Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

BMC Medical Genomics

Volumn 5, Issue , 2012, Pages

  Krithika, S ^a   Valladares Salgado, Adán ^b   Peralta, Jesus ^b   Escobedo De La Pẽa, Jorge ^c   Kumate Rodríguez, Jesus ^d   Cruz, Miguel ^b   Parra, Esteban J ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b HOSPITAL DE ESPECIALIDADES   (Mexico)

^c General Hospital   (Mexico)

^d INSTITUTO MEXICANO DEL SEGURO SOCIAL   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CHROMOSOME 12; COMPUTER PROGRAM; CONTROLLED STUDY; GENETIC VARIABILITY; GENOME; GENOTYPE; HAPLOTYPE MAP; HUMAN; HUMAN CELL; IMPUTATION; INFORMATION PROCESSING; MEXICO; MICROARRAY ANALYSIS; PRIORITY JOURNAL;

GENE FREQUENCY; GENETIC MARKERS; GENOME, HUMAN; GENOTYPING TECHNIQUES; HAPMAP PROJECT; HUMANS; MEXICO; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REFERENCE STANDARDS; SOFTWARE;

EID: 84862196987     PISSN: None     EISSN: 17558794     Source Type: Journal    
DOI: 10.1186/1755-8794-5-12     Document Type: Article

References (56)

1. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Kruglyak L, Nat Genet 1999 22 139 144 10.1038/9642 10369254 (Pubitemid 29264806)
2. Genome-wide association studies and the genetic dissection of complex traits. Sebastiani P, Timofeev N, Dworkis DA, Perls TT, Steinberg MH, Am J Hematol 2009 84 504 515 10.1002/ajh.21440 19569043
3. The road to genome-wide association studies. Kruglyak L, Nat Rev Genet 2008 9 314 318 18283274 (Pubitemid 351399831)
4. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN, Nat Rev Genet 2008 9 356 369 10.1038/nrg2344 18398418 (Pubitemid 351556063)
5. Human genetic variation and its contribution to complex traits. Frazer KA, Murray SS, Schork NJ, Topol EJ, Nat Rev Genet 2009 10 241 251 19293820
6. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. The Wellcome Trust Case Control Consortium, Nature 2007 447 661 678 10.1038/nature05911 17554300 (Pubitemid 46889737)
7. Progress in high throughput SNP genotyping methods. Tsuchihashi Z, Dracopoli NC, Pharmacogenomics J 2002 2 103 110 10.1038/sj.tpj.6500094 12049172 (Pubitemid 34982213)
8. High-throughput genomic technology in research and clinical management of breast cancer. Low YL, Wedrén S, Liu J, Evolving landscape of genetic epidemiological studies. Breast Cancer Res 2006 8 209
9. High-Throughput Single Nucleotide Polymorphisms Genotyping Technologies. Ku CS, Kasiman K, Chia KS, Encyclopedia of Life Sciences (ELS) Sons, Ltd, Chichester, John Wiley, 2009 http://www.els.net [doi: 10.1002/9780470015902. a0021631 ]
10. Genome-wide association studies: theoretical and practical concerns. Wang WY, Barratt BJ, Clayton DG, Todd JA, Nat Rev Genet 2005 6 109 118 10.1038/nrg1522 15716907 (Pubitemid 40179533)
11. Evaluating coverage of genome-wide association studies. Barrett JC, Cardon LR, Nat Genet 2006 38 659 662 10.1038/ng1801 16715099 (Pubitemid 43927307)
12. Evaluating and improving power in whole-genome association studies using fixed marker sets. Pe'er I, de Bakker PI, Maller J, Yelensky R, Altshuler D, Daly MJ, Nat Genet 2006 38 663 667 10.1038/ng1816 16715096 (Pubitemid 43927308)
13. Progress and challenges in genome-wide association studies in humans. Donnelly P, Nature 2008 456 728 731 10.1038/nature07631 19079049
14. Genotype imputation for genome-wide association studies. Marchini J, Howie B, Nat Rev Genet 2010 11 499 511 10.1038/nrg2796 20517342
15. Genotype Imputation. Li Y, Willer C, Sanna S, Abecasis G, Annu Rev Genomics Hum Genet 2009 10 387 406 10.1146/annurev.genom.9.081307.164242 19715440
16. Genotype-imputation accuracy across worldwide human populations. Huang L, Li Y, Singleton AB, Hardy JA, Abecasis G, Rosenberg NA, Scheet P, Am J Hum Genet 2009 84 2 235 250 10.1016/j.ajhg.2009.01.013 19215730
17. Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms. Anderson CA, Pettersson FH, Barrett JC, Am J Hum Genet 2008 83 112 119 10.1016/j.ajhg.2008.06.008 18589396
18. An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations. Almeida MA, Oliveira PS, Pereira TV, Krieger JE, Pereira AC, BMC Genet 2011 12 10 21251252
19. A new multipoint method for genome-wide association studies by imputation of genotypes. Marchini J, Howie B, Myers S, McVean G, Donnelly P, Nat Genet 2007 39 906 913 10.1038/ng2088 17572673 (Pubitemid 47014502)
20. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. Howie BN, Donnelly P, Marchini J, Plos Genet 2009 5 6 1000529 10.1371/journal.pgen.1000529 19543373
21. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR, Genet Epidemiol 2010 34 816 834 10.1002/gepi.20533 21058334
22. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Browning SR, Browning BL, Am J Hum Genet 2007 81 1084 1097 10.1086/521987 17924348 (Pubitemid 47580259)
23. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Scheet P, Stephens M, Am J Hum Genet 2006 78 629 644 10.1086/502802 16532393
24. PLINK: a tool set for whole-genome association and population-based linkage analyses. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC, Am J Hum Genet 2007 81 559 575 10.1086/519795 17701901 (Pubitemid 47330214)
25. Analyses and comparison of accuracy of different genotype imputation methods. Pei YF, Li J, Zhang L, Papasian CJ, Deng HW, PLoS One 2008 3 10 3551 10.1371/journal.pone.0003551 18958166
26. Missing data imputation and haplotype phase inference for genome-wide association studies. Browning SR, Hum Genet 2008 124 5 439 450 10.1007/s00439-008-0568-7 18850115
27. A comprehensive evaluation of SNP genotype imputation. Nothnagel M, Ellinghaus D, Schreiber S, Krawczak M, Franke A, Hum Genet 2009 125 2 163 171 10.1007/s00439-008-0606-5 19089453
28. Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies. Hao K, Chudin E, McElwee J, Schadt EE, BMC Genet 2009 10 27 19531258
29. Analyses and comparison of imputation-based association methods. Pei YF, Zhang L, Li J, Deng HW, PLoS One 2010 5 5 10827 10.1371/journal.pone.0010827 20520814
30. Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, Kumate J, McKeigue PM, Escobedo J, Cruz M: Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas. Parra EJ, Below JE, Krithika S, Valladares A, Barta JL, Cox NJ, Hanis CL, Wacher N, Garcia-Mena J, Hu P, Shriver MD, Diabetologia 2011 54 8 2038 2046 10.1007/s00125-011-2172-y 21573907
31. Genome-wide comparison of African-ancestry populations from CARe and other cohorts reveals signals of natural selection. Bhatia G, Patterson N, Pasaniuc B, Zaitlen N, Genovese G, Pollack S, Mallick S, Myers S, Tandon A, Spencer C, Palmer CD, Adeyemo AA, Akylbekova EL, Cupples LA, Divers J, Fornage M, Kao WH, Lange L, Li M, Musani S, Mychaleckyj JC, Ogunniyi A, Papanicolaou G, Rotimi CN, Rotter JI, Ruczinski I, Salako B, Siscovick DS, Tayo BO, Yang Q, et al. Am J Hum Genet 2011 89 3 368 381 10.1016/j.ajhg.2011.07.025 21907010
32. HLA DNA sequence variation among human populations: molecular signatures of demographic and selective events. Buhler S, Sanchez-Mazas A, PLoS One 2011 6 2 14643 10.1371/journal.pone.0014643 21408106
33. Natural selection and the distribution of identity-by-descent in the human genome. Albrechtsen A, Moltke I, Nielsen R, Genetics 2010 186 1 295 308 10.1534/genetics.110.113977 20592267
34. Practical issues in imputation-based association mapping. Guan Y, Stephens M, PLoS Genet 2008 4 12 1000279 10.1371/journal.pgen.1000279 19057666
35. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. Spencer CC, Su Z, Donnelly P, Marchini J, PLoS Genet 2009 5 5 1000477 10.1371/journal.pgen.1000477 19492015
36. Wellcome Trust Case Control Consortium, Mooser V, Francks C, Marchini J: Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJ, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, et al. Nat Genet 2010 42 5 436 40 10.1038/ng.572 20418889
37. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight BF, Hum Mol Genet 2008 17 R2 122 128 10.1093/hmg/ddn288 18852200
38. Meta-analysis in genome-wide association studies. Zeggini E, Ioannidis JP, Pharmacogenomics 2009 10 2 191 201 10.2217/14622416.10.2.191 19207020
39. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM, Allen JE, Downes K, Barrett JC, Healy BC, Mychaleckyj JC, Warram JH, Todd JA, Nat Genet 2008 40 12 1399 1401 10.1038/ng.249 18978792
40. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT, Piccio L, Raychaudhuri S, Tran D, Aubin C, Briskin R, Romano S, International MS Genetics Consortium, Baranzini SE, McCauley JL, Pericak-Vance MA, Haines JL, Gibson RA, Naeglin Y, Uitdehaag B, Matthews PM, Kappos L, Polman C, McArdle WL, Strachan DP, Evans D, Cross AH, Daly MJ, Compston A, Sawcer SJ, et al. Nat Genet 2009 41 7 776 782 10.1038/ng.401 19525953
41. Meta-analysis of three genome-wide association studies identifies susceptibility loci for colorectal cancer at 1q41, 3q26.2, 12q13.13 and 20q13.33. Houlston RS, Cheadle J, Dobbins SE, Tenesa A, Jones AM, Howarth K, Spain SL, Broderick P, Domingo E, Farrington S, Prendergast JG, Pittman AM, Theodoratou E, Smith CG, Olver B, Walther A, Barnetson RA, Churchman M, Jaeger EE, Penegar S, Barclay E, Martin L, Gorman M, Mager R, Johnstone E, Midgley R, Niittymäki I, Tuupanen S, Colley J, Idziaszczyk S, et al. Nat Genet 2010 42 11 973 977 10.1038/ng.670 20972440
42. Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies. International Parkinson Disease Genomics Consortium, Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simán-Sánchez J, Schulte C, Lesage S, Sveinbjörnsdáttir S, Stefánsson K, Martinez M, Hardy J, Heutink P, Brice A, Gasser T, Singleton AB, Wood NW, Lancet 2011 377 9766 641 649 21292315
43. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, Petrie JR, Travers ME, Bouatia-Naji N, Dimas AS, Nica A, Wheeler E, Chen H, Voight BF, Taneera J, Kanoni S, Peden JF, Turrini F, Gustafsson S, Zabena C, Almgren P, Barker DJ, Barnes D, Dennison EM, Eriksson JG, Eriksson P, Eury E, Folkersen L, Fox CS, Frayling TM, et al. Diabetes 2011 60 10 2624 2634 10.2337/db11-0415 21873549
44. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G, Ardlie K, Boström KB, Bergman RN, Bonnycastle LL, Borch-Johnsen K, Burtt NP, Chen H, Chines PS, Daly MJ, Deodhar P, Ding CJ, Doney AS, Duren WL, Elliott KS, Erdos MR, Frayling TM, Freathy RM, Gianniny L, Grallert H, Grarup N, et al. Nat Genet 2008 40 5 638 645 10.1038/ng.120 18372903 (Pubitemid 351601209)
45. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, et al. Science 2007 316 5829 1341 1345 10.1126/science.1142382 17463248 (Pubitemid 46871655)
46. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, Tay WT, Chen CH, Zhang Y, Yamamoto K, Katsuya T, Yokota M, Kim YJ, Ong RT, Nabika T, Gu D, Chang LC, Kokubo Y, Huang W, Ohnaka K, Yamori Y, Nakashima E, Jaquish CE, Lee JY, Seielstad M, Isono M, Hixson JE, Chen YT, Miki T, Zhou X, et al. Nat Genet 2011 43 6 531 8 10.1038/ng.834 21572416
47. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Kim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, Hwang JY, Oh JH, Kim DJ, Kim NH, Kim S, Hong EJ, Kim JH, Min H, Kim Y, Zhang R, Jia W, Okada Y, Takahashi A, Kubo M, Tanaka T, Kamatani N, Matsuda K, Consortium MAGIC, Park T, Oh B, Kimm K, Kang D, Shin C, Cho NH, et al. Nat Genet 2011 43 10 990 995 10.1038/ng.939 21909109
48. Transferability of tag SNPs in genetic association studies in multiple populations. de Bakker PI, Burtt NP, Graham RR, Guiducci C, Yelensky R, Drake JA, Bersaglieri T, Penney KL, Butler J, Young S, Onofrio RC, Lyon HN, Stram DO, Haiman CA, Freedman ML, Zhu X, Cooper R, Groop L, Kolonel LN, Henderson BE, Daly MJ, Hirschhorn JN, Altshuler D, Nat Genet 2006 38 11 1298 1303 10.1038/ng1899 17057720 (Pubitemid 44646292)
49. Estimating African American admixture proportions by use of population-specific alleles. Parra EJ, Marcini A, Akey J, Martinson J, Batzer MA, Cooper R, Forrester T, Allison DB, Deka R, Ferrell RE, Shriver MD, Am J Hum Genet 1998 63 6 1839 1851 10.1086/302148 9837836 (Pubitemid 30415746)
50. Geographic patterns of genome admixture in Latin American Mestizos. Wang S, Ray N, Rojas W, Parra MV, Bedoya G, Gallo C, Poletti G, Mazzotti G, Hill K, Hurtado AM, Camrena B, Nicolini H, Klitz W, Barrantes R, Molina JA, Freimer NB, Bortolini MC, Salzano FM, Petzl-Erler ML, Tsuneto LT, Dipierri JE, Alfaro EL, Bailliet G, Bianchi NO, Llop E, Rothhammer F, Excoffier L, Ruiz-Linares A, PLoS Genet 2008 4 3 1000037 10.1371/journal.pgen.1000037 18369456 (Pubitemid 351507870)
51. for the LACE Consortium. Development of a Panel of Genome-Wide Ancestry Informative Markers to Study Admixture Throughout the Americas. Galanter JM, Fernandez-Lopez JC, Gignoux CR, Barnholtz-Sloan J, Fernandez-Rozadilla C, Via M, Hidalgo-Miranda A, Contreras AV, Figueroa LU, Raska P, Jimenez-Sanchez G, Silva Zolezzi I, Torres M, Ponte CR, Ruiz Y, Salas A, Nguyen E, Eng C, Borjas L, Zabala W, Barreto G, Rondán González F, Ibarra A, Taboada P, Porras L, Moreno F, Bigham A, Gutierrez G, Brutsaert T, Leán-Velarde F, et al. PLoS Genet 2012 8 3 1002554 10.1371/journal.pgen.1002554
52. Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. Lettre G, Palmer CD, Young T, Ejebe KG, Allayee H, Benjamin EJ, Bennett F, Bowden DW, Chakravarti A, Dreisbach A, Farlow DN, Folsom AR, Fornage M, Forrester T, Fox E, Haiman CA, Hartiala J, Harris TB, Hazen SL, Heckbert SR, Henderson BE, Hirschhorn JN, Keating BJ, Kritchevsky SB, Larkin E, Li M, Rudock ME, McKenzie CA, Meigs JB, Meng YA, et al. PLoS Genet 2011 7 2 1001300 10.1371/journal.pgen.1001300 21347282
53. Practical considerations for imputation of untyped markers in admixed populations. Shriner D, Adeyemo A, Chen G, Rotimi CN, Genet Epidemiol 2010 34 3 258 265 19918757
54. Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium. Paaniuc B, Zaitlen N, Lettre G, Chen GK, Tandon A, Kao WH, Ruczinski I, Fornage M, Siscovick DS, Zhu X, Larkin E, Lange LA, Cupples LA, Yang Q, Akylbekova EL, Musani SK, Divers J, Mychaleckyj J, Li M, Papanicolaou GJ, Millikan RC, Ambrosone CB, John EM, Bernstein L, Zheng W, Hu JJ, Ziegler RG, Nyante SJ, Bandera EV, Ingles SA, et al. PLoS Genet 2011 7 4 1001371 10.1371/journal.pgen. 1001371 21541012
55. A generic coalescent-based framework for the selection of a reference panel for imputation. Paaniuc B, Avinery R, Gur T, Skibola CF, Bracci PM, Halperin E, Genet Epidemiol 2010 34 8 773 782 10.1002/gepi.20505 21058333
56. Use of weighted reference panels based on empirical estimates of ancestry for capturing untyped variation. Egyud MR, Gajdos ZK, Butler JL, Tischfield S, Le Marchand L, Kolonel LN, Haiman CA, Henderson BE, Hirschhorn JN, Hum Genet 2009 125 3 295 303 10.1007/s00439-009-0627-8 19184111