title: Estimation of allele frequency and association mapping using next-generation sequencing data.
abstract: 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.
authors.affiliation: Departments of Integrative Biology and Statistics, UC Berkeley, Berkeley, CA 94720, USA. suyeonkim@berkeley.edu
authors.forename: Su Yeon
authors.initials: SY
authors.lastname: Kim
authors.forename: Kirk E
authors.initials: KE
authors.lastname: Lohmueller
authors.forename: Anders
authors.initials: A
authors.lastname: Albrechtsen
authors.forename: Yingrui
authors.initials: Y
authors.lastname: Li
authors.forename: Thorfinn
authors.initials: T
authors.lastname: Korneliussen
authors.forename: Geng
authors.initials: G
authors.lastname: Tian
authors.forename: Niels
authors.initials: N
authors.lastname: Grarup
authors.forename: Tao
authors.initials: T
authors.lastname: Jiang
authors.forename: Gitte
authors.initials: G
authors.lastname: Andersen
authors.forename: Daniel
authors.initials: D
authors.lastname: Witte
authors.forename: Torben
authors.initials: T
authors.lastname: Jorgensen
authors.forename: Torben
authors.initials: T
authors.lastname: Hansen
authors.forename: Oluf
authors.initials: O
authors.lastname: Pedersen
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Rasmus
authors.initials: R
authors.lastname: Nielsen
mesh_terms: D005787:Gene Frequency; D005828:Genetics, Population / Q000379:methods*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016013:Likelihood Functions; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA