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  • 1
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    Integrating common and rare genetic variation in diverse human populations (2010)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2010-09-03
    Description: Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called 'HapMap 3', includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of 〈or=5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3173859/" 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/PMC3173859/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉International HapMap 3 Consortium -- Altshuler, David M -- Gibbs, Richard A -- Peltonen, Leena -- Dermitzakis, Emmanouil -- Schaffner, Stephen F -- Yu, Fuli -- Bonnen, Penelope E -- de Bakker, Paul I W -- Deloukas, Panos -- Gabriel, Stacey B -- Gwilliam, Rhian -- Hunt, Sarah -- Inouye, Michael -- Jia, Xiaoming -- Palotie, Aarno -- Parkin, Melissa -- Whittaker, Pamela -- Chang, Kyle -- Hawes, Alicia -- Lewis, Lora R -- Ren, Yanru -- Wheeler, David -- Muzny, Donna Marie -- Barnes, Chris -- Darvishi, Katayoon -- Hurles, Matthew -- Korn, Joshua M -- Kristiansson, Kati -- Lee, Charles -- McCarrol, Steven A -- Nemesh, James -- Keinan, Alon -- Montgomery, Stephen B -- Pollack, Samuela -- Price, Alkes L -- Soranzo, Nicole -- Gonzaga-Jauregui, Claudia -- Anttila, Verneri -- Brodeur, Wendy -- Daly, Mark J -- Leslie, Stephen -- McVean, Gil -- Moutsianas, Loukas -- Nguyen, Huy -- Zhang, Qingrun -- Ghori, Mohammed J R -- McGinnis, Ralph -- McLaren, William -- Takeuchi, Fumihiko -- Grossman, Sharon R -- Shlyakhter, Ilya -- Hostetter, Elizabeth B -- Sabeti, Pardis C -- Adebamowo, Clement A -- Foster, Morris W -- Gordon, Deborah R -- Licinio, Julio -- Manca, Maria Cristina -- Marshall, Patricia A -- Matsuda, Ichiro -- Ngare, Duncan -- Wang, Vivian Ota -- Reddy, Deepa -- Rotimi, Charles N -- Royal, Charmaine D -- Sharp, Richard R -- Zeng, Changqing -- Brooks, Lisa D -- McEwen, Jean E -- 068545/Wellcome Trust/United Kingdom -- 068545/Z/02/Wellcome Trust/United Kingdom -- 076113/Wellcome Trust/United Kingdom -- 077011/Wellcome Trust/United Kingdom -- 077014/Wellcome Trust/United Kingdom -- 082371/Wellcome Trust/United Kingdom -- 089061/Wellcome Trust/United Kingdom -- 089062/Wellcome Trust/United Kingdom -- 091746/Wellcome Trust/United Kingdom -- G0000934/Medical Research Council/United Kingdom -- P30 DK043351/DK/NIDDK NIH HHS/ -- U54 HG003273/HG/NHGRI NIH HHS/ -- England -- Nature. 2010 Sep 2;467(7311):52-8. doi: 10.1038/nature09298.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02138, USA. altshuler@molbio.mgh.harvard.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20811451" target="_blank"〉PubMed〈/a〉
    Keywords: *DNA Copy Number Variations ; *Genome, Human ; Human Genome Project ; Humans ; *Polymorphism, Single Nucleotide ; Population Groups/*genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 2
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    The landscape of recombination in African Americans (2011)
    Nature Publishing Group (NPG)
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    Publication Date: 2011-07-22
    Description: Recombination, together with mutation, gives rise to genetic variation in populations. Here we leverage the recent mixture of people of African and European ancestry in the Americas to build a genetic map measuring the probability of crossing over at each position in the genome, based on about 2.1 million crossovers in 30,000 unrelated African Americans. At intervals of more than three megabases it is nearly identical to a map built in Europeans. At finer scales it differs significantly, and we identify about 2,500 recombination hotspots that are active in people of West African ancestry but nearly inactive in Europeans. The probability of a crossover at these hotspots is almost fully controlled by the alleles an individual carries at PRDM9 (P value 〈 10(-245)). We identify a 17-base-pair DNA sequence motif that is enriched in these hotspots, and is an excellent match to the predicted binding target of PRDM9 alleles common in West Africans and rare in Europeans. Sites of this motif are predicted to be risk loci for disease-causing genomic rearrangements in individuals carrying these alleles. More generally, this map provides a resource for research in human genetic variation and evolution.