Ron Do

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes.

Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes

A Deep Look Into Our Genes Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective by Casals and Bertranpetit). Tennessen et al. (p. 64, published online 17 May), looking at all of the protein-coding genes in the human genome, and Nelson et al. (p. 100, published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health. Most functionally consequential variants in protein-coding genes are rare and, thus, difficult to find. As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

A High-Coverage Genome Sequence from an Archaic Denisovan Individual

Ancient Genomics The Denisovans were archaic humans closely related to Neandertals, whose populations overlapped with the ancestors of modern-day humans. Using a single-stranded library preparation method, Meyer et al. (p. 222, published online 30 August) provide a detailed analysis of a high-quality Denisovan genome. The genomic sequence provides evidence for very low rates of heterozygosity in the Denisova, probably not because of recent inbreeding, but instead because of a small population size. The genome sequence also illuminates the relationships between humans and archaics, including Neandertals, and establishes a catalog of genetic changes within the human lineage. A close-up look provides clues to the relationships between modern humans, Denisovans, and Neandertals. We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.

Loss-of-Function Mutations in APOC3, Triglycerides, and Coronary Disease

BACKGROUND Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. METHODS We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. RESULTS An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)). CONCLUSIONS Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).

Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction

Summary Myocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance1,2. When MI occurs early in life, the role of inheritance is substantially greater1. Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families3–8 whereas common variants at more than 45 loci have been associated with MI risk in the population9–15. Here, we evaluate the contribution of rare mutations to MI risk in the population. We sequenced the protein-coding regions of 9,793 genomes from patients with MI at an early age (≤50 years in males and ≤60 years in females) along with MI-free controls. We identified two genes where rare coding-sequence mutations were more frequent in cases versus controls at exome-wide significance. At low-density lipoprotein receptor (LDLR), carriers of rare, damaging mutations (3.1% of cases versus 1.3% of controls) were at 2.4-fold increased risk for MI; carriers of null alleles at LDLR were at even higher risk (13-fold difference). This sequence-based estimate of the proportion of early MI cases due to LDLR mutations is remarkably similar to an estimate made more than 40 years ago using total cholesterol16. At apolipoprotein A-V (APOA5), carriers of rare nonsynonymous mutations (1.4% of cases versus 0.6% of controls) were at 2.2-fold increased risk for MI. When compared with non-carriers, LDLR mutation carriers had higher plasma LDL cholesterol whereas APOA5 mutation carriers had higher plasma triglycerides. Recent evidence has connected MI risk with coding sequence mutations at two genes functionally related to APOA5, namely lipoprotein lipase15,17 and apolipoprotein C318,19. When combined, these observations suggest that, beyond LDL cholesterol, disordered metabolism of triglyceride-rich lipoproteins contributes to MI risk.

Searching for missing heritability: Designing rare variant association studies

Significance Discovering the genetic basis of common diseases, such as diabetes, heart disease, and schizophrenia, is a key goal in biomedicine. Genomic studies have revealed thousands of common genetic variants underlying disease, but these variants explain only a portion of the heritability. Rare variants are also likely to play an important role, but few examples are known thus far, and initial discovery efforts with small sample sizes have had only limited success. In this paper, we describe an analytical framework for the design of rare variant association studies of disease. It provides guidance with respect to sample size, as well as the roles of selection, disruptive and missense alleles, gene-specific allele frequency thresholds, isolated populations, gene sets, and coding vs. noncoding regions. Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set.

Exome sequencing and the genetic basis of complex traits

Shamil Sunyaev and colleagues present exome sequencing methods and their applications in studies to identify the genetic basis of human complex traits. They include analyses of the whole-exome sequences of 438 individuals from across several studies.

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.

Inactivating mutations in NPC1L1 and protection from coronary heart disease

BACKGROUND Ezetimibe lowers plasma levels of low-density lipoprotein (LDL) cholesterol by inhibiting the activity of the Niemann-Pick C1-like 1 (NPC1L1) protein. However, whether such inhibition reduces the risk of coronary heart disease is not known. Human mutations that inactivate a gene encoding a drug target can mimic the action of an inhibitory drug and thus can be used to infer potential effects of that drug. METHODS We sequenced the exons of NPC1L1 in 7364 patients with coronary heart disease and in 14,728 controls without such disease who were of European, African, or South Asian ancestry. We identified carriers of inactivating mutations (nonsense, splice-site, or frameshift mutations). In addition, we genotyped a specific inactivating mutation (p.Arg406X) in 22,590 patients with coronary heart disease and in 68,412 controls. We tested the association between the presence of an inactivating mutation and both plasma lipid levels and the risk of coronary heart disease. RESULTS With sequencing, we identified 15 distinct NPC1L1 inactivating mutations; approximately 1 in every 650 persons was a heterozygous carrier for 1 of these mutations. Heterozygous carriers of NPC1L1 inactivating mutations had a mean LDL cholesterol level that was 12 mg per deciliter (0.31 mmol per liter) lower than that in noncarriers (P=0.04). Carrier status was associated with a relative reduction of 53% in the risk of coronary heart disease (odds ratio for carriers, 0.47; 95% confidence interval, 0.25 to 0.87; P=0.008). In total, only 11 of 29,954 patients with coronary heart disease had an inactivating mutation (carrier frequency, 0.04%) in contrast to 71 of 83,140 controls (carrier frequency, 0.09%). CONCLUSIONS Naturally occurring mutations that disrupt NPC1L1 function were found to be associated with reduced plasma LDL cholesterol levels and a reduced risk of coronary heart disease. (Funded by the National Institutes of Health and others.).

Genetic Variants of FTO Influence Adiposity, Insulin Sensitivity, Leptin Levels, and Resting Metabolic Rate in the Quebec Family Study

OBJECTIVE—A genome-wide association study conducted by the Wellcome Trust Case Control Consortium recently associated single nucleotide polymorphisms (SNPs) in the FTO (fatso/fat mass and obesity associated) gene with type 2 diabetes. These associations were shown to be mediated by obesity. Other research groups found similar results in Europeans and Hispanics but not African Americans. The mechanism by which FTO influences obesity and type 2 diabetes is currently unknown. The present study investigated the role of two FTO SNPs (rs17817449 and rs1421085) in adiposity, insulin sensitivity, and body weight regulation, including energy intake and expenditure. RESEARCH DESIGN AND METHODS—We genotyped 908 individuals from the Quebec City metropolitan area that participated in the Quebec Family Study, a long-term study of extensively phenotyped individuals designed to investigate factors involved in adiposity. RESULTS—We found significant associations for both SNPs with several obesity-related phenotypes. In particular, rs17817449 was associated with BMI (P = 0.0014), weight (P = 0.0059), and waist circumference (P = 0.0021) under an additive model. In addition, this FTO SNP influenced fasting insulin (P = 0.011), homeostasis model assessment of insulin resistance (P = 0.038), and an insulin sensitivity index derived from an oral glucose tolerance test (P = 0.0091). Associations were also found with resting metabolic rate (RMR) (P = 0.042) and plasma leptin levels (P = 0.036). Adjustment for BMI abolished the associations with insulin sensitivity, RMR, and plasma leptin levels. CONCLUSIONS—These results confirm that genetic variation at the FTO locus contributes to the etiology of obesity, insulin resistance, and increased plasma leptin levels.