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      A Scan for Positively Selected Genes in the Genomes of Humans and Chimpanzees

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          Abstract

          Since the divergence of humans and chimpanzees about 5 million years ago, these species have undergone a remarkable evolution with drastic divergence in anatomy and cognitive abilities. At the molecular level, despite the small overall magnitude of DNA sequence divergence, we might expect such evolutionary changes to leave a noticeable signature throughout the genome. We here compare 13,731 annotated genes from humans to their chimpanzee orthologs to identify genes that show evidence of positive selection. Many of the genes that present a signature of positive selection tend to be involved in sensory perception or immune defenses. However, the group of genes that show the strongest evidence for positive selection also includes a surprising number of genes involved in tumor suppression and apoptosis, and of genes involved in spermatogenesis. We hypothesize that positive selection in some of these genes may be driven by genomic conflict due to apoptosis during spermatogenesis. Genes with maximal expression in the brain show little or no evidence for positive selection, while genes with maximal expression in the testis tend to be enriched with positively selected genes. Genes on the X chromosome also tend to show an elevated tendency for positive selection. We also present polymorphism data from 20 Caucasian Americans and 19 African Americans for the 50 annotated genes showing the strongest evidence for positive selection. The polymorphism analysis further supports the presence of positive selection in these genes by showing an excess of high-frequency derived nonsynonymous mutations.

          Abstract

          Humans and chimps diverged about 5 million years ago. This study seeks to find the genes that have undergone positive selection during the evolution of both lineages since that time.

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          Most cited references 46

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          Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models.

          Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.
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            Statistical methods for detecting molecular adaptation.

            The past few years have seen the development of powerful statistical methods for detecting adaptive molecular evolution. These methods compare synonymous and nonsynonymous substitution rates in protein-coding genes, and regard a nonsynonymous rate elevated above the synonymous rate as evidence for darwinian selection. Numerous cases of molecular adaptation are being identified in various systems from viruses to humans. Although previous analyses averaging rates over sites and time have little power, recent methods designed to detect positive selection at individual sites and lineages have been successful. Here, we summarize recent statistical methods for detecting molecular adaptation, and discuss their limitations and possible improvements.
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              Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene.

              Several codon-based models for the evolution of protein-coding DNA sequences are developed that account for varying selection intensity among amino acid sites. The "neutral model" assumes two categories of sites at which amino acid replacements are either neutral or deleterious. The "positive-selection model" assumes an additional category of positively selected sites at which nonsynonymous substitutions occur at a higher rate than synonymous ones. This model is also used to identify target sites for positive selection. The models are applied to a data set of the V3 region of the HIV-1 envelope gene, sequenced at different years after the infection of one patient. The results provide strong support for variable selection intensity among amino acid sites The neutral model is rejected in favor of the positive-selection model, indicating the operation of positive selection in the region. Positively selected sites are found in both the V3 region and the flanking regions.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                pbio
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                June 2005
                3 May 2005
                : 3
                : 6
                Affiliations
                1simpleBiological Statistics and Computational Biology, Cornell University Ithaca, New YorkUnited States of America
                2simpleCenter for Bioinformatics, University of Copenhagen Denmark
                3simpleMolecular Biology and Genetics, Cornell University Ithaca, New YorkUnited States of America
                4simpleApplied Biosystems Rockville, MarylandUnited States of America
                5simpleCelera Genomics Rockville, MarylandUnited States of America
                6simpleCelera Diagnostics Alameda, CaliforniaUnited States of America
                simpleSanger Institute United Kingdom
                Article
                10.1371/journal.pbio.0030170
                1088278
                15869325
                Copyright: © 2005 Nielsen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
                Categories
                Research Article
                Bioinformatics/Computational Biology
                Infectious Diseases
                Molecular Biology/Structural Biology
                Molecular Biology/Structural Biology
                Bioinformatics/Computational Biology
                Cancer Biology
                Cancer Biology
                Evolution
                Evolution
                Genetics/Genomics/Gene Therapy
                Genetics/Genomics/Gene Therapy
                Infectious Diseases
                Homo (Human)
                Primates

                Life sciences

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