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154982/" 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/PMC3154982/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Hinch, Anjali G -- Tandon, Arti -- Patterson, Nick -- Song, Yunli -- Rohland, Nadin -- Palmer, Cameron D -- Chen, Gary K -- Wang, Kai -- Buxbaum, Sarah G -- Akylbekova, Ermeg L -- Aldrich, Melinda C -- Ambrosone, Christine B -- Amos, Christopher -- Bandera, Elisa V -- Berndt, Sonja I -- Bernstein, Leslie -- Blot, William J -- Bock, Cathryn H -- Boerwinkle, Eric -- Cai, Qiuyin -- Caporaso, Neil -- Casey, Graham -- Cupples, L Adrienne -- Deming, Sandra L -- Diver, W Ryan -- Divers, Jasmin -- Fornage, Myriam -- Gillanders, Elizabeth M -- Glessner, Joseph -- Harris, Curtis C -- Hu, Jennifer J -- Ingles, Sue A -- Isaacs, William -- John, Esther M -- Kao, W H Linda -- Keating, Brendan -- Kittles, Rick A -- Kolonel, Laurence N -- Larkin, Emma -- Le Marchand, Loic -- McNeill, Lorna H -- Millikan, Robert C -- Murphy, Adam -- Musani, Solomon -- Neslund-Dudas, Christine -- Nyante, Sarah -- Papanicolaou, George J -- Press, Michael F -- Psaty, Bruce M -- Reiner, Alex P -- Rich, Stephen S -- Rodriguez-Gil, Jorge L -- Rotter, Jerome I -- Rybicki, Benjamin A -- Schwartz, Ann G -- Signorello, Lisa B -- Spitz, Margaret -- Strom, Sara S -- Thun, Michael J -- Tucker, Margaret A -- Wang, Zhaoming -- Wiencke, John K -- Witte, John S -- Wrensch, Margaret -- Wu, Xifeng -- Yamamura, Yuko -- Zanetti, Krista A -- Zheng, Wei -- Ziegler, Regina G -- Zhu, Xiaofeng -- Redline, Susan -- Hirschhorn, Joel N -- Henderson, Brian E -- Taylor, Herman A Jr -- Price, Alkes L -- Hakonarson, Hakon -- Chanock, Stephen J -- Haiman, Christopher A -- Wilson, James G -- Reich, David -- Myers, Simon R -- 090532/Wellcome Trust/United Kingdom -- CA060691/CA/NCI NIH HHS/ -- CA092447/CA/NCI NIH HHS/ -- CA100374/CA/NCI NIH HHS/ -- CA100598/CA/NCI NIH HHS/ -- CA1116460/CA/NCI NIH HHS/ -- CA1116460S1/CA/NCI NIH HHS/ -- CA121197/CA/NCI NIH HHS/ -- CA121197S2/CA/NCI NIH HHS/ -- CA127219/CA/NCI NIH HHS/ -- CA1326792/CA/NCI NIH HHS/ -- CA140388/CA/NCI NIH HHS/ -- CA141716/CA/NCI NIH HHS/ -- CA148085/CA/NCI NIH HHS/ -- CA148127/CA/NCI NIH HHS/ -- CA22453/CA/NCI NIH HHS/ -- CA54281/CA/NCI NIH HHS/ -- CA55769/CA/NCI NIH HHS/ -- CA58223/CA/NCI NIH HHS/ -- CA63464/CA/NCI NIH HHS/ -- CA68485/CA/NCI NIH HHS/ -- CA68578/CA/NCI NIH HHS/ -- CA77305/CA/NCI NIH HHS/ -- CA87895/CA/NCI NIH HHS/ -- CA88164/CA/NCI NIH HHS/ -- ES007784/ES/NIEHS NIH HHS/ -- ES011126/ES/NIEHS NIH HHS/ -- ES06717/ES/NIEHS NIH HHS/ -- ES10126/ES/NIEHS NIH HHS/ -- GM08016/GM/NIGMS NIH HHS/ -- GM091332/GM/NIGMS NIH HHS/ -- HD33175/HD/NICHD NIH HHS/ -- HG004726/HG/NHGRI NIH HHS/ -- HHSN268200960009C/PHS HHS/ -- HL084107/HL/NHLBI NIH HHS/ -- N01-HC-65226/HC/NHLBI NIH HHS/ -- P30 ES010126/ES/NIEHS NIH HHS/ -- R01 CA052689/CA/NCI NIH HHS/ -- R01 CA092447/CA/NCI NIH HHS/ -- R01 HG006399/HG/NHGRI NIH HHS/ -- R01 HL084107-04/HL/NHLBI NIH HHS/ -- R01-CA73629/CA/NCI NIH HHS/ -- U01 HG004168/HG/NHGRI NIH HHS/ -- U01 HG004168-03/HG/NHGRI NIH HHS/ -- Intramural NIH HHS/ -- Wellcome Trust/United Kingdom -- England -- Nature. 2011 Jul 20;476(7359):170-5. doi: 10.1038/nature10336.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Wellcome Trust Centre for Human Genetics, Oxford University, Roosevelt Drive, Oxford OX3 7BN, UK.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21775986" target="_blank"〉PubMed〈/a〉
    Keywords: Africa, Western/ethnology ; African Americans/*genetics ; Alleles ; Amino Acid Motifs ; Base Sequence ; Chromosome Mapping ; Crossing Over, Genetic/*genetics ; Europe/ethnology ; European Continental Ancestry Group/genetics ; Evolution, Molecular ; Female ; Gene Frequency ; Genetics, Population ; Genome, Human/*genetics ; Genomics ; Haplotypes/genetics ; Histone-Lysine N-Methyltransferase/chemistry/genetics/metabolism ; Humans ; Male ; Molecular Sequence Data ; Pedigree ; Polymorphism, Single Nucleotide/genetics ; Probability
