Flexible methods for estimating genetic distances from nucleotide data

Abstract

O_LIWith the increasing use of massively parallel sequencing approaches in evolutionary biology, the need for fast and accurate methods suitable to investigate genetic structure and evolutionary history are more important than ever. We propose new distance measures for estimating genetic distances between individuals when allelic variation, gene dosage and recombination could compromise standard approaches.\nC_LIO_LIWe present four distance measures based on single nucleotide polymorphisms (SNP) and evaluate them against previously published measures using coalescent-based simulations. Simulations were used to test (i) whether the measures give unbiased and accurate distance estimates, (ii) if they can accurately identify the genomic mixture of hybrid individuals and (iii) if they give precise (low variance) estimates.\nC_LIO_LIThe results showed that the SNP-based GENPOFAD distance we propose appears to work well in the widest circumstances. It was the most accurate method for estimating genetic distances and is also relatively good at estimating the genomic mixture of hybrid individuals.\nC_LIO_LIOur simulations provide benchmarks to compare the performance of different distance measures in specific situations.\nC_LI