Abstract
Population of ethnic mixtures can be useful in genetic studies. Admixture mapping, or mapping by admixture linkage disequilibrium (MALD), is specially developed for admixed populations and can supplement traditional genome-wide association analyses in the search for genetic variants underlying complex traits. Admixture mapping tests the association between a trait and locus-specific ancestries. The locus-specific ancestries are in linkage disequilibrium (LD), which is generated by an admixture process between genetically distinct ancestral populations. Because of the highly correlated-locus specific ancestries, admixture mapping performs many fewer independent tests across the genome than current genome-wide association analysis. Therefore, admixture mapping can be more powerful because it reduces the penalty due to multiple tests. In this chapter, we introduce the theory behind admixture mapping and explain how to conduct the analysis in practice.
References
Risch NJ (1992) Mapping genes for complex disease using association studies with recently admixed populations. Am J Hum Genet 51:13
McKeigue PM (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
Zhu X, Cooper RS, Elston RC (2004) Linkage analysis of a complex disease through use of admixed populations. Am J Hum Genet 74:1136–1153
Zhu X, Zhang S, Tang H, Cooper R (2006) A classical likelihood based approach for admixture mapping using EM algorithm. Hum Genet 120:431–445
Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA et al (2004) Methods for high-density admixture mapping of disease genes. Am J Hum Genet 74:979–1000
Hoggart CJ, Shriver MD, Kittles RA, Clayton DG, McKeigue PM (2004) Design and analysis of admixture mapping studies. Am J Hum Genet 74:965–978
Montana G, Pritchard JK (2004) Statistical tests for admixture mapping with case-control and cases-only data. Am J Hum Genet 75:771–789
Zhang C, Chen K, Seldin MF, Li H (2004) A hidden Markov modeling approach for admixture mapping based on case-control data. Genet Epidemiol 27:225–239
Zhu X, Tang H, Risch N (2008) Admixture mapping and the role of population structure for localizing disease genes. Adv Genet 60:547–569
Sha Q, Zhang X, Zhu X, Zhang S (2006) Analytical correction for multiple testing in admixture mapping. Hum Hered 62:55–63
Zhu X, Zhang S, Zhao H, Cooper RS (2002) Association mapping, using a mixture model for complex traits. Genet Epidemiol 23(2):181–196
Basu A, Tang H, Arnett D, CC G, Mosley T et al (2009) Admixture mapping of quantitative trait loci for BMI in African Americans: evidence for loci on chromosomes 3q, 5q, and 15q. Obesity (Silver Spring) 17:1226–1231
Zhu X, Young JH, Fox E, Keating BJ, Franceschini N, Kang S, Tayo B, Adeyemo A, Sun YV, Li Y et al (2011) Combined admixture mapping and association analysis identifies a novel blood pressure genetic locus on 5p13: contributions from the CARe consortium. Hum Mol Genet 20(11):2285–2295
Tang H, Coram M, Wang P, Zhu X, Risch N (2006) Reconstructing genetic ancestry blocks in admixed individuals. Am J Hum Genet 79:1–12
Sundquist A, Fratkin E, Do CB, Batzoglou S (2008) Effect of genetic divergence in identifying ancestral origin using HAPAA. Genome Res 18:676–682
Sankararaman S, Sridhar S, Kimmel G, Halperin E (2008) Estimating local ancestry in admixed populations. Am J Hum Genet 82:290–303
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587
Long JC (1991) The genetic structure of admixed populations. Genetics 127(2):417–428
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909
Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10:387–406
Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S, Cann HM, Barsh GS, Feldman M, Cavalli-Sforza LL et al (2008) Worldwide human relationships inferred from genome-wide patterns of variation. Science 319(5866):1100–1104
Baran Y, Pasaniuc B, Sankararaman S, Torgerson DG, Gignoux C, Eng C, Rodriguez-Cintron W, Chapela R, Ford JG, Avila PC et al (2012) Fast and accurate inference of local ancestry in Latino populations. Bioinformatics 28(10):1359–1367
Pasaniuc B, Sankararaman S, Kimmel G, Halperin E (2009) Inference of locus-specific ancestry in closely related populations. Bioinformatics 25(12):i213–i221
Brisbin A, Bryc K, Byrnes J, Zakharia F, Omberg L, Degenhardt J, Reynolds A, Ostrer H, Mezey JG, Bustamante CD (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
Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A et al (2009) Finding the missing heritability of complex diseases. Nature 461(7265):747–753
Bowden DW, An SS, Palmer ND, Brown WM, Norris JM, Haffner SM, Hawkins GA, Guo X, Rotter JI, Chen YD et al (2010) Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study. Hum Mol Genet 19(20):4112–4120
Zhu X, Feng T, Li Y, Lu Q, Elston RC (2010) Detecting rare variants for complex traits using family and unrelated data. Genet Epidemiol 34(2):171–187
Wang H, Zhu X (2014) De novo mutations discovered in 8 Mexican American families through whole genome sequencing. BMC Proc 8(Suppl 1 Genetic Analysis Workshop 18 Vanessa Olmo):S24
Veltman JA, Brunner HG (2012) De novo mutations in human genetic disease. Nat Rev Genet 13(8):565–575
O’Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, Karakoc E, Mackenzie AP, Ng SB, Baker C et al (2011) Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet 43(6):585–589
O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, Levy R, Ko A, Lee C, Smith JD et al (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485(7397):246–250
Girard SL, Gauthier J, Noreau A, Xiong L, Zhou S, Jouan L, Dionne-Laporte A, Spiegelman D, Henrion E, Diallo O et al (2011) Increased exonic de novo mutation rate in individuals with schizophrenia. Nat Genet 43(9):860–863
Wang H (2016) Local ancestry inference and its implication in searching for selection evidence in recent admixed population. Ph.D. dissertation, Case Western Reserve University
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575
Tang H, Choudhry S, Mei R, Morgan M, Rodriguez-Cintron W et al (2007) Recent genetic selection in the ancestral admixture of Puerto Ricans. Am J Hum Genet 81:626–633
Zhu X, Cooper RS (2007) Admixture mapping provides evidence of association of the VNN1 gene with hypertension. PLoS ONE 2:e1244
Wang YJ, Tayo BO, Bandyopadhyay A, Wang H, Feng T, Franceschini N, Tang H, Gao J, Sung YJ, Consortium CB et al (2014) The association of the vanin-1 N131S variant with blood pressure is mediated by endoplasmic reticulum-associated degradation and loss of function. PLoS Genet 10:e1004641
Smith MW, Patterson N, Lautenberger JA, Truelove AL, McDonald GJ et al (2004) A high-density admixture map for disease gene discovery in African Americans. Am J Hum Genet 74:1001–1013
Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004
Qin H, Zhu X (2012) Power comparison of admixture mapping and direct association analysis in genome-wide association studies. Genet Epidemiol 36:235–243
Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227
Acknowledgment
This work was supported by a grant from the National Human Genome Research Institute (HG003054).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Zhu, X., Wang, H. (2017). The Analysis of Ethnic Mixtures. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_25
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7274-6_25
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7273-9
Online ISBN: 978-1-4939-7274-6
eBook Packages: Springer Protocols