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 3
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    Reconstructing Native American population history (2012)
    Nature Publishing Group (NPG)
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    Publication Date: 2012-07-18
    Description: The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved. One contentious issue is whether the settlement occurred by means of a single migration or multiple streams of migration from Siberia. The pattern of dispersals within the Americas is also poorly understood. To address these questions at a higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. Here we show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call 'First American'. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan speakers on both sides of the Panama isthmus, who have ancestry from both North and South America.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615710/" 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/PMC3615710/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Reich, David -- Patterson, Nick -- Campbell, Desmond -- Tandon, Arti -- Mazieres, Stephane -- Ray, Nicolas -- Parra, Maria V -- Rojas, Winston -- Duque, Constanza -- Mesa, Natalia -- Garcia, Luis F -- Triana, Omar -- Blair, Silvia -- Maestre, Amanda -- Dib, Juan C -- Bravi, Claudio M -- Bailliet, Graciela -- Corach, Daniel -- Hunemeier, Tabita -- Bortolini, Maria Catira -- Salzano, Francisco M -- Petzl-Erler, Maria Luiza -- Acuna-Alonzo, Victor -- Aguilar-Salinas, Carlos -- Canizales-Quinteros, Samuel -- Tusie-Luna, Teresa -- Riba, Laura -- Rodriguez-Cruz, Maricela -- Lopez-Alarcon, Mardia -- Coral-Vazquez, Ramon -- Canto-Cetina, Thelma -- Silva-Zolezzi, Irma -- Fernandez-Lopez, Juan Carlos -- Contreras, Alejandra V -- Jimenez-Sanchez, Gerardo -- Gomez-Vazquez, Maria Jose -- Molina, Julio -- Carracedo, Angel -- Salas, Antonio -- Gallo, Carla -- Poletti, Giovanni -- Witonsky, David B -- Alkorta-Aranburu, Gorka -- Sukernik, Rem I -- Osipova, Ludmila -- Fedorova, Sardana A -- Vasquez, Rene -- Villena, Mercedes -- Moreau, Claudia -- Barrantes, Ramiro -- Pauls, David -- Excoffier, Laurent -- Bedoya, Gabriel -- Rothhammer, Francisco -- Dugoujon, Jean-Michel -- Larrouy, Georges -- Klitz, William -- Labuda, Damian -- Kidd, Judith -- Kidd, Kenneth -- Di Rienzo, Anna -- Freimer, Nelson B -- Price, Alkes L -- Ruiz-Linares, Andres -- BB/1021213/1/Biotechnology and Biological Sciences Research Council/United Kingdom -- GM057672/GM/NIGMS NIH HHS/ -- GM079558/GM/NIGMS NIH HHS/ -- GM079558-S1/GM/NIGMS NIH HHS/ -- HG006399/HG/NHGRI NIH HHS/ -- MH075007/MH/NIMH NIH HHS/ -- NS037484/NS/NINDS NIH HHS/ -- NS043538/NS/NINDS NIH HHS/ -- R01 GM079558/GM/NIGMS NIH HHS/ -- R01 GM100233/GM/NIGMS NIH HHS/ -- R01 HG006399/HG/NHGRI NIH HHS/ -- R21 DK073818/DK/NIDDK NIH HHS/ -- Canadian Institutes of Health Research/Canada -- England -- Nature. 2012 Aug 16;488(7411):370-4. doi: 10.1038/nature11258.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. reich@genetics.med.harvard.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22801491" target="_blank"〉PubMed〈/a〉
    Keywords: Americas ; Asia ; Cluster Analysis ; Emigration and Immigration/*history/statistics & numerical data ; Gene Flow ; Genetics, Population ; History, Ancient ; Humans ; Indians, North American/*genetics/*history ; Models, Genetic ; *Phylogeny ; Polymorphism, Single Nucleotide/genetics ; Siberia
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 4
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    Analysis of case-control association studies with known risk variants (2012)
    Oxford University Press
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    Publication Date: 2012-06-24
