Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

BMC Genomics

James J Chen³  Chia-Wei Chen²  Chien-Wei Lin²  Hsin-Chou Yang¹ 

[1] School of Public Health, National Defense Medical Center, Taipei, Taiwan;Institute of Statistical Science, Academia Sinica, No 128, Academia Road, Section 2, Nankang, Taipei, Taiwan;National Center for Toxicological Research, Food and Drug Administration, Little Rock, Arkansas, USA

关键词: Drug biotransformation;    Drug response;    Adverse drug reaction;    Pharmacogenetics;    Ancestry-informative marker (AIM);    Partial least squares (PLS);    Expression quantitative trait locus (eQTL);    Gene-based approach;   

Others  :  1217340
DOI  :  10.1186/1471-2164-15-319

 received in 2013-12-19, accepted in 2014-04-15,  发布年份 2014

【 摘 要 】

Background

Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).

Results

In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher’s exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher’s exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher’s exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.

Conclusions

In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm webcite. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

【 授权许可】

   
2014 Yang et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]The International HapMap Consortium: The international HapMap project. Nature 2003, 426(6968):789-796.
• [2]The International HapMap Consortium: Integrating ethics and science in the international HapMap project. Nat Rev Genet 2004, 5(6):467-475.
• [3]The International HapMap Consortium: A haplotype map of the human genome. Nature 2005, 437(7063):1299-1320.
• [4]The International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 2007, 449(7164):851-861.
• [5]The International HapMap 3 Consortium: Integrating common and rare genetic variation in diverse human populations. Nature 2010, 467(7311):52-58.
• [6]Encode Project Consortium: The ENCODE (ENCyclopedia of DNA elements) Project. Science 2004, 306(5696):636-640.
• [7]Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE, Kuehn MS, Taylor CM, Neph S, Koch CM, Asthana S, Malhotra A, Adzhubei I, Greenbaum JA, Andrews RM, Flicek P, Boyle PJ, Cao H, Carter NP, Clelland GK, Davis S, Day N, Dhami P, Dillon SC, Dorschner MO, Fiegler H, et al.: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 2007, 447(7146):799-816.
• [8]The ENCODE, Consortium P: A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 2011, 9(4):e1001046.
• [9]The 1000 Genomes Project Consortium: A map of human genome variation from population-scale sequencing. Nature 2010, 467(7319):1061-1073.
• [10]The 1000 Genomes Project Consortium: An integrated map of genetic variation from 1,092 human genomes. Nature 2012, 491(7422):56-65.
• [11]Thomas DC, Haile RW, Duggan D: Recent developments in genomewide association scans: A workshop summary and review. Am J Hum Genet 2005, 77(3):337-345.
• [12]Manolio TA: Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010, 363(2):166-176.
• [13]McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008, 9(5):356-369.
• [14]Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005, 6(2):95-108.
• [15]Wang WY, Barratt BJ, Clayton DG, Todd JA: Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 2005, 6(2):109-118.
• [16]Nica AC, Dermitzakis ET: Expression quantitative trait loci: present and future. Philos Trans R Soc B: Biol Sci 2013, 368(1620):20120362.
• [17]Montgomery SB, Dermitzakis ET: From expression QTLs to personalized transcriptomics. Nat Rev Genet 2011, 12(4):277-282.
• [18]Cheung VG, Spielman RS: The genetics of variation in gene expression. Nat Genet 2002, 32(Suppl):522-525.
