Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.

Understanding the genetic mechanisms underlying natural variation in gene expression is a central goal of both medical and evolutionary genetics, and studies of expression quantitative trait loci (eQTLs) have become an important tool for achieving this goal1. Although all eQTL studies so far have assayed messenger RNA levels using expression microarrays, recent advances in RNA sequencing enable the analysis of transcript variation at unprecedented resolution. We sequenced RNA from 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals that have been extensively genotyped by the International HapMap Project2. By pooling data from all individuals, we generated a map of the transcriptional landscape of these cells, identifying extensive use of unannotated untranslated regions and more than 100 new putative protein-coding exons. Using the genotypes from the HapMap project, we identified more than a thousand genes at which genetic variation influences overall expression levels or splicing. We demonstrate that eQTLs near genes generally act by a mechanism involving allele-specific expression, and that variation that influences the inclusion of an exon is enriched within and near the consensus splice sites. Our results illustrate the power of high-throughput sequencing for the joint analysis of variation in transcription, splicing and allele-specific expression across individuals.

BackgroundDNA methylation is an essential epigenetic mechanism involved in gene regulation and disease, but little is known about the mechanisms underlying inter-individual variation in methylation profiles. Here we measured methylation levels at 22,290 CpG dinucleotides in lymphoblastoid cell lines from 77 HapMap Yoruba individuals, for which genome-wide gene expression and genotype data were also available.ResultsAssociation analyses of methylation levels with more than three million common single nucleotide polymorphisms (SNPs) identified 180 CpG-sites in 173 genes that were associated with nearby SNPs (putatively in cis, usually within 5 kb) at a false discovery rate of 10%. The most intriguing trans signal was obtained for SNP rs10876043 in the disco-interacting protein 2 homolog B gene (DIP2B, previously postulated to play a role in DNA methylation), that had a genome-wide significant association with the first principal component of patterns of methylation; however, we found only modest signal of trans-acting associations overall. As expected, we found significant negative correlations between promoter methylation and gene expression levels measured by RNA-sequencing across genes. Finally, there was a significant overlap of SNPs that were associated with both methylation and gene expression levels.ConclusionsOur results demonstrate a strong genetic component to inter-individual variation in DNA methylation profiles. Furthermore, there was an enrichment of SNPs that affect both methylation and gene expression, providing evidence for shared mechanisms in a fraction of genes.

Here we present a draft genome sequence of the common chimpanzee (Pan troglodytes). Through comparison with the human genome, we have generated a largely complete catalogue of the genetic differences that have accumulated since the human and chimpanzee species diverged from our common ancestor, constituting approximately thirty-five million single-nucleotide changes, five million insertion/deletion events, and various chromosomal rearrangements. We use this catalogue to explore the magnitude and regional variation of mutational forces shaping these two genomes, and the strength of positive and negative selection acting on their genes. In particular, we find that the patterns of evolution in human and chimpanzee protein-coding genes are highly correlated and dominated by the fixation of neutral and slightly deleterious alleles. We also use the chimpanzee genome as an outgroup to investigate human population genetics and identify signatures of selective sweeps in recent human evolution.

The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation1-5. However, it remains difficult to identify the causal variants underlying eQTLs and little is known about the regulatory mechanisms by which they act. To address this gap, we used DNaseI sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines (LCLs), for which genome-wide genotypes and estimates of gene expression levels are also available6-8. We obtained a total of 2.7 billion uniquely mapped DNase-seq reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 9,595 locations at which DNase-seq read depth correlates significantly with genotype at a nearby SNP or indel (FDR=10%). We call such variants “DNaseI sensitivity Quantitative Trait Loci” (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16%) of dsQTLs are also associated with variation in the expression levels of nearby genes, (namely, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55% of eQTL SNPs are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome, and are likely to be important contributors to phenotypic variation.

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Accurate functional annotation of regulatory elements is essential for understanding global gene regulation. Here, we report a genome-wide map of 827,000 transcription factor binding sites in human lymphoblastoid cell lines, which is comprised of sites corresponding to 239 position weight matrices of known transcription factor binding motifs, and 49 novel sequence motifs. To generate this map, we developed a probabilistic framework that integrates cell- or tissue-specific experimental data such as histone modifications and DNase I cleavage patterns with genomic information such as gene annotation and evolutionary conservation. Comparison to empirical ChIP-seq data suggests that our method is highly accurate yet has the advantage of targeting many factors in a single assay. We anticipate that this approach will be a valuable tool for genome-wide studies of gene regulation in a wide variety of cell types or tissues under diverse conditions.

Sexual dimorphism in anatomical, physiological, and behavioural traits characterize many vertebrate species. In humans, sexual dimorphism is also observed in the prevalence, course, and severity of many common diseases, including cardiovascular diseases, autoimmune diseases, and asthma. Although sex differences in the endocrine and immune systems probably contribute to these observations, recent studies suggest that sex-specific genetic architecture also influences human phenotypes, including reproductive, physiological, and disease traits. It is likely that an underlying mechanism is differential gene regulation in males and females, particularly in sex steroid responsive genes. Genetic studies that ignore sex-specific effects in their design and interpretation could fail to identify a significant proportion of the genes that contribute to risk for complex diseases.Differences between males and females in anatomical, physiological, and behavioral traits characterize many vertebrate species, including humans. Although some may be apparent at birth, striking differences between the sexes most often emerge at or around the time of sexual maturation. It is thought that these are, in large part, due to sex hormone levels that differ in males and females beginning in utero and continuing throughout life 1 (Figure 1). The genetic contribution to sexual dimorphism was, until recently, less studied. Indeed, whereas genes on sex chromosomes contribute to many sexually dimorphic traits, the autosomal genome is generally assumed to be similar among the males and females of a species. Mechanisms for dosage compensation in HETEROGAMETIC species further assure that genetic contributions from the shared sex chromosome (X chromosome in mammals) is equivalent among males and females, at least for most genes 2 .Recent studies have challenged this paradigm, however, suggesting that natural variation within the autosomal genomes of many species also affects anatomical, physiological, and behavioral traits differently in males and females 3-5 . In this context, sex can be considered an 'environmental' variable that includes the cellular, metabolic, physiological, anatomical, and even behavioral differences between boys and girls (in childhood) or between men and women (in adulthood). Sex, then, may interact with genotype in a manner similar to other environmental factors (Figure 2). However, unlike most other environmental factors, sex is easily observable and (usually) unambiguous. Such sex-specific genetic architecture suggests new models of susceptibility for common diseases and sheds light on potential mechanisms of sexual dimorphism [Box 1] in human phenotypes.In this review, we argue that sex-specific genetic architecture is common in humans and that genotype-sex interactions contribute to differences in the prevalence, course, and severity of diseases as well as to other quantitative phenotypes. We provide recent examples of genotypesex interactions as evidence to support this argument and illustrate how patterns of t...