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7 - Admixture mapping for disease gene discovery

from Part II - Genome-wide studies in disease biology

Published online by Cambridge University Press:  18 December 2015

Randall C. Johnson
Affiliation:
Leidos Biomedical Research, Inc
Cheryl A. Winkler
Affiliation:
SAIC-National Cancer Institute, NIH
Meredith Yeager
Affiliation:
SAIC-National Cancer Institute, NIH
Krishnarao Appasani
Affiliation:
GeneExpression Systems, Inc., Massachusetts
Stephen W. Scherer
Affiliation:
University of Toronto
Peter M. Visscher
Affiliation:
University of Queensland
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Summary

Introduction

Mapping disease genes by admixture linkage disequilibrium (MALD) is a method that exploits observed associations between disease and ancestry. The sources of these observed associations generally fall into two categories: (1) environmental exposures, including factors such as diet, cultural practices and pathogens, and (2) heritable genetic risk modifiers. In a genome-wide association study (GWAS), association between a genetic marker and the disease are sought for, and any statistically significantly associated marker is inferred to be near a risk-modifying genetic variant. By contrast, a MALD study maps disease genes by identifying associations between ancestry and disease. Thus, loci with a statistically significantly different ancestral origin, when compared to the rest of the genome or to a control group, will be inferred to harbor a risk-modifying genetic variant. Additional research can then be carried out to identify the causal variant responsible for the observed association. The size of the associated region in a GWAS or MALD study is dependent upon the extent of genetic linkage at the locus in question.

Genetic linkage is fundamental to genetic association studies as a means to narrowing the search for causal variants, by identifying a chromosomal region associated with disease. When two markers are physically near each other on a chromosome, they are more likely to be inherited together, because there is a smaller chance of a crossover between the two during meiosis. The initial source of LD is mutation, in that a new allele arising at a locus by mutation necessarily occurs on a single chromosome, and is thus associated with all alleles carried on that specific chromosome (Bateson and Kilby, 1905; Morgan, 1910, 1911). In successive generations recombination breaks up this original chromosome, but even after 5000 generations – roughly the age of fully modern humans – chromosome segments of an average length of 20 kb will be inherited unbroken with probability less than 0.0001 (Matise et al., 2007). Random drift of allele frequencies, and selection for advantageous alleles against deleterious alleles, contribute to this process, in a complex and extensively studied pattern (Ohta, 1982; Sober, 1993; Keightley and Otto, 2006; Palaisa et al., 2004). As populations age, they acquire more variants and have more opportunity for recombination, which results in shorter blocks (or haplotypes) of LD, while younger populations tend to have longer LD blocks and fewer common variants (International HapMap Consortium et al., 2007).

Type
Chapter
Information
Genome-Wide Association Studies
From Polymorphism to Personalized Medicine
, pp. 89 - 105
Publisher: Cambridge University Press
Print publication year: 2016

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References

1000 Genomes Project Consortium, Abecasuis, G.R., Altshuler, D., et al. (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061–1073.Google Scholar
Alexander, D.H., Novembre, J. and Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Res., 19(9), 1655–1664.CrossRefGoogle ScholarPubMed
Altshuler, D., Daly, M.J. and Lander, E.S. (2008). Genetic mapping in human disease. Science, 322(5903), 881–888.CrossRefGoogle ScholarPubMed
Baran, Y., Pasaniuc, B., Sankararaman, S., et al. (2012). Fast and accurate inference of local ancestry in Latino populations. Bioinformatics, 28(10), 1359–1367.CrossRefGoogle ScholarPubMed
