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    A composite of multiple signals distinguishes causal variants in regions of positive selection (2010)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2010-01-09
    Description: The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Grossman, Sharon R -- Shlyakhter, Ilya -- Karlsson, Elinor K -- Byrne, Elizabeth H -- Morales, Shannon -- Frieden, Gabriel -- Hostetter, Elizabeth -- Angelino, Elaine -- Garber, Manuel -- Zuk, Or -- Lander, Eric S -- Schaffner, Stephen F -- Sabeti, Pardis C -- New York, N.Y. -- Science. 2010 Feb 12;327(5967):883-6. doi: 10.1126/science.1183863. Epub 2010 Jan 7.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. shari.grossman@post.harvard.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20056855" target="_blank"〉PubMed〈/a〉
    Keywords: Computational Biology/methods ; DNA, Intergenic/genetics ; Evolution, Molecular ; Genetic Loci ; *Genetic Variation ; *Genome, Human ; Haplotypes ; Humans ; Polymorphism, Genetic ; Population Groups/genetics ; Regulatory Sequences, Nucleic Acid/genetics ; *Selection, Genetic ; Software
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 2
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    Detecting novel associations in large data sets (2011)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2011-12-17
    Description: Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3325791/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3325791/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Reshef, David N -- Reshef, Yakir A -- Finucane, Hilary K -- Grossman, Sharon R -- McVean, Gilean -- Turnbaugh, Peter J -- Lander, Eric S -- Mitzenmacher, Michael -- Sabeti, Pardis C -- 090532/Wellcome Trust/United Kingdom -- P50 GM068763/GM/NIGMS NIH HHS/ -- P50 GM068763-09/GM/NIGMS NIH HHS/ -- T32 GM007753/GM/NIGMS NIH HHS/ -- U54 GM088558/GM/NIGMS NIH HHS/ -- U54 GM088558-03/GM/NIGMS NIH HHS/ -- U54GM088558/GM/NIGMS NIH HHS/ -- New York, N.Y. -- Science. 2011 Dec 16;334(6062):1518-24. doi: 10.1126/science.1205438.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dnreshef@mit.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22174245" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Animals ; Baseball/statistics & numerical data ; *Data Interpretation, Statistical ; Female ; Gene Expression ; Genes, Fungal ; Genomics/methods ; Humans ; Intestines/microbiology ; Male ; Metagenome ; Mice ; Obesity ; Saccharomyces cerevisiae/genetics
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
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