Mark J. Rieder

Selecting a Maximally Informative Set of Single-Nucleotide Polymorphisms for Association Analyses Using Linkage Disequilibrium

Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.

Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

It is well established that autism spectrum disorders (ASD) have a strong genetic component; however, for at least 70% of cases, the underlying genetic cause is unknown. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes—so-called sporadic or simplex families—we sequenced all coding regions of the genome (the exome) for parent–child trios exhibiting sporadic ASD, including 189 new trios and 20 that were previously reported. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19), for a total of 677 individual exomes from 209 families. Here we show that de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD. Moreover, 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected β-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes: CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3 and SCN1A. Combined with copy number variant (CNV) data, these results indicate extreme locus heterogeneity but also provide a target for future discovery, diagnostics and therapeutics.

Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans

Blood concentrations of lipoproteins and lipids are heritable risk factors for cardiovascular disease. Using genome-wide association data from three studies (n = 8,816 that included 2,758 individuals from the Diabetes Genetics Initiative specific to the current paper as well as 1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables reported in a companion paper in this issue) and targeted replication association analyses in up to 18,554 independent participants, we show that common SNPs at 18 loci are reproducibly associated with concentrations of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and/or triglycerides. Six of these loci are new (P < 5 × 10−8 for each new locus). Of the six newly identified chromosomal regions, two were associated with LDL cholesterol (1p13 near CELSR2, PSRC1 and SORT1 and 19p13 near CILP2 and PBX4), one with HDL cholesterol (1q42 in GALNT2) and five with triglycerides (7q11 near TBL2 and MLXIPL, 8q24 near TRIB1, 1q42 in GALNT2, 19p13 near CILP2 and PBX4 and 1p31 near ANGPTL3). At 1p13, the LDL-associated SNP was also strongly correlated with CELSR2, PSRC1, and SORT1 transcript levels in human liver, and a proxy for this SNP was recently shown to affect risk for coronary artery disease. Understanding the molecular, cellular and clinical consequences of the newly identified loci may inform therapy and clinical care.

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.

Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome

We demonstrate the successful application of exome sequencing to discover a gene for an autosomal dominant disorder, Kabuki syndrome (OMIM%147920). We subjected the exomes of ten unrelated probands to massively parallel sequencing. After filtering against existing SNP databases, there was no compelling candidate gene containing previously unknown variants in all affected individuals. Less stringent filtering criteria allowed for the presence of modest genetic heterogeneity or missing data but also identified multiple candidate genes. However, genotypic and phenotypic stratification highlighted MLL2, which encodes a Trithorax-group histone methyltransferase: seven probands had newly identified nonsense or frameshift mutations in this gene. Follow-up Sanger sequencing detected MLL2 mutations in two of the three remaining individuals with Kabuki syndrome (cases) and in 26 of 43 additional cases. In families where parental DNA was available, the mutation was confirmed to be de novo (n = 12) or transmitted (n = 2) in concordance with phenotype. Our results strongly suggest that mutations in MLL2 are a major cause of Kabuki syndrome.

Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations

Evidence for the etiology of autism spectrum disorders (ASDs) has consistently pointed to a strong genetic component complicated by substantial locus heterogeneity. We sequenced the exomes of 20 individuals with sporadic ASD (cases) and their parents, reasoning that these families would be enriched for de novo mutations of major effect. We identified 21 de novo mutations, 11 of which were protein altering. Protein-altering mutations were significantly enriched for changes at highly conserved residues. We identified potentially causative de novo events in 4 out of 20 probands, particularly among more severely affected individuals, in FOXP1, GRIN2B, SCN1A and LAMC3. In the FOXP1 mutation carrier, we also observed a rare inherited CNTNAP2 missense variant, and we provide functional support for a multi-hit model for disease risk. Our results show that trio-based exome sequencing is a powerful approach for identifying new candidate genes for ASDs and suggest that de novo mutations may contribute substantially to the genetic etiology of ASDs.