Bateson, W. and Kilby, H. (1905). Experimental studies in the physiology of heredity. R. Soc. Rep. Evol. Comm., 2, 1–55.Google Scholar
Bensen, J.T., Xu, Z., McKeigue, P.M., et al. (2014). Admixture mapping of prostate cancer in African Americans participating in the North Carolina–Louisiana Prostate Cancer Project (PCaP). The Prostate, 74(1), 1–9.CrossRefGoogle Scholar
Bostrom, M.A., Lu, L., Chou, J., et al. (2010). Candidate genes for non-diabetic ESRD in African Americans: a genome-wide association study using pooled DNA. Hum. Genet., 128(2), 195–204.CrossRefGoogle ScholarPubMed
Brisbin, A., Bryc, K., Zakharia, F., et al. (2012). PCAdmix: principal components-based assignment of ancestry along each chromosome in individuals with admixed ancestry from two or more populations. Hum. Biol., 84(4), 343–364.CrossRefGoogle ScholarPubMed
Chakraborty, R. and Weiss, K.M. (1986). Frequencies of complex diseases in hybrid populations. Am. J. Phys. Anthropol., 70(4), 489–503.CrossRefGoogle ScholarPubMed
Chakraborty, R. and Weiss, K.M. (1988). Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci. Proc. Natl Acad. Sci. USA, 85(23), 9119–9123.CrossRefGoogle ScholarPubMed
Cheng, C.-Y., Reich, D., Coresh, J., et al. (2010). Admixture mapping of obesity-related traits in African Americans: the Atherosclerosis Risk in Communities (ARIC) Study. Obesity (Silver Spring, Md.), 18(3), 563–572.CrossRefGoogle ScholarPubMed
Churchhouse, C. and Marchini, J. (2013). Multiway admixture deconvolution using phased or unphased ancestral panels. Genet. Epidemiol., 37(1), 1–12.CrossRefGoogle ScholarPubMed
Conley, M.E. and Casanova, J.-L. (2014). Discovery of single-gene inborn errors of immunity by next generation sequencing. Curr. Opin. Immunol., 30C, 17–23.CrossRefGoogle ScholarPubMed
Coram, M.A., Duan, Q., Hoffmann, T.J., et al. (2013). Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am. J. Hum. Genet., 92(6), 904–916.CrossRefGoogle ScholarPubMed
Divers, J., Palmer, N.D., Lu, L., et al. (2013). Admixture mapping of coronary artery calcified plaque in African Americans with type 2 diabetes mellitus. Cardiovasc. Genet., 6(1), 97–105.Google ScholarPubMed
Eichler, E.E., Flint, J., Gibson, G., et al. (2010). Missing heritability and strategies for finding the underlying causes of complex disease. Nature Rev. Genet., 11(6), 446–450.CrossRefGoogle ScholarPubMed
Falush, D., Stephens, M. and Pritchard, J.K. (2003). Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics, 164(4), 1567–1587.Google ScholarPubMed
Freedman, B.I., Kopp, J.B., Langefeld, C.D., et al. (2010). The apolipoprotein L1 (APOL1) gene and nondiabetic nephropathy in African Americans. J. Am. Soc. Nephrol., 21(9), 1422–1426.CrossRefGoogle ScholarPubMed
Freedman, B.I., Langefeld, C.D., Lu, L., et al. (2011). Differential effects of MYH9 and APOL1 risk variants on FRMD3 Association with diabetic ESRD in African Americans. PLoS Genet., 7(6), e1002150.CrossRefGoogle ScholarPubMed
Freedman, M.L., Haiman, C.A., Patterson, N., et al. (2006). Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc. Natl Acad. Sci. USA, 103(38), 14068–14073.CrossRefGoogle ScholarPubMed
Genovese, G., Friedman, D.J., Ross, M.D., et al. (2010). Association of trypanolytic APOL1 variants with kidney disease in African Americans. Science, 329(5993), 841–845.CrossRefGoogle ScholarPubMed
Genovese, G., Handsaker, R.E., Li, H., Kenny, E.E. and McCarroll, S.A. (2013). Mapping the human reference genome's missing sequence by three-way admixture in Latino genomes. Am. J. Hum. Genet., 93(3), 411–421.CrossRefGoogle ScholarPubMed
Guan, Y. (2014). Detecting structure of haplotypes and local ancestry. Genetics, 196(3), 625–642.CrossRefGoogle ScholarPubMed
Herrera-Paz, E.-F. (2014). The African Diaspora Power SNP Chip Developed via the CAAPA Consortium. Available at: http://www.academia.edu/7233528/The_African_Diaspora_Power_SNP_Chip_Developed_via_the_CAAPA_Consortium.
Hindorff, L.A., et al., A Catalog of Published Genome-Wide Association Studies. Available at: http://www.genome.gov/gwastudies/. Accessed July 16, 2014.
