Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies

Genome-wide disease-association mapping has been heralded as the study design of the next generation, but the lack of analytical methods to use genotype data fully is a large stumbling block. Here we describe an algorithm and statistical method that efficiently and exhaustively exploits haplotype information by subjecting alleles (a marker or contiguous sets of markers) from sliding windows of all sizes to transmission disequilibrium tests. By applying our method to simulated data and to Hirschsprung disease, we show that it can detect both common and rare disease variants of small effect. These results show that the theoretical benefits of genome-wide association studies are at last realizable.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.     

Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis

Exome sequencing has become a powerful and effective strategy for the discovery of genes underlying Mendelian disorders. However, use of exome sequencing to identify variants associated with complex traits has been more challenging, partly because the sample sizes needed for adequate power may be very large. One strategy to increase efficiency is to sequence individuals who are at both ends of a phenotype distribution (those with extreme phenotypes). Because the frequency of alleles that contribute to the trait are enriched in one or both phenotype extremes, a modest sample size can potentially be used to identify novel candidate genes and/or alleles. As part of the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP), we used an extreme phenotype study design to discover that variants in DCTN4, encoding a dynactin protein, are associated with time to first P. aeruginosa airway infection, chronic P. aeruginosa infection and mucoid P. aeruginosa in individuals with cystic fibrosis.     

Nested chromosomal deletions induced with retroviral vectors in mice

Chromosomal deletions, especially nested deletions, are major genetic tools in diploid organisms that facilitate the functional analysis of large chromosomal regions and allow the rapid localization of mutations to specific genetic intervals. In mice, well-characterized overlapping deletions are only available at a few chromosomal loci, partly due to drawbacks of existing methods. Here we exploit the random integration of a retrovirus to generate high-resolution sets of nested deletions around defined loci in embryonic stem (ES) cells, with sizes extending from a few kilobases to several megabases. This approach expands the application of Cre-loxP-based chromosome engineering because it not only allows the construction of hundreds of overlapping deletions, but also provides molecular entry points to regions based on the retroviral tags. Our approach can be extended to any region of the mouse genome.     

Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.     

Evaluating and improving power in whole-genome association studies using fixed marker sets

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.     

A genome-wide association study shows that common alleles of SMAD7 influence colorectal cancer risk

To identify risk variants for colorectal cancer (CRC), we conducted a genome-wide association study, genotyping 550,163 tag SNPs in 940 individuals with familial colorectal tumor (627 CRC, 313 advanced adenomas) and 965 controls. We evaluated selected SNPs in three replication sample sets (7,473 cases, 5,984 controls) and identified three SNPs in SMAD7 (involved in TGF-beta and Wnt signaling) associated with CRC. Across the four sample sets, the association between rs4939827 and CRC was highly statistically significant (P(trend) = 1.0 x 10(-12)).     

Transferability of tag SNPs in genetic association studies in multiple populations

A general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.     

Open-reading-frame sequence tags (OSTs) support the existence of at least 17,300 genes in C. elegans

The genome sequences of Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana have been predicted to contain 19,000, 13,600 and 25,500 genes, respectively. Before this information can be fully used for evolutionary and functional studies, several issues need to be addressed. First, the gene number estimates obtained in silico and not yet supported by any experimental data need to be verified. For example, it seems biologically paradoxical that C. elegans would have 50% more genes than Drosophilia. Second, intron/exon predictions need to be tested experimentally. Third, complete sets of open reading frames (ORFs), or "ORFeomes," need to be cloned into various expression vectors. To address these issues simultaneously, we have designed and applied to C. elegans the following strategy. Predicted ORFs are amplified by PCR from a highly representative cDNA library using ORF-specific primers, cloned by Gateway recombination cloning and then sequenced to generate ORF sequence tags (OSTs) as a way to verify identity and splicing. In a sample (n=1,222) of the nearly 10,000 genes predicted ab initio (that is, for which no expressed sequence tag (EST) is available so far), at least 70% were verified by OSTs. We also observed that 27% of these experimentally confirmed genes have a structure different from that predicted by GeneFinder. We now have experimental evidence that supports the existence of at least 17,300 genes in C. elegans. Hence we suggest that gene counts based primarily on ESTs may underestimate the number of genes in human and in other organisms.     

Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans

Large data sets on human genetic variation have been collected recently, but their usefulness for learning about history and natural selection has been limited by biases in the ways polymorphisms were chosen. We report large subsets of SNPs from the International HapMap Project that allow us to overcome these biases and to provide accurate measurement of a quantity of crucial importance for understanding genetic variation: the allele frequency spectrum. Our analysis shows that East Asian and northern European ancestors shared the same population bottleneck expanding out of Africa but that both also experienced more recent genetic drift, which was greater in East Asians.     

A new multipoint method for genome-wide association studies by imputation of genotypes

Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.     

RNA sequencing shows no dosage compensation of the active X-chromosome

Mammalian cells from both sexes typically contain one active X chromosome but two sets of autosomes. It has previously been hypothesized that X-linked genes are expressed at twice the level of autosomal genes per active allele to balance the gene dose between the X chromosome and autosomes (termed 'Ohno's hypothesis'). This hypothesis was supported by the observation that microarray-based gene expression levels were indistinguishable between one X chromosome and two autosomes (the X to two autosomes ratio (X:AA) ~1). Here we show that RNA sequencing (RNA-Seq) is more sensitive than microarray and that RNA-Seq data reveal an X:AA ratio of ~0.5 in human and mouse. In Caenorhabditis elegans hermaphrodites, the X:AA ratio reduces progressively from ~1 in larvae to ~0.5 in adults. Proteomic data are consistent with the RNA-Seq results and further suggest the lack of X upregulation at the protein level. Together, our findings reject Ohno’s hypothesis, necessitating a major revision of the current model of dosage compensation in the evolution of sex chromosomes.     

Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction

Noncoding variants at human chromosome 9p21 near CDKN2A and CDKN2B are associated with type 2 diabetes, myocardial infarction, aneurysm, vertical cup disc ratio and at least five cancers. Here we compare approaches to more comprehensively assess genetic variation in the region. We carried out targeted sequencing at high coverage in 47 individuals and compared the results to pilot data from the 1000 Genomes Project. We imputed variants into type 2 diabetes and myocardial infarction cohorts directly from targeted sequencing, from a genotyped reference panel derived from sequencing and from 1000 Genomes Project low-coverage data. Polymorphisms with frequency >5% were captured well by all strategies. Imputation of intermediate-frequency polymorphisms required a higher density of tag SNPs in disease samples than is available on first-generation genome-wide association study (GWAS) arrays. Our association analyses identified more comprehensive sets of variants showing equivalent statistical association with type 2 diabetes or myocardial infarction, but did not identify stronger associations than the original GWAS signals.     

Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer

Familial clustering studies indicate that breast cancer risk has a substantial genetic component. To identify new breast cancer risk variants, we genotyped approximately 300,000 SNPs in 1,600 Icelandic individuals with breast cancer and 11,563 controls using the Illumina Hap300 platform. We then tested selected SNPs in five replication sample sets. Overall, we studied 4,554 affected individuals and 17,577 controls. Two SNPs consistently associated with breast cancer: approximately 25% of individuals of European descent are homozygous for allele A of rs13387042 on chromosome 2q35 and have an estimated 1.44-fold greater risk than noncarriers, and for allele T of rs3803662 on 16q12, about 7% are homozygous and have a 1.64-fold greater risk. Risk from both alleles was confined to estrogen receptor-positive tumors. At present, no genes have been identified in the linkage disequilibrium block containing rs13387042. rs3803662 is near the 5' end of TNRC9 , a high mobility group chromatin-associated protein whose expression is implicated in breast cancer metastasis to bone.     

Identification of low-frequency variants associated with gout and serum uric acid levels

We tested 16 million SNPs, identified through whole-genome sequencing of 457 Icelanders, for association with gout and serum uric acid levels. Genotypes were imputed into 41,675 chip-genotyped Icelanders and their relatives, for effective sample sizes of 968 individuals with gout and 15,506 individuals for whom serum uric acid measurements were available. We identified a low-frequency missense variant (c.1580C>G) in ALDH16A1 associated with gout (OR = 3.12, P = 1.5 × 10(-16), at-risk allele frequency = 0.019) and serum uric acid levels (effect = 0.36 s.d., P = 4.5 × 10(-21)). We confirmed the association with gout by performing Sanger sequencing on 6,017 Icelanders. The association with gout was stronger in males relative to females. We also found a second variant on chromosome 1 associated with gout (OR = 1.92, P = 0.046, at-risk allele frequency = 0.986) and serum uric acid levels (effect = 0.48 s.d., P = 4.5 × 10(-16)). This variant is close to a common variant previously associated with serum uric acid levels. This work illustrates how whole-genome sequencing data allow the detection of associations between low-frequency variants and complex traits.