Abstract

BackgroundEstimation 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.ResultsWe 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.ConclusionsOverall, 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.

Highlights

  • Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping

  • We find that the LRTperforms better than the G-test based on either genotype calling method (Figure 5)

  • We have evaluated the performance of a likelihood method and genotype calling methods to estimate the minor allele frequency from next-generation sequencing data

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Summary

Introduction

Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates. 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. Allele frequencies can be used to infer past evolutionary events. 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 informative about the possible effects of natural selection. SNPs under the direct influence of negative selection are expected to be at

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