Inferring Population Structure and Admixture Proportions in Low Depth NGS Data [article]

Jonas Meisner, Anders Albrechtsen
2018 bioRxiv   pre-print
We here present two new methods for inferring population structure and admixture proportions in low depth next-generation sequencing data. Inference of population structure is essential in both population genetics and association studies and is often performed using principal component analysis or clustering-based approaches. Next-generation sequencing methods provide large amounts of genetic data but are associated with statistical uncertainty for especially low depth sequencing data. Models
more » ... cing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through principal component analysis in an iterative approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at
doi:10.1101/302463 fatcat:jbjgl6essvdzzfqwyptnott7fm