Hirschhorn, J.N., Lohmueller, K., Byrne, E. and Hirschhorn, K. (2002). A comprehensive review of genetic association studies. Genet. Med., 4(2), 45–61.CrossRefGoogle ScholarPubMed
Hoggart, C.J., Parra, E.J., Shriver, M.D., et al. (2003). Control of confounding of genetic associations in stratified populations. Am. J. Hum. Genet., 72(6), 1492–1504.CrossRefGoogle ScholarPubMed
Hoggart, C.J., Shriver, M.D., Kittles, R.A., et al. (2004). Design and analysis of admixture mapping studies. Am. J. Hum. Genet., 74(5), 965–978.CrossRefGoogle ScholarPubMed
Hu, Y., Willer, C., Zhan, X., Kang, H.M. and Abecasis, G.R. (2013). Accurate local-ancestry inference in exome-sequenced admixed individuals via off-target sequence reads. Am. J. Hum. Genet., 93(5), 891–899.CrossRefGoogle ScholarPubMed
International HapMap Consortium. (2005). A haplotype map of the human genome. Nature, 437(7063), 1299–1320.
International HapMap Consortium, Frazer, K.A., Ballinger, D.G., et al. (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449(7164), 851–861.CrossRefGoogle ScholarPubMed
Ionita-Laza, I., Lange, C. and M Laird, N. (2009). Estimating the number of unseen variants in the human genome. Proc. Natl Acad. Sci. USA, 106(13), 5008–5013.CrossRefGoogle ScholarPubMed
Jeff, J.M., Armstrong, L.L., Ritchie, M.D., et al. (2014). Admixture mapping and subsequent fine-mapping suggests a biologically relevant and novel association on chromosome 11 for type 2 diabetes in African Americans. PloS ONE, 9(3), e86931.CrossRefGoogle ScholarPubMed
Johnson, R.C., Nelson, G.W., Zagury, J.F. and Winkler, C.A. (2015). ALDsuite: dense marker MALD using principal components of ancestral linkage disequilibrium. Bioinformatics, 16(1), 23.Google ScholarPubMed
Kao, W.H.L., Klag, M.J., Meoni, L.A., et al. (2008). MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nature Genet., 40(10), 1185–1192.CrossRefGoogle ScholarPubMed
Keightley, P.D. and Otto, S.P. (2006). Interference among deleterious mutations favours sex and recombination in finite populations. Nature, 443(7107), 89–92.CrossRefGoogle ScholarPubMed
Kim-Howard, X., Sun, C., Molineros, J.E., et al. (2014). Allelic heterogeneity in NCF2 associated with systemic lupus erythematosus (SLE) susceptibility across four ethnic populations. Hum. Molec. Genet., 23(16), 1656–1668.CrossRefGoogle ScholarPubMed
Klein, R.J., Zeiss, C., Chow, E.J., et al. (2005). Complement factor H polymorphism in age-related macular degeneration. Science, 308(5720), 385–389.CrossRefGoogle ScholarPubMed
Kopp, J.B., Smith, M.W., Nelson, G.W., et al. (2008). MYH9 is a major-effect risk gene for focal segmental glomerulosclerosis. Nature Genet., 40(10), 1175–1184.CrossRefGoogle ScholarPubMed
Kopp, J.B., Nelson, G.W., Sampath, K., et al. (2011). APOL1 genetic variants in focal segmental glomerulosclerosis and HIV-associated nephropathy. J. Am. Soc. Nephrol., 22(11), 2129–2137.CrossRefGoogle ScholarPubMed
Lautenberger, J.A., Stephens, J.C., O'Brien, S.J. and Smith, M.W. (2000). Significant admixture linkage disequilibrium across 30 cM around the FY locus in African Americans. Am. J. Hum. Genet., 66(3), 969–978.CrossRefGoogle ScholarPubMed
Liu, E.Y., Li, M., Wang, W. and Li, Y. (2013). MaCH-admix: genotype imputation for admixed populations. Genet. Epidemiol., 37(1), 25–37.CrossRefGoogle ScholarPubMed
Loh, P.-R., Lipson, M., Patterson, N., et al. (2013). Inferring admixture histories of human populations using linkage disequilibrium. Genetics, 193(4), 1233–1254.CrossRefGoogle ScholarPubMed
MacLean, C.J. and Workman, P.L. (1973). Genetic studies on hybrid populations. I. Individual estimates of ancestry and their relation to quantitative traits. Ann. Hum. Genet., 36(3), 341–351.Google ScholarPubMed