    Description: Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P -value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. Availability: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/ Contact: nzaitlen@hsph.harvard.edu ; aprice@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 5
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    Genome-wide association study of ancestry-specific TB risk in the South African Coloured population (2014)
    Oxford University Press
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    Publication Date: 2014-01-11
    Description: The worldwide burden of tuberculosis (TB) remains an enormous problem, and is particularly severe in the admixed South African Coloured (SAC) population residing in the Western Cape. Despite evidence from twin studies suggesting a strong genetic component to TB resistance, only a few loci have been identified to date. In this work, we conduct a genome-wide association study (GWAS), meta-analysis and trans-ethnic fine mapping to attempt the replication of previously identified TB susceptibility loci. Our GWAS results confirm the WT1 chr11 susceptibility locus (rs2057178: odds ratio = 0.62, P = 2.71e –06 ) previously identified by Thye et al ., but fail to replicate previously identified polymorphisms in the TLR8 gene and locus 18q11.2. Our study demonstrates that the genetic contribution to TB risk varies between continental populations, and illustrates the value of including admixed populations in studies of TB risk and other complex phenotypes. Our evaluation of local ancestry based on the real and simulated data demonstrates that case-only admixture mapping is currently impractical in multi-way admixed populations, such as the SAC, due to spurious deviations in average local ancestry generated by current local ancestry inference methods. This study provides insights into identifying disease genes and ancestry-specific disease risk in multi-way admixed populations.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 6
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    Fast and accurate imputation of summary statistics enhances evidence of functional enrichment (2014)
    Oxford University Press
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    Publication Date: 2014-10-04
    Description: Motivation: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. Results: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (〉5%) and low-frequency (1–5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case–control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of $${\chi }^{2}$$ association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Availability and implementation: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/ . Contact: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary information: Supplementary materials are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
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    Improved ancestry inference using weights from external reference panels (2013)
    Oxford University Press
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    Publication Date: 2013-05-23
    Description: Motivation: Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies. Results: We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy ( R 2 ) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies. Availability: The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/ . Contact: aprice@hsph.harvard.edu or cychen@mail.harvard.edu . Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
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    Topics: Biology , Computer Science , Medicine
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  • 8
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    Genome-wide association study of ancestry-specific TB risk in the South African Coloured population (2013)
    Oxford University Press
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    Publication Date: 2013-09-20
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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