Parentage assignment with low density array data and low coverage sequence data

Abstract In this paper we developed and analysed a parentage assignment method for low density array data or low coverage sequence data. We tested the algorithm on a series of simulated datasets varying the genotyping error rate, number of potential sires, and availability of maternal information. We found that for array data, to obtain more than 99% sire assignment accuracy 250 SNPs were sufficient when the dam was unknown and 100 SNPs were sufficient when the dam was known and genotyped. These results replicated on a real pig dataset. We found that for sequence data, to obtain 99% sire assignment accuracy 500 reads on genotyped sites were sufficient when the dam was unknown, and 200 reads were sufficient when the dam was known. We also investigated the benefit of pre-correcting genotyping errors using an imputation algorithm, and utilizing linkage information to increase accuracy. We found that pre-correcting genotyping errors could drastically increase parentage assignment accuracy, but that there was little benefit to using linkage information when typed or sequence loci were distributed across multiple chromosomes. We provide this parentage assignment method as a standalone program called AlphaAssign.