serum, cells, urine, mucus, spinal fluid, or sweat.
  • the sample can be DNA or protein.
  • the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
  • the PCSK9 inhibitor can be a compound, protein, DNA, RNAi, CRISPR, or siRNA.
  • a subject comprising administering a composition that inhibits the function of PCSK9 to a subject, wherein the subject has been determined to lack a loss of function mutation in PCSK9.
  • the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
  • a subject lacking the loss of function mutation in PCSK9 can be a subject that does not contain the Arg46Leu mutation.
  • composition administered to the subject can be a compound, protein, DNA, RNAi, CRISPR, or siRNA.
  • Disclosed are methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, wherein the presence of a PCSK9 loss of function variant indicates that the subject is not in need of treatment for abdominal aortic aneurysm.
  • methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising
  • the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
  • Disclosed are methods of screening for test compositions that cause a loss of function mutation in PCSK9 comprising: contacting a PCSK9 gene with a test composition; detecting the presence of a mutation in the PCSK9 gene; and determining if the mutation is a loss of function mutation, wherein the presence of a loss of function mutation in PCSK9 indicates a test composition that causes a loss of function in PCSK9.
  • the presence of a loss of function mutation is first analyzed in the PCSK9 gene. If no loss of function mutation is detected then the PCSK9 gene can be contacted with a test composition.
  • the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
  • Disclosed are methods of screening for therapeutic candidates for treating abdominal aortic aneurysm compositions comprising: contacting a cell lacking a loss of function mutation in PCSK9 with a test composition; and determining if the test composition inhibits PCSK9 in the cell, wherein if the test composition inhibits PCSK9 then it is a therapeutic candidate for treating abdominal aortic aneurysm.
  • Disclosed are methods of identifying a subject in need of screening for the development of abdominal aortic aneurysm comprising determining in the subject the absence of a PCSK9 loss of function variant, wherein the absence of a a PCSK9 loss of function variant indicates a subject in need of screening for the development of abdominal aortic aneurysm.
  • the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
  • Disclosed are methods of inducing a loss of function mutation in PCSK9 comprising administering a test composition determined from the disclosed methods of screening for test compositions that cause a loss of function mutation in PCSK9.
  • the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
  • vectors comprising a loss of function PCSK9 variant, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
  • the vectors can be viral or non-viral vectors.
  • the term "vector”, as used herein, refers to a composition capable of transporting a nucleic acid.
  • a vector can be a plasmid, i.e., a circular double stranded piece of DNA into which additional DNA segments can be ligated.
  • a vector can be a viral vector, wherein additional DNA segments can be ligated into the viral genome.
  • a vector can autonomously replicate in a host cell into which they are introduced (e.g., bacterial vectors having a bacterial origin of replication and episomal mammalian vectors).
  • vectors e.g., non- episomal mammalian vectors
  • vectors can be integrated into the genome of a host cell upon introduction into the host cell, and thereby are replicated along with the host genome.
  • certain vectors can direct the expression of genes to which they are operatively linked.
  • Such vectors can be referred to as "recombinant expression vectors" (or simply, “expression vectors”).
  • the proteins encoded by the PCSK9 variants are expressed by inserting DNAs encoding the PCSK9 variants into expression vectors such that the genes are operatively linked to necessary expression control sequences such as transcriptional and translational control sequences.
  • Expression vectors include plasmids, retroviruses, adenoviruses, adeno-associated viruses (AAV), plant viruses such as cauliflower mosaic virus, tobacco mosaic virus, cosmids, YACs, EBV derived episomes, and the like.
  • nucleic acids comprising the PCSK9 variants can be ligated into a vector such that transcriptional and translational control sequences within the vector serve their intended function of regulating the transcription and translation of the PCSK9 variant.
  • the expression vector and expression control sequences are chosen to be compatible with the expression host cell used.
  • Nucleic acid sequences comprising the PCSK9 variants can be inserted into separate vectors or into the same expression vector.
  • a nucleic acid sequence comprising the PCSK9 variants can be inserted into the expression vector by standard methods (e.g., ligation of complementary restriction sites on the nucleic acid comprising the PCSK9 variants and vector, or blunt end ligation if no restriction sites are present).
  • the recombinant expression vectors can carry regulatory sequences that control the expression of the genetic variant in a host cell.
  • regulatory sequences include viral elements that direct high levels of protein expression in mammalian cells, such as promoters and/or enhancers derived from retroviral LTRs, cytomegalovirus (CMV) (such as the CMV
  • SV40 Simian Virus 40
  • AdMLP adenovirus major late promoter
  • polyoma strong mammalian promoters such as native immunoglobulin and actin promoters.
  • the recombinant expression vectors can carry additional sequences, such as sequences that regulate replication of the vector in host cells (e.g., origins of replication) and selectable marker genes.
  • the selectable marker gene facilitates selection of host cells into which the vector has been introduced (see e.g., U.S. Pat. Nos. 4,399,216, 4,634,665 and 5,179,017, incorporated herein by reference).
  • the selectable marker gene confers resistance to drugs, such as G418, hygromycin or methotrexate, on a host cell into which the vector has been introduced.
  • Preferred selectable marker genes include the dihydrofolate reductase (DHFR) gene (for use in dhfr-host cells with methotrexate selection/ amplification), the neo gene (for G418 selection), and the glutamate synthetase (GS) gene.
  • DHFR dihydrofolate reductase
  • GS glutamate synthetase
  • a cell comprising the disclosed vectors.
  • a cell can be transfected with a nucleic acid comprising the PCSK9 variants.
  • a cell comprising one or more of the PCKS9 variants can express the protein encoded by the one or more disclosed genetic variants and therefore, also disclosed are cells comprising a protein encoded by one or more PCSK9 variants.
  • kits can comprise an assay or assays for detecting one or more PCSK9 variants in a sample of a subject.
  • a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises a PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein.
  • the Cas protein can be a Type-II Cas9 protein or a gene encoding a Type-II Cas9 protein.
  • the Cas9 protein and the guide RNA do not naturally occur together
  • the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
  • the guide RNA sequence comprises the sequence of can comprise a sequence that binds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
  • the at least one gene product is a gene product from a PCSK9 loss of function variant
  • the method comprises administering a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the nucleic acid molecule which comprises the PCSK9 loss of function variant, whereby expression of the at least one gene product is altered.
  • the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
  • the at least one gene product is a gene product from a PCSK9 loss of function variant
  • the method comprises administering a vector that comprises a first regulatory element operable in a eukaryotic cell operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant; and a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Cas9 protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the target sequence, whereby expression of the at least one gene product is altered.
  • the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
  • the guide RNA sequence comprises the sequence of can comprise a sequence that binds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
  • Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one silencing agent to the cell, wherein said silencing agent silences or inhibits expression of the wild type PCSK9 in the cell.
  • the cell is inside a subject and thus the method occurs in vivo.
  • the silencing or inhibiting expression of PCSK9 in a cell occurs in vitro.
  • the silencing agent can be RNAi, CRISPR, or siRNA.
  • RNA comprises or forms a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure, and the RNA comprising the double-stranded structure inhibits expression of PCSK9.
  • the first strand comprises a sequence which corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.l.
  • the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.1.
  • RNA comprising a double-stranded structure comprising a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate sequences that hybridize to each other to form said double-stranded structure, and subsequently introducing the cell into a host, wherein said RNA comprising the double-stranded structure inhibits expression of the target gene in the cell in the host.
  • Stressing or“inhibiting,” as it is used herein, is a term generally used to refer to suppression, full or partial, of expression of a gene.
  • RNAs comprising a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure.
  • the first strand comprises a sequence which corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.l.
  • the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.1.
  • the disclosed nucleic acids that encode the PCSK9 variants or their modified forms can also be used to generate either transgenic animals or "knock out" animals which, in turn, are useful in the development and screening of therapeutically useful reagents as well as studying the mechanism of action of the genetic variant.
  • a transgenic animal e.g., a mouse or rat
  • a transgene is a DNA that is integrated into the genome of a cell from which a transgenic animal develops.
  • cDNA encoding one or more of the PCSK9 variants can be used to clone genomic DNA encoding the one or more of the disclosed genetic variants in accordance with established techniques and the genomic sequences used to generate transgenic animals that contain cells that express DNA encoding one or more of the PCSK9 variants.
  • TC total cholesterol
  • LDL low-density lipoprotein
  • HDL high-density lipoprotein
  • TG triglycerides
  • a GWAS was performed, including a discovery phase in MVP and a replication phase in the Global Lipids Genetics Consortium (GLGC) (Fig. 1).
  • association testing was performed among 297,626 white (European ancestry), black (African ancestry), and Hispanic MVP participants with lipids stratified by ethnicity followed by a meta analysis of results across all three groups.
  • Replication of MVP findings was conducted in one of two independent studies from the GLGC. Novel, genome-wide lipid-associated, low-frequency missense variants unique to black and Hispanic individuals were then examined. Results for predicted loss of gene function (pLoF) mutations were focused on, as these as associations have revealed target pathways for pharmacologic inactivation and modulation of cardiovascular risk.
  • pLoF predicted loss of gene function
  • a transcriptome-wide association study (TWAS) and a competitive gene set pathway analysis was then performed. Novel, genome-wide lipid-associated, low-frequency missense variants unique to black and Hispanic individuals were examined. Results for predicted loss of gene function (pLoF) mutations were focused on, as these associations have revealed target pathways for pharmacologic inactivation and modulation of cardiovascular risk.
  • pLoF loss of gene function
  • a PheWAS was performed for a set of DNA sequence variants within genes that have already emerged as therapeutic targets for lipid modulation, leveraging the full catalog of ICD-9 diagnosis codes in the VA EHR to better understand the consequences of pharmacologic modulation of these genes or their products.
  • AAAA abdominal aortic aneurysm
  • Admixture plots depicting the genetic background of the black and Hispanic groups are shown in Figures 5 and 6. Demographics and participant counts for a number of cardiometabolic traits for the 312,571 white, black, and Hispanic MVP participants that passed our quality control are depicted in Table 1.
  • Table 1 Demographic and clinical characteristics of black, white, and Hispanic individuals passing quality control in the Million Veteran Program
  • a participant To minimize potential confounding from the use of lipid-altering agents with variable adherence, a participant’s maximum LDL cholesterol, TG, and TC as well as his or her minimum HDL cholesterol were selected for genetic association analysis.
  • Table 2 summarizes characteristics at enrollment and the distribution lipid levels for MVP participants included in the analysis. Participants were largely male, 72% white, and while 39-46% of participants in each ancestral group had statin therapy prescriptions at the time of enrollment, only 8-9% were prescribed statin therapy at the time of their maximum LDL or TC measurement used for GW AS analysis.
  • Table 2 Demographic and clinical characteristics for 297,626 veterans in the Million Veteran
  • Triglycerides Triglycerides; TC, Total Cholesterol ii. Genetic Association Analysis of Lipids and Conditional Analysis
  • Table 2 Variance explained for 444 previously mapped independent genome-wide variants, 118 novel loci identified in this study, and 826 independent lipid genome-wide variants identified on conditional analysis in this study
  • a TWAS23 was performed using: 1) pre-computed weights from expression array data measured in peripheral blood from 1,245 unrelated control individuals from the Netherlands
  • NTR Twin Registry
  • RNA-seq data measured in adipose tissue from 563 control individuals from the Metabolic Syndrome in Men study (METSIM) and RNA-seq data from post-mortem liver (97 individuals) and tibial artery (285 individuals) tissue from the Genotype-Tissue Expression project (GTEx V6), and 2) combined MVP and GLGC summary statistics for each of the four lipid traits (Fig. 11).
  • this approach integrates information from expression reference panels (variant-expression correlation), GWAS summary statistics (variant-trait correlation), and linkage disequilibrium (LD) reference panels (variant-variant correlation) to assess the association between the cis-genetic component of expression and phenotype.
  • the TWAS identified 655 genome-wide significant (P ⁇ 5x10 8 ) gene-lipid associations (summed across expression reference panels) in a total of 333 distinct genes, including 194 that were significant in more than one tissue or lipid trait (Fig. 12-13 and 26-29).
  • the 333 distinct genes fell within 122 genomic loci, 117 of which were within a lipid GWAS region ( ⁇ lmB around a mapped sentinel GWAS variant) identified in a prior analysis or in the current study.
