Fine mapping of five Loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. (Articolo in rivista)

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  • Fine mapping of five Loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. (Articolo in rivista) (literal)
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  • Sanna S, Li B, Mulas A, Sidore C, Kang HM, Jackson AU, Piras MG, Usala G, Maninchedda G, Sassu A, Serra F, Palmas MA, Wood WH 3rd, Njølstad I, Laakso M, Hveem K, Tuomilehto, Lakka, Rauramaa R, Boehnke M, Cucca F, Uda M, Schlessinger , Nagaraja, Abecasis (2011)
    Fine mapping of five Loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability.
    in PLOS genetics
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  • Sanna S, Li B, Mulas A, Sidore C, Kang HM, Jackson AU, Piras MG, Usala G, Maninchedda G, Sassu A, Serra F, Palmas MA, Wood WH 3rd, Njølstad I, Laakso M, Hveem K, Tuomilehto, Lakka, Rauramaa R, Boehnke M, Cucca F, Uda M, Schlessinger , Nagaraja, Abecasis (literal)
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  • Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy 2Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America 3Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy 4Shardna Life Sciences, Pula, Italy 5Gene Expression and Genomics Unit, Research Resources Branch, National Institute on Aging, Baltimore, Maryland, United States of America 6Department of Community Medicine, Faculty of Health Sciences, University of Tromso, Tromso, Norway 7Department of Medicine, University of Eastern Finland, Kuopio, Finland 8Department of Public Health, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway 9Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland PLoS Genetics Public Library of Science Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability Serena Sanna, Bingshan Li, [...], and Gonçalo R. Abecasis Additional article information Abstract Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ~10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci. Author Summary Despite the striking success of genome-wide association studies in identifying genetic loci associated with common complex traits and diseases, much of the heritable risk for these traits and diseases remains unexplained. A higher resolution investigation of the genome through sequencing studies is expected to clarify the sources of this missing heritability. As a preview of what we might learn in these more detailed assessments of genetic variation, we used sequencing to identify potentially interesting variants in seven genes associated with low-density lipoprotein cholesterol (LDL-C) in 256 Sardinian individuals with extreme LDL-C levels, followed by large scale genotyping in 5,524 individuals, to examine newly discovered and previously described variants. We found that a combination of common and rare variants in these loci contributes to variation in LDL-C levels, and also that the initial estimate of the heritability explained by these loci doubled. Importantly, our results include a Sardinian-specific rare variant, highlighting the need for sequencing studies in isolated populations. Our results provide insights about what extensive whole-genome sequencing efforts are likely to reveal for the understanding of the genetic architecture of complex traits. Introduction In the past few years, genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with quantitative traits and diseases, providing valuable information about their underlying mechanisms (for a recent example, see [1]). More than 2,000 common variants appear associated with over 200 conditions (as reported by the NHGRI GWAS catalog on 12/2010) and for a few, like age-related macular degeneration [2] and type 1 diabetes [3], these common variants already account for a large fraction of trait heritability. In contrast, for most complex traits and diseases, common variants identified by GWAS confer relatively small increments in risk and explain only a small proportion of trait heritability [4]. For example, for low-density lipoprotein cholesterol (LDL-C), GWAS based on up to ~100,000 individuals examined at ~2.5 million common variants [1], [5]-[6], have identified 35 loci associated with trait variation, with some also involved in modulating the risk of cardiovascular diseases. Common variants at these loci are estimated to account for 12.2% of the variability in LDL-C levels, about one-fourth of its genetic variance [1]. Several hypotheses have been formulated about the nature of the remaining heritability of lipid levels and other complex traits [4], [7], ranging from the potential role of copy number variants to contributions from a large number of common SNPs each with very small effects. In our view, common and rare variants that are poorly represented in common genotyping arrays might account for an important fraction of trait heritability. Ignoring these variants might not only preclude identification of important trait associated loci but also compromise estimates of heritability. Thus, fine mapping appears the logical next step after GWAS. Here, we have focused on seven genes located in five of the loci associated with LDL-C in our original GWAS for blood lipid levels (APOE, APOC1, APOC2, SORT1, LDLR, APOB and PCSK9) [5]. A sixth locus (corresponding to SNP rs16996148) that included a large number of genes and no obvious functional candidates was not further examined here. Together, the 5 SNPs identified in the original GWAS analyses of these 5 loci in >8,000 individuals (with follow-up genotyping of >10,000 individuals) explained only 3.1% of LDL-C variability. We set out to re-assess the contribution of these loci to trait heritability by evaluating a broader spectrum of variants. To catalog genetic variation in these regions, we first sequenced the exons and flanking regions of the seven genes in 256 unrelated Sardinians [8], each with extremely low or high LDL-C, and in an additional 120 HapMap samples (parents from the 30 CEU and 30 YRI trios). To assess the effect of identified polymorphisms, we genotyped detected variants and additional variants selected based on an early release of the 1000 Genomes Project in a cohort of 5,524 volunteers from the SardiNIA project [8]. Our results show that at these five loci, a combination of