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10.1093/molbev/msv334 http://scihub22266oqcxt.onion/10.1093/molbev/msv334 C4776707!4776707 !26715629     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26715629 &cmd=llinks): Failed to open stream: HTTP request failed! 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Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data |pdf=|usr=}}{{26715629 }} gab.com Text Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data JO: Mol Biol Evol http://www.kidney.de/pget.php?&tw=1&pmid=26715629 #FOAvip To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. Twit Text FOAVip Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data ????Mol Biol Evol http://www.kidney.de/pget.php?&tw=1&pmid=26715629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26715629 #FOAvip English Wikipedia <ref>{{Cite pmid|26715629 }}</ref> Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data #MMPMID26715629 Duforet-Frebourg N ; Luu K ; Laval G ; Bazin E ; Blum MG Mol Biol Evol 2016[Apr]; 33 (4 ): 1082-93 PMID26715629 show ga To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. |*Genetics, Population [MESH] |*Selection, Genetic [MESH] |Adaptation, Physiological/*genetics [MESH] |Genome, Human [MESH] |Genomics [MESH] |Humans [MESH] |Principal Component Analysis/*methods [MESH] |Protein Structure, Tertiary [MESH] |Sequence Analysis, DNA [MESH]Detecting Genomic Signatures of Natural Selection with Principal Compo(epmc).pdf DeepDyve Pubget Overpricing