• [19]Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen KY, Morley M, Spielman RS: Natural variation in human gene expression assessed in lymphoblastoid cells. Nat Genet 2003, 33(3):422-425.
• [20]Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M: Mapping complex disease traits with global gene expression. Nat Rev Genet 2009, 10(3):184-194.
• [21]Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S, Mouy M, Steinthorsdottir V, Eiriksdottir GH, Bjornsdottir G, Reynisdottir I, Gudbjartsson D, Helgadottir A, Jonasdottir A, Styrkarsdottir U, Gretarsdottir S, Magnusson KP, Stefansson H, Fossdal R, Kristjansson K, Gislason HG, Stefansson T, Leifsson BG, Thorsteinsdottir U, Lamb JR, Gulcher JR, et al.: Genetics of gene expression and its effect on disease. Nature 2008, 452(7186):423-428.
• [22]Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, Kasarskis A, Zhang B, Wang S, Suver C, Zhu J, Millstein J, Sieberts S, Lamb J, GuhaThakurta D, Derry J, Storey JD, Avila-Campillo I, Kruger MJ, Johnson JM, Rohl CA, van Nas A, Mehrabian M, Drake TA, Lusis AJ, Smith RC, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, et al.: Mapping the genetic architecture of gene expression in human liver. PLoS Biol 2008, 6(5):e107.
• [23]Innocenti F, Cooper GM, Stanaway IB, Gamazon ER, Smith JD, Mirkov S, Ramirez J, Liu W, Lin YS, Moloney C, Aldred SF, Trinklein ND, Schuetz E, Nickerson DA, Thummel KE, Rieder MJ, Rettie AE, Ratain MJ, Cox NJ, Brown CD: Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet 2011, 7(5):e1002078.
• [24]Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature 2004, 430(7001):743-747.
• [25]Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT: Mapping determinants of human gene expression by regional and genome-wide association. Nature 2005, 437(7063):1365-1369.
• [26]Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, Hunt S, Kahl B, Antonarakis SE, Tavare S, Deloukas P, Dermitzakis ET: Genome-wide associations of gene expression variation in humans. PLoS Genet 2005, 1(6):e78.
• [27]Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D, Montgomery S, Tavare S, Deloukas P, Dermitzakis ET: Population genomics of human gene expression. Nat Genet 2007, 39(10):1217-1224.
• [28]Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman AK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang TP, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, et al.: The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet 2011, 7(2):e1002003.
• [29]Li Q, Seo JH, Stranger B, McKenna A, Pe’er I, Laframboise T, Brown M, Tyekucheva S, Freedman ML: Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 2013, 152(3):633-641.
• [30]Zhong H, Yang X, Kaplan LM, Molony C, Schadt EE: Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am J Hum Genet 2010, 86(4):581-591.
• [31]Yang TP, Beazley C, Montgomery SB, Dimas AS, Gutierrez-Arcelus M, Stranger BE, Deloukas P, Dermitzakis ET: Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics 2010, 26(19):2474-2476.
• [32]The GTEx Consortium: The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013, 45(6):580-585.
• [33]Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W, Sullivan PF, Wright FA: seeQTL: a searchable database for human eQTLs. Bioinformatics 2012, 28(3):451-452.
• [34]Creighton C, Hanash S: Mining gene expression databases for association rules. Bioinformatics 2003, 19(1):79-86.
• [35]Daye ZJ, Chen J, Li H: High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis. Biometrics 2012, 68(1):316-326.
