An Evolutionary Framework for Association Testing in Resequencing Studies

Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4

Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver Tissue

The discovery of expression quantitative trait loci (“eQTLs”) can help to unravel genetic contributions to complex traits. We identified genetic determinants of human liver gene expression variation using two independent collections of primary tissue profiled with Agilent (n = 206) and Illumina (n = 60) expression arrays and Illumina SNP genotyping (550K), and we also incorporated data from a published study (n = 266). We found that ∼30% of SNP-expression correlations in one study failed to replicate in either of the others, even at thresholds yielding high reproducibility in simulations, and we quantified numerous factors affecting reproducibility. Our data suggest that drug exposure, clinical descriptors, and unknown factors associated with tissue ascertainment and analysis have substantial effects on gene expression and that controlling for hidden confounding variables significantly increases replication rate. Furthermore, we found that reproducible eQTL SNPs were heavily enriched near gene starts and ends, and subsequently resequenced the promoters and 3′UTRs for 14 genes and tested the identified haplotypes using luciferase assays. For three genes, significant haplotype-specific in vitro functional differences correlated directly with expression levels, suggesting that many bona fide eQTLs result from functional variants that can be mechanistically isolated in a high-throughput fashion. Finally, given our study design, we were able to discover and validate hundreds of liver eQTLs. Many of these relate directly to complex traits for which liver-specific analyses are likely to be relevant, and we identified dozens of potential connections with disease-associated loci. These included previously characterized eQTL contributors to diabetes, drug response, and lipid levels, and they suggest novel candidates such as a role for NOD2 expression in leprosy risk and C2orf43 in prostate cancer. In general, the work presented here will be valuable for future efforts to precisely identify and functionally characterize genetic contributions to a variety of complex traits

A genome-wide scan for preeclampsia in the Netherlands

Preeclampsia, hallmarked by de novo hypertension and proteinuria in pregnancy, has a familial tendency. Recently, a large Icelandic genome-wide scan provided evidence for a maternal susceptibility locus for preeclampsia on chromosome 2p13 which was confirmed by a genome scan from Australia and New Zealand (NZ). The current study reports on a genome-wide scan of Dutch affected sib-pair families. In total 67 Dutch affected sib-pair families, comprising at least two siblings with proteinuric preeclampsia, eclampsia or HELLP-syndrome, were typed for 293 polymorphic markers throughout the genome and linkage analysis was performed. The highest allele sharing lod score of 1.99 was seen on chromosome 12q at 109.5 cM. Two peaks overlapped in the same regions between the Dutch and Icelandic genome-wide scan at chromosome 3p and chromosome 15q. No overlap was seen on 2p. Re-analysis in 38 families without HELLP-syndrome (preeclampsia families) and 34 families with at least one sibling with HELLP syndrome (HELLP families), revealed two peaks with suggestive evidence for linkage in the non-HELLP families on chromosome 10q (lod score 2.38, D10S1432, 93.9 cM) and 22q (lod score 2.41, D22S685, 32.4 cM). The peak on 12q appeared to be associated with HELLP syndrome; it increased to a lod score of 2.1 in the HELLP families and almost disappeared in the preeclampsia families. A nominal peak on chromosome 11 in the preeclampsia families showed overlap with the second highest peak in the Australian/NZ study. Results from our Dutch genome-wide scan indicate that HELLP syndrome might have a different genetic background than preeclampsia.Augusta MA Lachmeijer, Reynir Arngrímsson, Esther J Bastiaans, Michael L Frigge, Gerald Pals, Sigrun Sigurdardóttir, Hreinn Stéfansson, Birgir Pálsson, Dan Nicolae, Augustin Kong, Jan G Aarnoudse, Jeff R Gulcher, Guustaaf A Dekker, Leo P ten Kate and Kári Stéfansso

Chromatin marks identify critical cell types for fine mapping complex trait variants

If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant cell types. However, it is unclear which of the many marks are most useful in defining cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically cell type specific (P < 1 × 10(−6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(−5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T cells (P = 1 × 10(−4)), 67 SNPs for type 2 diabetes in pancreatic islet cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how cell type–specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation