A comparison of DMET Plus microarray and genome-wide technologies by assessing population substructure

Pharmacogenetics and Genomics

Volumn 26, Issue 4, 2016, Pages 147-153

  Jackson, Jami N ^a   Long, Kevin M ^b,c   He, Yijing ^c,d,e   Motsinger Reif, Alison A ^a,c   Mcleod, Howard L ^b,c,d,e   Jack, John ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b Ortelion LLC   (United States)

^c Pharmacogenetics for Every Nation Initiative ^*  (United States)

^d H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

^e CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

admixture; Affymetrix; cryptic relatedness; DMET Plus; inbreeding coefficients; metabolism; pharmacogenomics; population stratification; principal component analysis; transporters

Indexed keywords

CARRIER PROTEIN; DRUG METABOLIZING ENZYME;

ARTICLE; COMPARATIVE STUDY; COMPUTER PROGRAM; CONTROLLED STUDY; CORRELATION COEFFICIENT; DRUG METABOLISM; ETHNIC DIFFERENCE; GENE FREQUENCY; GENETIC ASSOCIATION; GENETIC LINKAGE; GENETIC MARKER; GENOTYPE; HAPLOTYPE MAP; HUMAN; INBREEDING; MICROARRAY ANALYSIS; MICROARRAY KIT; NONPARAMETRIC TEST; POPULATION STRUCTURE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RANDOM SAMPLE; SINGLE NUCLEOTIDE POLYMORPHISM;

EID: 84963812281     PISSN: 17446872     EISSN: 17446880     Source Type: Journal    
DOI: 10.1097/FPC.0000000000000200     Document Type: Article

References (34)

1. Pritchard JK, Rosenberg NA. Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 1999; 65: 220-228
2. Devlin B, Roeder K, Wasserman L. Genomic control, a new approach to genetic-based association studies. Theor Popul Biol 2001; 60: 155-166
3. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000; 155: 945-959
4. Enoch MA. Using ancestry-informative markers to define populations and detect population stratification. J Psychopharmacol 2006; 20 (Suppl): 19-26
5. The International HapMap Consortium. The International HapMap Project. Nature 2003; 426: 789-796
6. Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L, et al. Integrating common and rare genetic variation in diverse human populations. Nature 2010; 467: 52-58
7. Kühnisch J, Thiering E, Heitmüller D, Tiesler CT, Grallert H, Heinrich-Weltzien R, et al. Genome-wide association study (GWAS) for molar-incisor hypomineralization (MIH). Clin Oral Investig 2014; 18: 677-682
8. Craddock N, Hurles ME, Cardin N, Pearson RD, Plagnol V, Robson S, et al. Genome-wide association study of CNVs in 16, 000 cases of eight common diseases and 3000 shared controls. Nature 2010; 464: 713-720
9. Ma D, Salyakina D, Jaworski JM, Konidari I, Whitehead PL, Andersen AN, et al. A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann Hum Genet 2009; 73: 263-273
10. Rothman N, Garcia-Closa M, Chatterjee N, Malats N, Wu X, Figueroa JD, et al. A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci. Nat Genet 2010; 42: 978-984
11. Burmester J, Sedova M, Shapero M, Mansfield E. DMET microarray technology for pharmacogenomics-based personalized medicine. Methods Mol Biol 2010; 632: 99-124
12. Hoffman JM, Haidar CE, Wilkinson MR, Crews KR, Baker DK, Kornegay NM, et al. PG4KDS: A model for the clinical implementation of pre-emptive pharmacogenetics. Am J Med Genet C Semin Med Genet 2014; 166: 45-55
13. Deeken JF, Cormier T, Price DK, Sissung TM, Steinberg SM, Tran K, et al. A pharmacogenetic study of docetaxel and thalidomide in patients with castration-resistant prostate cancer using the DMET genotyping platform. Pharmacogenomics J 2010; 10: 191-199
14. de Graan AJ, Elens L, Smid M, Martens JW, Sparreboom A, Nieuweboer AJ, et al. A pharmacogenetic predictive model for paclitaxel clearance based on the DMET platform. Clin Cancer Res 2013; 19: 5210-5217
15. Suarez-Kurtz G. Pharmacogenomics in admixed populations. Trends Pharmacol Sci 2005; 26: 196-201
16. Man M, Farmen M, Dumaual C, Teng CH, Moser B, Irie S, et al. Genetic variation in metabolizing enzyme and transporter genes: comprehensive assessment in 3 major East Asian subpopulations with comparison to Caucasians and Africans. J Clin Pharmacol 2010; 50: 929-940
17. He YJ, Misher AD, Irvin W, Motsinger-Reif A, McLeod HL, Hoskins JM. Assessing the utility of whole genome amplified DNA as a template for DMET Plus array. Clin Chem Lab Med 2012; 50: 1329-1334
18. Hertz DL, Roy S, Jack J, Motsinger-Reif AA, Drobish A, Clark LS, et al. Genetic heterogeneity beyond CYP2C8. 3 does not explain differential sensitivity to paclitaxel-induced neuropathy. Breast Cancer Res Treat 2014; 145: 245-254
19. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904-909
20. Menozzi P, Piazza A, Cavalli-Sforza L. Synthetic maps of human gene frequencies in Europeans. Science 1978; 201: 786-792
21. Service SK, Teslovich TM, Fuchsberger C, Ramensky V, Yajnik P, Koboldt DC, et al. Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci. PLoS Genet 2014; 10: 1
22. Ma L, Wiggans GR, Wang S, Sonstegard TS, Yang J, Crooker BA, et al. Effect of sample stratification on dairy GWAS results. BMC Genomics 2012; 13: 536
23. Chen LS, Hutter CM, Potter JD, Liu Y, Prentice RL, Peters U, Hsu L. Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data. Am J Hum Genet 2010; 86: 860-871
24. Price AL, Butler J, Patterson N, Capelli C, Pascali VL, Scarnicci F, et al. Discerning the ancestry of European Americans in Genetic Association Studies. PLoS Genet 2008; 4: e236
25. Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G, et al. An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Gene 2009; 10: 39
26. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 2009; 19: 1655-1664
27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D, et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007; 81: 559-575
28. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013
29. Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A highperformance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 2012; 28: 3326-3328
30. Voight BF, Pritchard JK. Confounding from cryptic relatedness in case-control association studies. PLoS Genet 2005; 1: e32
31. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42: 565-569
32. Tian C, Plenge RM, Ransom M, Lee A, Villoslada P, Selmi C, et al. Analysis and application of European genetic substructure using 300K SNP information. PLoS Genet 2008; 4: e4
33. Heath SC, Gut IG, Brennan P, McKay JD, Bencko V, Fabianova E, et al. Investigation of the fine structure of European populations with applications to disease association studies. Eur J Med Genet 2008; 16: 1413-1429
34. Intarapanich A, Shaw PJ, Assawamakin A, Wangkumhang P, Ngamphiw C, Chaichoompu K, et al. Iterative pruning PCA improves resolution of highly structured populations. BMC Bioinformatics 2009; 10: 382