Use of Pharmacogenetic and Clinical Factors to Predict the Therapeutic Dose of Warfarin

Initiation of warfarin therapy using trial‐and‐error dosing is problematic. Our goal was to develop and validate a pharmacogenetic algorithm. In the derivation cohort of 1,015 participants, the independent predictors of therapeutic dose were: VKORC1 polymorphism −1639/3673 G>A (−28% per allele), body surface area (BSA) (+11% per 0.25 m2), CYP2C9*3 (−33% per allele), CYP2C9*2 (−19% per allele), age (−7% per decade), target international normalized ratio (INR) (+11% per 0.5 unit increase), amiodarone use (−22%), smoker status (+10%), race (−9%), and current thrombosis (+7%). This pharmacogenetic equation explained 53–54% of the variability in the warfarin dose in the derivation and validation (N= 292) cohorts. For comparison, a clinical equation explained only 17–22% of the dose variability (P < 0.001). In the validation cohort, we prospectively used the pharmacogenetic‐dosing algorithm in patients initiating warfarin therapy, two of whom had a major hemorrhage. To facilitate use of these pharmacogenetic and clinical algorithms, we developed a nonprofit website, http://www.WarfarinDosing.org.

Sequence variation in the human angiotensin converting enzyme

Angiotensin converting enzyme (encoded by the gene DCP1, also known as ACE) catalyses the conversion of angiotensin I to the physiologically active peptide angiotensin II, which controls fluid-electrolyte balance and systemic blood pressure. Because of its key function in the renin-angiotensin system, many association studies have been performed with DCP1. Nearly all studies have associated the presence (insertion, I) or absence (deletion, D) of a 287-bp Alu repeat element in intron 16 with the levels of circulating enzyme or cardiovascular pathophysiologies. Many epidemiological studies suggest that the DCP1*D allele confers increased susceptibility to cardiovascular disease; however, other reports have found no such association or even a beneficial effect (refs 4, 5, 6, 7). We present here the complete genomic sequence of DCP1 from 11 individuals, representing the longest contiguous scan (24 kb) for sequence variation in human DNA. We identified 78 varying sites in 22 chromosomes that resolved into 13 distinct haplotypes. Of the variant sites, 17 were in absolute linkage disequilibrium with the commonly typed Alu insertion/deletion polymorphism, producing two distinct and distantly related clades. We also identified a major subdivision in the Alu deletion clade that enables further analysis of the traits associated with this gene. The diversity uncovered in DCP1 is comparable to that described for other regions in the human genome. The highly correlated structure in DCP1 raises important issues for the determination of functional DNA variants within genes and genetic studies in humans based on marker association.

Population history and natural selection shape patterns of genetic variation in 132 genes.

Identifying regions of the human genome that have been targets of natural selection will provide important insights into human evolutionary history and may facilitate the identification of complex disease genes. Although the signature that natural selection imparts on DNA sequence variation is difficult to disentangle from the effects of neutral processes such as population demographic history, selective and demographic forces can be distinguished by analyzing multiple loci dispersed throughout the genome. We studied the molecular evolution of 132 genes by comprehensively resequencing them in 24 African-Americans and 23 European-Americans. We developed a rigorous computational approach for taking into account multiple hypothesis tests and demographic history and found that while many apparent selective events can instead be explained by demography, there is also strong evidence for positive or balancing selection at eight genes in the European-American population, but none in the African-American population. Our results suggest that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces. In addition, a region containing four contiguous genes on Chromosome 7 showed striking evidence of a recent selective sweep in European-Americans. More generally, our results have important implications for mapping genes underlying complex human diseases.

Optimal Unified Approach for Rare-Variant Association Testing with Application to Small-Sample Case-Control Whole-Exome Sequencing Studies

We propose in this paper a unified approach for testing the association between rare variants and phenotypes in sequencing association studies. This approach maximizes power by adaptively using the data to optimally combine the burden test and the nonburden sequence kernel association test (SKAT). Burden tests are more powerful when most variants in a region are causal and the effects are in the same direction, whereas SKAT is more powerful when a large fraction of the variants in a region are noncausal or the effects of causal variants are in different directions. The proposed unified test maintains the power in both scenarios. We show that the unified test corresponds to the optimal test in an extended family of SKAT tests, which we refer to as SKAT-O. The second goal of this paper is to develop a small-sample adjustment procedure for the proposed methods for the correction of conservative type I error rates of SKAT family tests when the trait of interest is dichotomous and the sample size is small. Both small-sample-adjusted SKAT and the optimal unified test (SKAT-O) are computationally efficient and can easily be applied to genome-wide sequencing association studies. We evaluate the finite sample performance of the proposed methods using extensive simulation studies and illustrate their application using the acute-lung-injury exome-sequencing data of the National Heart, Lung, and Blood Institute Exome Sequencing Project.