• [36]Massouras A, Waszak SM, Albarca-Aguilera M, Hens K, Holcombe W, Ayroles JF, Dermitzakis ET, Stone EA, Jensen JD, Mackay TF, Deplancke B: Genomic variation and its impact on gene expression in Drosophila melanogaster. PLoS Genet 2012, 8(11):e1003055.
• [37]Zichner T, Garfield DA, Rausch T, Stutz AM, Cannavo E, Braun M, Furlong EE, Korbel JO: Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res 2013, 23(3):568-579.
• [38]Hovatta I, Zapala MA, Broide RS, Schadt EE, Libiger O, Schork NJ, Lockhart DJ, Barlow C: DNA variation and brain region-specific expression profiles exhibit different relationships between inbred mouse strains: implications for eQTL mapping studies. Genome Biol 2007, 8(2):R25. BioMed Central Full Text
• [39]Chen L, Page GP, Mehta T, Feng R, Cui X: Single nucleotide polymorphisms affect both cis- and trans-eQTLs. Genomics 2009, 93(6):501-508.
• [40]Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, Tyler-Smith C, Carter N, Scherer SW, Tavare S, Deloukas P, Hurles ME, Dermitzakis ET: Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007, 315(5813):848-853.
• [41]Zhang W, Duan S, Kistner EO, Bleibel WK, Huang RS, Clark TA, Chen TX, Schweitzer AC, Biume JE, Cox NJ, Dolan ME: Evaluation of genetic variation contributing to differences in gene expression between populations. Am J Hum Genet 2008, 82(3):631-640.
• [42]Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, Ingle C, Beazley C, Gutierrez Arcelus M, Sekowska M, Gagnebin M, Nisbett J, Deloukas P, Dermitzakis ET, Antonarakis SE: Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 2009, 325(5945):1246-1250.
• [43]Rockman MV, Kruglyak L: Genetics of global gene expression. Nat Rev Genet 2006, 7(11):862-872.
• [44]Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG: Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet 2007, 39(2):226-231.
• [45]Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM: Gene-expression variation within and among human populations. Am J Hum Genet 2007, 80(3):502-509.
• [46]Broman KW: A model selection approach for the identification of quantitative trait loci in experimental crosses. J Roy Stat Soc Ser B (Stat Method) 2002, 64(4):641-656.
• [47]Sun W, Ibrahim JG, Zou F: Genomewide multiple-loci mapping in experimental crosses by iterative adaptive penalized regression. Genetics 2010, 185(1):349-359.
• [48]Kuhn K, Baker SC, Chudin E, Lieu MH, Oeser S, Bennett H, Rigault P, Barker D, McDaniel TK, Chee MS: A novel, high-performance random array platform for quantitative gene expression profiling. Genome Res 2004, 14(11):2347-2356.
• [49]Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14(13):1675-1680.
• [50]Steemers FJ, Chang WH, Lee G, Barker DL, Shen R, Gunderson KL: Whole-genome genotyping with the single-base extension assay. Nat Methods 2006, 3(1):31-33.
• [51]Steemers FJ, Gunderson KL: Whole genome genotyping technologies on the BeadArray™ platform. Biotechnol J 2007, 2(1):41-49.
• [52]Affymetrix: GeneChip human mapping 100K set. 2004.
• [53]Affymetrix: GeneChip Human Mapping 10K Array and Assay Kit. Affymetrix 2003.
• [54]Buil A, Martinez-Perez A, Perera-Lluna A, Rib L, Caminal P, Soria JM: A new gene-based association test for genome-wide association studies. BMC Proc 2009, 3(Suppl 7):S130. BioMed Central Full Text
• [55]Li MY, Wang K, Grant SFA, Hakonarson H, Li C: ATOM: a powerful gene-based association test by combining optimally weighted markers. Bioinformatics 2009, 25(4):497-503.
• [56]Yang HC, Liang YJ, Chung CM, Chen JW, Pan WH: Genome-wide gene-based association study. BMC Proc 2009, 3(7):S135.
• [57]Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, Hayward NK, Montgomery GW, Visscher PM, Martin NG, Macgregor S: A versatile gene-based test for genome-wide association studies. Am J Hum Genet 2010, 87(1):139-145.
