Gene Sampling Strategies for Multi-Locus Population Estimates of Genetic Diversity (θ)

Background: Theoretical work suggests that data from multiple nuclear loci provide better estimates of population genetic parameters than do single loci, but just how many loci are needed and how much sequence is required from each has been little explored. Methodology/Principle Findings: To investigate how much data is required to estimate the population genetic parameter θ (4Neμ) accurately under ideal circumstances, we simulated datasets of DNA sequences under three values of θ per site (0.1, 0.01, 0.001), varying in both the total number of base pairs sequenced per individual and the number of equal-length loci. From these datasets we estimated θ using the maximum likelihood coalescent framework implemented in the computer program Migrate. Our results corroborated the theoretical expectation that increasing the number of loci impacted the accuracy of the estimate more than increasing the sequence length at single loci. However, when the value of θ was low (0.001), the per-locus sequence length was also important for estimating θ accurately, something that has not been emphasized in previous work. Conclusions/Significance: Accurate estimation of θ required data from at least 25 independently evolving loci. Beyond this, there was little added benefit in terms of decreasing the squared coefficient of variation of the coalescent estimates relative to the extra effort required to sample more loci.