  • 5 TWAS genes fell outside of a previously mapped GWAS region, representing novel lipid genomic loci (Figure 30). Previous work has suggested that future lipid GWAS with larger sample sizes will likely confirm the novel lipid loci identified by TWAS.
  • MAGMA Multi-marker Analysis of GenoMic Annotation
  • FUMA pipeline was used as implemented in the FUMA pipeline to perform a competitive gene set analysis of curated gene sets and GO terms (pathways) obtained from the Molecular Signature Database, as well as a gene-property analysis for gene expression of GTex 25 tissues for LDL-C, TG, and HDL.
  • the pathway analysis revealed a significant enrichment for several biological processes related to lipoprotein metabolism including sterol homeostasis, acylglycerol homeostasis, chylomicron mediated transport, acyl reverse cholesterol transport, and regulation of lipoprotein lipase activity (P Bonferroni ⁇ 0.05).
  • MAGMA gene-property analysis revealed a significant enrichment of GWAS signal overlapping genes expressed in the liver, adrenal gland, and the ovary for LDL-C, subcutaneous and visceral adipose tissue, liver, adrenal gland, and pancreas for TG, and liver for HDL-C.
  • genotyped or imputed pLoF variants [variants annotated as: premature stop (nonsense), canonical splice-sites (splice-donor or splice-acceptor) or insertion/deletion variants that shifted frame (frameshift) by the Variant Effect Predictor software] was then studied. A total of 15 unique pLoF variants demonstrated genome-wide significant lipid associations across individuals of all three ethnic groups ( Figure 33).
  • a median of 65 unique ICD-9 diagnosis codes were leveraged per participant prior to enrollment in MVP to explore the spectrum of phenotypic consequences for 5 variants within genes targeted by lipid-lowering medicines.
  • Five lipid genes currently being targeted by pharmaceutical agents were selected and functional variants in these genes: two nonsense variants (LPL p.Ser474Ter, ANGPTL8 p.Glnl2lTer) and three missense variants (ANGPTL4 p.Glu40Lys, APOA5 p.Serl9Trp, PCSK9 p.Arg46Leu) were identified.
  • Phenotypes were considered to be significantly associated with a variant if they met a Bonferroni corrected P ⁇ 4.98 x 10-5 [0.05/1004 traits], a conservative threshold given the correlation structure present among PheWAS phenotypes.
  • Perilipin-l is required for lipid droplet formation, triglyceride storage, as well as free fatty acid metabolism, and frameshift pLoF mutations Perilipin-l have been reported to result in severe lipodystrophy.
  • BDNF encoding Brain-Derived Neurotrophic Factor
  • MVP Million Veteran Program
  • the MVP received ethical and study protocol approval from the VA Central Institutional Review Board (IRB) in accordance with the principles outlined in the Declaration of Helsinki.
  • DNA extracted from whole blood was genotyped using a customized Affymetrix Axiom biobank array, the MVP 1.0 Genotyping Array. With 723,305 total DNA sequence variants, the array is enriched for both common and rare variants of clinical significance in different ethnic backgrounds. Veterans of three mutually exclusive ethnic groups were identified for analysis: 1) non-Hispanic whites, 2) non-Hispanic blacks, and 3) Hispanics. Quality-control procedures were used to assign ancestry, remove low-quality samples and variants, and perform genotype imputation to the 1000 Genomes reference panel.
  • variant level quality control was performed using the EasyQC R package (www.R-project.org), and exclusion metrics included: ancestry specific Hardy - Weinberg equilibrium P ⁇ lxlO 20 , posterior call probability ⁇ 0.9, imputation quality /INFO ⁇ 0.3, minor allele frequency (MAF) ⁇ 0.0003, call rate ⁇ 97.5% for common variants (MAF > 1%), and call rate ⁇ 99% for rare variants (MAF ⁇ 1%). Variants were also excluded if they deviated > 10% from their expected allele frequency based on reference data from the 1000 Genomes Project.
  • EHR clinical laboratory data were available for MVP participants from as early as 2003.
  • the maximum LDL cholesterol/TG/TC, and minimum HDL cholesterol was extracted for each participant for analysis. These extreme values were selected to approximate plasma lipid concentrations in the absence of lipid lowering therapy.
  • For each phenotype LDL cholesterol, natural log transformed TG, HDL cholesterol, and TC), residuals were obtained after regressing on age, age2, sex, and 10 principal components of ancestry. Residuals were subsequently inverse normal transformed for association analysis.
  • Statin therapy prescription at enrollment was defined as the presence of a statin prescription in the EHR within 90 days before or after enrollment in MVP.
  • Statin therapy prescription at the maximum lipid measurement was defined as the presence of a statin prescription in the EHR within 90 days prior to the maximum lipid laboratory measurement used in the GW AS analysis.
  • Genotyped and imputed DNA sequence variants with a MAF > 0.0003 were tested for association with the inverse normal transformed residuals of lipid values through linear regression assuming an additive genetic model.
  • association testing was performed separately among individuals of each of three genetic ancestries (whites, blacks, and Hispanics) and then meta-analyzed results across ethnic groups using an inverse variance- weighted fixed effects method.
  • association P ⁇ 10 4 For variants with suggestive associations (association P ⁇ 10 4 ), replication was sought of the findings in one of two independent studies: the 2017 GLGC exome array meta-analysis or the 2013 GLGC“joint meta-analysis.” Replication was first performed using summary statistics from the 2017 GLGC exome array study. A total of 242,289 variants in up to 319,677 individuals were analyzed after quality control and were available for replication.
  • a TWAS was performed using summary statistics after a meta-analysis of 1.9 million overlapping variants among GLGC (predominantly European) and European MVP datasets ( Figure 11) and four gene-expression reference panels (NTR whole blood, METSIM adipose tissue, and tibial artery and liver from GTEx) in independent samples as previously described.
  • variant-expression weights in the l-mB cis locus were first computed with the BSLMM, which models effects on expression as a mixture of normal distributions to account for the sparse expression architecture.
  • TWAS w'Z/(w'Dw)l/2.
  • TWAS statistics were computed by using either the variants genotyped in each expression reference panel or imputed HapMap3:3 ⁇ 4variants.
  • P ⁇ 5 xl0-8 significantly more stringent than previously used Bonferroni corrections in prior TWAS26.
  • novel TWAS loci as a TWAS gene falling outside of a previously identified lipid GWAS region ( ⁇ lmB around a mapped sentinel GWAS variant).
  • the FUMA pipeline was used to obtain MAGMA expression profiles and perform a tissue expression profile analysis and a competitive gene set analysis for GO biological processes using the Molecular Signature Database (MsigDB 5.2).
  • MsigDB 5.2 Molecular Signature Database
  • Genomes2l imputed summary statistics from Stage 1 of our analysis (both ancestry-specific and combined trans-ethnic)
  • we identified enriched tissues (Bonferroni corrected P ⁇ 0.05) and GO biological processes with a Benjamini-Hochberg adjusted P ⁇ 5 xlO 8 for the HDL-C, LDL-C, and TG phenotypes.
  • the Variant Effect Predictor software was used to identify pLoF DNA sequence variants defined as: premature stop (nonsense), canonical splice-sites (splice-donor or splice- acceptor) or insertion/deletion variants that shifted frame (frameshift). These variants were then merged with data from the Exome Aggregation Consortium24 (Version 0.3.1), a publicly available catalogue of exome sequence data to confirm consistency in variant annotation. pLoF DNA sequence variants were required to be observed in at least 50 individuals, and set a statistical significance threshold of P ⁇ 5 x 10 8 (genome-wide significance). xi. Loss of PDE3B Gene Function and Coronary Artery Disease
  • a novel lipid association was identified for a pLoF mutation in the PDE3B gene (rsl50090666, p.Arg783Ter).
  • the mutation’s effects on risk for CAD were examined using logistic regression in five separate cohorts: MVP, UK Biobank, and 3 cohorts with exome sequencing: the Myocardial Infarction Genetics Consortium (MIGen), the Penn Medicine Biobank (PMBB), and DiscovEHR.
  • Phosphodiesterase 3B (PDE3B) regulates NLRP3 inflammasome in adipose tissue. Sci Rep 6, 28056 (2016).

Abstract

Disclosed are methods of detecting and screening for a PCSK9 variants. Disclosed are methods comprising: obtaining a sample from a subject; detecting whether a PCSK9 loss of function variant is present in the sample; diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and administering an effective amount of a PCSK9 inhibitor to the subject.

Description

PCSK9 VARIANTS
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0001] This invention was made with government support under #G002, 101-01BX03340, I01-BX002641, and I01-CX001025 awarded by Veterans Administration (VA) Cooperative Studies Program (CSP), T32 HL007734 and R01HL127564 awarded by the National Institute of Health. The government has certain rights in the invention.
BACKGROUND
[0002] Large-scale biobanks offer the potential to link genes to health traits documented in electronic health records (EHR) with unprecedented power. In turn, these discoveries are expected to improve our understanding of the etiology of common and complex diseases as well as our ability to treat and prevent these conditions. To this end, the Million Veteran Program (MVP) was established by the U.S. Veterans Health Administration in 2011 as a nationwide research program within the Veteran Administration (VA) healthcare system. The overarching goal of MVP is to reveal new biologic insights and clinical associations broadly relevant to human health and enhance the care of veterans through precision medicine.
[0003] Blood concentrations of total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides (TG) are heritable risk factors for cardiovascular disease, a highly prevalent condition among U.S. veterans. Genome wide association studies (GWAS) to date have identified at least 268 loci that influence these levels, many of which are under investigation as potential therapeutic targets. However, off- target effects have dampened enthusiasm for some of these molecules, and understanding the full spectrum of clinical consequences of a given DNA sequence variant through phenome-wide association scanning (“PheWAS”) may shed light on potential unintended effects as well as novel therapeutic indications.
BRIEF SUMMARY
[0004] A GWAS was perfromed, including a discovery phase in MVP and a replication phase in the Global Lipids Genetics Consortium (GLGC) (Fig. 1). In the discovery phase, an association testing was performed among 297,626 white, black, and Hispanic MVP participants with lipids stratified by ethnicity followed by a meta-analysis of results across all three groups. Replication of MVP findings was conducted in one of two independent studies from the GLGC. Genome-wide lipid-associated, low-frequency missense variants were examined unique to black and Hispanic individuals. Additionally a PheWAS was performed for a set of DNA sequence variants within genes that have already emerged as therapeutic targets for lipid modulation, leveraging the full catalog of ICD-9 diagnosis codes in the VA EHR to better understand the potential consequences of pharmacologic modulation of these genes or their products.
[0005] Disclosed are methods for determining a subject’s risk for having or developing abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, and wherein the presence of the variant indicates the subject's reduced risk for having or abdominal aortic aneurysm.
[0006] In some aspects, the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
[0007] In some aspects, the PCSK9 loss of function variant is determined from a sample obtained from the subject.
[0008] In some aspects, the PCSK9 loss of function variant is determined by amplifying or sequencing a nucleic acid sample obtained from the subject. In some aspects, the amplifying is performed using polymerase chain reaction (PCR). In some aspects, the amplifying or sequencing comprises using primers having sequences complementary to PCSK9 DNA or RNA sequences. For example, disclosed are primers and probes having sequences complementary to a portion of the PCSK9 nucleic acid sequence found in accession number NG_009061.1.
[0009] Disclosed are methods of detecting a PCSK9 loss of function variant (Arg46Leu) in a subject, said method comprising: obtaining a biological sample from a subject; detecting whether a PCSK9 loss of function variant (Arg46Leu) is present in the biological sample by by performing whole genome or whole exome sequencing.
[0010] Disclosed are methods comprising: obtaining a sample from a subject; detecting whether a PCSK9 loss of function variant is present in the sample; diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and administering an effective amount of a PCSK9 inhibitor to the subject.
[0011] Disclosed are methods of treating a subject comprising administering a composition that inhibits the function of PCSK9 to a subject, wherein the subject has been determined to lack a loss of function mutation in PCSK9.
[0012] Disclosed are methods of screening for test compositions that cause a loss of function mutation in PCSK9 comprising: contacting a PCSK9 gene with a test composition; detecting the presence of a mutation in the PCSK9 gene; and determining if the mutation is a loss of function mutation, wherein the presence of a loss of function mutation in PCSK9 indicates a test composition that causes a loss of function in PCSK9.
[0013] Disclosed are methods of screening for therapeutic candidates for treating abdominal aortic aneurysm compositions comprising: contacting a cell lacking a loss of function mutation in PCSK9 with a test composition; and determining if the test composition inhibits PCSK9 in the cell, wherein if the test composition inhibits PCSK9 then it is a therapeutic candidate for treating abdominal aortic aneurysm.
[0014] Disclosed are vectors comprising a loss of function PCSK9 variant, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
[0015] Disclosed are cells comprising any of the disclosed vectors.
[0016] Disclosed are methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, wherein the presence of a PCSK9 loss of function variant indicates that the subject is not in need of treatment for abdominal aortic aneurysm.
[0017] Disclosed are methods of identifying a subject in need of screening for the development of abdominal aortic aneurysm comprising determining in the subject the absence of a PCSK9 loss of function variant, wherein the absence of a a PCSK9 loss of function variant indicates a subject in need of screening for the development of abdominal aortic aneurysm.
[0018] Disclosed are engineered, non-naturally occurring CRISPR-CAS systems comprising: a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises a PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein.
[0019] Disclosed are methods of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a) a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and b) a Cas protein or gene encoding a Cas protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the nucleic acid molecule which comprises the PCSK9 loss of function variant, whereby expression of the at least one gene product is altered.
[0020] Disclosed are methods of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a vector that comprises a) a first regulatory element operable in a eukaryotic cell operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and b) a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Cas9 protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the target sequence, whereby expression of the at least one gene product is altered.
[0021] Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one silencing agent to the cell, wherein said silencing agent silences or inhibits expression of the wild type PCSK9 in the cell.
[0022] Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one RNA to the cell in an amount sufficient to inhibit the expression of PCSK9, wherein the RNA comprises or forms a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure, and the RNA comprising the double-stranded structure inhibits expression of PCSK9.
[0023] Disclosed are RNAs comprising a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure.
[0024] Disclosed are methods of inhibiting expression of PCSK9 in a cell comprising: (a) isolating the cell; (b) contacting the cell with a RNA comprising a double-stranded structure comprising a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate sequences that hybridize to each other to form said double-stranded structure, and (c) subsequently introducing the cell into a host, wherein said RNA comprising the double-stranded structure inhibits expression of the target gene in the cell in the host.
[0025] Additional advantages of the disclosed method and compositions will be set forth in part in the description which follows, and in part will be understood from the description, or may be learned by practice of the disclosed method and compositions. The advantages of the disclosed method and compositions will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the disclosed method and compositions and together with the description, serve to explain the principles of the disclosed method and compositions.
[0027] Figures 1A and 1B show a diagram of a GWAS Study Design a) DNA sequence variants across 3 separate ancestry groups in the Million Veteran Program were meta-analyzed using an inverse-variance weighted fixed effects meta-analysis in the discovery phase. Variants with suggestive association were then brought forward for independent replication b) DNA sequence variants with suggestive association (P < 10 4) in discovery were brought forward for independent replication and tested using summary statistics from either 1) the 2017 exome-array focused GLGC meta-analysis (exome chip replication) or 2) the 2013“joint meta-analysis” (joint meta-analysis replication) from the GLGC. Abbreviations: MVP, Million Veteran Program; GWAS, genome-wide association study; EHR, electronic health record; GLGC,
Global Lipids Genetics Consortium
[0028] Figures 2A-2C show Genetic Variation in Million Veteran Program Participants. Histogram of rare (MAF 0.0003-0.05, a) and common (MAF 0.05-0.5, b) variants passing quality control stratified by ethnicity in the MVP. c) The number of pLoF variants passing quality control for white, black, and Hispanic participants in MVP. Each pLoF annotation (frameshift, splice donor/acceptor, stop gain) is depicted by a separate color. Abbreviations: MVP, Million Veteran Program; pLoF, predicted Loss of Function
[0029] Figures 3A-3D show a comparison of 354 Independent Lipid Associated Variants Across Ethnicities. Allele frequencies observed in white individuals (x-axes) compared to black (a, R = 0.72,) or Hispanic (b, R = 0.96) individuals for lipid-associated variants. Effect estimates for LDL cholesterol association in white individuals (x-axes) compared to black (c, b = 1.07) or Hispanic (d, b = 1.06) individuals. Abbreviations: SD, Standard Deviations; LDL, Low-Density Lipoprotein; R = Pearson correlation coefficient
[0030] Figures 4A and 4B show PCSK9 46Leu Carrier Disease Associations and Lipid Associations with Abdominal Aortic Aneurysm a) Forest plot for a representative 33 of the 1004 disorders tested in the PCSK9 p.Arg46Leu PheWAS. Statistically significant associations are shown with an asterisk b) Association of 223 variant lipid genetic risk score with abdominal aortic aneurysm in a multivariable Mendelian randomization analysis. Odds ratios are displayed per l-standard deviation genetically increased or decreased lipid fraction. Abbreviations: HDL, High-Density Lipoprotein; LDL, Low-Density Lipoprotein.
[0031] Figure 5 shows a supervised ADMIXTURE analysis was performed on all MVP samples using 1000 Genomes Project reference samples as the reference panel. Following training of the ADMIXTURE model on 5 populations representing East Asia (CHB), Europe (GBR), East Africa (LWK), South American (PEL), and West Africa (YRI), individuals with at least 50% African (LWK or YRI) ancestry and self-identifying as“non-Hispanic” and“black” were assigned to a separate MVP“black” population. The x-axis depicts each of the 57,332 samples assigned to this group, the Y-axis shows the percentage of each reference population per sample.
[0032] Figure 6 shows a supervised ADMIXTURE analysis was performed on all MVP samples using 1000 Genomes Project reference samples as the reference panel. Following training of the ADMIXTURE model on 5 populations representing East Asia (CHB), Europe (GBR), East Africa (LWK), South American (PEL), and West Africa (YRI), individuals self- identifying as“Hispanic” were assigned to a separate MVP“Hispanic” population. The x-axis depicts each of the 24,743 samples assigned to this group, the Y-axis shows the percentage of each reference population per sample.
[0033] Figure 7 shows a plot of the Z score of association (b/SE) for 444 independent lipid exome-wide associated (P < 2.2x10-7) DNA sequence variants per trait as reported in the published GLGC 2017 exome chip analysis3 and in our MVP discovery GW AS analysis aligned to the lipid raising allele. A strong association (linear regression P < 1.0 xlO-lOO) between published (GLGC) and MVP Z scores was observed for each trait. Abbreviations: SE, standard error; GLGC, Global Lipids Genetics Consortium; MVP, Million Veteran Program; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Choleterol; TG, Triglycerides; TC, Total Cholesterol
[0034] Figure 8 shows a plot of the effect estimates (b) for 444 independent lipid exome- wide associated (P < 2.2x 10-7) DNA sequence variants per trait as reported in the published GLGC 2017 exome chip analysis3 and in our MVP discovery GW AS analysis. The effect estimate between MVP discovery and published (GLGC) b values demonstrated evidence of the winner’s curse (b = 0.72, 0.90, 0.85, 0.96 for LDL-C, TG, TC, and HDL-C, respectively after exclusion of extreme outliers). Abbreviations: GLGC, Global Lipids Genetics Consortium; MVP, Million Veteran Program; HDL-C, High-Density Lipoprotein Cholesterol; TG,
Triglycerides; LDL-C, Low-Density Lipoprotein Cholesterol; TC, Total Cholesterol [0035] Figure 9 shows the expected association P values versus the observed distribution of P values for LDL cholesterol, TG, TC, and HDL cholesterol association are displayed.
Quantile-quantile plots were inspected for ancestry specific analyses, and genomic control values were < 1.20 for each racial group (data not shown). The inflation observed (LGC = 1.08- 1.13) is comparable to that observed in other studies of polygenic traits with similar large sample sizes (n > 300,000)4,5. Abbreviations: HDL, High-Density Lipoprotein Cholesterol; TG, Triglycerides; LDL, Low-Density Lipoprotein Cholesterol; TC, Total Cholesterol; MVP,
Million Veteran Program
[0036] Figures 10A - 10F show a,b) Effect estimates for TG association in white individuals (x-axes) compared to black (a, b = 0.76) or Hispanic (b, b =0.91) individuals. c,d) Effect estimates for TC association in white individuals (x-axes) compared to black (c, b = 0.95) or Hispanic (d, b = 1.08) individuals. e,f) Effect estimates for HDL-C association in white individuals (x-axes) compared to black (e, b = 0.88) or Hispanic (f, b = 1.04) individuals.
Abbreviations: SD, Standard Deviations; HDL-C, High-Density Lipoprotein Cholesterol; TG, Triglycerides; TC, Total Cholesterol.
[0037] Figure 11 shows a flow chart for generation of summary statistics used in GCTA- COJO approximate stepwise conditional analysis. The GCTA-COJO software requires GWAS summary statistics and an LD-matrix of a representative group of samples with similar genetic ancestry to those used for the GWAS. As such, summary statistics were combined from MVP (European ancestry subgroup), the GLGC 2017 exome chip analysis (predominantly European ancestry) and the GLGC 2013“joint meta-analysis” (predominantly European ancestry) via an inverse-variance weight fixed effects meta-analysis. These combined results were then used with an LD-matrix of 10,000 randomly selected European samples from the UK Biobank interim release6 for GCTA-COJO stepwise conditional analysis.
[0038] Figure 12 shows a plot of -loglO(P) for lipid-gene associations by chromosomal position for all genes analyzed in the TWAS. The genes nearest to the top associated variants are displayed.
[0039] Figure 13 shows a graph of the 655 genome-wide (P < 5x10 8) gene-lipid associations for each of four tissues (adipose, liver, tibial artery, and whole blood) resulting from the lipids TWAS analysis.
[0040] Figure 14 shows the association results of previously reported genome-wide significant loci for HDL cholesterol in the MVP lipids discovery (trans-ethnic) analysis. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g, (CETP)] if applicable. ** Refers to the 1 million base-pair window around a previously described lipid variant. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error; I, Insertion; D, Deletion.
[0041] Figure 15 shows the association results of previously reported genome-wide significant loci for LDL cholesterol in the MVP lipids discovery (trans-ethnic) analysis. *
Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [eg, (CYP26A)] if applicable. ** Refers to the 1 million base-pair window around a previously described lipid variant. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error; I, Insertion; D, Deletion.
[0042] Figure 16 shows the association results of previously reported genome-wide significant loci for TG in the MVP lipids discovery (trans-ethnic) analysis. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g, (CETP)] if applicable. ** Refers to the 1 million base-pair window around a previously described lipid variant. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error; I, Insertion; D, Deletion.
[0043] Figure 17 shows the association results of previously reported genome-wide significant loci for TC in the MVP lipids discovery (trans-ethnic) analysis. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g, (CETP)] if applicable. ** Refers to the 1 million base-pair window around a previously described lipid variant. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error; I, Insertion; D, Deletion.
[0044] Figure 18 shows the novel genome-wide significant loci for HDL in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
(BDNF)]. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; EAF SE, Standard Error in Allele Frequency; Het 12, Heterogeneity I-Sqaured Statistic; SE, Standard Error.
[0045] Figure 19 shows the novel genome-wide significant loci for LDL in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
(THOP1)]. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; EAF SE, Standard Error in Allele Frequency; Het 12, Heterogeneity I-Sqaured Statistic; SE, Standard Error.
[0046] Figure 20 shows the novel genome-wide significant loci for TG in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
(BDNF)]. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; EAF SE, Standard Error in Allele Frequency; Het 12, Heterogeneity I-Squared Statistic; SE, Standard Error.
[0047] Figure 21 shows the novel genome-wide significant loci for TC in the MVP lipids GW AS following independent replication. * Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g,
(ARL11)]. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; EAF SE, Standard Error in Allele Frequency; Het 12, Heterogeneity I-Sqaured Statistic; SE, Standard Error.
[0048] Figure 22 shows the 223 variants (across 223 distinct loci) used for a weighted genetic risk score. Effect estimates/P values are taken from 2017 GLGC exome array analysis.
* Genes for variants that are outside the transcript boundary of a protein-coding gene are shown with nearest the gene in parentheses [e.g, (ARL11)]. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error.
[0049] Figure 23 shows the increase in variance explained as a function of the number of repeated measures in MVP non-Hispanic whites (for a fixed sample size of 171,314 MVP participants; only individuals with five or more measures were included). Variance explained was calculated using a genetic risk score of 223 previously described lipid hits with previous effect sizes.