Manolio, T.A., Collins, F.S., Cox, N.J., et al. (2009). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753.CrossRefGoogle ScholarPubMed
Maples, B.K., Gravel, S., Kenny, E.E. and Bustamante, C.D. (2013). RFMix: A discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet., 93(2), 278–288.CrossRefGoogle ScholarPubMed
Martin, E.R., Lai, E.H., Gilbert, J.R., et al. (2000). SNPing away at complex diseases: analysis of single-nucleotide polymorphisms around APOE in Alzheimer disease. Am. J. Hum. Genet., 67(2), 383–394.CrossRefGoogle ScholarPubMed
Matise, T.C., Chen, F., Chen, W., et al. (2007). A second-generation combined linkage physical map of the human genome. Genome Res., 17(12), 1783–1786.CrossRefGoogle ScholarPubMed
McDonough, C.W., Palmer, N.D., Hicks, P.J., et al. (2011). A genome-wide association study for diabetic nephropathy genes in African Americans. Kidney Int., 79(5), 563–572.CrossRefGoogle ScholarPubMed
McKeigue, P.M. (1997). Mapping genes underlying ethnic differences in disease risk by linkage disequilibrium in recently admixed populations. Am. J. Hum. Genet., 60(1), 188.Google ScholarPubMed
McKeigue, P.M. (1998). Mapping genes that underlie ethnic differences in disease risk: methods for detecting linkage in admixed populations, by conditioning on parental admixture. Am. J. Hum. Genet., 63, 241–251.CrossRefGoogle ScholarPubMed
McKeigue, P.M., Carpenter, J.R., Parra, E.J. and Shriver, M.D. (2000a). Estimation of admixture and detection of linkage in admixed populations by a Bayesian approach: application to African-American populations. Ann. Hum. Genet., 64(Pt 2), 171–186.CrossRefGoogle ScholarPubMed
McKeigue, P.M., Colombo, M., Agakov, F., et al. (2013). Extending admixture mapping to nuclear pedigrees: application to sarcoidosis. Genet. Epidemiol., 37(3), 256–266.CrossRefGoogle ScholarPubMed
Mezaka, I., Legzdina, L., Waugh, R., Close, T.J. and Rostoks, N. (2012). Genetic diversity in Latvian spring barley association mapping population. In Zhang, G., Li, C. and Liu, X. (Eds), Advances in Barley Science: Proceedings of the 11th International Barley Genetics Symposium. Dordrecht: Springer Netherlands, pp. 25–35.Google Scholar
Molineros, J.E., Maiti, A.K., Sun, C., et al. (2013). Admixture mapping in lupus identifies multiple functional variants within IFIH1 associated with apoptosis, inflammation, and autoantibody production. PLoS Genet., 9(2), e1003222.CrossRefGoogle ScholarPubMed
Montana, G. and Pritchard, J.K. (2004). Statistical tests for admixture mapping with case-control and cases-only data. Am. J. Hum. Genet., 75(5), 771–789.CrossRefGoogle ScholarPubMed
Morgan, T.H. (1910). Sex limited inheritance in drosophila. Science, 32(812), 120–123.CrossRefGoogle ScholarPubMed
Morgan, T.H. (1911). Random segregation versus coupling in medellian inheritance. Science, 34(873), 384.CrossRefGoogle Scholar
Morris, A.P., Voight, B.F., Teslovich, T.M., et al. (2012). Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature Genet., 44(9), 981–990.Google ScholarPubMed
Moutsianas, L. and Morris, A.P. (2014). Methodology for the analysis of rare genetic variation in genome-wide association and re-sequencing studies of complex human traits. Brief. Funct. Genom., 13(5), 362–370.CrossRefGoogle ScholarPubMed
Nalls, M.A., Wilson, J.G., Patterson, N.J., et al. (2008). Admixture mapping of white cell count: genetic locus responsible for lower white blood cell count in the Health ABC and Jackson Heart studies. Am. J. Hum. Genet., 82(1), 81–87.CrossRefGoogle ScholarPubMed
Ning, B., Su, Z., Mei, N., et al. (2014). Toxicogenomics and cancer susceptibility: advances with next-generation sequencing. J. Environ. Sci. Health. Part C, Environ. Carcin. Ecotoxicol. Rev., 32(2), 121–158.CrossRefGoogle ScholarPubMed
