FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2113547573> ?p ?o ?g. }

• W2113547573 endingPage "245" @default.
• W2113547573 startingPage "233" @default.
• W2113547573 abstract "Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98th or <2nd percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments. Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98th or <2nd percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments. Elevated low-density lipoprotein cholesterol (LDL-C) is one of the cardinal risk factors for coronary artery disease, the leading cause of death in the United States.1Roger V.L. Go A.S. Lloyd-Jones D.M. Benjamin E.J. Berry J.D. Borden W.B. Bravata D.M. Dai S. Ford E.S. Fox C.S. et al.American Heart Association Statistics Committee and Stroke Statistics SubcommitteeHeart disease and stroke statistics—2012 update: a report from the American Heart Association.Circulation. 2012; 125: e2-e220Crossref PubMed Scopus (0) Google Scholar LDL-C is a complex trait whose variation is influenced by the environment and genes; approximately 40%–50% of the variation is estimated as heritable.2Pilia G. Chen W.M. Scuteri A. Orrú M. Albai G. Dei M. Lai S. Usala G. Lai M. Loi P. et al.Heritability of cardiovascular and personality traits in 6,148 Sardinians.PLoS Genet. 2006; 2: e132Crossref PubMed Scopus (376) Google Scholar, 3de Miranda Chagas S.V. Kanaan S. Chung Kang H. Cagy M. de Abreu R.E. da Silva L.A. Garcia R.C. Garcia Rosa M.L. Environmental factors, familial aggregation and heritability of total cholesterol, low density lipoprotein-cholesterol and high density lipoprotein-cholesterol in a Brazilian population assisted by the Family Doctor Program.Public Health. 2011; 125: 329-337Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Rare mutations have been identified in families affected by Mendelian forms of lipid-related disorders. Family members carrying these rare variants typically demonstrate extreme lipid phenotypes in childhood and, for those with high LDL-C, premature cardiovascular disease. Family studies have shown that extremely high cholesterol levels can result from mutations in LDLR (MIM 606945), PCSK9 (MIM 607786), APOB (MIM 107730), ABCG5 (MIM 605459), ABCG8 (MIM 605460), and LDLRAP1 (MIM 605747), whereas extremely low cholesterol levels can result from mutations in PCSK9, MTTP (MIM 590075), APOB (Rahalkar and Hegele4Rahalkar A.R. Hegele R.A. Monogenic pediatric dyslipidemias: classification, genetics and clinical spectrum.Mol. Genet. Metab. 2008; 93: 282-294Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar), and ANGPTL35Musunuru K. Pirruccello J.P. Do R. Peloso G.M. Guiducci C. Sougnez C. Garimella K.V. Fisher S. Abreu J. Barry A.J. et al.Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia.N. Engl. J. Med. 2010; 363: 2220-2227Crossref PubMed Scopus (507) Google Scholar (MIM 603874). Targeted sequencing studies in subjects with low cholesterol levels have detected rare mutations in LDLR,6Brown M.S. Goldstein J.L. A receptor-mediated pathway for cholesterol homeostasis.Science. 1986; 232: 34-47Crossref PubMed Scopus (4358) Google Scholar PCSK9,7Cohen J. Pertsemlidis A. Kotowski I.K. Graham R. Garcia C.K. Hobbs H.H. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9.Nat. Genet. 2005; 37: 161-165Crossref PubMed Scopus (1071) Google Scholar and NPC1L18Cohen J.C. Pertsemlidis A. Fahmi S. Esmail S. Vega G.L. Grundy S.M. Hobbs H.H. Multiple rare variants in NPC1L1 associated with reduced sterol absorption and plasma low-density lipoprotein levels.Proc. Natl. Acad. Sci. USA. 2006; 103: 1810-1815Crossref PubMed Scopus (328) Google Scholar (MIM 608010), but the overall contribution of rare and low-frequency variants to population variation in cholesterol levels remains poorly defined. Genome-wide association studies (GWASs) focused primarily on common variants have identified 157 loci associated with lipid levels, including LDL-C.9Teslovich T.M. Musunuru K. Smith A.V. Edmondson A.C. Stylianou I.M. Koseki M. Pirruccello J.P. Ripatti S. Chasman D.I. Willer C.J. et al.Biological, clinical and population relevance of 95 loci for blood lipids.Nature. 