[0050] Figure 24 shows examples of PCSK9 variants.
[0051] Figure 25 shows no significant associations were observed for ANGPTL8 p.Glnl2lTer (rs 145464906). Abbreviations: ICD, International Classification of Disease; SE, Standard Error; EAF, Effect Allele Frequency.
[0052] Figure 26 shows a transcriptome-wide association study (TWAS) results for HDL Cholesterol in 4 tissues. Abbreviations: Chr, Chromosome; Pos, Position; Top GWAS rsid, rsID of the most significant GWAS variant in locus; Top GWAS Zscore, Z-score of the most significant GWAS variant in locus; Top eQTL rsid, rsID of the best eQTL in the locus; Top eQTL Zscore, Z-score of the best eQTL in the locus; GWAS Zscore for Top eQTL Variant, GWAS Z-score for this eQTL.
[0053] Figure 27 shows a transcriptome-wide association study (TWAS) results for LDL Cholesterol in 4 tissues. Abbreviations: Chr, Chromosome; Pos, Position; Top GWAS rsid, rsID of the most significant GW AS variant in locus; Top GWAS Zscore, Z-score of the most significant GWAS variant in locus; Top eQTL rsid, rsID of the best eQTL in the locus; Top eQTL Zscore, Z-score of the best eQTL in the locus; GWAS Zscore for Top eQTL Variant, GWAS Z-score for this eQTL.
[0054] Figure 28 shows a transcriptome-wide association study (TWAS) results for TG in 4 tissues. Abbreviations: Chr, Chromosome; Pos, Position; Top GWAS rsid, rsID of the most significant GWAS variant in locus; Top GWAS Zscore, Z-score of the most significant GWAS variant in locus; Top eQTL rsid, rsID of the best eQTL in the locus; Top eQTL Zscore, Z-score of the best eQTL in the locus; GWAS Zscore for Top eQTL Variant, GWAS Z-score for this eQTL.
[0055] Figure 29 shows a transcriptome-wide association study (TWAS) results for TC in 4 tissues. Abbreviations: Chr, Chromosome; Pos, Position; Top GWAS rsid, rsID of the most significant GWAS variant in locus; Top GWAS Zscore, Z-score of the most significant GWAS variant in locus; Top eQTL rsid, rsID of the best eQTL in the locus; Top eQTL Zscore, Z-score of the best eQTL in the locus; GWAS Zscore for Top eQTL Variant, GWAS Z-score for this eQTL.
[0056] Figure 30 shows a transcriptome-wide association study (TWAS) results in loci not identified in previous GLGC or current MVP Lipids GWAS.
[0057] Figure 31 shows black-specific novel low-frequency protein-altering variants associated with lipids. Abbreviations: Anno, Annotation; EA, Effect Allele; NEA, Non Effect
Allele; LDL, Low-Density Lipoprotein Cholesterol; TC, Total Cholesterol; Exome Chip Beta/P refers to the effect estimate/P from the 2017 GLGC exome chip meta-analysis.
[0058] Figure 32 shows hispanic-specific novel low-frequency protein-altering variants associated with lipids. * Also observed at a genome-wide level with TC in blacks (b = -0.22, SE
= 0.01, P = 2.84 x 10-94). Abbreviations: Anno, Annotation; EA, Effect Allele; NEA, Non
Effect Allele; HDL, High-Density Lipoprotein Cholesterol; TC, Total Cholesterol; Exome Chip
Beta/P refers to the effect estimate/P from the 2017 GLGC exome chip meta-analysis.
[0059] Figure 33 shows genome-wide significant pLoF variants for lipids in the MVP discovery analysis. * pLoF Confidence reflects the reported annotation by the VEP software
(PMID: 20562413), LOFTEE Plugin in which a series of filters are applied to candidate pLoF variants. Confident means the variant does not fail any filters. Not Confident means the mutation fails at least one of these filters. A full list of filters is provided at
h†tps:. ''gi†huh.convLonradjk/ioftee. ** Sub-genome-wide in the MVP discovery analysis, brought over the genome-wide threshold with replication from DiscovEHR Study. Abbreviations: EA, Effect Allele; NEA, Non-effect Allele; EAF, Effect Allele Frequency; SE, Standard Error.
DETAILED DESCRIPTION
[0060] The disclosed method and compositions may be understood more readily by reference to the following detailed description of particular embodiments and the Example included therein and to the Figures and their previous and following description.
[0061] It is to be understood that the disclosed method and compositions are not limited to specific synthetic methods, specific analytical techniques, or to particular reagents unless otherwise specified, and, as such, may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.
[0062] Disclosed are materials, compositions, and components that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed method and compositions. These and other materials are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these materials are disclosed that while specific reference of each various individual and collective combinations and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. Thus, if a class of molecules A, B, and C are disclosed as well as a class of molecules D, E, and F and an example of a combination molecule, A-D is disclosed, then even if each is not individually recited, each is individually and collectively contemplated. Thus, is this example, each of the combinations A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D. Likewise, any subset or combination of these is also specifically contemplated and disclosed. Thus, for example, the sub-group of A-E, B-F, and C- E are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D. This concept applies to all aspects of this application including, but not limited to, steps in methods of making and using the disclosed compositions. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods, and that each such combination is specifically contemplated and should be considered disclosed.
A. Definitions
[0063] It is understood that the disclosed method and compositions are not limited to the particular methodology, protocols, and reagents described as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims.
[0064] It must be noted that as used herein and in the appended claims, the singular forms "a ", "an", and "the" include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to "a variant" includes a plurality of such variants, reference to "the variant" is a reference to one or more variants and equivalents thereof known to those skilled in the art, and so forth.
[0065] “Optional” or“optionally” means that the subsequently described event,
circumstance, or material may or may not occur or be present, and that the description includes instances where the event, circumstance, or material occurs or is present and instances where it does not occur or is not present.
[0066] Ranges may be expressed herein as from "about" one particular value, and/or to "about" another particular value. When such a range is expressed, also specifically
contemplated and considered disclosed is the range-· from the one particular value and/or to the other particular value unless the context specifically indicates otherwise. Similarly, when values are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms another, specifically contemplated embodiment that should be considered disclosed unless the context specifically indicates otherwise. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint unless the context specifically indicates otherwise. Finally, it should be understood that all of the individual values and sub-ranges of values contained within an explicitly disclosed range are also specifically contemplated and should be considered disclosed unless the context specifically indicates otherwise. The foregoing applies regardless of whether in particular cases some or all of these embodiments are explicitly disclosed.
[0067] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed method and compositions belong. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present method and compositions, the particularly useful methods, devices, and materials are as described. Publications cited herein and the material for which they are cited are hereby specifically incorporated by reference. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such disclosure by virtue of prior invention. No admission is made that any reference constitutes prior art. The discussion of references states what their authors assert, and applicants reserve the right to challenge the accuracy and pertinency of the cited documents. It will be clearly understood that, although a number of publications are referred to herein, such reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art.
[0068] Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as“comprising” and“comprises,” means“including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps. In particular, in methods stated as comprising one or more steps or operations it is specifically contemplated that each step comprises what is listed (unless that step includes a limiting term such as“consisting of’), meaning that each step is not intended to exclude, for example, other additives, components, integers or steps that are not listed in the step.
B. Methods for Determining Risk of Abdominal Aortic Aneurysm
[0069] Disclosed are methods for determining a subject’s risk for having or developing abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, and wherein the presence of the variant indicates the subject's reduced risk for having or developing abdominal aortic aneurysm.
[0070] In some aspects, the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu. In some aspects, the PCSK9 loss of function or damaging variant can be any of those found in Figure 24. In some aspects the PCSK9 loss of function or damaging variant can be any of those variants provided in the ExAc Browser (Beta) Exome Agregation Consortium as found at http://exac.broadinstitute.org/.
[0071] In some aspects, the PCSK9 loss of function variant is determined from a sample obtained from the subject. The sample obtained from the subject can be, for example, blood, plasma, serum, cells, urine, mucus, spinal fluid, or sweat.
[0072] In some aspects, the PCSK9 loss of function variant is determined by amplifying or sequencing a nucleic acid sample obtained from the subject. In some aspects, the amplifying can be performed using polymerase chain reaction (PCR). In some aspects, the amplifying or sequencing comprises using primers having sequences complementary to PCSK9 DNA or RNA sequences. For example, disclosed are primers and probes having sequences complementary to a portion of the PCSK9 nucleic acid sequence found in accession number NG_009061.1
C. Methods of Detecting PCSK9 Loss of Function Variants
[0073] Disclosed are methods of detecting a PCSK9 loss of function variant in a subject, said method comprising: obtaining a biological sample from a subject; detecting whether a
PCSK9 loss of function variant is present in the biological sample by contacting the biological sample with an anti- PCSK9 loss of function variant antibody or antigen binding fragment thereof and detecting binding between the PCSK9 loss of function variant and the antibody, or fragment thereof. In some aspects, the PCSK9 loss of function variant has the mutation
Arg46Leu.
[0074] Disclosed are methods of detecting one or more PCSK9 loss of function or damaging variants in a subject, said method comprising: obtaining a biological sample from a subject; detecting whether one or more PCSK9 loss of function or damaging variants are present in the biological sample by performing whole genome or whole exome sequencing. After detecting the presence of a variant the effect of these variant on function of the protein or expression of the protein can be predicted. pLOFs can lead to truncation of a protein, splice site problems, or frameshifts.
[0075] Disclosed are methods comprising: obtaining a sample from a subject; detecting whether a PCSK9 loss of function variant is present in the sample; diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and administering an effective amount of a PCSK9 inhibitor to the subject. In some aspects, the sample can be, but is not limited to, blood, plasma, serum, cells, urine, mucus, spinal fluid, or sweat. In some aspects, the sample can be DNA or protein.
[0076] In some aspects, the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
[0077] In some aspects, the PCSK9 inhibitor can be a compound, protein, DNA, RNAi, CRISPR, or siRNA.
D. Methods of Treating
[0078] Disclosed are methods of treating a subject comprising administering a composition that inhibits the function of PCSK9 to a subject, wherein the subject has been determined to lack a loss of function mutation in PCSK9. In some aspects, the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu. Thus, a subject lacking the loss of function mutation in PCSK9 can be a subject that does not contain the Arg46Leu mutation.
[0079] In some aspects, the composition administered to the subject can be a compound, protein, DNA, RNAi, CRISPR, or siRNA.
[0080] Disclosed are methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, wherein the presence of a PCSK9 loss of function variant indicates that the subject is not in need of treatment for abdominal aortic aneurysm. Thus, also disclosed are methods for identifying a subject in need of treatment for abdominal aortic aneurysm comprising
determining in the subject the lack of a PCSK9 loss of function variant, wherein the lack of a PCSK9 loss of function variant indicates that the subject is in need of treatment for abdominal aortic aneurysm. In some aspects, the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
E. Methods of Screening
[0081] Disclosed are methods of screening for test compositions that cause a loss of function mutation in PCSK9 comprising: contacting a PCSK9 gene with a test composition; detecting the presence of a mutation in the PCSK9 gene; and determining if the mutation is a loss of function mutation, wherein the presence of a loss of function mutation in PCSK9 indicates a test composition that causes a loss of function in PCSK9. In some aspects, prior to contacting a PCSK9 gene with a test composition, the presence of a loss of function mutation is first analyzed in the PCSK9 gene. If no loss of function mutation is detected then the PCSK9 gene can be contacted with a test composition.
[0082] In some aspects, the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
[0083] Disclosed are methods of screening for therapeutic candidates for treating abdominal aortic aneurysm compositions comprising: contacting a cell lacking a loss of function mutation in PCSK9 with a test composition; and determining if the test composition inhibits PCSK9 in the cell, wherein if the test composition inhibits PCSK9 then it is a therapeutic candidate for treating abdominal aortic aneurysm.
