Identification and analysis of genomic regions with large between-population differentiation in humans

Date

2008-01-01

Author

Myles, S.
Tang, K.
Somel, Mehmet
Green, R. E.
Kelso, J.
Stoneking, M.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The primary aim of genetic association and linkage studies is to identify genetic variants that contribute to phenotypic variation within human populations. Since the overwhelming majority of human genetic variation is found within populations, these methods are expected to be effective and can likely be extrapolated from one human population to another. However, they may lack power in detecting the genetic variants that contribute to phenotypes that differ greatly between human populations. Phenotypes that show large differences between populations are expected to be associated with genomic regions exhibiting large allele frequency differences between populations. Thus, from genome-wide polymorphism data genomic regions with large allele frequency differences between populations can be identified, and evaluated as candidates for large between-population phenotypic differences. Here we use allele frequency data from similar to 1.5 million SNPs from three human populations, and present an algorithm that identifies genomic regions containing SNPs with extreme Fst. We demonstrate that our candidate regions have reduced heterozygosity in Europeans and Chinese relative to African-Americans, and are likely enriched with genes that have experienced positive natural selection. We identify genes that are likely responsible for phenotypes known to differ dramatically between human populations and present several candidates worthy of future investigation. Our list of high Fst genomic regions is a first step in identifying the genetic variants that contribute to large phenotypic differences between populations, many of which have likely experienced positive natural selection. Our approach based on between population differences can compliment traditional within population linkage and association studies to uncover novel genotype-phenotype relationships.

Subject Keywords

Genetics(clinical), Genetics

URI

https://hdl.handle.net/11511/38238

Collections

Department of Biology, Article