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Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case‐control genetic association studies

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  • Tamar Sofer
  • Elizabeth D. Schifano
  • David C. Christiani
  • Xihong Lin

Abstract

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case‐control genetic association studies. To boost analysis power, we assume that the SNP‐specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case‐control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo‐score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information‐like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case‐control genetic association study.

Suggested Citation

  • Tamar Sofer & Elizabeth D. Schifano & David C. Christiani & Xihong Lin, 2017. "Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case‐control genetic association studies," Biometrics, The International Biometric Society, vol. 73(4), pages 1210-1220, December.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1210-1220
    DOI: 10.1111/biom.12680
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    References listed on IDEAS

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    1. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    2. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    3. Johnson, Brent A. & Lin, D.Y. & Zeng, Donglin, 2008. "Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 672-680, June.
    4. Gao, Xin & Song, Peter X.-K., 2010. "Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1531-1540.
    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. Wenjiang J. Fu, 2003. "Penalized Estimating Equations," Biometrics, The International Biometric Society, vol. 59(1), pages 126-132, March.
    7. Robert B. Davies, 1980. "The Distribution of a Linear Combination of χ2 Random Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(3), pages 323-333, November.
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