[0084] Disclosed are methods of identifying a subject in need of screening for the development of abdominal aortic aneurysm comprising determining in the subject the absence of a PCSK9 loss of function variant, wherein the absence of a a PCSK9 loss of function variant indicates a subject in need of screening for the development of abdominal aortic aneurysm. In some aspects, the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
F. Methods of Inducing Loss of Function PCSK9 Variants
[0085] Disclosed are methods of inducing a loss of function mutation in PCSK9 comprising administering a test composition determined from the disclosed methods of screening for test compositions that cause a loss of function mutation in PCSK9. In some aspects, the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
G. Vectors
[0086] Disclosed are vectors comprising a loss of function PCSK9 variant, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
[0087] In some aspects, the vectors can be viral or non-viral vectors. The term "vector", as used herein, refers to a composition capable of transporting a nucleic acid. In some cases, a vector can be a plasmid, i.e., a circular double stranded piece of DNA into which additional DNA segments can be ligated. In some cases, a vector can be a viral vector, wherein additional DNA segments can be ligated into the viral genome. In some cases, a vector can autonomously replicate in a host cell into which they are introduced (e.g., bacterial vectors having a bacterial origin of replication and episomal mammalian vectors). In other cases, vectors (e.g., non- episomal mammalian vectors) can be integrated into the genome of a host cell upon introduction into the host cell, and thereby are replicated along with the host genome. Moreover, certain vectors can direct the expression of genes to which they are operatively linked. Such vectors can be referred to as "recombinant expression vectors" (or simply, "expression vectors").
[0088] In some aspects, the proteins encoded by the PCSK9 variants are expressed by inserting DNAs encoding the PCSK9 variants into expression vectors such that the genes are operatively linked to necessary expression control sequences such as transcriptional and translational control sequences. Expression vectors include plasmids, retroviruses, adenoviruses, adeno-associated viruses (AAV), plant viruses such as cauliflower mosaic virus, tobacco mosaic virus, cosmids, YACs, EBV derived episomes, and the like. In some instances nucleic acids comprising the PCSK9 variants can be ligated into a vector such that transcriptional and translational control sequences within the vector serve their intended function of regulating the transcription and translation of the PCSK9 variant. The expression vector and expression control sequences are chosen to be compatible with the expression host cell used. Nucleic acid sequences comprising the PCSK9 variants can be inserted into separate vectors or into the same expression vector. A nucleic acid sequence comprising the PCSK9 variants can be inserted into the expression vector by standard methods (e.g., ligation of complementary restriction sites on the nucleic acid comprising the PCSK9 variants and vector, or blunt end ligation if no restriction sites are present).
[0089] In addition to a nucleic acid sequence comprising the PCSK9 variants, the recombinant expression vectors can carry regulatory sequences that control the expression of the genetic variant in a host cell. It will be appreciated by those skilled in the art that the design of the expression vector, including the selection of regulatory sequences can depend on such factors as the choice of the host cell to be transformed, the level of expression of protein desired, etc. Preferred regulatory sequences for mammalian host cell expression include viral elements that direct high levels of protein expression in mammalian cells, such as promoters and/or enhancers derived from retroviral LTRs, cytomegalovirus (CMV) (such as the CMV
promoter/enhancer), Simian Virus 40 (SV40) (such as the SV40 promoter/enhancer), adenovirus, (e.g., the adenovirus major late promoter (AdMLP)), polyoma and strong mammalian promoters such as native immunoglobulin and actin promoters. For further description of viral regulatory elements, and sequences thereof, see e.g., U.S. Pat. Nos.
5,168,062, 4,510,245 and 4,968,615. Methods of expressing polypeptides in bacterial cells or fungal cells, e.g., yeast cells, are also well known in the art.
[0090] In addition to a nucleic acid sequence comprising the PCSK9 variants and regulatory sequences, the recombinant expression vectors can carry additional sequences, such as sequences that regulate replication of the vector in host cells (e.g., origins of replication) and selectable marker genes. The selectable marker gene facilitates selection of host cells into which the vector has been introduced (see e.g., U.S. Pat. Nos. 4,399,216, 4,634,665 and 5,179,017, incorporated herein by reference). For example, typically the selectable marker gene confers resistance to drugs, such as G418, hygromycin or methotrexate, on a host cell into which the vector has been introduced. Preferred selectable marker genes include the dihydrofolate reductase (DHFR) gene (for use in dhfr-host cells with methotrexate selection/ amplification), the neo gene (for G418 selection), and the glutamate synthetase (GS) gene.
H. Cells
[0091] Disclosed are cells comprising the disclosed vectors. In some instances, a cell can be transfected with a nucleic acid comprising the PCSK9 variants. In some instances, a cell comprising one or more of the PCKS9 variants can express the protein encoded by the one or more disclosed genetic variants and therefore, also disclosed are cells comprising a protein encoded by one or more PCSK9 variants.
I. Kits
[0092] The materials described above as well as other materials can be packaged together in any suitable combination as a kit useful for performing, or aiding in the performance of, the disclosed method. It is useful if the kit components in a given kit are designed and adapted for use together in the disclosed method. For example disclosed are kits that can comprise an assay or assays for detecting one or more PCSK9 variants in a sample of a subject.
J. Engineered CRISPR-CAS System
[0093] Disclosed are engineered, non-naturally occurring CRISPR-CAS system comprising: a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises a PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein. In some aspects, the Cas protein can be a Type-II Cas9 protein or a gene encoding a Type-II Cas9 protein. In some aspects, the Cas9 protein and the guide RNA do not naturally occur together
[0094] In some aspects of the engineered, non-naturally occurring CRISPR-CAS system, the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
[0095] In some aspects, the guide RNA sequence comprises the sequence of can comprise a sequence that binds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
K. Methods of Altering Expression of a Gene Product
[0096] Disclosed are methods of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and a Cas protein or gene encoding a Cas protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the nucleic acid molecule which comprises the PCSK9 loss of function variant, whereby expression of the at least one gene product is altered. In some aspects, the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
[0097] Disclosed are methods of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a vector that comprises a first regulatory element operable in a eukaryotic cell operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant; and a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Cas9 protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the target sequence, whereby expression of the at least one gene product is altered. In some aspects, the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
[0098] In some aspects, the guide RNA sequence comprises the sequence of can comprise a sequence that binds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
L. Methods of Silencing/Inhibiting Expression of PCSK9
[0099] Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one silencing agent to the cell, wherein said silencing agent silences or inhibits expression of the wild type PCSK9 in the cell.
[00100] In some aspects, the cell is inside a subject and thus the method occurs in vivo. In some aspects, the silencing or inhibiting expression of PCSK9 in a cell occurs in vitro.
[00101] In some aspects, the silencing agent can be RNAi, CRISPR, or siRNA.
[00102] Disclosed are methods of silencing or inhibiting expression of wild type PCSK9 in a cell comprising providing at least one RNA to the cell in an amount sufficient to inhibit the expression of PCSK9, wherein the RNA comprises or forms a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure, and the RNA comprising the double-stranded structure inhibits expression of PCSK9.
[00103] In some aspects, the first strand comprises a sequence which corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.l. In some aspects, the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.1.
[00104] Disclosed are methods of inhibiting expression of PCSK9 in a cell comprising:
isolating the cell; contacting the cell with a RNA comprising a double-stranded structure comprising a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate sequences that hybridize to each other to form said double-stranded structure, and subsequently introducing the cell into a host, wherein said RNA comprising the double-stranded structure inhibits expression of the target gene in the cell in the host.
[00105] "Silencing" or“inhibiting,” as it is used herein, is a term generally used to refer to suppression, full or partial, of expression of a gene.
M. RNA
[00106] Disclosed are RNAs comprising a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure. [00107] In some aspects, the first strand comprises a sequence which corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.l. In some aspects, the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l.1.
N. Animal Models
[00108] The disclosed nucleic acids that encode the PCSK9 variants or their modified forms can also be used to generate either transgenic animals or "knock out" animals which, in turn, are useful in the development and screening of therapeutically useful reagents as well as studying the mechanism of action of the genetic variant. A transgenic animal (e.g., a mouse or rat) is an animal having cells that contain a transgene, which transgene was introduced into the animal or an ancestor of the animal at a prenatal, e.g., an embryonic stage. A transgene is a DNA that is integrated into the genome of a cell from which a transgenic animal develops. In some instances, cDNA encoding one or more of the PCSK9 variants can be used to clone genomic DNA encoding the one or more of the disclosed genetic variants in accordance with established techniques and the genomic sequences used to generate transgenic animals that contain cells that express DNA encoding one or more of the PCSK9 variants.
Examples
A. Genetics of Blood Lipids Among -300,000 Multi-Ethnic Participants of the Million Veteran Program
[00109] Large-scale biobanks offer the potential to link genes to health traits documented in electronic health records (EHR) with unprecedented power. In turn, these discoveries are expected to improve the understanding of the etiology of common and complex diseases as well as the ability to treat and prevent these conditions. To this end, the Million Veteran Program (MVP) was established by the U.S. Veterans Health Administration in 2011 as a nationwide research program within the Veteran Administration (VA) healthcare system. The overarching goal of MVP is to reveal new biologic insights and clinical associations broadly relevant to human health and enhance the care of veterans through precision medicine.
[00110] Blood concentrations of total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides (TG) are heritable risk factors for cardiovascular disease, a highly prevalent condition among U.S. veterans. Genome wide association studies (GWAS) to date have identified at least 268 loci that influence these levels, many of which are under investigation as potential therapeutic targets. However, off- target effects have dampened enthusiasm for some of these molecules, and understanding the full spectrum of clinical consequences of a given DNA sequence variant through phenome-wide association scanning (“PheWAS”) can shed light on potential unintended effects as well as novel therapeutic indications.
[00111] A GWAS was performed, including a discovery phase in MVP and a replication phase in the Global Lipids Genetics Consortium (GLGC) (Fig. 1). In the discovery phase, association testing was performed among 297,626 white (European ancestry), black (African ancestry), and Hispanic MVP participants with lipids stratified by ethnicity followed by a meta analysis of results across all three groups. Replication of MVP findings was conducted in one of two independent studies from the GLGC. Novel, genome-wide lipid-associated, low-frequency missense variants unique to black and Hispanic individuals were then examined. Results for predicted loss of gene function (pLoF) mutations were focused on, as these as associations have revealed target pathways for pharmacologic inactivation and modulation of cardiovascular risk. Finally, a PheWAS was performed for a set of DNA sequence variants within genes that have already emerged as therapeutic targets for lipid modulation, leveraging the full catalog of ICD-9 diagnosis codes in the VA EHR to better understand the potential consequences of
pharmacologic modulation of these genes or their products.
[00112] A transcriptome-wide association study (TWAS) and a competitive gene set pathway analysis was then performed. Novel, genome-wide lipid-associated, low-frequency missense variants unique to black and Hispanic individuals were examined. Results for predicted loss of gene function (pLoF) mutations were focused on, as these associations have revealed target pathways for pharmacologic inactivation and modulation of cardiovascular risk. A PheWAS was performed for a set of DNA sequence variants within genes that have already emerged as therapeutic targets for lipid modulation, leveraging the full catalog of ICD-9 diagnosis codes in the VA EHR to better understand the consequences of pharmacologic modulation of these genes or their products. Lastly, the causal relationship of lipids on abdominal aortic aneurysm (AAA) development were explored through a multivariate Mendelian randomization analysis.
1. Results
i. Demographic and Clinical Characteristics of Genotyped Participants in the
Million Veteran Program
[00113] A total of 353,323 veterans had genetic data available in MVP, with clinical phenotypes recorded in the VA EHR over 3,088,030 patient-years prior to enrollment (median of 10.0 years per participant) and 61,747,974 distinct clinical encounters (median of 99 per participant). Veterans were categorized into three mutually exclusive ancestral groups for association analysis: 1) non-Hispanic whites, 2) non-Hispanic blacks, and 3) Hispanics.
Admixture plots depicting the genetic background of the black and Hispanic groups are shown in Figures 5 and 6. Demographics and participant counts for a number of cardiometabolic traits for the 312,571 white, black, and Hispanic MVP participants that passed our quality control are depicted in Table 1.
Table 1: Demographic and clinical characteristics of black, white, and Hispanic individuals passing quality control in the Million Veteran Program
Figure imgf000024_0001
* Diseases are defined by International Classification of Disease, Ninth Edition (ICD-9) diagnosis codes. Abbreviations: SD, Standard Deviation
[00114] A subset of 297,626 participants passing quality control had at least 1 laboratory measurement of blood lipids in their EHR. These individuals collectively had a total of 15,456,328 lab entries for blood lipids, or a median of 12 measures per lipid fraction per participant. To minimize potential confounding from the use of lipid-altering agents with variable adherence, a participant’s maximum LDL cholesterol, TG, and TC as well as his or her minimum HDL cholesterol were selected for genetic association analysis. Table 2 summarizes characteristics at enrollment and the distribution lipid levels for MVP participants included in the analysis. Participants were largely male, 72% white, and while 39-46% of participants in each ancestral group had statin therapy prescriptions at the time of enrollment, only 8-9% were prescribed statin therapy at the time of their maximum LDL or TC measurement used for GW AS analysis.