rare and common variants, some novel and some previously identified, are associated with LDL-C, and that, taken together they double the variance explained by the common variants detected in GWAS. Results To refine the contribution of five loci implicated by GWAS in the variability of LDL-C, we sequenced the exons and flanking regions of seven genes in 256 unrelated Sardinians [8] with LDL-C levels that were either extremely low (116 individuals, mean LDL-C = 70.4±16.0 mg/dl) or high (140 individuals, mean LDL-C = 205.9±19.6 mg/dl) (Materials and Methods), as well as an additional 120 HapMap samples (parents from the 30 CEU and 30 YRI trios). Observed heterozygosity per base pair per individual was 1.28×10-3 in the selected Sardinian individuals, 1.31×10-3 in the CEU and 1.99×10-3 in the YRI. Sequencing identified 782 variants, all submitted to dbSNP and now included in dbSNP releases 130 and later (for a complete list see Table S1). As expected, more variants were found in the HapMap YRI samples than in the HapMap CEU or in Sardinian individuals with extreme lipid levels (Table S2). Overall, we observed a 2[ratio]1 trend for enrichment of rare variants (MAF<1%) in the high LDL-C group compared to the low LDL-C group, similar to the observation by Johansen and colleagues [9] (Table S3), but this enrichment was only statistically significant for APOB (P = 0.03 using an exact test). To test for LDL-C association, we used logistic regression to compare individuals in the two categories, yielding 10 variants (in APOE, APOC1, SORT1, APOB, and PCKS9) with P<0.1 (Table S4). Because of the modest number of sequenced individuals and because no signal reached significance after Bonferroni adjustment, we judged these initial association analyses - which focused only on sequenced samples and only at coding regions - inconclusive. In addition to the loci discussed so far, our re-sequencing and genotyping effort also included B3GALT4 and B4GALT4, two loci that approached genome-wide significance in our initial GWAS analysis (each with 5×10-80.88 (Table 1, Figure 1A and Figure S2). In those three genes, the variant showing strongest association was non-coding and not in strong linkage disequilibrium (r2>0.4) with any tested coding variant. The most strongly associated marker at the SORT1 locus, rs583104 (p-value = 1.2×10-9) was in high LD (r2 = 0.77) with rs12740374 (p-value = 2.2×10-8), an intronic SNP in the CELSR2 gene that alters the hepatic expression of the SORT1 gene by creating a C/EBP (CCAAT/enhancer binding protein) transcription factor binding site [11]. Both markers were genotyped, so that under the hypothesis that rs12740374 is the causal variant underlying this association signal, the modest difference in p-values may be attributable to statistical fluctuation. Figure 1 Figure 1 Regional Association plots. At the remaining two loci, APOE and PCSK9, evidence for association peaked at low frequency (1-5%) variants not in strong linkage disequilibrium with the original GWAS signals. In both cases our analyses pointed to variants that were well studied in other contexts, but which are not included in typical GWAS panels or in the HapMap panel of European haplotypes commonly used to impute missing genotypes. Thus these variants were missed in previous GWAS analyses. In PCSK9, variant rs11591147, which leads to a non-synonymous R46L change in exon 1, was more strongly associated (P = 2.9×10-15, frequency (T) = 0.037, effect = -12.9 mg/dl; Table 1) than GWAS variant rs11206510, a SNP ~10 Kb upstream of the transcription start site of the gene (P = 5.7×10-7, frequency (C) = 0.24, effect = -3.7 mg/dl) (Figure 1C). Furthermore, rs11591147 explained the GWAS association signal (association at GWAS variant rs11206510 became non-significant (P = 0.013) when non-synonymous variant R46L/rs11591147 was included as a covariate, Figure 1D). This coding variant has been previously implicated in the regulation of blood lipid levels, including LDL-C, and in the susceptibility to coronary and ischemic heart disease [12]-[13]. At the APOE gene cluster, the strongest evidence of association was observed at the missense variant (R176C, also known as R158C [14]) rs7412 (P = 1.8×10-31, frequency (T) = 0.037, effect = -18.8 mg/dl) (Figure 1E). This variant did not account for the previously reported GWAS signal; marker rs4420638 indeed remained significantly associated (P = 6.4×10-10) after adjusting for rs7412. The missense variants at APOE and PCSK9 were not typed in the HapMap II data set, and were only recently added to genotyping arrays (Illumina 1MDuo). Thus they have not been assessed by any GWAS reported to date. We next conditioned on the top association signal at each of the 5 loci and sought to identify additional independently associated variants. To declare statistical significance at secondary signals, we used a p-value threshold of 1×10-4; corresponding to an adjustment for 500 independent tests across the five regions examined. At LDLR, we found an independently associated rare missense variant (rs72658864/V578A, P = 2.5×10-6 in the basic model, P = 3.9×10-6 in the conditional model, frequency (C) = 0.005; effect = 23.7 mg/dl) (Table 1 and Figure 1B). This variant appears to be specific to Sardinia (where we identified it in our SardiNIA cohort [8] by Sanger sequencing in 3 out of 256 individuals with extreme LDL-C; by Illumina genotyping in 51 out of 5,800 randomly ascertained individuals; and by Solexa sequencing in 1 out of 505 individuals, unpublished data). It is absent in the HapMap data set, not detected in 280 Northern European individuals sequenced within the 1000 Genomes Project, and monomorphic in >10,000 Finnish [15]-[16] and Norwegian [17]-[19] individuals genotyped with the MetaboChip (Materials and Methods, Table S6 and Table S7). Reassuringly, the variant was also observed, albeit with a lower frequency (0.00035), in TaqMan genotyping an independent sample of 5,661 Sardinians from different villages in Sardinia [20] (Materials and Methods). The change in lipid levels associated with this rare variant (23.7 mg/dl) is 4 times greater than that observed for the strongest associated common variant at the locus (5.7 mg/dl for rs73015013). At the APOE locus, we found a strong independent signal at non-synonymous variant rs429358 (C130R, also known as C112R [14]) (Table 1 and Figure 1F)(P = 1.2×10-12 in the basic model, P = 5.8×10-11 in the conditional analysis, frequency (C) = 0.071, effect = 9.3 mg/dl), which, together with rs7412, defines the three major isoforms of APOE (?2, ?3 and ?4) [14], [21]. This variant strongly correlates (r2 = 0.96) with the originally reported GWAS signal, rs4420638 (P = 4.6×10-12, frequency (G) = 0.097, effect = 7.8 mg/dl). So, at this locus, the initial GWAS analysis picked up one independent signal (a proxy of rs429358/C130R) but missed the strongest associated variant in the region (rs7412/R176C). There was no clear evidence for residual association after accounting for the two missense variants (Figure S3). Interestingly, the frequency of the derived allele C at rs429358 was remarkably lower in Sardinia (freq = 0.07, see Table 1) than that observed in the Finnish and Norwegian individuals (see Table S7) and several other European ancestry samples (freq~0.20) [22]-[24], resulting in a strikingly lower frequency of the ?4 haplotype (2.5% vs. 15%) [22]. Finally, at PCSK9, we observed a possible independent association at SNP rs2479415, in the non-coding region flanking the transcript (P = 1.1×10-7 in the basic model, P = 8×10-5 in the conditional model, frequency (T) = 0.59, effect = -3.6 mg/dl) (Table 1 and Figure 1D). This variant showed an independent trend also in ~10,000 Finnish and Norwegian individuals (one-sided P = 0.055 after conditioning for rs11591147). When the 5 GWAS SNPs were replaced by the 8 variants described here (1 each for SORT1 and APOB, 2 for APOE, PCSK9 and LDLR) the variance accounted for by those loci increased from 3.1% to 6.5%. Similar estimates were also obtained with ~10,000 Finnish and Norwegian individuals, where, on average, analysis of these 8 variants increased variance explained from 3.5% to 7.1% (Table 2 and Materials and Methods). Table 2 Table 2 Heritability estimates in all study samples. Discussion We conducted fine mapping of five loci associated with LDL-C at an unprecedented level of resolution. In particular, we sequenced individuals with extreme phenotype levels, and subsequently genotyped variants identified by us and by the 1000 Genomes Project in a larger sample. In a final step we also imputed additional variants in the region to account for limitations of genotyping assay design. At all but one of the loci, APOB, the most strongly associated variant was directly genotyped or sequenced, suggesting that our initial selection included the crucial variants. In three loci, we found strongly associated rare or low frequency variants - which (except for a variant in LDLR, which appears to be specific to Sardinia) had been extensively characterized in previous non-GWAS studies. In these cases, although the associated variants had been previously described, they had not been thoroughly examined in together with GWAS associated variants at the same loci - so that the relative contributions of GWAS identified SNPs and previously described variants remained unclear. In summary, we observed that: At SORT1 and APOB loci, association peaked at variants with similar effect size and frequency to the variants identified in GWAS; At the LDLR locus, in addition to confirming the GWAS signal, a rare variant with a large effect was found. This variant is currently unique to the island of Sardinia; At the APOE locus, an independently associated low frequency variant was identified. The signal was previously missed in GWAS because the variant was not included in the available genotyping chips or in the HapMap reference panels. An independently associated common variant similar in frequency and effect size to the original GWAS signal was also identified. At the last locus, PCSK9, the GWAS signal could be explained by a low frequency coding variant not included in the available GWAS genotyping chips or in the HapMap reference panels. Furthermore, there was evidence for one other independently associated variant. The strongest signals identified at APOE (both variants) and PCSK9 (the top hit) are likely to be the causal variants underlying the association signals. For SORT1, the variant exhibiting strongest association appears to be in strong linkage disequilibrium with a recently proposed functional polymorphism. In contrast, biological interpretation remains unclear for the other identified polymorphisms and requires further studies. Our results lead to several important major conclusions. First, it is striking that prior LDL-C GWAS have often missed signals due to low frequency variants (in two of the loci examined here, we identified strongly associated variants with frequency 1-5% that were missed in the original GWAS, because they were untyped or missing on imputation panels and poorly tagged by nearby SNPs). Sequencing in individuals with extreme trait values, along with large-scale imputation and genotyping, provided a better evaluation of the contribution of these loci to variation in LDL-C levels. A similar design was recently used to fine-map loci associated with fetal hemoglobin levels, a trait for which three loci can now account for about half of total variance [25]. Second, we show that in one of the five loci we fine-mapped, a previously missed low frequency variant can account for the GWAS signal - consistent with the hypothesis that at least some GWAS signals will be due to disequilibrium with nearby low frequency or rare variants [26]. There is considerable debate on how frequently this scenario will occur [27]. Our observations are compatible with some of the arguments made on both sides of this debate [26]-[27]. For example, in the case of PCSK9, a single low frequency variant explains the observed common variant association signal but did not appear to reduce the ability of the genome-wide association study to localize the functional element of interest. Furthermore, the effect of this variant was too small to be detectable in most linkage studies (including our own linkage analysis of >35,000 relative pairs in Sardinia). Further, a single low frequency variant (and not a cluster of variants) was sufficient to explain this association signal. Finally, our results show that if estimates are based only on the common variation assessed through GWAS, heritability at identified