Correction for population stratification in random forest analysis

International Journal of Epidemiology

Volumn 41, Issue 6, 2012, Pages 1798-1806

  Zhao, Yang ^a,b   Chen, Feng ^b   Zhai, Rihong ^a   Lin, Xihong ^a   Wang, Zhaoxi ^a   Su, Li ^a   Christiani, David C ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b NANJING MEDICAL UNIVERSITY   (China)

Author keywords

Genome wide association study; Population stratification; Random forest

Indexed keywords

LACTASE;

ALLELE; DATA INTERPRETATION; DATA SET; EPIDEMIOLOGY; GENETIC ANALYSIS; GENOME; NUCLEIC ACID; NUMERICAL METHOD; PHENOTYPE; POLYMORPHISM; POPULATION STRUCTURE; PUBLIC HEALTH; SUBPOPULATION;

ARTICLE; CLUSTER ANALYSIS; GENE FREQUENCY; GENETIC ASSOCIATION; GENOTYPE; OUTCOME ASSESSMENT; POPULATION DYNAMICS; POPULATION STRATIFICATION; POPULATION STRUCTURE; PRIORITY JOURNAL; RANDOM FOREST; SINGLE NUCLEOTIDE POLYMORPHISM;

CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; GENETICS, POPULATION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MODELS, STATISTICAL; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84872122626     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dys183     Document Type: Article

References (33)

1. Thomas DC, Haile RW, Duggan D. Recent developments in genomewide association scans: a workshop summary and review. Am J Hum Genet 2005;77:337-45.
2. McCarthy MI, Hirschhorn JN. Genome-wide association studies: past, present and future. Hum Mol Genet 2008; 17(R2):R100-01.
3. Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M. Genome-wide association studies in diverse populations. Nat Rev Genet 2010;11:356-66.
4. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95-108.
5. Breiman L. Random forests. Mach Learn 2001;45:5-32.
6. Nicodemus KK, Malley JD, Strobl C, Ziegler A. The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics 2010;11:110.
7. Goldstein BA, Hubbard AE, Cutler A, Barcellos LF. An application of random forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genet 2010;11:49.
8. Maenner MJ, Denlinger LC, Langton A, Meyers KJ, Engelman CD, Skinner HG. Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests. BMC Proc 2009;3(Suppl 7):S88.
9. Kim Y, Wojciechowski R, Sung H et al. Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects. BMC Proc 2009;3(Suppl 7):S64.
10. Sun YV, Cai Z, Desai K et al. Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests. BMC Proc 2007;1(Suppl 1):S62.
11. Ziegler A, Konig IR, Thompson JR. Biostatistical aspects of genome-wide association studies. Biom J 2008;50:8-28.
12. Lander ES, Schork NJ. Genetic dissection of complex traits. Science 1994;265:2037-48.
13. Freedman ML, Reich D, Penney KL et al. Assessing the impact of population stratification on genetic association studies. Nat Genet 2004;36:388-93.
14. Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet 2004;36:512-17.
15. 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;38:904-09.
16. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet 2010;11:459-63.
17. Li Q, Yu K. Improved correction for population stratification in genome-wide association studies by identifying hidden population structures. Genet Epidemiol 2008;32: 215-26.
18. Sun YV. Multigenic modeling of complex disease by random forests. Adv Genet 2010;72:73-99.
19. Nicodemus KK, Malley JD. Predictor correlation impacts machine learning algorithms: implications for genomic studies. Bioinformatics 2009;25:1884-90.
20. Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley Online Library, 1990.
21. Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Series B Stat Methodol 2001;63:411-23.
22. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins, 2008.
23. Campbell CD, Ogburn EL, Lunetta KL et al. Demonstrating stratification in a European American population. Nat Genet 2005;37:868-72.
24. Qin H, Morris N, Kang SJ et al. Interrogating local population structure for fine mapping in genome-wide association studies. Bioinformatics 2010;26:2961-68.
25. Li M, Reilly MP, Rader DJ, Wang LS. Correcting population stratification in genetic association studies using a phylogenetic approach. Bioinformatics 2010;26: 798-806.
26. Asomaning K, Miller DP, Liu G et al. Second hand smoke, age of exposure and lung cancer risk. Lung Cancer 2008; 61:13-20.
27. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010;34: 816-34.
28. Liaw A, Wiener M. Classification and regression by randomForest. R News 2002;2:18-22.
29. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet 2006;2:e190.
30. Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics 2008;9:307.
31. Li Y, Willer C, Sanna S, Abecasis G. Genotype imputation. Annu Rev Genomics Hum Genet 2009;10:387-406.
32. Zheng J, Li Y, Abecasis GR, Scheet P. A comparison of approaches to account for uncertainty in analysis of imputed genotypes. Genet Epidemiol 2011;35:102-10.
33. Amaratunga D, Cabrera J, Lee YS. Enriched random forests. Bioinformatics 2008;24:2010-14.