Gene set of nuclear-encoded mitochondrial regulators is enriched for common inherited variation in obesity

There are hints of an altered mitochondrial function in obesity. Nuclear-encoded genes are relevant for mitochondrial function (3 gene sets of known relevant pathways: (1) 16 nuclear regulators of mitochondrial genes, (2) 91 genes for oxidative phosphorylation and (3) 966 nuclear-encoded mitochondrial genes). Gene set enrichment analysis (GSEA) showed no association with type 2 diabetes mellitus in these gene sets. Here we performed a GSEA for the same gene sets for obesity. Genome wide association study (GWAS) data from a case-control approach on 453 extremely obese children and adolescents and 435 lean adult controls were used for GSEA. For independent confirmation, we analyzed 705 obesity GWAS trios (extremely obese child and both biological parents) and a population-based GWAS sample (KORA F4, n = 1,743). A meta-analysis was performed on all three samples. In each sample, the distribution of significance levels between the respective gene set and those of all genes was compared using the leading-edge-fraction-comparison test (cut-offs between the 50(th) and 95(th) percentile of the set of all gene-wise corrected p-values) as implemented in the MAGENTA software. In the case-control sample, significant enrichment of associations with obesity was observed above the 50(th) percentile for the set of the 16 nuclear regulators of mitochondrial genes (p(GSEA,50) = 0.0103). This finding was not confirmed in the trios (p(GSEA,50) = 0.5991), but in KORA (p(GSEA,50) = 0.0398). The meta-analysis again indicated a trend for enrichment (p(MAGENTA,50) = 0.1052, p(MAGENTA,75) = 0.0251). The GSEA revealed that weak association signals for obesity might be enriched in the gene set of 16 nuclear regulators of mitochondrial genes

Geographical Affinities of the HapMap Samples

The HapMap samples were collected for medical-genetic studies, but are also widely used in population-genetic and evolutionary investigations. Yet the ascertainment of the samples differs from most population-genetic studies which collect individuals who live in the same local region as their ancestors. What effects could this non-standard ascertainment have on the interpretation of HapMap results?We compared the HapMap samples with more conventionally-ascertained samples used in population- and forensic-genetic studies, including the HGDP-CEPH panel, making use of published genome-wide autosomal SNP data and Y-STR haplotypes, as well as producing new Y-STR data. We found that the HapMap samples were representative of their broad geographical regions of ancestry according to all tests applied. The YRI and JPT were indistinguishable from independent samples of Yoruba and Japanese in all ways investigated. However, both the CHB and the CEU were distinguishable from all other HGDP-CEPH populations with autosomal markers, and both showed Y-STR similarities to unusually large numbers of populations, perhaps reflecting their admixed origins.The CHB and JPT are readily distinguished from one another with both autosomal and Y-chromosomal markers, and results obtained after combining them into a single sample should be interpreted with caution. The CEU are better described as being of Western European ancestry than of Northern European ancestry as often reported. Both the CHB and CEU show subtle but detectable signs of admixture. Thus the YRI and JPT samples are well-suited to standard population-genetic studies, but the CHB and CEU less so

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package

Multi-locus genome-wide association analysis supports the role of glutamatergic synaptic transmission in the etiology of major depressive disorder

Major depressive disorder (MDD) is a common psychiatric illness characterized by low mood and loss of interest in pleasurable activities. Despite years of effort, recent genome-wide association studies (GWAS) have identified few susceptibility variants or genes that are robustly associated with MDD. Standard single-SNP (single nucleotide polymorphism)-based GWAS analysis typically has limited power to deal with the extensive heterogeneity and substantial polygenic contribution of individually weak genetic effects underlying the pathogenesis of MDD. Here, we report an alternative, gene-set-based association analysis of MDD in an effort to identify groups of biologically related genetic variants that are involved in the same molecular function or cellular processes and exhibit a significant level of aggregated association with MDD. In particular, we used a text-mining-based data analysis to prioritize candidate gene sets implicated in MDD and conducted a multi-locus association analysis to look for enriched signals of nominally associated MDD susceptibility loci within each of the gene sets. Our primary analysis is based on the meta-analysis of three large MDD GWAS data sets (total N = 4346 cases and 4430 controls). After correction for multiple testing, we found that genes involved in glutamatergic synaptic neurotransmission were significantly associated with MDD (set-based association P = 6.9 X 10(-4)). This result is consistent with previous studies that support a role of the glutamatergic system in synaptic plasticity and MDD and support the potential utility of targeting glutamatergic neurotransmission in the treatment of MDD