loci is likely to be underestimated. A more complete dissection, including common, low frequency and rare variants (some of which will be population specific), dramatically increased the proportion of heritability associated with the 5 loci examined here, from 3.1% to 6.5%. Notably, the variance explained by each locus increased when a rare variant was found as a primary or secondary hit (LDLR, APOE and PCSK9), even when the top GWAS SNP highly correlates with a strong association signal (LDLR and APOE). By contrast, only slight improvements were observed at loci where the most associated marker highly correlated with the GWAS SNPs and there was no evidence for additional independent signals, even when the GWAS variant is unlikely to be functional (SORT1 and APOB). Genome-wide association studies have proven to be an extremely productive strategy for identifying regions of the genome associated with complex traits, often leading to unexpected insights into complex trait biology. A major efficiency of these studies is that, by focusing on a subset of variants that can be genotyped using array based platforms, they can conveniently and economically survey many common variants in large numbers of individuals. Our results emphasize the utility of these genome-wide studies in identifying trait association regions, but also emphasize that caution is needed when genome-wide study results are used to quantify the overall contribution of a locus to trait heritability. In our opinion, and consistent with our results, accurate estimates of heritability will require more extensive examination of each identified locus. Broadly, this observation is consistent with recent simulation studies [28] which explore, in the context of a dichotomous trait, the relationship between effect sizes observed at GWAS SNPs and at true causal variants for the same locus. These simulation studies suggest that, most of the time, effect sizes estimated from GWAS would be similar to true effect sizes but that, some of the time, effect sizes estimated from GWAS might substantially underestimate the true effect size - especially in a scenario where rare variants are more likely to be causal. In cases where the effect size was underestimated by GWAS variants, a noticeable increase in heritability ensues. It is also interesting to note that the effect sizes estimated here for rare and low frequency variants (all >10 mg/dl) are larger than the effect sizes of any of the common variants identified in GWAS studies. Effect sizes of more rare alleles associated with familial hypercholesterolemia are even larger (see [29] for examples of PCSK9 variants with effects >100 mg/dl). This is consistent with the intuition that alleles with a large impact on LDL-C levels will be under strong natural selection and will, thus, be prevented from reaching high frequency in the population. Although rare and low frequency alleles with more modest impacts on LDL-C values are also likely to exist, we cannot detect them using available sample sizes and their detection must await studies of much larger sample sizes. In conclusion, these results underline that the subsequent sequencing of the coding regions around GWAS associations in individuals with extreme values followed by large scale imputation and genotyping is an important step in assessing the contribution of associated genomic regions to trait heritability. If similar trends to those described here are observed at the remaining LDL-C associated loci, extending our approach described to all known LDL-C susceptibility loci could lead to an increase in the proportion of variance they explain from ~12% to ~24%, exceeding half of the genetic variance for this trait. Due to economic considerations, our sequencing efforts focused on the coding regions of each gene and only on genes that appeared very likely to be involved in lipid metabolism. In each locus, we augmented the set of discovered variants with variants discovered by the 1000 Genomes Project, but that will likely miss very rare as well as population specific variants. We expect that more extensive fine-mapping efforts that more comprehensively examine non-coding regions could identify additional trait associated variants. Ultimately, unbiased whole genome sequencing based association analyses might be required to fully explain the heritability of a trait like LDL-C, facilitating the comprehensive assessment of rare, population specific, and non-SNP variation. In the meantime, directed sequencing and large scale genotyping appears to be a promising approach. Materials and Methods Ethics statement All individuals studied and all analyses on their samples were done according to the Declaration of Helsinki and were approved by the local medical ethics and institutional review committees. Samples description The SardiNIA project is a population based study of aging-related traits that includes 6,148 related individuals from the Ogliastra region of Sardinia, Italy [8], [30]. During physical examination, a blood sample was collected from each individual and divided into two aliquots, one for DNA extraction and the other to characterize several blood phenotypes, including lipids levels. Specifically, LDL-C values were derived using the Friedwald formula that combines HDL and total cholesterol levels. The Finnish and Norwegian individuals are Type 2 Diabetes patients and unaffected individuals collected from several studies. Specifically, Finnish studies are: Dehko 2D 2007 (D2D 2007), Dose Responses to Exercise Training (DrsEXTRA), Diabetes Prevention Study (DPS), FUSION stage 2 [15] samples (from ACTION LADA, D2D 2004, FINRISK 1987, FINRISK 2002, Health 2000, Savitaipale) and Metabolic Syndrome in Men (METSIM) [16]; Norwegian studies are: The Nord- Trøndelag Health Study (HUNT 2) [17]-[18] and The Tromsø Study (TROMSØ) [19]. Baseline clinic characteristics of the SardiNIA, Finnish and Norwegian studies are reported in Table S7. The independent Sardinian sample used for assessing the frequency of the rare variant at LDLR consists of 5,661 individuals belonging to 884 families enrolled in the SharDNA study [20], which recruited volunteers from a cluster of villages located in the Ogliastra region: Talana, Urzulei, Baunei, Triei, Seui, Seulo, Ussassai, Perdasdefogu, Escalaplano and Loceri. Observed heterozygotes were unrelated to those observed in the SardiNIA study based on demographic records to track origin of individuals up to 10 generations. Sequencing Sequencing of the 256 Sardinians and the 120 HapMap samples (parents from the 30 CEU and 30 YRI trios) was carried out at the University of Washington Genome Sequencing Center through the NHLBI Resequencing & Genotyping Service (Debbie Nickerson, PI). To select the 256 individuals to be sequenced, we adjusted LDL levels by age and sex and then identified individuals in the top and bottom 5% of the distribution (individuals under lipid-lowering therapy were not considered). Among those, we selected all unrelated individuals who had at least one sibling in the study and were genotyped with 500 K or 10 K arrays [30], to facilitate downstream follow-up and imputation analyses. Among the 782 variants detected by sequencing, two loss-of-function variants were observed. However, these were identified only on HapMap samples (see Table S8). A common in-frame insertion in APOB was observed in Sardinia and in HapMap CEU samples but was not associated with LDL-C after multiple testing adjustment (rs17240441, P = 3.0×10-4; see Figure S1C and S1D, Table S8). The observed heterozygosity per bp/per individual was 0.00128, 0.00131 and 0.00199 in Sardinia, CEU and YRI samples, respectively. Concordance rate of HapMap II and III phases genotypes with those obtained from Sanger sequencing was 99.63%, while a lower rate (98.1%) was observed with genotypes obtained from the low-pass sequencing 1000 Genomes Project (43 CEU and 42 YRI samples were c????????????????‘E??????HQµÁ9???HQµÁ9????f5?????f5????enetics, Wellcome Trust Sanger Institute, Hinxton, U.K.; the 2Department of Twin Research and Genetic Epidemiology, King's College London, London, U.K.; 3Istituto di Neurogenetica e Neurofarmacologia, CNR, Monserrato, Italy; the 4Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; the 5Institute for Community Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany; the 6Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts; the 7National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts; 8CNRS-UMR-8090, Institut Pasteur de Lille and Lille 2 University, Lille, France; the 9MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, U.K.; the 10Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K.; the 11Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.; the 12Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts; the 13Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; the 14Department of Medicine, Harvard Medical School, Boston, Massachusetts; the 15Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, U.K.; the 16Wellcome Trust Sanger Institute, Hinxton, Cambridge, U.K.; the 17Hematology Division, Children's Hospital Boston, Boston, Massachusetts; the 18Cardiovascular Research Institute, MedStar Research Institute, Washington Hospital Center, Washington, D.C.; the 19Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; the 20Cardiovascular Institute, University of Pennsylvania, Philadelphia, Pennsylvania; 21Medicine, University of Ottawa Heart Institute, Ottawa, Canada; the 22Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts; 23Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany; 24Medizinische Klinik II, Universität zu Lübeck, Lübeck, Germany; the 25Department of Health Sciences, University of Leicester, Leicester, U.K.; the 26McKusick-Nathans Institute of Genetic Medicine and Department of Medicine, Johns Hopkins University, Baltimore, Maryland; 27INSERM U70, Villejuif, France; 28University Paris-Sud, Orsay, France; the 29Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas; the 30Division of Endocrinology and Diabetes, Graduate School Molecular Endocrinology and Diabetes, University of Ulm, Ulm, Germany; the 31Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland; 32Biological Psychology, VU University Amsterdam, Amsterdam, the Netherlands; the 33Department of Medicine III, Division Prevention and Care of Diabetes, University of Dresden, Dresden, Germany; the 34Department of Medicine, University of Leipzig, Leipzig, Germany; the 35Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland; the 36Department of Epidemiology and Public Health, Imperial College London, London, U.K.; the 37Clinical Trials Services Unit, University of Oxford, Oxford, U.K.; the 38Departments of Epidemiology, Biostatistics, and Medicine, Johns Hopkins University, Baltimore, Maryland; the 39Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland; 40Cardiovascular Epidemiology and Genetics, Institut Municipal D'investigacio Medica, and CIBER Epidemiología y Salud Publica, Barcelona, Spain; the 41Clinical Research Branch, National Institute on Aging, Baltimore, Maryland; the 42Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Boston, Massachusetts; 43Dipartimento di Ricerca Cardiovascolare, Istituto di Ricerche Farmacologiche \"Mario Negri,\" Milano, Italy; the 44Department of Cardiovascular Medicine, University of Oxford, Oxford, U.K.; 45Immunology and Transfusion Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany; the 46Division of Community Health Sciences, St George's, University of London, London, U.K.; 47Cardiovascular Sciences, University of Leicester, Leicester, U.K.; the 48Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; the 49Human Genetics Unit, Medical Research Council, Edinburgh, U.K.; 50Statistical Genetics, Centre National de Genotypage, Evry, France; the 51Institute for Clinical Diabetology, German Diabetes Centre, Leibniz Centre at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; the 52Interfaculty Institute for Genetics and Functional Genomics, Ernst Moritz Arndt University Greifswald, Greifswald, Germany; the 53Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan; 54Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland; 55LURIC Study nonprofit LLC, Freiburg, Germany; 56Genetics, Drug Discovery, GlaxoSmithKline, King of Prussia, Pennsylvania; 57Statistics, deCODE genetics, Reykjavik, Iceland; the 58National Heart and Lung Institute, Hammersmith Hospital Campus, Imperial College London, London, U.K.; the 59Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland; the 60Interdisciplinary Centre for Clinical Research, University