Search

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797-22,562 persons, aged 18-104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 x 10(-8)), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 x 10(-11); LDL, P = 2.6 x 10(-10)), TMEM57 (TC, P = 5.4 x 10(-10)), CTCF-PRMT8 region (HDL, P = 8.3 x 10(-16)), DNAH11 (LDL, P = 6.1 x 10(-9)), FADS3-FADS2 (TC, P = 1.5 x 10(-10); LDL, P = 4.4 x 10(-13)) and MADD-FOLH1 region (HDL, P = 6 x 10(-11)). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene-environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.

The genome-wide distribution of linkage disequilibrium (LD) determines the strategy for selecting markers for association studies, but it varies between populations. We assayed LD in large samples (200 individuals) from each of 11 well-described population isolates and an outbred European-derived sample, using SNP markers spaced across chromosome 22. Most isolates show substantially higher levels of LD than the outbred sample and many fewer regions of very low LD (termed 'holes'). Young isolates known to have had relatively few founders show particularly extensive LD with very few holes; these populations offer substantial advantages for genome-wide association mapping.

Efforts to identify gene variants associated with susceptibility to common diseases use three approaches: pedigree and affected sib-pair linkage studies and association studies of population samples. The different aims of these study designs reflect their derivation from biological versus epidemiological traditions. Similar principles regarding determination of the evidence levels required to consider the results statistically significant apply to both linkage and association studies, however. Such determination requires explicit attention to the prior probability of particular findings, as well as appropriate correction for multiple comparisons. For most common diseases, increasing the sample size in a study is a crucial step in achieving statistically significant genetic mapping results. Recent studies suggest that the technology and statistical methodology will soon be available to make well-powered studies feasible using any of these approaches.