Table 2: Demographic and clinical characteristics for 297,626 veterans in the Million Veteran
Program lipids analysis
Figure imgf000025_0001
Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; TG,
Triglycerides; TC, Total Cholesterol ii. Genetic Association Analysis of Lipids and Conditional Analysis
[00115] 19.3, 31.4, and 30.4 million variants in white, black, and Hispanic veterans, respectively, were successfully imputed [INFO > 0.3, minor allele frequency (MAF) > 0.0003] using the 1000 Genomes Project reference panel (Table 2). Black and Hispanic participants had substantially more variants available for analysis, reflecting the known greater genetic diversity within these populations. We also identified 6,657 pLoF variants in 4,294 genes across the three ethnicities (Fig. 2).
[00116] The Z scores and effect estimates from the published literature were compared with those observed in MVP for 444 previously reported exome-wide significant variants for lipids that were imputed using HapMap. A strong correlation of genetic associations was found across all four traits, validating the lipid phenotypes defined through EHR (Fig. 7,8).
[00117] Association testing was performed separately among individuals of each of three ancestries (whites, blacks, and Hispanics) in the initial discovery analysis and then meta- analyzed results across ancestry groups using an inverse variance-weighted fixed effects method (Fig. la, Fig. 9). Following trans-ethnic meta-analysis in the discovery phase of the study, a total of 46,526 variants at 188 of the 268 known loci for lipids met the genome-wide significance threshold (P < 5x10-8) (Figures 14-17). Pairwise comparisons of the allele frequencies and effect estimates were performed between whites and blacks as well as between whites and Hispanics for 354 of the 444 previously established independent genome-wide significant variants for lipids which were well imputed in all three ancestral groups in MVP (Fig. 3). A much stronger correlation between white and Hispanic effect allele frequencies (Pearson correlation coefficient R = 0.96) than between whites and blacks (R = 0.72) was noted, likely reflecting the greater European admixture in the MVP Hispanic participants. The correlation in effects estimates among the three ethnicities varied by lipid trait (Fig. 3, Fig. 10).
[00118] Replication for variants within MVP with suggestive associations (P < 1 x 10-4) was sought in one of two independent studies (Figure lb). Replication was first performed using summary statistics from the 2017 GLGC exome array meta-analysis. If a DNA sequence variant was not available for replication in the above exome array-focused study, we sought replication of remaining variants from publicly available summary statistics from the 2013 GLGC“joint meta-analysis. A total of 170,925 variants demonstrated suggestive association (P<l0-4) in the MVP discovery analysis. Among these variants, 39,663 were also available for in silico replication in at least one of the two GLGC studies involving up to 319,677 additional individuals. Significant novel associations were defined as those that were at least nominally significant in replication (P<0.05) and had an overall P < 5 xlO 8 (genome-wide significance) in the discovery and replication cohorts combined. Following replication, 118 novel loci exceeded genome-wide significance (P < 5xl0 8, Figures 18-21). Minor allele frequencies (MAF) of lead variants ranged from 0.08% to 49.9%, with effect sizes ranging from 0.01 to 0.243 standard deviations. For example, carriers of a rare missense mutation in the gene encoding Sorting Nexin-8 [SNX8 p.Ile4l4Thr, (rsl44787l22) MAF = 0.35% in MVP] demonstrated a 0.10 standard deviation (3.8 mg/dL) higher plasma LDL cholesterol after testing in 587,481 individuals.
[00119] At any given genetic locus, more than one variant may independently affect plasma lipid levels. A conditional analysis was performed using combined summary statistics from MVP and publicly available data from GLGC for each of the four plasma lipid fractions (Fig. 11). A total of 826 independently associated lipid variants were identified across 118 novel and 268 previously identified loci (data not shown).
iii. Variance Explained and Gain Using Multiple Lipid Measurements
[00120] The previously mapped 444 lipid variants explain about 7.5-10.5% of the phenotypic variance in lipid levels in the MVP population. The 118 novel loci explain an additional 0.38- 0.74% in phenotypic variance, and the 826 independent variants identified in the conditional analysis increase the overall phenotypic variance explained to 8.8-12.3% (Table 2).
Table 2: Variance explained for 444 previously mapped independent genome-wide variants, 118 novel loci identified in this study, and 826 independent lipid genome-wide variants identified on conditional analysis in this study
Figure imgf000027_0001
[00121] The impact of multiple lipid measurements was subsequently explored in an analysis restricted to 171,314 European MVP participants with > 5 lipid measurements in their EHR. A weighted genetic risk score (GRS) of 223 variants was constructed across 268 of the previously mapped loci with effect estimates available in the 2017 GLGC exome array analysis summary statistics (Figure 22). Generally across the four lipid traits, the GRS explained a larger proportion of the phenotypic variance with an increasing number of lipid measurements included in the analysis (Figure 23). In addition, when the maximal/minimal lipid values were used as in GWAS, the GRS explained more total variance than when using up to 5 lipid measurements for the LDL-C, TG, and TC phenotypes
iv. Transcriptome-wide Association Study
[00122] A TWAS23 was performed using: 1) pre-computed weights from expression array data measured in peripheral blood from 1,245 unrelated control individuals from the Netherlands
Twin Registry (NTR), RNA-seq data measured in adipose tissue from 563 control individuals from the Metabolic Syndrome in Men study (METSIM), and RNA-seq data from post-mortem liver (97 individuals) and tibial artery (285 individuals) tissue from the Genotype-Tissue Expression project (GTEx V6), and 2) combined MVP and GLGC summary statistics for each of the four lipid traits (Fig. 11). Briefly, this approach integrates information from expression reference panels (variant-expression correlation), GWAS summary statistics (variant-trait correlation), and linkage disequilibrium (LD) reference panels (variant-variant correlation) to assess the association between the cis-genetic component of expression and phenotype. The results yield candidate causal genes from the GWAS results under the assumption that the causal mechanism of the tested genes involves changes in cis-expression.
[00123] In total, the TWAS identified 655 genome-wide significant (P < 5x10 8) gene-lipid associations (summed across expression reference panels) in a total of 333 distinct genes, including 194 that were significant in more than one tissue or lipid trait (Fig. 12-13 and 26-29). The 333 distinct genes fell within 122 genomic loci, 117 of which were within a lipid GWAS region (± lmB around a mapped sentinel GWAS variant) identified in a prior analysis or in the current study. However, 5 TWAS genes fell outside of a previously mapped GWAS region, representing novel lipid genomic loci (Figure 30). Previous work has suggested that future lipid GWAS with larger sample sizes will likely confirm the novel lipid loci identified by TWAS.
v. Tissue Expression Enrichment and Competitive Gene Set Pathway Analysis
[00124] Multi-marker Analysis of GenoMic Annotation (MAGMA) was used as implemented in the FUMA pipeline to perform a competitive gene set analysis of curated gene sets and GO terms (pathways) obtained from the Molecular Signature Database, as well as a gene-property analysis for gene expression of GTex25 tissues for LDL-C, TG, and HDL. As expected, the pathway analysis revealed a significant enrichment for several biological processes related to lipoprotein metabolism including sterol homeostasis, acylglycerol homeostasis, chylomicron mediated transport, acyl reverse cholesterol transport, and regulation of lipoprotein lipase activity (P Bonferroni < 0.05). MAGMA gene-property analysis revealed a significant enrichment of GWAS signal overlapping genes expressed in the liver, adrenal gland, and the ovary for LDL-C, subcutaneous and visceral adipose tissue, liver, adrenal gland, and pancreas for TG, and liver for HDL-C.
vi. Low-Frequency Missense Variant Lipid Associations Specific to Blacks and
Hispanics
[00125] Ancestry specific low-frequency (MAF < 5%) missense variants were studied, as these variants have been suggested to have a higher likelihood of causality. Several novel, low- frequency missense variants associated with one or more lipid levels at genome wide significance that were specific to blacks or Hispanics were identified. A total of 5 variants associated with LDL cholesterol and/or TC among blacks (Figure 31) were found and 2 associated with HDL cholesterol and/or TC among Hispanics (Figure 32) in PCSK9, LDLR, APOB, and ABCA1. All 10 associations were directionally consistent in the 2017 GLGC exome chip meta-analysis with 9 reaching nominal significance (p < 0.05) among 17,009 blacks and 5,084 Hispanics in GLGC. In addition, the 7 variants identified were either monomorphic or had a MAF of < 0.0005 in the -215,000 white veterans in MVP. Of note, the low-frequency 443Thr allele was observed in PCSK9 within Hispanics to be 8 fold more common in blacks (MAF = 0.011 in Hispanics versus 0.092 in blacks). This variant was also found to be associated with TC in blacks at genome-wide significance.
vii. Predicted Loss of Gene Function Lipid Associations
[00126] The subset of genotyped or imputed pLoF variants [variants annotated as: premature stop (nonsense), canonical splice-sites (splice-donor or splice-acceptor) or insertion/deletion variants that shifted frame (frameshift) by the Variant Effect Predictor software] was then studied. A total of 15 unique pLoF variants demonstrated genome-wide significant lipid associations across individuals of all three ethnic groups (Figure 33). Known pLoF associations were replicated at PCSK9, APOC3, ANGPTL8, LPL, CD36, and HBB and genome-wide significant associations of comparable magnitude of effect in each of the three ethnic groups for 2 pLoF variants: APOC3 c.55+lG>A and LPL p.Ser747Ter were observed.
viii. PheWAS of Low-Frequency, Lipid Therapy Genetic Variants
[00127] A median of 65 unique ICD-9 diagnosis codes were leveraged per participant prior to enrollment in MVP to explore the spectrum of phenotypic consequences for 5 variants within genes targeted by lipid-lowering medicines. Five lipid genes currently being targeted by pharmaceutical agents were selected and functional variants in these genes: two nonsense variants (LPL p.Ser474Ter, ANGPTL8 p.Glnl2lTer) and three missense variants (ANGPTL4 p.Glu40Lys, APOA5 p.Serl9Trp, PCSK9 p.Arg46Leu) were identified. Phenotypes were considered to be significantly associated with a variant if they met a Bonferroni corrected P < 4.98 x 10-5 [0.05/1004 traits], a conservative threshold given the correlation structure present among PheWAS phenotypes.