of Leipzig, Leipzig, Germany; the 61Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland; 62Centre National de Genotypage, Evry, France; 63Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania; the 64Department of Clinical Sciences, Lund University, Malmö, Sweden; the 65Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; the 66Diabetes and Lipids Research Laboratory, Madrid, Spain; 67Fundación Investigación Biomédica del Hospital Clínico San Carlos, Madrid, Spain; the 68Department of Social Medicine, University of Bristol, Bristol, U.K.; the 69Institute of Human Genetics, Technische Universität München, München, Germany; the 70Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; the 71Department of Genetics, University of North Carolina, Chapel Hill, North Carolina; the 72Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts; the 73Institute for Clinical Chemistry and Laboratory Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany; the 74Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota; the 75Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 76The Institute of Molecular Medicine, University of Helsinki, Helsinki, Finland; 77Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland; 78Andrija Stampar School of Public Health, University of Zagreb Medical School, Zagreb, Croatia; 79Gen-Info Ltd., Zagreb, Croatia; the 80Institute of Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; the 81Department of Human Genetics, Faculty of Medicine, McGill University Montreal, Montreal, Canada; the 82Genome Quebec Innovation Centre, Montreal, Canada; the 83Department of Metabolic Diseases, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; the 84Croatian Centre for Global Health, University of Split Medical School, Split, Croatia; the 85Gerontology Research Center, National Institute on Aging, Baltimore, Maryland; 86Leibniz-Institut für Arterioskleroseforschung an der Universität Münster, Universität Münster, Münster, Germany; the 87General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts; the 88Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington; 89Population Genomics, deCODE genetics, Reykjavik, Iceland; the 90Faculty of Medicine, University of Iceland, Reykjavik, Iceland; the 91LIFE Study Centre, University of Leipzig, Leipzig, Germany; the 92MedStar Research Institute, Baltimore, Maryland; the 93Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany; 94Klinikum Grosshadern, Munich, Germany; the 95Department of Neurology, Technische Universität München, München, Germany; the 96Wellcome Trust Case Control Consortium; the 97Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; the 98Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts; 99Public Health and Primary Care, Clinical Gerontology Unit, University of Cambridge, Cambridge, U.K.; the 100Pathology Division, Children's Hospital Boston, Boston, Massachusetts; 101Genomic Medicine, Hammersmith Hospital, Imperial College London, London, U.K.; 102Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, U.K.; and 103Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland.? Heñ ??????8JµÁ9???8JµÁ9???????????????????8JµÁ9????L4????????????????????tion, Harvard School of Public Health, Boston, MA, USA 46 Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 47 Sections of General Internal Medicine, Preventive Medicine and Endocrinology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA 48 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden 49 MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, BS8 2BN, UK 50 Centre for Cancer Genetic Epidemiology, Departments of Oncology and Public Health and Primary Care, University of Cambridge, Cambridge, UK 51 MPRI, Merck & Co., Inc, 126 Lincoln Ave, Rahway, NJ 07065, USA 52 National Institute for Health and Welfare, Finland 53 Department of General Practice and Primary health Care, University of Helsinki, Finland 54 Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland 55 Folkhalsan Research Centre, Helsinki, Finland 56 Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA 57 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA 58 Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA 60 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA 61 Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA 62 Laboratory of Neurogenetics, National Institute of Ageing, Bethesda, MD, USA 63 Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands 64 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK 65 NHLBI Center for Population Studies, Bethesda, MD, USA 66 Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA, USA 67 Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, CB2 0QQ, UK 68 Medical School; University of Zagreb; Zagreb, 10000; Croatia 69 Department of Obstetrics and Gynaecology, Erasmus MC, Rotterdam, the Netherlands 70 Human Genetics, Genome Institute of Singapore, Singapore 71 Division of Cardiology, Boston University School of Medicine, USA 72 Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia 73 Avon Longitudinal Study of Parents and Children (ALSPAC), Department of Social Medicine, University of Bristol, BS8 2BN, UK 74 Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, USA 75 Department of Pediatrics, University of Iowa, Iowa City, IA, USA 76 Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA 77 Department of Oral and Dental Science, University of Bristol, BS1 2LY, UK 78 Department of Medicine, Indiana University School of Medicine, Indiana, USA 79 Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA 80 Genetic and Molecular Epidemiology Laboratory, McMaster University, 1200 Main St. W MDCL Rm. 3206, Hamilton, ON, L8N3Z5, Canada 81 Amgen, 1 Kendall Square, Building 100, Cambridge, MA 02139, USA 83 School of Women's and Infants' Health, The University of Western Australia, Australia 84 Gen Info Ltd; Zagreb, 10000; Croatia 85 Cardiovascular Disease, Merck Research Laboratory, Rahway, NJ 07065, USA 86 Croatian Centre for Global Health; University of Split Medical School; Split, 21000; Croatia 87 Gerontology Research Center, National Institute on Aging, Baltimore, Maryland, USA 88 UOC Geriatria - Istituto Nazionale Ricovero e Cura per Anziani IRCCS - Rome, Italy 89 Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA 90 Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, USA 91 Division of Community Health Sciences, St. George's, University of London, London, UK 92 Icelandic Cancer Registry, Reykjavik, Iceland 93 Departments of Epidemiology and Public Health and Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 94 Department of Internal Medicine, BH-10 Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland 95 Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany 96 Klinikum Grosshadern, Munich, Germany 97 Molecular Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia 98 Division of Cardiology, Brigham and Women's Hospital 99 Faculty of Medicine, University of Iceland, Reykjavik, Iceland 100 Department of Paediatrics, University of Cambridge, Cambridge, UK Istituto di Neurogenetica e Neurofarmacologia del CNR, Monserrato, 09042 Cagliari, Italy. ??