2010; 466: 707-713Crossref PubMed Scopus (2787) Google Scholar Although GWASs have identified loci with robust evidence of association with LDL-C, only 10%–12% of the total variance in LDL-C can be attributed to these common variants,9Teslovich T.M. Musunuru K. Smith A.V. Edmondson A.C. Stylianou I.M. Koseki M. Pirruccello J.P. Ripatti S. Chasman D.I. Willer C.J. et al.Biological, clinical and population relevance of 95 loci for blood lipids.Nature. 2010; 466: 707-713Crossref PubMed Scopus (2787) Google Scholar despite 40%–50% estimated heritability.2Pilia G. Chen W.M. Scuteri A. Orrú M. Albai G. Dei M. Lai S. Usala G. Lai M. Loi P. et al.Heritability of cardiovascular and personality traits in 6,148 Sardinians.PLoS Genet. 2006; 2: e132Crossref PubMed Scopus (376) Google Scholar, 3de Miranda Chagas S.V. Kanaan S. Chung Kang H. Cagy M. de Abreu R.E. da Silva L.A. Garcia R.C. Garcia Rosa M.L. Environmental factors, familial aggregation and heritability of total cholesterol, low density lipoprotein-cholesterol and high density lipoprotein-cholesterol in a Brazilian population assisted by the Family Doctor Program.Public Health. 2011; 125: 329-337Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar We evaluated the hypothesis that rare or low-frequency variants, which are not well covered by GWASs and not easily imputed, are also associated with LDL-C. In the current study, we performed a two-stage association study to evaluate low-frequency variation in protein-coding regions across the genome for association with LDL-C. We examined the spectrum of coding variants in associated genes in an unbiased manner. To address these goals, the NHLBI Grand Opportunity (GO) Exome Sequencing Project (ESP)10Tennessen J.A. Bigham A.W. O’Connor T.D. Fu W. Kenny E.E. Gravel S. McGee S. Do R. Liu X. Jun G. et al.Broad GOSeattle GONHLBI Exome Sequencing ProjectEvolution and functional impact of rare coding variation from deep sequencing of human exomes.Science. 2012; 337: 64-69Crossref PubMed Scopus (1215) Google Scholar completed exome sequencing and analysis of 2,005 individuals, including 307 individuals with extremely high and 247 with extremely low LDL-C (>98th percentile or <2nd percentile) from population-based cohorts (stage 1). We followed up with the most promising 17 genes in 1,302 additional sequenced individuals, including 157 individuals with extremely high and 144 with extremely low LDL-C (stage 2). We also performed genotype-based follow-up of variants in 15 genes in up to 52,221 participants from population-based cohorts. We selected samples from seven population-based cohorts: Atherosclerosis Risk in Communities (ARIC),11The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators.Am. J. Epidemiol. 1989; 129: 687-702Crossref PubMed Scopus (2873) Google Scholar Coronary Artery Risk Development in Young Adults (CARDIA),12Friedman G.D. Cutter G.R. Donahue R.P. Hughes G.H. Hulley S.B. Jacobs Jr., D.R. Liu K. Savage P.J. CARDIA: study design, recruitment, and some characteristics of the examined subjects.J. Clin. Epidemiol. 1988; 41: 1105-1116Abstract Full Text PDF PubMed Scopus (1223) Google Scholar the Cardiovascular Health Study (CHS),13Fried L.P. Borhani N.O. Enright P. Furberg C.D. Gardin J.M. Kronmal R.A. Kuller L.H. Manolio T.A. Mittelmark M.B. Newman A. et al.The Cardiovascular Health Study: design and rationale.Ann. Epidemiol. 1991; 1: 263-276Abstract Full Text PDF PubMed Scopus (1990) Google Scholar the Framingham Heart Study (FHS),14Dawber T.R. Meadors G.F. Moore Jr., F.E. Epidemiological approaches to heart disease: the Framingham Study.Am. J. Public Health Nations Health. 