Common DNA sequence variation influences 3-dimensional conformation of the human genome.

BACKGROUND:The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. RESULTS:To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. CONCLUSION:Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized

Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits.

The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell-derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes

Candidate-Based Approaches to Identify Genetic Variation Influencing Type 2 Diabetes and Quantitative Traits

Type 2 diabetes (T2D) is a metabolic disorder characterized by insulin resistance and impaired insulin secretion that affects more than 20 million Americans, although the genetic component of the disorder is largely unknown. Individual genetic susceptibility to type 2 diabetes and other complex traits is the result of variation that is both common in human populations and rare, de novo and inherited mutations. We adopted a diverse set of genetics, genomics and informatics approaches to prioritize candidate genomic regions and variants and perform in-depth, targeted analysis of their contributions to type 2 diabetes susceptibility and related trait variability. Our initial efforts focused on the selection of candidate genes relevant to a complex trait by developing a metric to weight the relevance of functional gene annotations to the known biology of a trait. We used this method to select candidate genes for type 2 diabetes and performed a T2D case-control and quantitative trait association study in 2,335 Finnish individuals from the FUSION study. After follow-up in additional samples, we identified several variants that might contribute to T2D susceptibility. Genomic regions associated with plasma levels of HDL cholesterol and triglycerides were re-sequenced in individuals with trait-extreme values. Our analysis revealed a denser set of common and rare functional target variants including several non-synonymous, 3' UTR, and non-coding SNPs and indels. Finally, we utilized two approaches to identify candidate functional non-coding variants that may directly contribute to trait susceptibility. First, we used Formaldehyde-assisted isolation of regulatory elements (FAIRE) coupled with high-throughput sequencing to identify nucleosome-depleted regions in pancreatic islets. We used islet FAIRE-seq data to identify SNPs associated with T2D that potentially alter islet transcriptional regulation. A SNP in TCF7L2, rs7903146, was located in a FAIRE-seq site and demonstrated allelic differences in islet chromatin openness and enhancer activity, suggesting that it may contribute functionally to T2D susceptibility. Second, we used transcription factor binding site motifs to computationally predict variants that have allelic differences in regulatory activity. Taken together, these results suggest that identifying candidate genomic regions can successfully enrich for variation important for type 2 diabetes and other complex traits

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Genetic Discovery and Risk Prediction for Type 1 Diabetes in Individuals Without High-Risk HLA-DR3/DR4 Haplotypes.

OBJECTIVE: More than 10% of patients with type 1 diabetes (T1D) do not have high-risk HLA-DR3 or -DR4 haplotypes with distinct clinical features, such as later onset and reduced insulin dependence. We aimed to identify genetic drivers of T1D in the absence of DR3/DR4 and improve prediction of T1D risk in these individuals. RESEARCH DESIGN AND METHODS: We performed T1D association and fine-mapping analyses in 12,316 non-DR3/DR4 samples. Next, we performed heterogeneity tests to examine differences in T1D risk variants in individuals without versus those with DR3/DR4 haplotypes. We further assessed genome-wide differences in gene regulatory element and biological pathway enrichments between the non-DR3/DR4 and DR3/DR4 cohorts. Finally, we developed a genetic risk score (GRS) to predict T1D in individuals without DR3/DR4 and compared with an existing T1D GRS. RESULTS: A total of 18 T1D risk variants in non-DR3/DR4 samples were identified. Risk variants at the MHC and multiple other loci genome wide had heterogeneity in effects on T1D dependent on DR3/DR4 status, and non-DR3/DR4 T1D had evidence for a greater polygenic burden. T1D-associated variants in non-DR3/DR4 were more enriched for regulatory elements and pathways involved in antigen presentation, innate immunity, and β-cells and depleted in T cells compared with DR3/DR4. A non-DR3/DR4 GRS outperformed an existing risk score GRS2 in discriminating non-DR3/DR4 T1D from no diabetes (area under the curve 0.867; P = 7.48 × 10-32) and type 2 diabetes (0.907; P = 4.94 × 10-44). CONCLUSIONS: In total, we identified heterogeneity in T1D genetic risk dependent on high-risk HLA-DR3/DR4 haplotype, which uncovers disease mechanisms and enables more accurate prediction of T1D across the HLA background