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  • 1
    Publication Date: 2012-05-19
    Description: Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4319976/" 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/PMC4319976/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Nelson, Matthew R -- Wegmann, Daniel -- Ehm, Margaret G -- Kessner, Darren -- St Jean, Pamela -- Verzilli, Claudio -- Shen, Judong -- Tang, Zhengzheng -- Bacanu, Silviu-Alin -- Fraser, Dana -- Warren, Liling -- Aponte, Jennifer -- Zawistowski, Matthew -- Liu, Xiao -- Zhang, Hao -- Zhang, Yong -- Li, Jun -- Li, Yun -- Li, Li -- Woollard, Peter -- Topp, Simon -- Hall, Matthew D -- Nangle, Keith -- Wang, Jun -- Abecasis, Goncalo -- Cardon, Lon R -- Zollner, Sebastian -- Whittaker, John C -- Chissoe, Stephanie L -- Novembre, John -- Mooser, Vincent -- T32 HG002536/HG/NHGRI NIH HHS/ -- New York, N.Y. -- Science. 2012 Jul 6;337(6090):100-4. doi: 10.1126/science.1217876. Epub 2012 May 17.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Quantitative Sciences, GlaxoSmithKline (GSK), Research Triangle Park, NC 27709, USA. matthew.r.nelson@gsk.com〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22604722" target="_blank"〉PubMed〈/a〉
    Keywords: African Americans/genetics ; Asian Continental Ancestry Group ; Disease/*genetics ; European Continental Ancestry Group/genetics ; Gene Frequency ; Genetic Association Studies ; Genetic Predisposition to Disease ; *Genetic Variation ; *Genome, Human ; Geography ; High-Throughput Nucleotide Sequencing ; Humans ; Molecular Targeted Therapy ; Multifactorial Inheritance ; Mutation Rate ; Pharmacogenetics ; Phenotype ; Polymorphism, Single Nucleotide ; Population Growth ; Sample Size ; Selection, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2014-12-16
    Description: Ancestry analysis from genetic data plays a critical role in studies of human disease and evolution. Recent work has introduced explicit models for the geographic distribution of genetic variation and has shown that such explicit models yield superior accuracy in ancestry inference over nonmodel-based methods. Here we extend such work to introduce a method that models admixture between ancestors from multiple sources across a geographic continuum. We devise efficient algorithms based on hidden Markov models to localize on a map the recent ancestors ( e.g. , grandparents) of admixed individuals, joint with assigning ancestry at each locus in the genome. We validate our methods by using empirical data from individuals with mixed European ancestry from the Population Reference Sample study and show that our approach is able to localize their recent ancestors within an average of 470 km of the reported locations of their grandparents. Furthermore, simulations from real Population Reference Sample genotype data show that our method attains high accuracy in localizing recent ancestors of admixed individuals in Europe (an average of 550 km from their true location for localization of two ancestries in Europe, four generations ago). We explore the limits of ancestry localization under our approach and find that performance decreases as the number of distinct ancestries and generations since admixture increases. Finally, we build a map of expected localization accuracy across admixed individuals according to the location of origin within Europe of their ancestors.
    Electronic ISSN: 2160-1836
    Topics: Biology
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