1951; 41: 279-281Crossref PubMed Google Scholar the Jackson Heart Study (JHS),15Taylor Jr., H.A. Wilson J.G. Jones D.W. Sarpong D.F. Srinivasan A. Garrison R.J. Nelson C. Wyatt S.B. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.Ethn. Dis. 2005; 15 (S6-4–S6-17)Google Scholar the Multi-Ethnic Study of Atherosclerosis (MESA),16Bild D.E. Bluemke D.A. Burke G.L. Detrano R. Diez Roux A.V. Folsom A.R. Greenland P. Jacob Jr., D.R. Kronmal R. Liu K. et al.Multi-ethnic study of atherosclerosis: objectives and design.Am. J. Epidemiol. 2002; 156: 871-881Crossref PubMed Scopus (2539) Google Scholar and the Women’s Health Initiative (WHI).17The Women’s Health Initiative Study GroupDesign of the Women’s Health Initiative clinical trial and observational study.Control. Clin. Trials. 1998; 19: 61-109Abstract Full Text Full Text PDF PubMed Scopus (2091) Google Scholar Of the 2,005 individuals with exome sequence data in stage 1, 854 (43%) were African American (AA) and the remainder (n = 1,153 [57%]) were European American (EA) (Table S1, available online). We calculated fasting LDL-C by using the Friedewald formula.18Friedewald W.T. Levy R.I. Fredrickson D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.Clin. Chem. 1972; 18: 499-502Crossref PubMed Scopus (64) Google Scholar For individuals on lipid-lowering medication, we estimated pretreatment LDL-C values by dividing treated LDL-C values by 0.75 to model a 25% reduction in LDL-C on therapy. We then regressed estimated pretreatment LDL-C levels (or actual LDL-C levels for those not on lipid-lowering therapies) on sex, age, and age squared within both cohort and ethnic (EA and AA) groups. Residuals were then combined across studies, within ethnicity strata, for selection of extreme LDL-C levels. Participants with extreme levels of LDL-C (Table S1) were selected from four population-based cohorts: ARIC,11The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators.Am. J. Epidemiol. 1989; 129: 687-702Crossref PubMed Scopus (2873) Google Scholar CHS,13Fried L.P. Borhani N.O. Enright P. Furberg C.D. Gardin J.M. Kronmal R.A. Kuller L.H. Manolio T.A. Mittelmark M.B. Newman A. et al.The Cardiovascular Health Study: design and rationale.Ann. Epidemiol. 1991; 1: 263-276Abstract Full Text PDF PubMed Scopus (1990) Google Scholar FHS,14Dawber T.R. Meadors G.F. Moore Jr., F.E. Epidemiological approaches to heart disease: the Framingham Study.Am. J. Public Health Nations Health. 1951; 41: 279-281Crossref PubMed Google Scholar and JHS15Taylor Jr., H.A. Wilson J.G. Jones D.W. Sarpong D.F. Srinivasan A. Garrison R.J. Nelson C. Wyatt S.B. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.Ethn. Dis. 2005; 15 (S6-4–S6-17)Google Scholar and represented the 1st and 99th percentile tails in EA individuals (n = 156 high LDL-C and 137 low LDL-C) and the 2nd and 98th percentile tails in AA individuals (n = 151 high LDL-C and 110 low LDL-C). Additional samples not selected for LDL-C levels came from ESP studies (n = 1,451) on the basis of the following phenotypes: early-onset myocardial infarction cases and controls, ischemic stroke cases, blood pressure extremes, and body mass index (BMI); also included was a set of randomly selected samples among participants with near-complete phenotype data across a range of traits. Stage 2 samples (n = 1,302 [66.2%] AA) were selected from the same seven cohorts as stage 1 and included individuals in the 1st and 99th percentile tails of LDL-C in EA individuals (n = 61 high LDL-C and 63 low LDL-C) and 2nd and 98th percentile tails in AA individuals (n = 96 high LDL-C and 81 low LDL-C). Stage 1 samples included an 18-fold enrichment of extreme samples in EA individuals and a 12-fold enrichment of extreme samples in AA individuals (stage 2 samples were 21-fold and 7-fold for EA and AA individuals, respectively). Additional information about these samples and the distribution of LDL-C are given in Table S1 and Figure S1. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national), and all individuals provided informed consent. Protocols were evaluated by individual institutional review boards. Exome sequencing was performed at the University of Washington (UW; stage 1, n = 773; stage 2, n = 858) and at the Broad Institute of Harvard and MIT (Broad; stage 1, n = 1,232; stage 2, n = 444). DNA samples were quality controlled by concentration estimation by Pico Green and, in some cases, by gel electrophoresis and real-time-PCR-based genotyping. For the majority of the samples other than those from the WHI, initial quality control (QC) was done centrally at the University of Vermont prior to shipping to the UW and the Broad. Both centers prepared DNA samples by subjecting genomic DNA to shearing and then ligating sequencing adaptors. Exome capture for the samples was performed with the Roche Nimblegen SeqCap EZ (UW) or Agilent SureSelect Human All Exon 50 Mb (Broad) according to the manufacturers’ instructions. Paired-end sequencing (2 × 76 bp) was carried out with Illumina GAII and HiSeq sequencing instruments. For QC purposes prior to the release of sequence data, samples were initially converted from real-time base calls to qseq.txt files with the use of Bustard and aligned to the human reference sequence (UCSC Genome Browser, hg19) with the Burrows-Wheeler Aligner.19Li H. Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics. 2009; 25: 1754-1760Crossref PubMed Scopus (26860) Google Scholar We performed duplicate removal and indel realignment by using the Genome Analysis Toolkit (GATK).20McKenna A. Hanna M. Banks E. Sivachenko A. Cibulskis K. Kernytsky A. Garimella K. Altshuler D. Gabriel S. Daly M. DePristo M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (14887) Google Scholar After the use of GATK filters, samples were required to reach at least 20× coverage over 70% of the exome target. Prior to the release of individual-level sequence reads, sequence data were required to match known fingerprint genotypes for their respective samples. Variant calls were evaluated on both bulk and per-sample properties for novel (absent from dbSNP) and known variant counts, transition/transversion (Ti/Tv) ratio, heterozygote/homozygote ratio, and insertion/deletion ratio. Both bulk and sample metrics were compared to historical values for exome sequencing projects at the two centers. DNA samples that failed laboratory QC were requeued for library preparation and sequencing. A subset of these data is available from dbGaP under accession numbers phs000279, phs000401, phs000354, phs000362, phs000285, phs000399, phs000347, phs000546, phs000556, phs000581, phs000398, phs000402, phs000582, phs000422, phs000400, phs000327, phs000403, phs000296, phs000254, phs000518, phs000281, phs000291, phs000290, and phs000335. An average of 130 million mapped reads were generated per sample, and 95.5% of bases reached a recalibrated quality score of Q20 or greater. A total of 63.8% of the reads mapped to the exonic target region, and the mean depth of targeted regions was 127×. To generate high-quality genotype calls for analysis, we removed reads with map quality < 20 prior to variant calling with the University of Michigan’s multisample SNP-calling pipeline UMAKE (H.M.K. and G.J., unpublished data). To reduce the number of sequencing variants miscalled because of sequencing and alignment artifacts, the UMAKE pipeline uses a support vector machine (SVM)21Joachims T. Making large-scale Support Vector Machine Learning Practical.in: Schölkopf B. Burges C.J.C. Smola A.J. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge1999: 169-184Google Scholar to exclude likely sequencing artifacts by using a battery of SNP quality metrics (Table S9). These include allelic balance (the proportional representation of each allele in likely heterozygotes), base quality distribution for sites supporting the reference and alternate alleles, and the distribution of supporting evidence between strands and sequencing cycle, among others. We used variants identified by dbSNP or 1000 Genomes as the positive training set and used variants that failed multiple filters as the negative training set. We found this method to be effective at removing sequencing artifacts while preserving good-quality data, as indicated by the Ti/Tv ratio for previously known and newly identified variant sites, the proportion of high-frequency variants overlapping with those in dbSNP, and the ratio of synonymous to nonsynonymous variants, as well as attempts at validation of a subset of sites. With the use of SVM filtering, 19,775 coding variants (5.72%) were removed.21Joachims T. Making large-scale Support Vector Machine Learning Practical.in: Schölkopf B. Burges C.J.C. Smola A.J. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge1999: 169-184Google Scholar The genotype concordance rate among five duplicate pairs blindly sequenced at both sequencing centers was 99.97%, and the concordance of nonhomozygous reference genotype calls was 98.97%. The genotype concordance rate for 289 AA samples genotyped at 5,051 autosomal markers with Metabochip was 98.8%, and genotype concordance was 98.7% for 526 markers with minor allele frequency (MAF) < 1%. Allelic concordance rates were 99.39% for all markers and 99.3% for variants with MAF < 1%. Stage 2 samples were exome sequenced with the same technical and bioinformatics pipeline as those in stage 1, although variants were called and filtered as a separate batch. We only analyzed stage 2 genes that reached p < 1 × 10−5 in stage 1. To reduce any differences between samples sequenced at different centers, we called variants only for the targeted region of the sample and marked them as missing if they were outside the target region. Although we initially observed a batch effect between sequencing centers, this was essentially eliminated, as determined from quantile-quantile plots, by the application of a call-rate filter. We used SVM filtering to further refine the results and saw no significant evidence of differences between sequencing centers. All extreme-LDL-C samples were sequenced at the UW. We attempted calling insertion-deletion polymorphisms with SAMtools;22Li H. Handsaker B. Wysoker A. Fennell T. Ruan J. Homer N. Marth G. Abecasis G. Durbin R. 1000 Genome Project Data Processing SubgroupThe Sequence Alignment/Map format and SAMtools.Bioinformatics. 2009; 25: 2078-2079Crossref PubMed Scopus (31812) Google Scholar however, the concordance rate of the resultant indel calls was only 70.9% among our duplicate pairs, so we excluded these calls from this analysis. We identified related individuals by applying a maximum-likelihood algorithm23Epstein M.P. Duren W.L. Boehnke M. Improved inference of relationship for pairs of individuals.Am. J. Hum. Genet. 2000; 67: 1219-1231Abstract Full Text Full Text PDF PubMed Scopus (220) Google Scholar as implemented in RelativeFinder and by examining the mean and SD of the identity-by-state estimate for putative first- or second-degree relatives. In each group of related individuals, we prioritized (1) individuals with extreme LDL-C and (2) the individual with the highest genotype call rate and excluded all putative first- or second-degree relatives (n = 35). We determined the number of sequenced reads that mapped to the Y and X chromosomes, grouped the ratio of Y and X chromosome reads into two clusters, and excluded outliers on the basis of their reported sex (n = 3, Figure S2). We performed principal-component analysis as implemented in the PLINK software package24Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A. Bender D. Maller J. Sklar P. de Bakker P.I. Daly M.J. Sham P.C. PLINK: a tool set for whole-genome association and population-based linkage analyses.Am. J. Hum. Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (19746) Google Scholar and used the first and second principal components (PC1 and PC2, respectively) as covariates for all analyses. PC1 had a squared correlation of 0.988 with estimates of European ancestry among AA samples (ancestry estimated with SEQMIX). Of 2,038 individuals with exome sequence data, 2,005 (including 554 LDL-C extremes) passed all sample-level QC and were included in the final analyses (Table S1). We used ANNOVAR25Wang K. Li M. Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.Nucleic Acids Res. 2010; 38: e164Crossref PubMed Scopus (7933) Google Scholar with GENCODE genes (v.7; UCSC Genome Browser, hg19) to annotate variants as nonsense, splice, read-through, missense, synonymous, UTR, or noncoding and selected the most deleterious annotation for each variant (i.e., if missense in one transcript and synonymous in another, the variant was considered to be missense). We considered splice variants to be those that altered either the first two or the last two nucleotides of an intron (essential splice donor and acceptor sites). The following RefSeq accession numbers were used for annotating variants in significant genes: NM_000384.2 (APOB), NM_001195802.1 (LDLR), NM_174936.3 (PCSK9), and NM_138814.3 (PNPLA5). In stage 1, we identified 588,226 genetic variants in the protein-coding regions of genes (exome). Of these, 3,093 (0.5%) were splice variants, 6,958 (1.1%) were nonsense variants, 345,569 (58.5%) were missense variants, and 232,182 (39.5%) were synonymous variants (Table 1). On average, each EA individual had 16.6 splice variants, 46.9 nonsense variants (stop-gained), 15.9 read-through variants (loss of stop codon), 5,865 missense variants, and 7,089 synonymous variants. By comparison, each AA individual had, on average, 24.0 splice variants, 53.5 nonsense variants, 17.7 read-through variants, 7,284 missense variants, and 9,113 synonymous variants. The number of unique variants (not seen in any other individual in our study) also differed by ethnic group. Each EA individual had, on average, 1 unique splice variant, 2.5 unique nonsense variants, and 91 unique missense variants. AA individuals had an average of 1 unique splice variant, 2.4 unique nonsense variants, and 110 unique missense variants (Table S8).Table 1Study Sample: Stages 1 and 2AA IndividualsEA IndividualsAll Sequenced Samples (EA and AA)Total No. of DNA SamplesPopulation- Based Samples SequencedExtremely High LDL-C SequencedExtremely Low LDL-C SequencedAA Sequenced SamplesTotal No. of DNA SamplesPopulation-Based Samples SequencedExtremely High LDL-C SequencedExtremely Low LDL-C SequencedEA Sequenced SamplesStage 1n17,62859115111085444,9878601561371,1512,005Mean LDL-C (SD)-138.9 (34.1)243.0 (31.9)52.7 (12.6)143.7 (60.9)-131.8 (31.2)265.8 (48.6)50.1 (13.8)140.0 (64.7)142.8 (63.7)Range-57–230195–39822–7822–398-46–224180–47914–8014–47914–479Stage 2n4,42268596818625,00031661634401,302Mean LDL-C (SD)-140.7 (33.4)246.1 (48.5)53.7 (16.3)144.3 (55.7)-131.2 (27.6)228.4 (37.9)54.9 (15.3)133.8 (54.0)140.7 (55.4)Range-70–230195–47212–7912–472-74–201189–40620–9120–40612–472Stages 1 and 2n22,0501,2762471911,71449,9871,1762172001,5933,307Mean LDL-C (SD)-139.9 (33.7)244.2 (39.1)53.2 (14.3)145.3 (58.9)-131.7 (30.2)255.3 (48.8)51.6 (14.4)138.5 (62.2)142.0 (60.6)Range-57–230195–47212–7912–472-46–224180–47914–9114–47912–479Abbreviations are as follows: AA, African American; and EA, European American. Open table in a new tab Abbreviations are as follows: AA, African American; and EA, European American. Single-variant tests for medication-adjusted LDL-C were performed by linear regression with covariates for age, sex, ethnicity (AA versus EA), primary phenotype (early-onset myocardial infarction cases and controls, ischemic stroke cases, blood-pressure extremes, BMI, and random set), and PC1 and PC2 as implemented in PLINK.24Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A. Bender D. Maller J. Sklar P. de Bakker P.I. Daly M.J. Sham P.C. PLINK: a tool set for whole-genome association and population-based linkage analyses.Am. J. Hum. Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (19746) Google Scholar We excluded variants with a genotype call rate < 50% and MAF < 1%. Burden tests that aggregated certain classes of variants within each gene across the genome were performed with the combined multivariate and collapsing (CMC) test26Li B. Leal S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.Am. J. Hum. Genet. 2008; 83: 311-321Abstract Full Text Full Text PDF PubMed Scopus (1101) Google Scholar with multiple frequency thresholds for variant inclusion (MAF < 5%, 1%, 0.5%, and 0.1%) and different classes of variants: (1) nonsynonymous and splice and (2) loss of function (LoF; nonsense, read-through, and splice)—only for MAF < 5%. We used multiple frequency thresholds because the inclusion of low-frequency benign variants might have diluted a signal seen with a small number of functional rare variants, but we also wanted to test low-frequency variants because these would be present in higher numbers of individuals and might be functional. Because we expected most LoF variants to be functional, we opted to use only a single frequency threshold (MAF < 5%). We used a CMC model that assigns samples as carriers or noncarriers of rare variants in a particular gene and tests for association with LDL-C values by using a linear regression model.26Li B. Leal S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.Am. J. Hum. Genet. 2008; 83: 311-321Abstract Full Text Full Text PDF PubMed Scopus (1101) Google ScholarLDL=β0+β1burdenscore+β2age+β3sex+β4PC1+β5PC2+β6ethnicity+β7ESPphenotype. The effect sizes for the burden of rare variants (shown in Table 2) were estimated in all samples with the same model. We considered estimating effect sizes from the nonextreme samples only, but this resulted in the exclusion of many individuals who carry rare variants at these genes (seven out of eight carriers at APOB were excluded, for example). Instead, we estimated the aggregate effect sizes from the entire sample, including extreme individuals, which might have resulted in upwardly biased estimates. Extremely large population-based samples will be required to provide unbiased effect-size estimates for very rare variants, which are currently unavailable. We tested for heterogeneity between aggregate effect sizes in AA and EA samples by performing analyses separately in AA and EA samples and then explicitly testing for heterogeneity (METAL).27Willer C.J. Li Y. Abecasis G.R. METAL: fast and efficient meta-analysis of genomewide association scans.Bioinformatics. 2010; 26: 2190-2191Crossref PubMed Scopus (2634) Google ScholarTable 2Genes with a Burden of Rare or Low-Frequency Variants Significantly Associated with LDL-C by Exome SequencingGeneGenomic LocationOptimal Burden TestAA Individuals (Stages 1 and 2, n = 1,714)EA Individuals (Stages 1 and 2, n = 1,593)Stages 1 and 2 (n = 3,307)Stages 1–3 (n = 5,391)No. of variantsBurden Frequency (%)No. of VariantsBurden Frequency (%)Burden Effect Size in mg/dl (SE)p Val" @default.
• W2113547573 created "2016-06-24" @default.
• W2113547573 creator A5000325789 @default.
• W2113547573 creator A5000537292 @default.
• W2113547573 creator A5002821639 @default.
• W2113547573 creator A5003810507 @default.
• W2113547573 creator A5003856334 @default.
• W2113547573 creator A5004148044 @default.
• W2113547573 creator A5004946791 @default.
• W2113547573 creator A5006966752 @default.
• W2113547573 creator A5007613197 @default.
• W2113547573 creator A5008481316 @default.
• W2113547573 creator A5009996821 @default.
• W2113547573 creator A5011512882 @default.
• W2113547573 creator A5011940218 @default.
• W2113547573 creator A5013273481 @default.