• [58]Li MX, Gui HS, Kwan JS, Sham PC: GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet 2011, 88(3):283-293.
• [59]Baye TM, Wilke RA: Mapping genes that predict treatment outcome in admixed populations. Pharmacogenom J 2010, 10(6):465-477.
• [60]Chen LS, Hsu L, Gamazon ER, Cox NJ, Nicolae DL: An exponential combination procedure for set-based association tests in sequencing studies. Am J Hum Genet 2012, 91(6):977-986.
• [61]Sharpe AG, Ramsay L, Sanderson LA, Fedoruk MJ, Clarke WE, Li R, Kagale S, Vijayan P, Vandenberg A, Bett KE: Ancient orphan crop joins modern era: gene-based SNP discovery and mapping in lentil. BMC Genomics 2013, 14:192. BioMed Central Full Text
• [62]Yang H-C, Chen C-W: Region-based and pathway-based QTL mapping using a p-value combination method. BMC Proc 2011, 5(Suppl 9):S43. BioMed Central Full Text
• [63]Nguyen DV, Rocke DM: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002, 18(1):39-50.
• [64]Nguyen DV, Rocke DM: Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics 2002, 18(9):1216-1226.
• [65]Boulesteix AL: PLS dimension reduction for classification with microarray data. Stat Appl Genet Mol Biol 2004, 3:33.
• [66]Chun H, Ballard DH, Cho J, Zhao H: Identification of association between disease and multiple markers via sparse partial least-squares regression. Genet Epidemiol 2011, 35(6):479-486.
• [67]Turkmen AS, Lin S: Gene-based partial least-squares approaches for detecting rare variant associations with complex traits. BMC Proc 2011, 5(9):S19.
• [68]Zhang F, Guo X, Deng HW: Multilocus association testing of quantitative traits based on partial least-squares analysis. PLoS ONE 2011, 6(2):e16739.
• [69]Serre D, Paabo S: Evidence for gradients of human genetic diversity within and among continents. Genome Res 2004, 14(9):1679-1685.
• [70]Halder I, Shriver M, Thomas M, Fernandez JR, Frudakis T: A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications. Hum Mutat 2008, 29(5):648-658.
• [71]Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G, Kittles R, Alarcon-Riquelme ME, Gregersen PK, Belmont JW, De La Vega FM, Seldin MF: An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Genet 2009, 10:39.
• [72]Paschou P, Lewis J, Javed A, Drineas P: Ancestry informative markers for fine-scale individual assignment to worldwide populations. J Med Genet 2010, 47(12):835-847.
• [73]Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics 2000, 155(2):945-959.
• [74]Kosoy R, Nassir R, Tian C, White PA, Butler LM, Silva G, Kittles R, Alarcon-Riquelme ME, Gregersen PK, Belmont JW, De La Vega FM, Seldin MF: Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America. Hum Mutat 2009, 30(1):69-78.
• [75]Pomeroy R, Duncan G, Sunar-Reeder B, Ortenberg E, Ketchum M, Wasiluk H, Reeder D: A low-cost, high-throughput, automated single nucleotide polymorphism assay for forensic human DNA applications. AnBio 2009, 395(1):61-67.
• [76]Phillips C, Salas A, Sanchez JJ, Fondevila M, Gomez-Tato A, Alvarez-Dios J, Calaza M, Casares de Cal M, Ballard D, Lareu MV, Carracedo A, Consortium SN: Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs. Forensic Sci Int Genet 2007, 1(3-4):273-280.
• [77]Rosenberg NA, Li LM, Ward R, Pritchard JK: Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 2003, 73(6):1402-1422.
• [78]Londin ER, Keller MA, Maista C, Smith G, Mamounas LA, Zhang R, Madore SJ, Gwinn K, Corriveau RA: CoAIMs: a cost-effective panel of ancestry informative markers for determining continental origins. PLoS ONE 2010, 5(10):e13443.
• [79]Tian C, Hinds DA, Shigeta R, Kittles R, Ballinger DG, Seldin MF: A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping. Am J Hum Genet 2006, 79(4):640-649.
