title: Secondary phenotype analysis in ascertained family designs: application to the Leiden longevity study.
abstract: The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype, a number of additional traits, known as secondary phenotypes, are routinely recorded, and typically, associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated. Several methods have been proposed in the literature to overcome the problem, but they are limited to case-control studies and not directly applicable to more complex designs, such as the multiple-cases family studies. A proper secondary phenotype analysis, in this case, is complicated by the within families correlations on top of the biased sampling design. We propose a novel approach to accommodate the ascertainment process while explicitly modelling the familial relationships. Our approach pairs existing methods for mixed-effects models with the retrospective likelihood framework and uses a multivariate probit model to capture the association between the mixed type primary and secondary phenotypes. To examine the efficiency and bias of the estimates, we performed simulations under several scenarios for the association between the primary phenotype, secondary phenotype and genetic markers. We will illustrate the method by analysing the association between triglyceride levels and glucose (secondary phenotypes) and genetic markers from the Leiden Longevity Study, a multiple-cases family study that investigates longevity. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
authors.affiliation: Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: Renaud
authors.identifier: 0000-0002-9665-883X
authors.initials: R
authors.lastname: Tissier
authors.affiliation: Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: Roula
authors.initials: R
authors.lastname: Tsonaka
authors.affiliation: Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: Simon P
authors.initials: SP
authors.lastname: Mooijaart
authors.affiliation: Department of Molecular Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: Eline
authors.initials: E
authors.lastname: Slagboom
authors.affiliation: Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands.|Department of Statistics, University of Leeds, U.K.
authors.forename: Jeanine J
authors.initials: JJ
authors.lastname: Houwing-Duistermaat
mesh_terms: D056808:Biostatistics; D001786:Blood Glucose / Q000378:metabolism; D003198:Computer Simulation; D005190:Family; D056726:Genetic Association Studies; D005819:Genetic Markers; D006801:Humans; D016013:Likelihood Functions; D008136:Longevity / Q000235:genetics* / Q000502:physiology; D008957:Models, Genetic; D015233:Models, Statistical; D009426:Netherlands; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012189:Retrospective Studies; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D005819:Genetic Markers; D014280:Triglycerides
keywords: ascertainment; family data; genetic association and heritability; mixed models; multivariate probit model