Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia.

By K. Galinsky, Gaurav Bhatia, Po-Ru Loh, Stoyan Georgiev, Sayan Mukherjee, Nick Patterson, Alkes L Price. 2016

370 citations recorded by the source corpus.

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Pcadapt: An R Package to Perform Genome Scans for Selection Based on Principal Component Analysis (2016, methodology): “…based on related techniques of multivariate analysis including principal component analysis (PCA), factor models, and non-negative matrix factorization (Duforet-Frebourg et al., 2014; Hao et al., 2016; Galinsky et al., 2016; Chen et al., 2016; Duforet-Frebourg et al., 2016; Martins et al., 2016).”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, methodology): “We computed principal components (PCs) using an algorithm (fastPCA [36]) which performs well on datasets with hundreds of thousands of samples by approximating only the top n PCs that explain the most variation, where n is specified in advance.”
Mixed model association for biobank-scale data sets (2018, methodology): “Projecting out top PCs (which can be computed rapidly using FastPCA [9]) improves the conditioning of the matrix computations that BOLT-LMM implicitly performs, roughly halving the number of iterations required for convergence (Supplementary Table 7).”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, methodology): “If PCs are not available, we would recommend efficient PCA tools such as fastPCA or FlashPCA 63,64 .”