Zhi-Xiang Zhu | bioRxiv Science

bioRxiv2015

Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) 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 a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method 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. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, 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. Finally, 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.

Genetics↗

bioRxiv2015

EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations

We apply the statistical framework for genome-wide association studies (GWAS) to eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterise the structure of genetic data. The approach does not require discrete sub-populations and thus it can be utilized in any genetic data where the underlying population structure is unknown, or where the interest is assessing divergence along a gradient. Through theory and simulation study we show that our approach can identify regions under selection along gradients of ancestry. In real data, we confirm this by demonstrating LCT to be under selection between HapMap CEU-TSI cohorts, and validated this selection signal across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU-TSI cohort and within the POPRES sample, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. Controlling for population stratification is of great importance in any quantitative genetic study and our approach also provides a simple, fast, and accurate way of predicting principal components in independent samples. With ever increasing sample sizes across many fields, this approach is likely to be greatly utilized to gain individual-level eigenvectors avoiding the computational challenges associated with conducting singular value decomposition in large datasets. We have developed freely available software to facilitate the application of the methods.

Genetics↗