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MBE Advance Access originally published online on September 18, 2006
Molecular Biology and Evolution 2006 23(12):2283-2287; doi:10.1093/molbev/msl123
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Letters

Higher Intensity of Purifying Selection on >90% of the Human Genes Revealed by the Intrinsic Replacement Mutation Rates

Sankar Subramanian*,{dagger},1 and Sudhir Kumar*,{dagger}

* Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University
{dagger} School of Life Sciences, Arizona State University

E-mail: s.kumar{at}asu.edu.

For over 3 decades, the rate of replacement mutations has been assumed to be equal to, and estimated from, the rate of "strictly" neutral sequence divergence in noncoding regions and in silent-codon positions where mutations do not alter the amino acid encoded. This assumption is fundamental to estimating the fraction of harmful protein mutations and to identifying adaptive evolution at individual codons and proteins. We show that the assumption is not justifiable because a much larger fraction of codon positions is involved in hypermutable CpG dinucleotides as compared with the introns, leading to a higher expected replacement mutation rate per site in a vast majority of the genes. Consideration of this difference reveals a higher intensity of purifying natural selection than previously inferred in human genes. We also show that a much smaller number of genes are expected to be evolving with positive selection than that predicted using sequence divergence at intron and silent positions in the human genome. These patterns indicate the need for using new approaches for estimating rates of amino acid–altering mutations in order to find positively selected genes and codons in genomes that contain hypermutable CpG's.

Key Words: adaptive evolution • mutation rate • test of selection • comparative genomics


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