• W2113547573 creator A5013403466 @default.
• W2113547573 creator A5015477558 @default.
• W2113547573 creator A5016233543 @default.
• W2113547573 creator A5017009749 @default.
• W2113547573 creator A5017009982 @default.
• W2113547573 creator A5017252332 @default.
• W2113547573 creator A5017572190 @default.
• W2113547573 creator A5017961709 @default.
• W2113547573 creator A5020525249 @default.
• W2113547573 creator A5021225119 @default.
• W2113547573 creator A5022172869 @default.
• W2113547573 creator A5022420096 @default.
• W2113547573 creator A5022649363 @default.
• W2113547573 creator A5022738034 @default.
• W2113547573 creator A5023169260 @default.
• W2113547573 creator A5027800297 @default.
• W2113547573 creator A5029811259 @default.
• W2113547573 creator A5030267686 @default.
• W2113547573 creator A5031530236 @default.
• W2113547573 creator A5032020402 @default.
• W2113547573 creator A5033780024 @default.
• W2113547573 creator A5034697802 @default.
• W2113547573 creator A5035342009 @default.
• W2113547573 creator A5036685109 @default.
• W2113547573 creator A5036791650 @default.
• W2113547573 creator A5038431304 @default.
• W2113547573 creator A5039611937 @default.
• W2113547573 creator A5040770914 @default.
• W2113547573 creator A5043655881 @default.
• W2113547573 creator A5043975637 @default.
• W2113547573 creator A5044048476 @default.
• W2113547573 creator A5045835301 @default.
• W2113547573 creator A5047340885 @default.
• W2113547573 creator A5047959482 @default.
• W2113547573 creator A5048738826 @default.
• W2113547573 creator A5048844887 @default.
• W2113547573 creator A5048882210 @default.
• W2113547573 creator A5048908501 @default.
• W2113547573 creator A5050276649 @default.
• W2113547573 creator A5052029446 @default.
• W2113547573 creator A5052604218 @default.
• W2113547573 creator A5052849556 @default.
• W2113547573 creator A5052984251 @default.
• W2113547573 creator A5053681099 @default.
• W2113547573 creator A5054234274 @default.
• W2113547573 creator A5054956552 @default.
• W2113547573 creator A5055433188 @default.
• W2113547573 creator A5056299676 @default.
• W2113547573 creator A5056557315 @default.
• W2113547573 creator A5058314296 @default.
• W2113547573 creator A5059089739 @default.
• W2113547573 creator A5059340927 @default.
• W2113547573 creator A5059560495 @default.
• W2113547573 creator A5060214482 @default.
• W2113547573 creator A5061263756 @default.
• W2113547573 creator A5061931771 @default.
• W2113547573 creator A5062296084 @default.
• W2113547573 creator A5062804698 @default.
• W2113547573 creator A5062883630 @default.
• W2113547573 creator A5063164828 @default.
• W2113547573 creator A5064323494 @default.
• W2113547573 creator A5064529676 @default.
• W2113547573 creator A5065262945 @default.
• W2113547573 creator A5065704891 @default.
• W2113547573 creator A5067109963 @default.
• W2113547573 creator A5068555852 @default.
• W2113547573 creator A5070735232 @default.
• W2113547573 creator A5071606253 @default.
• W2113547573 creator A5073710029 @default.
• W2113547573 creator A5073734270 @default.
• W2113547573 creator A5074117194 @default.
• W2113547573 creator A5074771810 @default.
• W2113547573 creator A5075061959 @default.
• W2113547573 creator A5075343690 @default.
• W2113547573 creator A5078482348 @default.
• W2113547573 creator A5078559681 @default.
• W2113547573 creator A5078574182 @default.
• W2113547573 creator A5078999064 @default.
• W2113547573 creator A5079207212 @default.
• W2113547573 creator A5081217777 @default.
• W2113547573 creator A5083006483 @default.
• W2113547573 creator A5083671777 @default.

• next