Natural Selection on Genes that Underlie Human Disease Susceptibility

Ran Blekhman, Orna Man, Leslie Herrmann, Adam R. Boyko, Amit Indap, Carolin Kosiol, Carlos D. Bustamante, Kosuke M. Teshima, Molly Przeworski

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169 Scopus citations


What evolutionary forces shape genes that contribute to the risk of human disease? Do similar selective pressures act on alleles that underlie simple versus complex disorders [1-3]? Answers to these questions will shed light onto the origin of human disorders (e.g., [4]) and help to predict the population frequencies of alleles that contribute to disease risk, with important implications for the efficient design of mapping studies [5-7]. As a first step toward addressing these questions, we created a hand-curated version of the Mendelian Inheritance in Man database (OMIM). We then examined selective pressures on Mendelian-disease genes, genes that contribute to complex-disease risk, and genes known to be essential in mouse by analyzing patterns of human polymorphism and of divergence between human and rhesus macaque. We found that Mendelian-disease genes appear to be under widespread purifying selection, especially when the disease mutations are dominant (rather than recessive). In contrast, the class of genes that influence complex-disease risk shows little signs of evolutionary conservation, possibly because this category includes targets of both purifying and positive selection.

Original languageEnglish (US)
Pages (from-to)883-889
Number of pages7
JournalCurrent Biology
Issue number12
StatePublished - Jun 25 2008
Externally publishedYes

Bibliographical note

Funding Information:
Thanks to G. Coop, N. Cox, A. Di Rienzo, Y. Gilad, C. Ober, G. Sella, M. Stephens, J. D. Wall, W. Wen, and especially J. Pritchard for helpful discussions and/or comments about the manuscript. We are also grateful to the Rhesus Macaque Genome Sequencing and Analysis Consortium in general and to A. Siepel in particular for providing a set of human-rhesus alignments, to K. Bullaughey and A. Fledel-Alon for help with bioinformatics, and to M. Groat, L. Stephens, and B. Rashidi for their aid in the curation of the OMIM database. This work was supported by an Alfred P. Sloan fellowship in Computational Molecular Biology and by a National Institutes of Health (NIH) grant GM79558 to M.P.; R.B. is supported by a NIH grant GM077959 to Y. Gilad.




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