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Assessing the evolutionary impact of amino acid mutations in the human genome.

ABSTRACT: Quantifying the distribution of fitness effects among newly arising mutations in the human genome is key to resolving important debates in medical and evolutionary genetics. Here, we present a method for inferring this distribution using Single Nucleotide Polymorphism (SNP) data from a population with non-stationary demographic history (such as that of modern humans). Application of our method to 47,576 coding SNPs found by direct resequencing of 11,404 protein coding-genes in 35 individuals (20 European Americans and 15 African Americans) allows us to assess the relative contribution of demographic and selective effects to patterning amino acid variation in the human genome. We find evidence of an ancient population expansion in the sample with African ancestry and a relatively recent bottleneck in the sample with European ancestry. After accounting for these demographic effects, we find strong evidence for great variability in the selective effects of new amino acid replacing mutations. In both populations, the patterns of variation are consistent with a leptokurtic distribution of selection coefficients (e.g., gamma or log-normal) peaked near neutrality. Specifically, we predict 27-29% of amino acid changing (nonsynonymous) mutations are neutral or nearly neutral (|s|<0.01%), 30-42% are moderately deleterious (0.01%<|s|<1%), and nearly all the remainder are highly deleterious or lethal (|s|>1%). Our results are consistent with 10-20% of amino acid differences between humans and chimpanzees having been fixed by positive selection with the remainder of differences being neutral or nearly neutral. Our analysis also predicts that many of the alleles identified via whole-genome association mapping may be selectively neutral or (formerly) positively selected, implying that deleterious genetic variation affecting disease phenotype may be missed by this widely used approach for mapping genes underlying complex traits.

SUBMITTER: Boyko AR

PROVIDER: S-EPMC2377339 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Assessing the evolutionary impact of amino acid mutations in the human genome.

Boyko Adam R AR Williamson Scott H SH Indap Amit R AR Degenhardt Jeremiah D JD Hernandez Ryan D RD Lohmueller Kirk E KE Adams Mark D MD Schmidt Steffen S Sninsky John J JJ Sunyaev Shamil R SR White Thomas J TJ Nielsen Rasmus R Clark Andrew G AG Bustamante Carlos D CD

PLoS genetics 20080530 5

Quantifying the distribution of fitness effects among newly arising mutations in the human genome is key to resolving important debates in medical and evolutionary genetics. Here, we present a method for inferring this distribution using Single Nucleotide Polymorphism (SNP) data from a population with non-stationary demographic history (such as that of modern humans). Application of our method to 47,576 coding SNPs found by direct resequencing of 11,404 protein coding-genes in 35 individuals (20 ...[more]

PMID: 18516229

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Project description:Human myxovirus resistant protein A (MxA), encoded by the myxovirus resistance 1 (Mx1) gene, is an interferon (IFN)-triggered dynamin-like multi-domain GTPase involved in innate immune responses against viral infections. Recent studies suggest that MxA is associated with several human cancers and may be a tumor suppressor and a promising biomarker for IFN therapy. Mx1 gene mutations in the coding region for MxA have been discovered in many types of cancer, suggesting potential biological associations between mutations in MxA protein and corresponding cancers. In this study, we performed a systematic analysis based on the crystal structures of MxA and elucidated how these mutations specifically affect the structure and therefore the function of MxA protein.Cancer-associated Mx1 mutations were collected and screened from the COSMIC database. Twenty-two unique mutations that cause single amino acid alterations in the MxA protein were chosen for the analysis. Amino acid sequence alignment was performed using Clustal W to check the conservation level of mutation sites in Mx proteins and dynamins. Structural analysis of the mutants was carried out with Coot. Structural models of selected mutants were generated by the SWISS-MODEL server for comparison with the corresponding non-mutated structures. All structural figures were generated using PyMOL.We analyzed the conservation level of the single-point mutation sites and mapped them on different domains of MxA. Through individual structural analysis, we found that some mutations severely affect the stability and function of MxA either by disrupting the intra-/inter-molecular interactions supported by the original residues or by incurring unfavorable configuration alterations, whereas other mutations lead to gentle or no interference to the protein stability and function because of positions or polarity features. The potential clinical value of the mutations that lead to drastic influence on MxA protein is also assessed.Among all of the reported tumor-associated single-point mutations, seven of them notably affect the structure and function of MxA and therefore deserve more attention with respect to potential clinical applications. Our research provides an example for systematic analysis and consequence evaluation of single-point mutations on a given cancer-related protein.

Project description:BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Dataset Information

Assessing the evolutionary impact of amino acid mutations in the human genome.

Publications

Assessing the evolutionary impact of amino acid mutations in the human genome.

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