Estimation of allele frequency and association mapping using next-generation sequencing data

Background: Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates. Results: We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data. Conclusions: Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score. Background The frequency of an allele in the population is a fundamental quantity in human statistical genetics. This quantity forms the basis of many population and medical genetic studies. Many evolutionary forces change allele frequencies. Consequently, allele frequencies can be used to infer past evolutionary events. For example, allele frequencies at single nucleotide polymorphisms (SNPs) can be used to infer the demographic history of a population [1,2]. Patterns of allele frequency are also informative about the possible effects of natural selection. After a completed selective sweep, an excess of low-frequency and high-frequency derived SNPs is expected around the selected site [3-6]. Conversely, SNPs under the direct influence of negative selection are expected to be at