title: Genotype and SNP calling from next-generation sequencing data.
abstract: Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.
authors.affiliation: Department of Integrative Biology, University of California, Berkeley, CA 94720, USA. rasmus_nielsen@berkeley.edu
authors.forename: Rasmus
authors.initials: R
authors.lastname: Nielsen
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Paul
authors.forename: Anders
authors.initials: A
authors.lastname: Albrechtsen
authors.forename: Yun S
authors.initials: YS
authors.lastname: Song
mesh_terms: D000483:Alleles; D002874:Chromosome Mapping; D003627:Data Interpretation, Statistical; D030342:Genetic Diseases, Inborn / Q000235:genetics; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D017422:Sequence Analysis, DNA