Effects of sample selection bias on the accuracy of population structure and ancestry inference

G3: Genes, Genomes, Genetics

Volumn 4, Issue 5, 2014, Pages 901-911

  Shringarpure, Suyash ^a   Xing, Eric P ^b  

^a STANFORD UNIVERSITY   (United States)

^b Carnegie Mellon University ^*  (United States)

Author keywords

Admixture analysis; Biased sampling; Global ancestry; Population structure

Indexed keywords

ACCURACY; ANCESTOR; ANCESTRY INFERENCE; ARTICLE; CATTLE; CONTROLLED STUDY; EFFECTIVE POPULATION SIZE; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENETIC VARIABILITY; GENOTYPE; NONHUMAN; POPULATION DYNAMICS; POPULATION GENETICS; POPULATION STRUCTURE; SAMPLING BIAS; SELECTION BIAS; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; STRATIFIED SAMPLE; STRUCTURE ANALYSIS;

EID: 84901302427     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.113.007633     Document Type: Article

References (32)

1. Alexander, D. H., J. Novembre, and K. Lange, 2009 Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19: 1655-1664.
2. Baran, Y., B. Pasaniuc, S. Sankararaman, D. G. Torgerson, C. Gignoux et al., 2012. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics 28: 1359-1367.
3. Cavalli-Sforza, L. L., 2005. The Human Genome Diversity Project: past, present and future. Nat. Rev. Genet. 6: 333-340.
4. Cortes, C., M. Mohri, M. Riley, and A. Rostamizadeh, 2008. Sample selection bias correction theory. Algorithmic Learning Theory 5254: 16.
5. Davidson, I., and B. Zadrozny, 2005. An improved categorization of classifiers sensitivity on sample selection bias, pp. 605-608 in Fifth IEEE International Conference on Data Mining ICDM05. Available at: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1565737&queryText%3DAn+improved+categorization+of+classifiers+sensitivity+on+sample+selection+bias. Accessed March 26, 2014.
6. Engelhardt, B. E., and M. Stephens, 2010. Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis. PLoS Genet. 6: 12.
7. Falush, D., M. Stephens, and J. K. Pritchard, 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567-1587.
8. Gibbs, R. A., 2003. The International HapMap Project. Nature 426: 789-796.
9. Heckman, J. J., 1979. Sample selection bias as a specification error. Econometrica 47: 153-161.
10. Hoggart, C. J., E. J. Parra, M. D. Shriver, C. Bonilla, R. A. Kittles et al., 2003. Control of confounding of genetic associations in stratified populations. Am. J. Hum. Genet. 72: 1492-1504.
11. Hudson, R. R., 2002. Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18: 337-338.
12. Huelsenbeck, J. P., and P. Andolfatto, 2007. Inference of population structure under a Dirichlet process prior. Genetics 175: 1787-1802.
13. Kaeuffer, R., D. Réale, D. W. Coltman, and D. Pontier, 2007. Detecting population structure using STRUCTURE software: effect of background linkage disequilibrium. Heredity 99: 374-380.
14. McTavish, E. J., J. E. Decker, R. D. Schnabel, J. F. Taylor, and D. M. Hillis, 2013. a Data from: New World cattle show ancestry from multiple independent domestication events. Available at: http://datadryad.org/resource/doi:10.5061/dryad.42tr0. Accessed March 26, 2014.
15. McTavish, E. J., J. E. Decker, R. D. Schnabel, J. F. Taylor, and D. M. Hillis, 2013. b New World cattle show ancestry from multiple independent domestication events. Proc. Natl. Acad. Sci. USA 110: E1398-E1406.
16. McVean, G., 2009. A genealogical interpretation of principal components analysis. PLoS Genet. 5: e1000686.
17. Nelson, M. R., K. Bryc, K. S. King, A. Indap, A. R. Boyko et al., 2008. The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research. Am. J. Hum. Genet. 83: 347-358.
18. Novembre, J., and M. Stephens, 2008. Interpreting principal component analyses of spatial population genetic variation. Nat. Genet. 40: 646-649.
19. Novembre, J., T. Johnson, K. Bryc, Z. Kutalik, A. R. Boyko et al., 2008. Genes mirror geography within Europe. Nature 456: 98-101.
20. Patterson, N., A. L. Price, and D. Reich, 2006. Population structure and eigenanalysis. PLoS Genet. 2: e190.
21. Phillips, S. J., M. Dudík, J. Elith, C. H. Graham, A. Lehmann et al., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19: 181-197.
22. Price, A. L., N. J. Patterson, R. M. Plenge, M. E. Weinblatt, N. A. Shadick et al., 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38: 904-909.
23. Price, A. L., A. Tandon, N. Patterson, K. C. Barnes, N. Rafaels et al., 2009. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet. 5: e1000519.
24. Pritchard, J. K., M. Stephens, and P. Donnelly, 2000. Inference of population structure from multilocus genotype data. Genetics 155: 945-959.
25. Rosenberg, N. A., J. K. Pritchard, J. L. Weber, H. M. Cann, K. K. Kidd et al., 2002. Genetic Structure of Human Populations. Science 298: 2381-2385.
26. Sankararaman, S., G. Kimmel, E. Halperin, and M. I. Jordan, 2008. On the inference of ancestries in admixed populations. Genome Res. 18: 668-675.
27. Shringarpure, S., and E. P. Xing, 2009. mStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations. Genetics 182: 575-593.
28. Tang, H., J. Peng, P. Wang, and N. J. Risch, 2005. Estimation of individual admixture: analytical and study design considerations. Genet. Epidemiol. 28: 289-301.
29. Vella, F., 1998. Estimating models with sample selection bias: a survey. J. Hum. Resour. 33: 127-169.
30. Wall, J. D., R. Jiang, C. Gignoux, G. K. Chen, C. Eng et al., 2011. Genetic variation in Native Americans, inferred from Latino SNP and resequencing data. Mol. Biol. Evol. 28: 2231-2237.
31. Zadrozny, B. 2004. Learning and evaluating classifiers under sample selection bias, pp. 114 in Twenty-First International Conference on Machine Learning ICML 04. ACM, New York, NY.
32. Zadrozny, B., J. Langford, and N. Abe, 2003. Cost-sensitive learning by costproportionate example weighting, pp. 435-442 in Third IEEE International Conference on Data Mining, ICDM 2003, Available at: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1250950&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1250950. Accessed March 27, 2014.