Evidence of widespread selection on standing variation in Europe at height-associated SNPs.

Strong signatures of positive selection at newly arising genetic variants are well documented in humans(1-8), but this form of selection may not be widespread in recent human evolution(9). Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation(10-12). By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome wide, are systematically elevated in Northern Europeans compared with Southern Europeans (P < 4.3 × 10(-4)). This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients ∼10(-3)-10(-5) per allele) rather than genetic drift alone (P < 10(-15))

Across-cohort QC analyses of GWAS summary statistics from complex traits.

Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics Fst statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy

Genetic evidence of assortative mating in humans

In human populations, assortative mating is almost univer­sally positive, with similarities between partners for quantit­ative phenotypes, common disease risk, beha­vi­our6, social factors and personality. The causes and genetic consequences of assortative mating remain un­re­solved because partner similarity can arise from different mechanisms: phenotypic assortment based on mate choice, partner interaction and convergence in phenotype over time, or social homogamy where individuals pair according to social or environmental background. Here, we present theory and an analytical approach to test for genetic evidence of assortative mating and find a correlation in genetic value among partners for a range of phenotypes. Across three independent samples of 24,662 spousal pairs in total, we infer a correlation at trait-associated loci between partners for height (0.200, 0.004 standard error, SE) that matched the phenotypic correlation (0.201, 0.004 SE), and a correlation at trait-associated loci for BMI (0.143, 0.007 SE) that was significantly lower than the phenotypic value (0.228, 0.004 SE). We extend our analysis to the UK Biobank study (7,780 pairs), finding evidence of a correlation at trait-associated loci for waist-to-hip ratio (0.101, 0.041 SE), systolic blood pressure (0.138, 0.064 SE) and educational attainment (0.654, 0.014 SE). Our results imply that mate choice, combined with widespread pleiotropy among traits, affects the genomic architecture of traits in humans

Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma.

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function