[00128] A total of 176,913 white veterans were available for analysis after quality control. Among these individuals, 33 statistically significant phenotypic associations were identified across the 5 variants, all of which are correlated with lipids (Figure 25). Known associations with CAD were replicated for LPL33, ANGPTL414, and PCSK919. Notably, carriers of TG- lowering/HDL-C-raising mutations in ANGPTL4 (p.Glu40Lys, 7,013 carriers) were also found to have a reduced risk of type 2 diabetes (Fig. 5a). The type 2 diabetes association were replicated for the ANGPTL4 p.E40K variant in an independent sample of -452,000 participants in the recently published trans-ethnic diabetes GWAS4l [(OR =0.89, 95% Cl = 0.86-0.93, P =9.24x10-10, Fig. 5b). In addition, carriers of LDL-C-lowering mutations in PCSK9
(p. Arg46Leu, 5,537 carriers) also demonstrated a reduced risk of AAA (Fig. 6a).
ix. Lipids and Abdominal Aortic Aneurysm Mendelian Randomization Analysis
[00129] To further explore the causal relationship of lipids on AAA development, a multivariate Mendelian randomization analysis was performed using a weighted GRS of 223 lipid associated variants and summary data from a GW AS of 5,002 AAA cases and 139,968 controls in MVP. Consistent with the PheWAS results, a 1 -standard deviation genetically elevated LDL-C was associated with an increased risk of AAA (OR = 1.47, 95% Cl =1.28-1.68, P = 4.4xl0 8). Furthermore, a l-standard deviation genetically elevated HDL-C was associated with a decreased risk of AAA (OR = 0.79, 95% Cl = 0.68-0.91, P = 0.001); and a l-standard deviation genetically elevated TG was associated with an increased risk of AAA (OR = 1.4, 95% Cl = 1.18-1.66, P = 8.5xl0 5, Fig. 6b). An MR-Egger analysis indicated no pleiotropic bias of the lipid genetic instruments [MR-Egger intercept P > 0.05 for all 3 lipid fractions (Table 4)]. Table 4 - Association of 223 Variant Lipid Genetic Risk Score With Abdominal Aortic Aneurysm (AAA) Risk. The 4 different Mendelian randomization (MR) methods used to determine this association were conventional inverse weighted MR, MR-Egger, weighted median MR, and multivariable MR. Abbreviations: SE, Standard Error
Figure imgf000030_0001
Figure imgf000030_0002
Figure imgf000030_0003
2. Discussion
[00130] Data was leveraged from the Million Veteran Program to investigate the inherited basis of blood lipids using EHR-based laboratory measures in nearly 300,000 U.S. veterans. First, 188 previously identified loci were confirmed; furthermore, an additional 118 novel genome-wide significant loci were uncovered. Next, a total of 826 independent lipid associated variants were identified increasing the phenotypic variance explained by nearly 2%. A TWAS was performed in four tissues identifying 5 additional novel lipid loci at a genome-wide level of significance, and a pathway analysis was performed highlighting lipid transport mechanisms in the GWAS results. Ancestry-specific effects of rare coding variation on lipids among white, black, and Hispanic participants were identified and 15 pLoF mutations associated with lipids at a genome-wide level of significance were identified, including a protein-truncating variant in PDE3B that lowers TG, raises HDL cholesterol, and protects against CAD. Finally, the full spectrum of phenotypic consequences for mutations in lipid genes emerging as therapeutic targets, identifying protective effects of pLoF mutations in PCSK9 for abdominal aortic aneurysm and in ANGPTL4 for type 2 diabetes were examined.
[00131] There is enormous potential of a large-scale multi-ethnic biobank built within an integrated health care system in the discovery of the genetic basis of a broad spectrum of human traits. Specifically, the VA’s mature nationwide EHR was leveraged to efficiently extract existing repeated laboratory measures of lipids collected during the course of clinical care in nearly 300,000 veterans over a median of 10 years for GWAS analysis. Subsequent meta analysis (combined N>600,000) with existing datasets increased the number of known independent genetic lipid associations to nearly 400. These results highlight an increase in variance explained with multiple lipid measurements, and multiple lipid pathways with links to human disease. For example, common variants near genes such as COL4A2 and ITGA1 identified for LDL cholesterol/TC indicate links to extracellular matrix and cell adhesion biology, two pathways recently implicated by GWAS of CAD. Carriers of a rare missense mutation in the gene encoding Perilipin-l (PLIN1 p.Leu90Pro) possess a markedly higher plasma HDL cholesterol (0.243 standard deviations). In humans, Perilipin-l is required for lipid droplet formation, triglyceride storage, as well as free fatty acid metabolism, and frameshift pLoF mutations Perilipin-l have been reported to result in severe lipodystrophy. A variant downstream of BDNF (encoding Brain-Derived Neurotrophic Factor) was found to be associated with HDL cholesterol and TG levels, supporting recent evidence linking this gene with metabolic syndrome and diabetes. These findings not only improve the understanding of the genetic basis of dyslipidemia, but also provide insights into targets for the development of novel therapeutic agents.
[00132] There is a benefit of studying individuals with a diverse ethnic background. Such a design can provide valuable incremental information on the nature of previously identified human genetic associations. In MVP, nearly 60,000 black and 25,000 Hispanic veterans were examined for analysis, representing one of the largest - if not the largest - single-cohort GW AS to date for these ethnic groups for any trait. Among these individuals, we compared the effect estimates and allele frequencies of lipid-associated variants across ancestral group and identified 7 novel, low-frequency coding variants associated with lipids only in non-European populations. Conversely, a shared genetic architecture across all three racial groups for pLoF variation at the LPL and APOC3 loci was confirmed. Previous work identifying low-frequency missense and pLoF variation in lipid genes have led to the development of the next generation of
pharmaceutical agents for cardiovascular disease. Expansion of these efforts to larger sample sizes and additional ancestries may help explain differences in blood lipid levels and risk of atherosclerosis among select populations.
[00133] Phenome-wide association scanning across a large-scale EHR-based biobank to predict both potentially adverse as well as beneficial consequences of artificially inhibiting gene function can be beneficial. Here, evidence is provided that pharmacologic PCSK9 inhibition can reduce abdominal aortic aneurysm risk in addition to its known effects on atherosclerotic cardiovascular disease. This finding is further supported by: the Mendelian randomization results; a recently published analysis using an independent AAA dataset; and a recent report demonstrating that a PCSK9 gain-of-function mutation augments AAA development in a mouse model. However, we also recognize the possibility that these results may be a consequence of pleiotropic effects induced by a high phenotypic correlation between AAA and the presence of advanced atherosclerotic disease. Future PheWAS efforts can reveal associations that facilitate prioritization of drugs currently in development, repurposing of therapies already in clinical use, or prediction of adverse or off-target effects prior to investigation through expensive and time- consuming clinical trials.
[00134] In conclusion, >100 new genetic signals were identified for blood lipid levels utilizing a biobank that exploits existing EHRs of U.S. veterans.
3. Methods
[00135] The design of the Million Veteran Program (MVP) has been previously described. Briefly, individuals aged 19 to 104 years have been recruited from more than 50 VA Medical Centers nationwide since 2011. Each veteran’s EHR data are being integrated into the MVP biorepository, including inpatient International Classification of Diseases (ICD-9) diagnosis codes, Current Procedural Terminology (CPT) procedure codes, clinical laboratory
measurements, and reports of diagnostic imaging modalities. The MVP received ethical and study protocol approval from the VA Central Institutional Review Board (IRB) in accordance with the principles outlined in the Declaration of Helsinki.
i. Genetic Data
[00136] DNA extracted from whole blood was genotyped using a customized Affymetrix Axiom biobank array, the MVP 1.0 Genotyping Array. With 723,305 total DNA sequence variants, the array is enriched for both common and rare variants of clinical significance in different ethnic backgrounds. Veterans of three mutually exclusive ethnic groups were identified for analysis: 1) non-Hispanic whites, 2) non-Hispanic blacks, and 3) Hispanics. Quality-control procedures were used to assign ancestry, remove low-quality samples and variants, and perform genotype imputation to the 1000 Genomes reference panel.
ii. Variant Quality Control
[00137] Prior to imputation, variants that were poorly called (genotype missingness > 5%) or that deviated from their expected allele frequency based on reference data from the 1000 Genomes Project were excluded. After pre-phasing using EAGLE v2, genotypes from the 1000 Genomes Project phase 3, version 5 reference panel were imputed into Million Veteran Program (MVP) participants via Minimac3 software. Ethnicity-specific principal component analysis was performed using the EIGENSOFT software.
[00138] Following imputation, variant level quality control was performed using the EasyQC R package (www.R-project.org), and exclusion metrics included: ancestry specific Hardy - Weinberg equilibrium P <lxlO 20, posterior call probability < 0.9, imputation quality /INFO <0.3, minor allele frequency (MAF) < 0.0003, call rate < 97.5% for common variants (MAF > 1%), and call rate < 99% for rare variants (MAF < 1%). Variants were also excluded if they deviated > 10% from their expected allele frequency based on reference data from the 1000 Genomes Project.
iii. EHR-Based Lipid Phenotypes
[00139] EHR clinical laboratory data were available for MVP participants from as early as 2003. The maximum LDL cholesterol/TG/TC, and minimum HDL cholesterol was extracted for each participant for analysis. These extreme values were selected to approximate plasma lipid concentrations in the absence of lipid lowering therapy. For each phenotype (LDL cholesterol, natural log transformed TG, HDL cholesterol, and TC), residuals were obtained after regressing on age, age2, sex, and 10 principal components of ancestry. Residuals were subsequently inverse normal transformed for association analysis. Statin therapy prescription at enrollment was defined as the presence of a statin prescription in the EHR within 90 days before or after enrollment in MVP. Statin therapy prescription at the maximum lipid measurement was defined as the presence of a statin prescription in the EHR within 90 days prior to the maximum lipid laboratory measurement used in the GW AS analysis.
iv. MVP Association Analysis
[00140] Genotyped and imputed DNA sequence variants with a MAF > 0.0003 were tested for association with the inverse normal transformed residuals of lipid values through linear regression assuming an additive genetic model. In a discovery analysis, association testing was performed separately among individuals of each of three genetic ancestries (whites, blacks, and Hispanics) and then meta-analyzed results across ethnic groups using an inverse variance- weighted fixed effects method. For variants with suggestive associations (association P < 10 4), replication was sought of the findings in one of two independent studies: the 2017 GLGC exome array meta-analysis or the 2013 GLGC“joint meta-analysis.” Replication was first performed using summary statistics from the 2017 GLGC exome array study. A total of 242,289 variants in up to 319,677 individuals were analyzed after quality control and were available for replication.
[00141] If a DNA sequence variant was not available for replication in the above exome array-focused study, replication was sought from publicly available summary statistics from the 2013 GLGC“joint meta-analysis.” An additional 2,044,165 variants in up to 188,587 individuals were available for replication in this study. In total, 2,286,454 DNA sequence variants in up to 319,677 individuals were available for independent replication. If a variant was available for replication in both studies, replication was prioritized using summary statistics from the 2017 GLGC exome array study given its larger sample size. Significant novel associations were defined as those that were at least nominally significant in replication (P<0.05) and had an overall P < 5 xlO 8 (genome-wide significance) in the discovery and replication cohorts combined. Novel loci were defined as being greater than 1 mB away from a known lipid genome-wide associated lead variant. Additionally, linkage disequilibrium information from the 1000 Genomes Project21 was used to determine independent variants where a locus extended beyond 1 mB.
v. Conditional Analysis
[00142] Given that individual level data for the prior GLGC lipid analyses are not publicly available, we used the COJO-GCTA software to perform an approximate, stepwise conditional analysis to identify independent variants within lipid-associated loci. We used summary statistics after a meta-analysis of 1.9 million overlapping variants across the GLGC (predominantly European) and European MVP datasets (Figure 11). An LD-matrix obtained from 10,000 unrelated European individuals randomly sampled from the UK Biobank interim release was used for this analysis.
vi. Variance Explained and Gain Using Multiple Lipid Measurements
[00143] The proportion of variance explained by the set of 444 previously mapped independent lipid variants, the 118 novel lipid loci identified in the study, and the 826 independent lipid variants identified from conditional analysis using ridge regression with the glmnet R package were estimated. The variance explained was determined after tuning the hyperparameter (lambda) to approximate an optimal value, and then calculating the model R2 after performing linear regression with the inverse normal transformed lipid outcome and each set (444, 118, 826) of independent genome-wide variants as predictors.
[00144] To assess the impact of multiple lipid measurements, the variance explained for a GRS of 223 previously described GWAS lipid variants weighted by their previously reported effect sizes as a function of the number of lipid measurements was estimated (Figure 22). This analysis was performed using one, two, three, four, and five lipid measurements for each individual starting with their measurement closest to enrollment and moving backward in time. To account for the use of statin therapy, individuals with evidence of a statin prescription in their EHR at the time of enrollment had their LDL-C/TC values adjusted by dividing by 0.7/0.8, respectively as previously described. In addition, the variance explained by the maximal TG, LDL-C/TC, and minimal HDL-C from the EHR was calculated without adjustment for lipid lowering therapy. A set of 171,314 European MVP participants was focused on with > 5 lipid measurements available for this analysis.
vii. Lipids Transcriptome-wide Association Study
[00145] A TWAS was performed using summary statistics after a meta-analysis of 1.9 million overlapping variants among GLGC (predominantly European) and European MVP datasets (Figure 11) and four gene-expression reference panels (NTR whole blood, METSIM adipose tissue, and tibial artery and liver from GTEx) in independent samples as previously described. In brief, for a given gene, variant-expression weights in the l-mB cis locus were first computed with the BSLMM, which models effects on expression as a mixture of normal distributions to account for the sparse expression architecture. Given weights w, lipid Z scores Z, and variant-correlation (LD) matrix D; the association between predicted expression and lipids (i.e., the TWAS statistic) was estimated as ZTWAS = w'Z/(w'Dw)l/2. TWAS statistics were computed by using either the variants genotyped in each expression reference panel or imputed HapMap3:¾variants. To account for multiple hypotheses we applied a genome-wide significant P value threshold (P < 5 xl0-8), significantly more stringent than previously used Bonferroni corrections in prior TWAS26. We defined novel TWAS loci as a TWAS gene falling outside of a previously identified lipid GWAS region (± lmB around a mapped sentinel GWAS variant).
viii. Tissue Expression Enrichment and Competitive Gene Set Pathway Analysis
[00146] The FUMA pipeline was used to obtain MAGMA expression profiles and perform a tissue expression profile analysis and a competitive gene set analysis for GO biological processes using the Molecular Signature Database (MsigDB 5.2). Using 1000 Genomes2l imputed summary statistics from Stage 1 of our analysis (both ancestry-specific and combined trans-ethnic), we identified enriched tissues (Bonferroni corrected P < 0.05) and GO biological processes with a Benjamini-Hochberg adjusted P < 5 xlO 8 for the HDL-C, LDL-C, and TG phenotypes.
ix. Identification of Independent Low-Frequency Coding Variant Lipid
Associations Specific to Blacks and Hispanics
[00147] The P value and linkage disequilibrium-driven clumping procedure in PLINK version l.90b (—clump) was used to identify associations between low-frequency coding variants and lipids specific to blacks and Hispanics. Input included summary lipid association statistics from our MVP 1000 Genomes imputed genome-wide association study of black and Hispanic individuals, and reference linkage disequilibrium panels of 661 African (AFR) and 347 Ad Mixed American (AMR) samples from 1000 Genomes phase 3 whole genome sequencing data. Variants were clumped with stringent r2 (0.01) and P (< 5 x 10 8) thresholds in a 1 mega base region surrounding the lead variant at each locus to reveal independent index variants at genome-wide significance. From this list of independent variants, we report novel protein- altering variants specific to blacks and Hispanics at a MAF < 0.05.
x. Loss of Gene Function Analysis
[00148] The Variant Effect Predictor software was used to identify pLoF DNA sequence variants defined as: premature stop (nonsense), canonical splice-sites (splice-donor or splice- acceptor) or insertion/deletion variants that shifted frame (frameshift). These variants were then merged with data from the Exome Aggregation Consortium24 (Version 0.3.1), a publicly available catalogue of exome sequence data to confirm consistency in variant annotation. pLoF DNA sequence variants were required to be observed in at least 50 individuals, and set a statistical significance threshold of P < 5 x 10 8 (genome-wide significance). xi. Loss of PDE3B Gene Function and Coronary Artery Disease
[00149] A novel lipid association was identified for a pLoF mutation in the PDE3B gene (rsl50090666, p.Arg783Ter). For carriers of damaging mutations in Phosphodiesterase 3B, the mutation’s effects on risk for CAD were examined using logistic regression in five separate cohorts: MVP, UK Biobank, and 3 cohorts with exome sequencing: the Myocardial Infarction Genetics Consortium (MIGen), the Penn Medicine Biobank (PMBB), and DiscovEHR. In studies with exome sequencing, pLoF variants were combined with missense variants predicted to be damaging or possibly damaging by each of 5 computer prediction algorithms (LRT score, MutationTaster, PolyPhen-2, HumDiv, PolyPhen-2 HumVar, and SIFT) as performed previously. Because any individual damaging mutation was rare, variants were aggregated together for subsequent phenotypic analysis. Logistic regression on disease status was performed, adjusting for age, sex, and principal components of ancestry as appropriate. Effects of PDE3B damaging mutations were pooled across studies using an inverse-variance weighted fixed effects meta-analysis. A P < 0.05 threshold for statistical significance was set.
xii. PheWAS of Variation in Genes Targeted by Lipid Lowering Therapies
[00150] For a set of DNA sequence variants within genes targeted by lipid-lowering medicines, a PheWAS was performed leveraging the full catalog of EHR ICD-9 diagnosis codes. Five lipid genes currently being targeted by pharmaceutical agents were selected and functional variants were identified in these genes: two nonsense variants (LPL p.Ser474Ter, ANGPTL8 p.Glnl2lTer) and three missense variants (ANGPTL4 p.Glu40Lys, APOA5 p.Serl9Trp, PCSK9 p.Arg46Leu). Phenotypes were considered to be significantly associated with a variant if they met a Bonferroni corrected P < 4.98 x 10 5 [0.05/1004 traits]. For replication of our ANGPTL4 p.E40K type 2 diabetes finding, the PheWAS results were combined with publically available data from the recently published trans-ethnic type 2 diabetes GWAS41 using an inverse variance-weighted fixed effects method.
[00151] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the method and compositions described herein. Such equivalents are intended to be encompassed by the following claims. References
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Claims

CLAIMS We claim:
1. A method for determining a subject’s risk for having or developing abdominal aortic aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, and wherein the presence of the variant indicates the subject's reduced risk for having or abdominal aortic aneurysm.
2. The method of claim 1, wherein the PCSK9 loss of function variant results in a
PCSK9 protein having the mutation Arg46Leu.
3. The method of claims 1-2, wherein the PCSK9 loss of function variant is determined from a sample obtained from the subject.
4. The method of claims 1-3, wherein the PCSK9 loss of function variant is determined by amplifying or sequencing a nucleic acid sample obtained from the subject.
5. The method of claim 4, wherein the amplifying is performed using polymerase chain reaction (PCR).
6. The method of claims 4-5, wherein the amplifying or sequencing comprises using primers having sequences complementary to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
7. A method of detecting a PCSK9 loss of function variant (Arg46Leu) in a subject, said method comprising: a. obtaining a biological sample from a subject; b. detecting whether a PCSK9 loss of function variant (Arg46Leu) is present in the biological sample by performing whole genome or whole exome sequencing.
8. A method comprising: a. obtaining a sample from a subject; b. detecting whether a PCSK9 loss of function variant is present in the sample; c. diagnosing the subject as having a greater likelihood of responding to PCSK9 inhibitors when there is an absence of the PCSK9 loss of function variant; and d. administering an effective amount of a PCSK9 inhibitor to the subject.
9. The method of claim 7, wherein the PCSK9 loss of function variant results in a
PCSK9 protein having the mutation Arg46Leu.
10. The method of claims 7-9, wherein the sample is DNA or protein.
11. The method of claims 7-10, wherein the PCSK9 inhibitor is a compound, protein, DNA, RNAi, CRISPR, or siRNA.
12. A method of treating a subject comprising administering a composition that inhibits the function of PCSK9 to a subject, wherein the subject has been determined to lack a loss of function mutation in PCSK9.
13. The method of claim 12, wherein the composition is a compound, protein, DNA, RNAi, CRISPR, or siRNA.
14. The method of claims 12-13, wherein the PCSK9 loss of function variant results in a PCSK9 protein having the mutation Arg46Leu.
15. A method of screening for test compositions that cause a loss of function mutation in PCSK9 comprising: a. contacting a PCSK9 gene with a test composition; b. detecting the presence of a mutation in the PCSK9 gene; and c. determining if the mutation is a loss of function mutation, wherein the
presence of a loss of function mutation in PCSK9 indicates a test composition that causes a loss of function in PCSK9.
16. The method of claim 15, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
17. A method of screening for therapeutic candidates for treating abdominal aortic
aneurysm compositions comprising: a. contacting a cell lacking a loss of function mutation in PCSK9 with a test composition; and b. determining if the test composition inhibits PCSK9 in the cell, wherein if the test composition inhibits PCSK9 then it is a therapeutic candidate for treating abdominal aortic aneurysm.
18. A method of inducing a loss of function mutation in PCSK9 comprising administering a test composition determined from the method of claims 15-17.
19. A vector comprising a loss of function PCSK9 variant, wherein the loss of function mutation in PCSK9 results in a PCSK9 protein having the mutation Arg46Leu.
20. A cell comprising the vector of claim 19.
21. A method for identifying a subject in need of treatment for abdominal aortic
aneurysm comprising determining in the subject the presence of a PCSK9 loss of function variant, wherein the presence of a PCSK9 loss of function variant indicates that the subject is not in need of treatment for abdominal aortic aneurysm.
22. A method of identifying a subject in need of screening for the development of
abdominal aortic aneurysm comprising determining in the subject the absence of a PCSK9 loss of function variant, wherein the absence of a a PCSK9 loss of function variant indicates a subject in need of screening for the development of abdominal aortic aneurysm.
23. An engineered, non-naturally occurring CRISPR-CAS system comprising: a) a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises a PCSK9 loss of function variant, and b) a Cas protein or gene encoding a Cas protein.
24. The engineered, non-naturally occurring CRISPR-CAS system of claim 23, wherein the Cas protein is a Type-II Cas9 protein or a gene encoding a Type-II Cas9 protein.
25. The engineered, non-naturally occurring CRISPR-CAS system of claim 24, wherein the Cas9 protein and the guide RNA do not naturally occur together.
26. The engineered, non-naturally occurring CRISPR-CAS system of claims 23-25, wherein the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
27. A method of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a) a guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and b) a Cas protein or gene encoding a Cas protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the nucleic acid molecule which comprises the PCSK9 loss of function variant, whereby expression of the at least one gene product is altered.
28. The of altering expression of at least one gene product of claim 27, wherein the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
29. A method of altering expression of at least one gene product, wherein the at least one gene product is a gene product from a PCSK9 loss of function variant, wherein the method comprises administering a vector that comprises a) a first regulatory element operable in a eukaryotic cell operably linked to at least one nucleotide sequence encoding a CRISPR-Cas system guide RNA that hybridizes with a target sequence, wherein the target sequence comprises the PCSK9 loss of function variant, and b) a second regulatory element operable in a eukaryotic cell operably linked to a nucleotide sequence encoding a Cas9 protein, whereby the guide RNA targets the target sequence and the Cas9 protein cleaves the target sequence, whereby expression of the at least one gene product is altered.
30. The method of altering expression of at least one gene product of claim 29, wherein the PCSK9 loss of function variant comprises the mutation Arg46Leu in the PCSK9 protein.
31. A method of silencing or inhibiting expression of wild type PCSK9 in a cell
comprising providing at least one silencing agent to the cell, wherein said silencing agent silences or inhibits expression of the wild type PCSK9 in the cell.
32. The method of silencing or inhibiting expression of wild type PCSK9 in a cell of claim 31, wherein the cell is inside a subject and thus the method occurs in vivo.
33. The method of silencing or inhibiting expression of wild type PCSK9 in a cell of claim 31, wherein the silencing or inhibiting expression of PCSK9 in a cell occurs in vitro.
34. The method of silencing or inhibiting expression of wild type PCSK9 in a cell of claims 31-33, wherein the silencing agent is RNAi, CRISPR, or siRNA.
35. A method of silencing or inhibiting expression of wild type PCSK9 in a cell
comprising providing at least one RNA to the cell in an amount sufficient to inhibit the expression of PCSK9, wherein the RNA comprises or forms a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure, and the RNA comprising the double-stranded structure inhibits expression of PCSK9.
36. The method of silencing or inhibiting expression of wild type PCSK9 in a cell of claim 35, wherein the first strand comprises a sequence which corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number
NG_00906l. l.
37. The method of silencing or inhibiting expression of wild type PCSK9 in a cell of claims 35-36, wherein the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l .1.
38. A RNA comprising a double-stranded structure containing a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate complementary sequences that hybridize to each other to form said double-stranded structure.
39. The RNA of claim 38, wherein the first strand comprises a sequence which
corresponds to a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l .1.
40. The RNA of claims 38-39, wherein the second strand comprises a sequence that can bind to, or is complementary to, a portion of the PCSK9 nucleic acid sequence found in accession number NG_00906l. l.
41. A method of inhibiting expression of PCSK9 in a cell comprising: (a) isolating the cell; (b) contacting the cell with a RNA comprising a double-stranded structure comprising a first strand comprising a ribonucleotide sequence which corresponds to a nucleotide sequence of PCSK9 and a second strand comprising a ribonucleotide sequence which is complementary to the nucleotide sequence of PCSK9, wherein the first and the second ribonucleotide sequences are separate sequences that hybridize to each other to form said double-stranded structure, and (c) subsequently introducing the cell into a host, wherein said RNA comprising the double-stranded structure inhibits expression of the target gene in the cell in the host.
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