Ochs-Balcom, H.M., Preus, L., Wactawski-Wende, J., et al. (2013). Association of DXA-derived bone mineral density and fat mass with African ancestry. J. Clin. Endocrinol. Metab., 98(4), E713–717.CrossRefGoogle ScholarPubMed
Ohta, T. (1982). Linkage disequilibrium due to random genetic drift in finite subdivided populations. Proc. Natl Acad. Sci. USA, 79(6), 1940–1944.CrossRefGoogle ScholarPubMed
Palaisa, K., Morgante, M., Tingey, S. and Rafalski, A. (2004). Long-range patterns of diversity and linkage disequilibrium surrounding the maize Y1 gene are indicative of an asymmetric selective sweep. Proc. Natl Acad. Sci. USA, 101(26), 9885–9890.CrossRefGoogle ScholarPubMed
Parker, M.M., Foreman, M.G., Abel, H.J., et al. (2014). Admixture mapping identifies a quantitative trait locus associated with FEV1/FVC in the COPD Gene Study. Genet. Epidemiol., 38(7), 652–659.CrossRefGoogle Scholar
Parra, E.J., Marcini, A., Akey, J., et al. (1998). Estimating African American admixture proportions by use of population-specific alleles. Am. J. Hum. Genet., 63(6), 1839–1851.CrossRefGoogle ScholarPubMed
Pasaniuc, B., Sankararaman, S., Kimmel, G. and Halperin, E. (2009). Inference of locus-specific ancestry in closely related populations. Bioinformatics, 25(12), i213–221.CrossRefGoogle ScholarPubMed
Pasaniuc, B., Zaitlen, N., Lettre, G., et al. (2011). Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium. PLoS Genet., 7(4), e1001371.CrossRefGoogle Scholar
Patterson, N., Hattangadi, N., Lane, B., et al. (2004). Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet., 74(5), 979–1000.CrossRefGoogle ScholarPubMed
Patterson, N., Moorjani, P., Luo, Y., et al. (2012). Ancient admixture in human history. Genetics, 192(3), 1065–1093.CrossRefGoogle ScholarPubMed
Price, A.L., Weale, M.E., Patterson, N., et al. (2008). Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet., 83(1), 132–135.CrossRefGoogle ScholarPubMed
Price, A.L., Tandon, A., Patterson, N., et al. (2009). Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet., 5(6), e1000519.CrossRefGoogle ScholarPubMed
Price, P., James, I., Fernandez, S. and French, M.A. (2004). Alleles of the gene encoding IL-1α may predict control of plasma viraemia in HIV-1 patients on highly active antiretroviral therapy. AIDS, 18(11), 1495–1501.CrossRefGoogle ScholarPubMed
Pritchard, J.K., Stephens, M. and Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155(2), 945–959.Google ScholarPubMed
Rabbani, B., Tekin, M. and Mahdieh, N. (2014). The promise of whole-exome sequencing in medical genetics. J. Hum. Genet., 59(1), 5–15.CrossRefGoogle ScholarPubMed
Redden, D.T., Divers, J., Vaughan, L.K., et al. (2006). Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLoS Genet., 2(8), e137.CrossRefGoogle ScholarPubMed
Reich, D., Patterson, N., De Jager, P.L., et al. (2005). A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nature Genet., 37(10), 1113–1118.CrossRefGoogle ScholarPubMed
Risch, N., Burchard, E., Ziv, E. and Tang, H. (2002). Categorization of humans in biomedical research: genes, race and disease. Genome Biol., 3(7), comment2007.CrossRefGoogle ScholarPubMed
Rodriguez, J.M., Bercovici, S., Elmore, M. and Batzoglou, S. (2013). Ancestry inference in complex admixtures via variable-length Markov chain linkage models. J. Comput. Biol., 20(3), 199–211.CrossRefGoogle ScholarPubMed
Rogers, S.M. (2012). Mapping the genomic architecture of ecological speciation in the wild: does linkage disequilibrium hold the key?Molec. Ecol., 21(21), 5155–5158.CrossRefGoogle ScholarPubMed
Saint Pierre, A. and Génin, E. (2014). How important are rare variants in common disease?Brief. Funct. Genom., 13(5), 353–361.CrossRefGoogle ScholarPubMed
