Genome-wide association study identifies multiple loci influencing human serum metabolite levels

Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10(-10)) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.     

The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology

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.     

Recombination rates in admixed individuals identified by ancestry-based inference

Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally variable regions, admixture does not substantially disrupt the factors controlling recombination rates in humans.     

Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies

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.     

Screening a large reference sample to identify very low frequency sequence variants: comparisons between two genes

Most human sequence variation is in the form of single-nucleotide polymorphisms (SNPs). It has been proposed that coding-region SNPs (cSNPs) be used for direct association studies to determine the genetic basis of complex traits. The success of such studies depends on the frequency of disease-associated alleles, and their distribution in different ethnic populations. If disease-associated alleles are frequent in most populations, then direct genotyping of candidate variants could show robust associations in manageable study samples. This approach is less feasible if the genetic risk from a given candidate gene is due to many infrequent alleles. Previous studies of several genes demonstrated that most variants are relatively infrequent (<0.05). These surveys genotyped small samples (n<75) and thus had limited ability to identify rare alleles. Here we evaluate the prevalence and distribution of such rare alleles by genotyping an ethnically diverse reference sample that is more than six times larger than those used in previous studies (n=450). We screened for variants in the complete coding sequence and intron-exon junctions of two candidate genes for neuropsychiatric phenotypes: SLC6A4, encoding the serotonin transporter; and SLC18A2, encoding the vesicular monoamine transporter. Both genes have unique roles in neuronal transmission, and variants in either gene might be associated with neurobehavioral phenotypes.