• [80]Tian C, Kosoy R, Lee A, Ransom M, Belmont JW, Gregersen PK, Seldin MF: Analysis of East Asia genetic substructure using genome-wide SNP arrays. PLoS ONE 2008, 3(12):e3862.
• [81]Kidd JR, Friedlaender FR, Speed WC, Pakstis AJ, De La Vega FM, Kidd KK: Analyses of a set of 128 ancestry informative single-nucleotide polymorphisms in a global set of 119 population samples. Investig Genet 2011, 2(1):1. BioMed Central Full Text
• [82]Yang HC, Wang PL, Lin CW, Chen CH: Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations. BMC Genomics 2012, 13(1):346. BioMed Central Full Text
• [83]Kayser M, De Knijff P: Improving human forensics through advances in genetics, genomics and molecular biology. Nat Rev Genet 2011, 12(3):179-192.
• [84]Goldstein DB, Tate SK, Sisodiya SM: Pharmacogenetics goes genomic. Nat Rev Genet 2003, 4(12):937-947.
• [85]Roses AD: Pharmacogenetics and drug development: The path to safer and more effective drugs. Nat Rev Genet 2004, 5(9):645-656.
• [86]Daly AK: Genome-wide association studies in pharmacogenomics. Nat Rev Genet 2010, 11(4):241-246.
• [87]Motsinger-Reif AA, Jorgenson E, Relling MV, Kroetz DL, Weinshilboum R, Cox NJ, Roden DM: Genome-wide association studies in pharmacogenomics: successes and lessons. Pharmacogenet Genomics 2013, 23(8):383-394.
• [88]Wood AJJ, Zhou HH: Ethnic-differences in drug disposition and responsiveness. Clin Pharmacokinet 1991, 20(5):350-373.
• [89]Xie HG, Kim RB, Wood AJJ, Stein CM: Molecular basis of ethnic differences in drug disposition and response. Annu Rev Pharmacol Toxicol 2001, 41:815-850.
• [90]Yasuda SU, Zhang L, Huang SM: The role of ethnicity in variability in response to drugs: Focus on clinical pharmacology studies. Clin Pharmacol Ther 2008, 84(3):417-423.
• [91]Maisano Delser P, Fuselli S: Human loci involved in drug biotransformation: worldwide genetic variation, population structure, and pharmacogenetic implications. Hum Genet 2013, 132(5):563-577.
• [92]Visscher H, Ross CJ, Dube MP, Brown AM, Phillips MS, Carleton BC, Hayden MR: Application of principal component analysis to pharmacogenomic studies in Canada. Pharmacogenomics J 2009, 9(6):362-372.
• [93]Seldin MF, Price AL: Application of ancestry informative markers to association studies in European Americans. PLoS Genet 2008, 4(1):e5.
• [94]Hamburg MA, Collins FS: The Path to Personalized Medicine. N Engl J Med 2010, 363(4):301-304.
• [95]Chung WH, Hung SI, Hong HS, Hsih MS, Yang LC, Ho HC, Wu JY, Chen YT: Medical genetics: a marker for Stevens-Johnson syndrome. Nature 2004, 428(6982):486.
• [96]Inc A: BRLMM: An improved genotype calling method for the GeneChip human mapping 500K array set. 2006.
• [97]Guo SW, Thompson EA: Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 1992, 48(2):361-372.
• [98]Benjamini Y, Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Statist Soc B 1995, 57(1):289-300.
• [99]Vandervoet H: Comparing the Predictive Accuracy of Models Using a Simple Randomization Test. Chemometrics Intellig Lab Syst 1994, 25(2):313-323.
• [100]Nordhausen K, Oja H: Multivariate L-1 Methods: The Package MNM. J Stat Softw 2011, 43(5):1-28.
• [101]Roberts RL, Joyce PR, Mulder RT, Begg EJ, Kennedy MA: A common P-glycoprotein polymorphism is associated with nortriptyline-induced postural hypotension in patients treated for major depression. Pharmacogenom J 2002, 2(3):191-196.
• [102]Campa D, Gioia A, Tomei A, Poli P, Barale R: Association of ABCB1/MDR1 and OPRM1 gene polymorphisms with morphine pain relief. Clin Pharmacol Ther 2008, 83(4):559-566.