¡ff??????8JµÁ9??? v4????????????????????miology, Harvard School of Public Health, Boston, Massachusetts, USA 148Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA 149Genetic Epidemiology & Clinical Research Group, Department of Public Health & Clinical Medicine, Section for Medicine, UmeÃ¥ University Hospital, UmeÃ¥, Sweden 150London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK 151Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA 152The Welch Center for Prevention, Epidemiology, and Clinical Research, School of Medicine and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21287, USA 153Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55454, USA 154Department of Endocrinology and Diabetes, Norfolk and Norwich University Hospital NHS Trust, Norwich, NR1 7UY, UK 155Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio 70210, Finland 156Faculty of Health Science, University of Southern Denmark, Odense, Denmark 157Institute of Biomedical Science, Faculty of Health Science, University of Copenhagen, Denmark 158Department of Neurology, General Central Hospital, 39100 Bolzano, Italy 159Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany 160Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany 161Klinikum Grosshadern, Munich, Germany 162School of Medicine, University of Split, Soltanska 2, 21000 Split, Croatia 163Gen-Info Ltd, Ruzmarinka 17, 10000 Zagreb, Croatia 164Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA 165Department of Medicine, Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA 166Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA 167National Institute for Health and Welfare, Unit of Diabetes Prevention, Helsinki, Finland 168Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington, USA 169Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, NIH, Baltimore, Maryland, USA 170Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland 171Lab of Cardiovascular Sciences, National Institute on Aging, NIH, Baltimore, Maryland, USA 172Department of Clinical Sciences/Clinical Chemistry, University of Oulu, Box 5000, Fin-90014 University of Oulu, Finland 173National Institute of Health and Welfare, Aapistie 1, P.O. Box 310, Fin-90101 Oulu, Finland 174Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, USA?stitE???????à????????‘¢affiliazioni?????Università di Cagliari??‘¤affiliazioni????§1 Laboratory of Cardiovascular Sciences, National Institute on Aging, NIH, Baltimore, USA; 2 Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy 09042; 3 Longitudinal Study Section, National Institute on Aging, NIH, Baltimore, USA; and 4 Laboratory of Genetics, National Institute on Aging, NIH, Baltimore, USA??‘«affiliazioni????@1Istituto di Neurogenetica e Neurofarmacologia, Consiglio Nazionale delle Ricerche, Cagliari, Italy 2 Dipartimento di Scienze Biomediche e Biotecnologie, Università’ degli Studi di Cagliari, Cagliari, Italy 3 Ente Ospedaliero Galliera, Genova, Italy 4 Fondazione Policlinico Mangiagalli e Regina Elena, Milano, Italy??‘¯affiliazioni????âIstituto di Neurogenetica e Neurofarmacologia, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Italy. Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy. Center for Advanced Studies, Research and Development in Sardinia (CRS4), Laboratorio di Bioinformatica, Parco tecnologico della Sardegna, Pula, Italy. CRS4, Laboratorio di Genomica, Parco tecnologico della Sardegna, Pula, Italy. Centro Sclerosi Multipla, Dipartimento di Scienze Neurologiche e Cardiovascolari, Università di Cagliari, Cagliari, Italy. Istituto di Neurologia Clinica, Università di Sassari, Sassari, Italy. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. Dipartimento di Scienze Biomediche e biotecnologie, Università di Cagliari, Cagliari, Italy. Azienda Ospedaliera Brotzu, Centro Trasfusionale, Cagliari, Italy. Azienda Sanitaria Locale 1, Sassari, Italy. Azienda Ospedaliera Brotzu, Divisione di Neurologia, Cagliari, Italy. Presidio Ospedaliero, Divisione Neurologia, Ozieri, Italy. Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland, USA.??‘°affiliazioni????)1Department of Mathematics and Statistics, Calvin College, Grand Rapids, MI 49546 2Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 3Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari, Italy 09042. 4Bioinformatics Graduate Program, The University of Michigan Medical School, Ann Arbor, MI 48109. 5 National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland??‘²affiliazioni????À1-Istituto di Neurogenetica e Neurofarmacologia CNR- Cittadella Universitaria- Monserrato, Cagliari, Italy. 2- Neuropsichiatria Infantile- Azienda Ospedaliero- Universitaria, Cagliari, Italy.??‘³affiliazioni????¢1- Dipartimento di Scienze Chirurgiche e Odontostomatologiche, Università di Cagliari. 2- Istituto di Neurogenetica e Neurofarmacologia CNR, Monserrato, Cagliari.??‘´affiliazioni????¦Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy??‘·affiliazioni???