title: Semiparametric Bayesian modeling of random genetic effects in family-based association studies.
abstract: We consider the inference problem of estimating covariate and genetic effects in a family-based case-control study where families are ascertained on the basis of the number of cases within the family. However, our interest lies not only in estimating the fixed covariate effects but also in estimating the random effects parameters that account for varying correlations among family members. These random effects parameters, though weakly identifiable in a strict theoretical sense, are often hard to estimate due to the small number of observations per family. A hierarchical Bayesian paradigm is a very natural route in this context with multiple advantages compared with a classical mixed effects estimation strategy based on the integrated likelihood. We propose a fully flexible Bayesian approach allowing nonparametric modeling of the random effects distribution using a Dirichlet process prior and provide estimation of both fixed effect and random effects parameters using a Markov chain Monte Carlo numerical integration scheme. The nonparametric Bayesian approach not only provides inference that is less sensitive to parametric specification of the random effects distribution but also allows possible uncertainty around a specific genetic correlation structure. The Bayesian approach has certain computational advantages over its mixed-model counterparts. Data from the Prostate Cancer Genetics Project, a family-based study at the University of Michigan Comprehensive Cancer Center including families having one or more members with prostate cancer, are used to illustrate the proposed methods. A small-scale simulation study is carried out to compare the proposed nonparametric Bayes methodology with a parametric Bayesian alternative.
authors.affiliation: Department of Quantitative Health Sciences, The Cleveland Clinic Foundation, Cleveland, OH 44195, USA. zhangl3@ccf.org
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
authors.forename: Bo
authors.initials: B
authors.lastname: Hu
authors.forename: Victor
authors.initials: V
authors.lastname: Moreno
authors.forename: Kathleen A
authors.initials: KA
authors.lastname: Cooney
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008390:Markov Chains; D008824:Michigan / Q000453:epidemiology; D009010:Monte Carlo Method; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000453:epidemiology / Q000235:genetics*; D018570:Risk Assessment
chemical_list: D017430:Prostate-Specific Antigen
references.citation: Biometrics. 2006 Jun;62(2):488-94
references.pmid: 16918913
references.citation: Biometrics. 2008 Jun;64(2):635-44
references.pmid: 17680831
references.citation: Am J Hum Genet. 2000 Jan;66(1):251-61
references.pmid: 10631155
references.citation: Am J Epidemiol. 1999 Apr 15;149(8):693-705
references.pmid: 10206618
references.citation: Am J Hum Genet. 2000 Mar;66(3):1119-31
references.pmid: 10712222
references.citation: Biometrics. 2004 Sep;60(3):739-46
references.pmid: 15339297
references.citation: Am J Epidemiol. 1978 Oct;108(4):299-307
references.pmid: 727199
references.citation: Am J Epidemiol. 2002 May 1;155(9):875-80
references.pmid: 11978593
references.citation: Biometrics. 2007 Sep;63(3):690-8
references.pmid: 17403104
references.citation: Genet Epidemiol. 2007 Dec;31(8):883-93
references.pmid: 17565751
references.citation: Stat Methods Med Res. 2000 Dec;9(6):563-87
references.pmid: 11308071
references.citation: Am J Hum Genet. 2000 Jul;67(1):244-8
references.pmid: 10831398
references.citation: Biometrics. 1984 Dec;40(4):961-71
references.pmid: 6534418
references.citation: Biometrics. 1998 Sep;54(3):921-38
references.pmid: 9750242