Correcting for Hidden Population Structure in Single Marker Association Testing and Estimation

  • Daniel O. Stram
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Chapter 2 discussed both relatedness of study participants and hidden population structure in terms of the correlations induced between the number of copies, n iA and n jA , of a diallelic genetic variant carried by two individuals i and j. In Chap. 3 we discussed the requirement for association studies of unrelated subjects that the outcomes of interest, Y i , be independent between study subjects. In this chapter we will expand on this initial discussion (1) to examine the impact of non-independence on the distribution of statistical tests for the influence of alleles (here a and A) on phenotype or disease risk, and (2) how non-independence between individuals’ outcomes can arise as a direct result of correlation among the genotypes of study subjects due to hidden strata or relatedness or due to other factors (e.g., cultural/behavioral) that act as confounders of genetic associations. The chapter introduces several basic approaches for dealing with population structure in single marker association analyses and shows how all these methods deal, at least in part, with the fundamental problem of the analysis of correlated phenotypes. At the heart of these methods is the empirical estimation of a relationship matrix (more precisely a covariance structure matrix) that describes the relative relatedness of individuals. The statistical methods for dealing with covariances in estimation of single marker effects fall into three categories: fixed effects models utilizing adjustment for eigenvectors (“principal components”) of this matrix; random effects methods dealing explicitly with the relationship matrix as a covariance matrix of random effects in extended generalized linear modeling; and retrospective methods, which invert the usual generalized linear modeling procedures so that the conditional distribution of the genetic markers given the phenotypes (rather than the reverse) is used for inference in genetic association studies. Our discussion of all these approaches is unified around the theme of dealing with false-positive associations that are due to unrecognized inflation of the variance of estimators relied upon in traditional regression methods when correlated data are analyzed. Finally the relative performance of the various methods is described in various settings.

Keywords

Cholesterol Obesity Estrogen Covariance Recombination 

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Daniel O. Stram
    • 1
  1. 1.Department of Preventive MedicineUniversity of Southern California Keck School of MedicineLos AngelesUSA

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