Multiple Sclerosis Risk Variant HLA-DRB1*1501 Associates with High Expression of DRB1 Gene in Different Human Populations

The human leukocyte antigen (HLA) DRB1*1501 has been consistently associated with multiple sclerosis (MS) in nearly all populations tested. This points to a specific antigen presentation as the pathogenic mechanism though this does not fully explain the disease association. The identification of expression quantitative trait loci (eQTL) for genes in the HLA locus poses the question of the role of gene expression in MS susceptibility. We analyzed the eQTLs in the HLA region with respect to MS-associated HLA-variants obtained from genome-wide association studies (GWAS). We found that the Tag of DRB1*1501, rs3135388 A allele, correlated with high expression of DRB1, DRB5 and DQB1 genes in a Caucasian population. In quantitative terms, the MS-risk AA genotype carriers of rs3135388 were associated with 15.7-, 5.2- and 8.3-fold higher expression of DQB1, DRB5 and DRB1, respectively, than the non-risk GG carriers. The haplotype analysis of expression-associated variants in a Spanish MS cohort revealed that high expression of DRB1 and DQB1 alone did not contribute to the disease. However, in Caucasian, Asian and African American populations, the DRB1*1501 allele was always highly expressed. In other immune related diseases such as type 1 diabetes, inflammatory bowel disease, ulcerative colitis, asthma and IgA deficiency, the best GWAS-associated HLA SNPs were also eQTLs for different HLA Class II genes. Our data suggest that the DR/DQ expression levels, together with specific structural properties of alleles, seem to be the causal effect in MS and in other immunopathologies rather than specific antigen presentation alone

Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility

Variations in gene expression level might lead to phenotypic diversity across individuals or populations. Although many human genes are found to have differential mRNA levels between populations, the extent of gene expression that could vary within and between populations largely remains elusive. To investigate the dynamic range of gene expression, we analyzed the expression variability of ∼18, 000 human genes across individuals within HapMap populations. Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population. By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5′ and 3′UTRs) of these variable genes show consistently elevated population heterozygosity. We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations. Functional analysis revealed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling in HIV-1 infection, and for HIV-interacting proteins that control viral entry, replication, and propagation. This observation combined with the finding that known human HIV host factors show substantially elevated expression variability, collectively suggest that gene expression variability might explain differential HIV susceptibility across individuals

Abundant Quantitative Trait Loci Exist for DNA Methylation and Gene Expression in Human Brain

A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We present a set of integrated experiments that investigate the effects of common genetic variability on DNA methylation and mRNA expression in four human brain regions each from 150 individuals (600 samples total). We find an abundance of genetic cis regulation of mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation across the genome. We show peak enrichment for cis expression QTLs to be approximately 68,000 bp away from individual transcription start sites; however, the peak enrichment for cis CpG methylation QTLs is located much closer, only 45 bp from the CpG site in question. We observe that the largest magnitude quantitative trait loci occur across distinct brain tissues. Our analyses reveal that CpG methylation quantitative trait loci are more likely to occur for CpG sites outside of islands. Lastly, we show that while we can observe individual QTLs that appear to affect both the level of a transcript and a physically close CpG methylation site, these are quite rare. We believe these data, which we have made publicly available, will provide a critical step toward understanding the biological effects of genetic variation

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis