Natural selection is a significant force that shapes the architecture of the human genome and introduces diversity across global populations. The question of whether advantageous mutations have arisen in the human genome as a result of single or multiple mutation events remains unanswered except for the fact that there exist a handful of genes such as those that confer lactase persistence, affect skin pigmentation, or cause sickle cell anemia. We have developed a long-range-haplotype method for identifying genomic signatures of positive selection to complement existing methods, such as the integrated haplotype score (iHS) or cross-population extended haplotype homozygosity (XP-EHH), for locating signals across the entire allele frequency spectrum. Our method also locates the founder haplotypes that carry the advantageous variants and infers their corresponding population frequencies. This presents an opportunity to systematically interrogate the whole human genome whether a selection signal shared across different populations is the consequence of a single mutation process followed subsequently by gene flow between populations or of convergent evolution due to the occurrence of multiple independent mutation events either at the same variant or within the same gene. The application of our method to data from 14 populations across the world revealed that positive-selection events tend to cluster in populations of the same ancestry. Comparing the founder haplotypes for events that are present across different populations revealed that convergent evolution is a rare occurrence and that the majority of shared signals stem from the same evolutionary event.

Original publication

DOI

10.1016/j.ajhg.2013.04.021

Type

Journal article

Journal

Am J Hum Genet

Publication Date

06/06/2013

Volume

92

Pages

866 - 881

Keywords

Chromosomes, Human, Computer Simulation, Evolution, Molecular, Founder Effect, Gene Frequency, Genetics, Population, Genome, Human, Haplotypes, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Principal Component Analysis, Selection, Genetic, Software