TY - JOUR
T1 - Effect of linkage disequilibrium on the identification of functional variants
AU - Thomas, Alun
AU - Abel, Haley J.
AU - Di, Yanming
AU - Faye, Laura L.
AU - Jin, Jing
AU - Liu, Jin
AU - Wu, Zheyan
AU - Paterson, Andrew D.
PY - 2011
Y1 - 2011
N2 - We summarize the contributions of Group 9 of Genetic Analysis Workshop 17. This group addressed the problems of linkage disequilibrium and other longer range forms of allelic association when evaluating the effects of genotypes on phenotypes. Issues raised by long-range associations, whether a result of selection, stratification, possible technical errors, or chance, were less expected but proved to be important. Most contributors focused on regression methods of various types to illustrate problematic issues or to develop adaptations for dealing with high-density genotype assays. Study design was also considered, as was graphical modeling. Although no method emerged as uniformly successful, most succeeded in reducing false-positive results either by considering clusters of loci within genes or by applying smoothing metrics that required results from adjacent loci to be similar. Two unexpected results that questioned our assumptions of what is required to model linkage disequilibrium were observed. The first was that correlations between loci separated by large genetic distances can greatly inflate single-locus test statistics, and, whether the result of selection, stratification, possible technical errors, or chance, these correlations seem overabundant. The second unexpected result was that applying principal components analysis to genome-wide genotype data can apparently control not only for population structure but also for linkage disequilibrium.
AB - We summarize the contributions of Group 9 of Genetic Analysis Workshop 17. This group addressed the problems of linkage disequilibrium and other longer range forms of allelic association when evaluating the effects of genotypes on phenotypes. Issues raised by long-range associations, whether a result of selection, stratification, possible technical errors, or chance, were less expected but proved to be important. Most contributors focused on regression methods of various types to illustrate problematic issues or to develop adaptations for dealing with high-density genotype assays. Study design was also considered, as was graphical modeling. Although no method emerged as uniformly successful, most succeeded in reducing false-positive results either by considering clusters of loci within genes or by applying smoothing metrics that required results from adjacent loci to be similar. Two unexpected results that questioned our assumptions of what is required to model linkage disequilibrium were observed. The first was that correlations between loci separated by large genetic distances can greatly inflate single-locus test statistics, and, whether the result of selection, stratification, possible technical errors, or chance, these correlations seem overabundant. The second unexpected result was that applying principal components analysis to genome-wide genotype data can apparently control not only for population structure but also for linkage disequilibrium.
KW - Graphical modeling
KW - Higher criticism
KW - Principal components analysis
KW - Robust regression
KW - Score tests
KW - Two-stage study designs
UR - http://www.scopus.com/inward/record.url?scp=82455219101&partnerID=8YFLogxK
U2 - 10.1002/gepi.20660
DO - 10.1002/gepi.20660
M3 - Article
C2 - 22128051
AN - SCOPUS:82455219101
SN - 0741-0395
VL - 35
SP - S115-S119
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - SUPPL. 1
ER -