title: Inference of population mutation rate and detection of segregating sites from next-generation sequence data.
abstract: We live in an age in which our ability to collect large amounts of genome-wide genetic variation data offers the promise of providing the key to the understanding and treatment of genetic diseases. Over the next few years this effort will be spearheaded by so-called next-generation sequencing technologies, which provide vast amounts of short-read sequence data at relatively low cost. This technology is often used to detect unknown variation in regions that have been linked with a given disease or phenotype. However, error rates are significant, leading to some nontrivial issues when it comes to interpreting the data. In this article, we present a method with which to address questions of widespread interest: calling variants and estimating the population mutation rate. We show performance of the method using simulation studies before applying our approach to an analysis of data from the 1000 Genomes project.
authors.affiliation: Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA. chulkang@usc.edu
authors.forename: Chul Joo
authors.initials: CJ
authors.lastname: Kang
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation; D005828:Genetics, Population; D015894:Genome, Human / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D059645:Mutation Rate; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*