title: Ancestral alleles in the human genome based on population sequencing data.
abstract: Ancestral allele information is useful for genetics studies. Previously, the identification of ancestral alleles was primarily based on sequence alignments between species. Alternative ways to identify ancestral alleles were proposed in this study based on population sequencing data. The methods described here utilized the diversity between haplotypes harboring ancestral and newly emerged alleles. Simulations showed that these methods were reliable for identifying ancestral alleles when the variants had not aged too greatly. Application to the human genome sequencing data suggested the role of indels in maintaining the GC content in the human genome. The deletion-to-insertion ratios and GC proportions were correlated depending on the sizes of insertions and deletions in the direction of increasing GC content. There were GC-biased fixations in single base-pair insertions and AT-biased fixations in single base-pair deletions in the results based on the proposed methods. In the current study, GC-biased gene conversions in nucleotide substitutions were very slight or insignificant. In the variants of several quantitative trait loci (QTLs), slight GC-biased gene conversion was observed in nucleotide substitutions. For the QTL indels, insertions were observed more often than deletions, and deletion-biased fixation was observed, providing new insights into the evolution of functional genes.
authors.affiliation: Natural Science Research Institute, Yonsei University, Seoul, Korea.
authors.forename: Leeyoung
authors.initials: L
authors.lastname: Park
mesh_terms: D000483:Alleles; D001482:Base Composition; D003198:Computer Simulation; D019143:Evolution, Molecular; D005785:Gene Conversion; D005787:Gene Frequency; D005828:Genetics, Population; D015894:Genome, Human; D006239:Haplotypes; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D054643:INDEL Mutation; D008957:Models, Genetic; D016254:Mutagenesis, Insertional; D040641:Quantitative Trait Loci