People of the British Isles: preliminary analysis of genotypes and surnames in a UK control population

There is a great deal of interest in fine scale population structure in the UK, both as a signature of historical immigration events and because of the effect population structure may have on disease association studies. Although population structure appears to have a minor impact on the current generation of genome-wide association studies, it is likely to play a significant part in the next generation of studies designed to search for rare variants. A powerful way of detecting such structure is to control and document carefully the provenance of the samples involved. Here we describe the collection of a cohort of rural UK samples (The People of the British Isles), aimed at providing a well-characterised UK control population that can be used as a resource by the research community as well as providing fine scale genetic information on the British population. So far, some 4,000 samples have been collected, the majority of which fit the criteria of coming from a rural area and having all four grandparents from approximately the same area. Analysis of the first 3,865 samples that have been geocoded indicates that 75% have a mean distance between grandparental places of birth of 37.3km, and that about 70% of grandparental places of birth can be classed as rural. Preliminary genotyping of 1,057 samples demonstrates the value of these samples for investigating fine scale population structure within the UK, and shows how this can be enhanced by the use of surnames

Eight common genetic variants associated with serum dheas levels suggest a key role in ageing mechanisms

Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands-yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15×10-36), SULT2A1 (rs2637125; p = 2.61×10-19), ARPC1A (rs740160; p = 1.56×10-16), TRIM4 (rs17277546; p = 4.50×10-11), BMF (rs7181230; p = 5.44×10-11), HHEX (rs2497306; p = 4.64×10-9), BCL2L11 (rs6738028; p = 1.72×10-8), and CYP2C9 (rs2185570; p = 2.29×10-8). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS

Genetic Variation and Population Substructure in Outbred CD-1 Mice: Implications for Genome-Wide Association Studies

Outbred laboratory mouse populations are widely used in biomedical research. Since little is known about the degree of genetic variation present in these populations, they are not widely used for genetic studies. Commercially available outbred CD-1 mice are drawn from an extremely large breeding population that has accumulated many recombination events, which is desirable for genome-wide association studies. We therefore examined the degree of genome-wide variation within CD-1 mice to investigate their suitability for genetic studies. The CD-1 mouse genome displays patterns of linkage disequilibrium and heterogeneity similar to wild-caught mice. Population substructure and phenotypic differences were observed among CD-1 mice obtained from different breeding facilities. Differences in genetic variation among CD-1 mice from distinct facilities were similar to genetic differences detected between closely related human populations, consistent with a founder effect. This first large-scale genetic analysis of the outbred CD-1 mouse strain provides important considerations for the design and analysis of genetic studies in CD-1 mice

Interpreting Meta-Analyses of Genome-Wide Association Studies

Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect

Epistasis: Obstacle or Advantage for Mapping Complex Traits?

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic

Natural selection on cork oak: allele frequency reveals divergent selection in cork oak populations along a temperature cline

A recent study of population divergence at neutral markers and adaptive traits in cork oak has observed an association between genetic distances at locus QpZAG46 and genetic distances for leaf size and growth. In that study it was proposed that certain loci could be linked to genes encoding for adaptive traits in cork oak and, thus, could be used in adaptation studies. In order to investigate this hypothesis, here we (1) looked for associations between molecular markers and a set of adaptive traits in cork oak, and (2) explored the effects of the climate on among-population patterns in adaptive traits and molecular markers. For this purpose, we chose 9-year-old plants originating from thirteen populations spanning a broad range of climatic conditions. Plants established in a common garden site were genotyped at six nuclear microsatellites and phenotypically characterized for six functional traits potentially related to plant performance. Our results supported the proposed linkage between locus QpZAG46 and genes encoding for leaf size and growth. Temperature caused adaptive population divergence in leaf size and growth, which was expressed as differences in the frequencies of the alleles at locus QpZAG46

A Genetic Risk Score Combining Ten Psoriasis Risk Loci Improves Disease Prediction

Psoriasis is a chronic, immune-mediated skin disease affecting 2–3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS) combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS) and a weighted (wGRS) approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7) versus 12.09 (SD 1.8), p = 4.577×10−40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63–14.57), p = 2.010×10−65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC). The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10−8). Additionally, the AUC for HLA-C alone (rs10484554) was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18), highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10−6) and family history (p = 0.020). Using a liability threshold model, we estimated that the 10 risk loci account for only11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date

A study assessing the association of glycated hemoglobin a1C (HbA1C) associated variants with HbA1C, chronic kidney disease and diabetic retinopathy in populations of asian ancestry

10.1371/journal.pone.0079767PLoS ONE811-POLN