title: Marginal genetic effects estimation in family and twin studies using random-effects models.
abstract: Random-effects models are often used in family-based genetic association studies to properly capture the within families relationships. In such models, the regression parameters have a conditional on the random effects interpretation and they measure, e.g., genetic effects for each family. Estimating parameters that can be used to make inferences at the population level is often more relevant than the family-specific effects, but not straightforward. This is mainly for two reasons: First the analysis of family data often requires high-dimensional random-effects vectors to properly model the familial relationships, for instance when members with a different degree of relationship are considered, such as trios, mix of monozygotic and dizygotic twins, etc. The second complication is the biased sampling design, such as the multiple cases families design, which is often employed to enrich the sample with genetic information. For these reasons deriving parameters with the desired marginal interpretation can be challenging. In this work we consider the marginalized mixed-effects models, we discuss challenges in applying them in ascertained family data and propose penalized maximum likelihood methodology to stabilize the parameter estimation by using external information on the disease prevalence or heritability. The performance of our methodology is evaluated via simulation and is illustrated on data from Rheumatoid Arthritis patients, where we estimate the marginal effect of HLA-DRB1*13 and shared epitope alleles across three different study designs and combine them using meta-analysis.
authors.affiliation: Department of Medical Statistics and BioInformatics, Leiden University Medical Center, Post Zone S5-P, PO Box 9600, 2300 RC Leiden, The Netherlands.
authors.forename: Roula
authors.initials: R
authors.lastname: Tsonaka
authors.affiliation: Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Diane
authors.initials: D
authors.lastname: van der Woude
authors.affiliation: Department of Medical Statistics and BioInformatics, Leiden University Medical Center, Post Zone S5-P, PO Box 9600, 2300 RC Leiden, The Netherlands.
authors.forename: Jeanine
authors.initials: J
authors.lastname: Houwing-Duistermaat
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics / Q000276:immunology; D015982:Bias; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D003430:Cross-Sectional Studies / Q000706:statistics & numerical data; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005190:Family; D056726:Genetic Association Studies / Q000706:statistics & numerical data*; D059811:HLA-DRB1 Chains / Q000235:genetics; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D012044:Regression Analysis; D018433:Twin Studies as Topic / Q000706:statistics & numerical data
chemical_list: D059811:HLA-DRB1 Chains
keywords: Ascertainment; Heritability; Marginalized mixed models; Penalized ML; Prevalence