• [103]Iakoubova OA, Tong CH, Rowland CM, Kirchgessner TG, Young BA, Arellano AR, Shiffman D, Sabatine MS, Campos H, Packard CJ, Pfeffer MA, White TJ, Braunwald E, Shepherd J, Devlin JJ, Sacks FM: Association of the Trp719Arg polymorphism in kinesin-like protein 6 with myocardial infarction and coronary heart disease in 2 prospective trials: the CARE and WOSCOPS trials. J Am Coll Cardiol 2008, 51(4):435-443.
• [104]Bare LA, Morrison AC, Rowland CM, Shiffman D, Luke MM, Iakoubova OA, Kane JP, Malloy MJ, Ellis SG, Pankow JS, Willerson JT, Devlin JJ, Boerwinkle E: Five common gene variants identify elevated genetic risk for coronary heart disease. Genet Med 2007, 9(10):682-689.
• [105]Peng P, Lian J, Huang RS, Xu L, Huang Y, Ba Y, Yang X, Huang X, Dong C, Zhang L, Ye M, Zhou J, Duan S: Meta-analyses of KIF6 Trp719Arg in coronary heart disease and statin therapeutic effect. PLoS ONE 2012, 7(12):e50126.
• [106]Iakoubova OA, Sabatine MS, Rowland CM, Tong CH, Catanese JJ, Ranade K, Simonsen KL, Kirchgessner TG, Cannon CP, Devlin JJ, Braunwald E: Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: results from the PROVE IT-TIMI 22 study. J Am Coll Cardiol 2008, 51(4):449-455.
• [107]Cooper R, Cutler J, Desvigne-Nickens P, Fortmann SP, Friedman L, Havlik R, Hogelin G, Marler J, McGovern P, Morosco G, Mosca L, Pearson T, Stamler J, Stryer D, Thom T: Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the national conference on cardiovascular disease prevention. Circulation 2000, 102(25):3137-3147.
• [108]Jolly S, Vittinghoff E, Chattopadhyay A, Bibbins-Domingo K: Higher cardiovascular disease prevalence and mortality among younger blacks compared to whites. Am J Med 2010, 123(9):811-818.
• [109]Shiffman D, Sabatine MS, Louie JZ, Kirchgessner TG, Iakoubova OA, Campos H, Devlin JJ, Sacks FM: Effect of pravastatin therapy on coronary events in carriers of the KIF6 719Arg allele from the cholesterol and recurrent events trial. Am J Cardiol 2010, 105(9):1300-1305.
• [110]Li Y, Iakoubova OA, Shiffman D, Devlin JJ, Forrester JS, Superko HR: KIF6 polymorphism as a predictor of risk of coronary events and of clinical event reduction by statin therapy. Am J Cardiol 2010, 106(7):994-998.
• [111]Deeken JF, Cormier T, Price DK, Sissung TM, Steinberg SM, Tran K, Liewehr DJ, Dahut WL, Miao X, Figg WD: A pharmacogenetic study of docetaxel and thalidomide in patients with castration-resistant prostate cancer using the DMET genotyping platform. Pharmacogenom J 2010, 10(3):191-199.
• [112]Ingelman-Sundberg M, Sim SC, Gomez A, Rodriguez-Antona C: Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol Ther 2007, 116(3):496-526.
• [113]Caldwell MD, Awad T, Johnson JA, Gage BF, Falkowski M, Gardina P, Hubbard J, Turpaz Y, Langaee TY, Eby C, King CR, Brower A, Schmelzer JR, Glurich I, Vidaillet HJ, Yale SH, Qi Zhang K, Berg RL, Burmester JK: CYP4F2 genetic variant alters required warfarin dose. Blood 2008, 111(8):4106-4112.
• [114]Gibson G: The environmental contribution to gene expression profiles. Nat Rev Genet 2008, 9(8):575-581.
• [115]Stranger BE, Montgomery SB, Dimas AS, Parts L, Stegle O, Ingle CE, Sekowska M, Smith GD, Evans D, Gutierrez-Arcelus M, Price A, Raj T, Nisbett J, Nica AC, Beazley C, Durbin R, Deloukas P, Dermitzakis ET: Patterns of cis regulatory variation in diverse human populations. PLoS Genet 2012, 8(4):e1002639.
• [116]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012, 40(Database issue):D109-D114.
• [117]Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25-29.
• [118]Stone M, Brooks RJ: Continuum Regression: Cross-Validated Sequentially Constructed Prediction embracing Ordinary Least Squares, Partial Least Squares and Principal Components Regression. J R Statist Soc B 1990, 52(2):237-269.