#g1 Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK 2 Genetics of Complex Traits, Peninsula Medical School, University of Exeter, UK 3 deCODE Genetics, Reykjavik, Iceland 4 Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston MA 02215, USA 5 Harvard Medical School, Boston, Massachusetts, USA 6 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 7 Department of Public Health, Indiana University School of Medicine, Indiana, USA 8 Melvin and Bren Simon Cancer Center, Indiana University, Indiana, USA 9 The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA 10 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA 11 Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands 12 Queensland Statistical Genetics, Queensland Institute of Medical Research, Brisbane, Australia 13 The University of Queensland, Brisbane, Australia 14 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland 15 Estonian Genome Center, University of Tartu, Tartu, Estonia 16 Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia 17 Genotyping Core Facility, Estonian Biocenter, Tartu, Estonia 18 Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark 19 Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands 20 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indiana, USA 21 Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland 22 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland 23 Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA 24 Department of Twin Research and Genetic Epidemiology, King's College London, London, UK 25 Istituto di Neurogenetica e Neurofarmacologia, Consiglio Nazionale delle Ricerche, Cagliari, Italy 26 Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, USA 27 Icelandic Heart Association, Kopavogur, Iceland 28 University of Iceland, Reykjavik, Iceland 29 Netherlands Consortium of Healthy Aging, Rotterdam, the Netherlands 30 Genetic-Epidemiology Unit, Department of Epidemiology and Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands 31 Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany 32 Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy 33 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 34 MRC Human Genetics Unit; Institute of Genetics and Molecular Medicine, Western General Hospital; Edinburgh, UK 35 Scripps Genomic Medicine, The Scripps Translational Science Institute, and The Scripps Research Institute, La Jolla, CA, USA 36 Medical Genetics, Department of Reproductive Sciences and Development, University of Trieste, Trieste, Italy 37 Centre for Genetic Epidemiology and Biostatistics University of Western Australia, Australia 38 Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland 39 Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy 40 Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK 41 Tulane University, New Orleans, LA, USA 42 Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA 43 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA 44 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892, USA 45 Department of Nutrition, Harvard School of Public Health, Boston, MA, USA 46 Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 47 Sections of General Internal Medicine, Preventive Medicine and Endocrinology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA 48 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden 49 MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, BS8 2BN, UK 50 Centre for Cancer Genetic Epidemiology, Departments of Oncology and Public Health and Primary Care, University of Cambridge, Cambridge, UK 51 MPRI, Merck & Co., Inc, 126 Lincoln Ave, Rahway, NJ 07065, USA 52 National Institute for Health and Welfare, Finland 53 Department of General Practice and Primary health Care, University of Helsinki, Finland 54 Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland 55 Folkhalsan Research Centre, Helsinki, Finland 56 Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA 57 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA 58 Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA 60 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA 61 Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA 62 Laboratory of Neurogenetics, National Institute of Ageing, Bethesda, MD, USA 63 Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands 64 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK 65 NHLBI Center for Population Studies, Bethesda, MD, USA 66 Hebrew SeniorLife Institute for Aging Research and Harvard Medical School, Boston, MA, USA 67 Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, CB2 0QQ, UK 68 Medical School; University of Zagreb; Zagreb, 10000; Croatia 69 Department of Obstetrics and Gynaecology, Erasmus MC, Rotterdam, the Netherlands 70 Human Genetics, Genome Institute of Singapore, Singapore 71 Division of Cardiology, Boston University School of Medicine, USA 72 Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia 73 Avon Longitudinal Study of Parents and Children (ALSPAC), Department of Social Medicine, University of Bristol, BS8 2BN, UK 74 Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, USA 75 Department of Pediatrics, University of Iowa, Iowa City, IA, USA 76 Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA 77 Department of Oral and Dental Science, University of Bristol, BS1 2LY, UK 78 Department of Medicine, Indiana University School of Medicine, Indiana, USA 79 Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA 80 Genetic and Molecular Epidemiology Laboratory, McMaster University, 1200 Main St. W MDCL Rm. 3206, Hamilton, ON, L8N3Z5, Canada 81 Amgen, 1 Kendall Square, Building 100, Cambridge, MA 02139, USA 83 School of Women's and Infants' Health, The University of Western Australia, Australia 84 Gen Info Ltd; Zagreb, 10000; Croatia 85 Cardiovascular Disease, Merck Research Laboratory, Rahway, NJ 07065, USA 86 Croatian Centre for Global Health; University of Split Medical School; Split, 21000; Croatia 87 Gerontology Research Center, National Institute on Aging, Baltimore, Maryland, USA 88 UOC Geriatria - Istituto Nazionale Ricovero e Cura per Anziani IRCCS - Rome, Italy 89 Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA 90 Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, USA 91 Division of Community Health Sciences, St. George's, University of London, London, UK 92 Icelandic Cancer Registry, Reykjavik, Iceland 93 Departments of Epidemiology and Public Health and Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 94 Department of Internal Medicine, BH-10 Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland 95 (literal)
Titolo
  • Fine mapping of five Loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. (literal)
Abstract
  • Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ~10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci. (literal)
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