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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: 2014-01-28
    Description: Ancient genomic sequences have started to reveal the origin and the demographic impact of farmers from the Neolithic period spreading into Europe. The adoption of farming, stock breeding and sedentary societies during the Neolithic may have resulted in adaptive changes in genes associated with immunity and diet. However, the limited data available from earlier hunter-gatherers preclude an understanding of the selective processes associated with this crucial transition to agriculture in recent human evolution. Here we sequence an approximately 7,000-year-old Mesolithic skeleton discovered at the La Brana-Arintero site in Leon, Spain, to retrieve a complete pre-agricultural European human genome. Analysis of this genome in the context of other ancient samples suggests the existence of a common ancient genomic signature across western and central Eurasia from the Upper Paleolithic to the Mesolithic. The La Brana individual carries ancestral alleles in several skin pigmentation genes, suggesting that the light skin of modern Europeans was not yet ubiquitous in Mesolithic times. Moreover, we provide evidence that a significant number of derived, putatively adaptive variants associated with pathogen resistance in modern Europeans were already present in this hunter-gatherer.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269527/" 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/PMC4269527/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Olalde, Inigo -- Allentoft, Morten E -- Sanchez-Quinto, Federico -- Santpere, Gabriel -- Chiang, Charleston W K -- DeGiorgio, Michael -- Prado-Martinez, Javier -- Rodriguez, Juan Antonio -- Rasmussen, Simon -- Quilez, Javier -- Ramirez, Oscar -- Marigorta, Urko M -- Fernandez-Callejo, Marcos -- Prada, Maria Encina -- Encinas, Julio Manuel Vidal -- Nielsen, Rasmus -- Netea, Mihai G -- Novembre, John -- Sturm, Richard A -- Sabeti, Pardis -- Marques-Bonet, Tomas -- Navarro, Arcadi -- Willerslev, Eske -- Lalueza-Fox, Carles -- F32 GM106656/GM/NIGMS NIH HHS/ -- F32GM106656/GM/NIGMS NIH HHS/ -- R01 HG007089/HG/NHGRI NIH HHS/ -- R01-HG007089/HG/NHGRI NIH HHS/ -- England -- Nature. 2014 Mar 13;507(7491):225-8. doi: 10.1038/nature12960. Epub 2014 Jan 26.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Institut de Biologia Evolutiva, CSIC-UPF, Barcelona 08003, Spain [2]. ; 1] Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, DK-1350 Copenhagen K, Denmark [2]. ; Institut de Biologia Evolutiva, CSIC-UPF, Barcelona 08003, Spain. ; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California 90095, USA. ; 1] Department of Integrative Biology, University of California, Berkeley, California 94720, USA [2] Department of Biology, Pennsylvania State University, 502 Wartik Laboratory, University Park, Pennsylvania 16802, USA. ; Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark. ; I.E.S.O. 'Los Salados', Junta de Castilla y Leon, E-49600 Benavente, Spain. ; Junta de Castilla y Leon, Servicio de Cultura de Leon, E-24071 Leon, Spain. ; Center for Theoretical Evolutionary Genomics, University of California, Berkeley, California 94720, USA. ; Department of Medicine and Nijmegen Institute for Infection, Inflammation and Immunity, Radboud University Nijmegen Medical Centre, 6500 Nijmegen, The Netherlands. ; Department of Human Genetics, University of Chicago, Illinois 60637, USA. ; Institute for Molecular Bioscience, Melanogenix Group, The University of Queensland, Brisbane, Queensland 4072, Australia. ; 1] Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA [2] Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA. ; 1] Institut de Biologia Evolutiva, CSIC-UPF, Barcelona 08003, Spain [2] Institucio Catalana de Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Catalonia, Spain. ; 1] Institut de Biologia Evolutiva, CSIC-UPF, Barcelona 08003, Spain [2] Institucio Catalana de Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Catalonia, Spain [3] Centre de Regulacio Genomica (CRG), Barcelona 08003, Catalonia, Spain [4] National Institute for Bioinformatics (INB), Barcelona 08003, Catalonia, Spain. ; Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, DK-1350 Copenhagen K, Denmark.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24463515" target="_blank"〉PubMed〈/a〉
    Keywords: Agriculture/history ; *Alleles ; Biological Evolution ; Caves ; European Continental Ancestry Group/*genetics ; Eye Color/genetics ; *Fossils ; Genome, Human/genetics ; Genomics ; History, Ancient ; Humans ; Immunity/*genetics ; Lactose Intolerance/genetics ; Male ; Pigmentation/*genetics ; Polymorphism, Single Nucleotide/genetics ; Principal Component Analysis ; Skeleton ; Skin Pigmentation/genetics ; Spain/ethnology
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2010-03-20
    Description: Advances in genome technology have facilitated a new understanding of the historical and genetic processes crucial to rapid phenotypic evolution under domestication. To understand the process of dog diversification better, we conducted an extensive genome-wide survey of more than 48,000 single nucleotide polymorphisms in dogs and their wild progenitor, the grey wolf. Here we show that dog breeds share a higher proportion of multi-locus haplotypes unique to grey wolves from the Middle East, indicating that they are a dominant source of genetic diversity for dogs rather than wolves from east Asia, as suggested by mitochondrial DNA sequence data. Furthermore, we find a surprising correspondence between genetic and phenotypic/functional breed groupings but there are exceptions that suggest phenotypic diversification depended in part on the repeated crossing of individuals with novel phenotypes. Our results show that Middle Eastern wolves were a critical source of genome diversity, although interbreeding with local wolf populations clearly occurred elsewhere in the early history of specific lineages. More recently, the evolution of modern dog breeds seems to have been an iterative process that drew on a limited genetic toolkit to create remarkable phenotypic diversity.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3494089/" 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/PMC3494089/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Vonholdt, Bridgett M -- Pollinger, John P -- Lohmueller, Kirk E -- Han, Eunjung -- Parker, Heidi G -- Quignon, Pascale -- Degenhardt, Jeremiah D -- Boyko, Adam R -- Earl, Dent A -- Auton, Adam -- Reynolds, Andy -- Bryc, Kasia -- Brisbin, Abra -- Knowles, James C -- Mosher, Dana S -- Spady, Tyrone C -- Elkahloun, Abdel -- Geffen, Eli -- Pilot, Malgorzata -- Jedrzejewski, Wlodzimierz -- Greco, Claudia -- Randi, Ettore -- Bannasch, Danika -- Wilton, Alan -- Shearman, Jeremy -- Musiani, Marco -- Cargill, Michelle -- Jones, Paul G -- Qian, Zuwei -- Huang, Wei -- Ding, Zhao-Li -- Zhang, Ya-Ping -- Bustamante, Carlos D -- Ostrander, Elaine A -- Novembre, John -- Wayne, Robert K -- R01 GM083606/GM/NIGMS NIH HHS/ -- R01 GM083606-03/GM/NIGMS NIH HHS/ -- ZIC HG200365-01/Intramural NIH HHS/ -- ZIC HG200365-02/Intramural NIH HHS/ -- ZIC HG200365-03/Intramural NIH HHS/ -- England -- Nature. 2010 Apr 8;464(7290):898-902. doi: 10.1038/nature08837. Epub 2010 Mar 17.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Ecology and Evolutionary Biology, 621 Charles E. Young Drive South, University of California, Los Angeles, California 90095, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20237475" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Animals, Domestic/classification/*genetics ; Animals, Wild/classification/genetics ; Breeding ; Computational Biology ; Dogs/classification/*genetics ; Evolution, Molecular ; Far East/ethnology ; Genome/*genetics ; Haplotypes/*genetics ; Middle East/ethnology ; Phenotype ; Phylogeny ; Polymorphism, Single Nucleotide/*genetics ; Wolves/classification/genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 4
    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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  • 5
    Publication Date: 2014-10-04
    Description: Motivation: Unique modeling and computational challenges arise in locating the geographic origin of individuals based on their genetic backgrounds. Single-nucleotide polymorphisms (SNPs) vary widely in informativeness, allele frequencies change non-linearly with geography and reliable localization requires evidence to be integrated across a multitude of SNPs. These problems become even more acute for individuals of mixed ancestry. It is hardly surprising that matching genetic models to computational constraints has limited the development of methods for estimating geographic origins. We attack these related problems by borrowing ideas from image processing and optimization theory. Our proposed model divides the region of interest into pixels and operates SNP by SNP. We estimate allele frequencies across the landscape by maximizing a product of binomial likelihoods penalized by nearest neighbor interactions. Penalization smooths allele frequency estimates and promotes estimation at pixels with no data. Maximization is accomplished by a minorize–maximize (MM) algorithm. Once allele frequency surfaces are available, one can apply Bayes’ rule to compute the posterior probability that each pixel is the pixel of origin of a given person. Placement of admixed individuals on the landscape is more complicated and requires estimation of the fractional contribution of each pixel to a person’s genome. This estimation problem also succumbs to a penalized MM algorithm. Results: We applied the model to the Population Reference Sample (POPRES) data. The model gives better localization for both unmixed and admixed individuals than existing methods despite using just a small fraction of the available SNPs. Computing times are comparable with the best competing software. Availability and implementation: Software will be freely available as the OriGen package in R. Contact: ranolaj@uw.edu or klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 6
    Publication Date: 2014-02-20
    Description: : forqs is a forward-in-time simulation of recombination, quantitative traits and selection. It was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. Availability and implementation : forqs is implemented as a command-line C++ program. Source code and binary executables for Linux, OSX and Windows are freely available under a permissive BSD license: https://bitbucket.org/dkessner/forqs . Contact: jnovembre@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
    Publication Date: 2013-04-16
    Description: DNA samples are often pooled, either by experimental design or because the sample itself is a mixture. For example, when population allele frequencies are of primary interest, individual samples may be pooled together to lower the cost of sequencing. Alternatively, the sample itself may be a mixture of multiple species or strains (e.g., bacterial species comprising a microbiome or pathogen strains in a blood sample). We present an expectation–maximization algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, as well as the calculation of proportions of different species within a metagenomics sample. Our method outperforms existing methods based on single-site allele frequencies, as well as simple approaches using sequence read data. We have implemented the method in a freely available open-source software tool.
    Print ISSN: 0737-4038
    Electronic ISSN: 1537-1719
    Topics: Biology
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  • 8
    Publication Date: 2014-02-27
    Description: The site frequency spectrum (SFS) is of primary interest in population genetic studies, because the SFS compresses variation data into a simple summary from which many population genetic inferences can proceed. However, inferring the SFS from sequencing data is challenging because genotype calls from sequencing data are often inaccurate due to high error rates and if not accounted for, this genotype uncertainty can lead to serious bias in downstream analysis based on the inferred SFS. Here, we compare two approaches to estimate the SFS from sequencing data: one approach infers individual genotypes from aligned sequencing reads and then estimates the SFS based on the inferred genotypes (call-based approach) and the other approach directly estimates the SFS from aligned sequencing reads by maximum likelihood (direct estimation approach). We find that the SFS estimated by the direct estimation approach is unbiased even at low coverage, whereas the SFS by the call-based approach becomes biased as coverage decreases. The direction of the bias in the call-based approach depends on the pipeline to infer genotypes. Estimating genotypes by pooling individuals in a sample (multisample calling) results in underestimation of the number of rare variants, whereas estimating genotypes in each individual and merging them later (single-sample calling) leads to overestimation of rare variants. We characterize the impact of these biases on downstream analyses, such as demographic parameter estimation and genome-wide selection scans. Our work highlights that depending on the pipeline used to infer the SFS, one can reach different conclusions in population genetic inference with the same data set. Thus, careful attention to the analysis pipeline and SFS estimation procedures is vital for population genetic inferences.
    Print ISSN: 0737-4038
    Electronic ISSN: 1537-1719
    Topics: Biology
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  • 9
    Publication Date: 2010-01-21
    Print ISSN: 0737-4038
    Electronic ISSN: 1537-1719
    Topics: Biology
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  • 10
    Publication Date: 2013-01-30
    Print ISSN: 0737-4038
    Electronic ISSN: 1537-1719
    Topics: Biology
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