Sankararaman, S., Kimmel, G., Halperin, E. and Jordan, M.I. (2008a). On the inference of ancestries in admixed populations. Genome Res., 18(4), 668–675.CrossRefGoogle ScholarPubMed
Sankararaman, S., Sridhar, S. and Kimmel, G. (2008b). Estimating local ancestry in admixed populations. Am. J. Hum. Genet., 82(2), 290–303.CrossRefGoogle ScholarPubMed
Schork, N.J., Murray, S.S., Frazer, K.A. and Topol, E.J. (2009). Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Develop., 19(3), 212–219.CrossRefGoogle ScholarPubMed
Schwartz, A.G., Wenzlaff, A.S., Bock, C.H., et al. (2011). Admixture mapping of lung cancer in 1812 African-Americans. Carcinogenesis, 32(3), 312–317.CrossRefGoogle ScholarPubMed
Seldin, M.F., Pasaniuc, B. and Price, A.L. (2011). New approaches to disease mapping in admixed populations. Nature Rev. Genet., 12(8), 523–528.CrossRefGoogle ScholarPubMed
Sham, P.C. and Purcell, S.M. (2014). Statistical power and significance testing in large-scale genetic studies. Nature Rev. Genet., 15(5), 335–346.CrossRefGoogle ScholarPubMed
Sober, E. (1993). The Nature of Selection: Evolutionary Theory in Philosophical Focus. University of Chicago Press, Chicago, IL.Google Scholar
Stephens, J.C., Briscoe, D. and O'Brien, S.J. (1994). Mapping by admixture linkage disequilibrium in human populations: limits and guidelines. Am. J. Hum. Genet., 55, 809–824.Google ScholarPubMed
Sundquist, A., Fratkin, E., Do, C.B. and Batzoglou, S. (2008). Effect of genetic divergence in identifying ancestral origin using HAPAA. Genome Res., 18(4), 676–682.CrossRefGoogle ScholarPubMed
Tang, H., Peng, J., Wang, P. and Risch, N.J. (2005). Estimation of individual admixture: analytical and study design considerations. Genet. Epidemiol., 28(4), 289–301.CrossRefGoogle ScholarPubMed
Tang, H., Coram, M., Wang, P., Zhu, X. and Risch, N. (2006). Reconstructing genetic ancestry blocks in admixed individuals. Am. J. Hum. Genet., 79(1), 1–12.CrossRefGoogle ScholarPubMed
Tang, H., Siegmund, D.O., Johnson, N.A., Romieu, I. and London, S.J. (2010). Joint testing of genotype and ancestry association in admixed families. Genet. Epidemiol., 34(8), 783–791.CrossRefGoogle ScholarPubMed
Thoday, J.M. (1969). Limitations to genetic comparison of populations. J. Biosoc. Sci., Suppl. 1, 3–14.Google ScholarPubMed
Tishkoff, S.A., Dietzch, E., Speed, W., et al. (1996). Global patterns of linkage disequilibrium at the CD4 locus and modern human origins. Science, 271(5254), 1380–1387.CrossRefGoogle ScholarPubMed
Torgerson, D.G., Gignoux, C.R., Galanter, J.M., et al. (2012). Case-control admixture mapping in Latino populations enriches for known asthma-associated genes. J. Allergy Clin. Immunol., 130(1), 76–82.e12.CrossRefGoogle ScholarPubMed
Winkler, C.A., Nelson, G.W. and Smith, M.W. (2010). Admixture mapping comes of age. Annu. Rev. Genom. Hum. Genet., 11, 65–89.CrossRefGoogle ScholarPubMed
Wojcik, G.L., Thio, C.L., Kao, W.H., et al. (2014). Admixture analysis of spontaneous hepatitis C virus clearance in individuals of African descent. Genes Immun., 15(4), 241–246.CrossRefGoogle ScholarPubMed
Yang, J.J., Li, J., Buu, A. and Williams, L.K. (2013). Efficient inference of local ancestry. Bioinformatics, 29(21), 2750–2756.CrossRefGoogle ScholarPubMed
Zhang, Y. (2013). De novo inference of stratification and local admixture in sequencing studies. BMC Bioinformatics, 14(Suppl. 5), S17.Google ScholarPubMed
Zhao, H., Pfeiffer, R. and Gail, M.H. (2003). Haplotype analysis in population genetics and association studies. Pharmacogenomics, 4(2), 171–178.CrossRefGoogle ScholarPubMed

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