Optimum study design for detecting imprinting and maternal effects based on partial likelihood.
Despite spectacular advances in molecular genomic technologies in the past two decades, resources available for genomic studies are still finite and limited, especially for family-based studies. Hence, it is important to consider an optimum study design to maximally utilize limited resources to increase statistical power in family-based studies. A particular question of interest is whether it is more profitable to genotype siblings of probands or to recruit more independent families. Numerous studies have attempted to address this study design issue for simultaneous detection of imprinting and maternal effects, two important epigenetic factors for studying complex diseases. The question is far from settled, however, mainly due to the fact that results and recommendations in the literature are based on anecdotal evidence from limited simulation studies rather than based on rigorous statistical analysis. In this article, we propose a systematic approach to study various designs based on a partial likelihood formulation. We derive the asymptotic properties and obtain formulas for computing the information contents of study designs being considered. Our results show that, for a common disease, recruiting additional siblings is beneficial because both affected and unaffected individuals will be included. However, if a disease is rare, then any additional siblings recruited are most likely to be unaffected, thus contributing little additional information; in such cases, additional families will be a better choice with a fixed amount of resources. Our work thus offers a practical strategy for investigators to select the optimum study design within a case-control family scheme before data collection.
Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio 43210, U.S.A.
Fangyuan
F
Zhang
Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, Quebec H3A 0B9, Canada.
Abbas
A
Khalili
Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, Ohio 43210, U.S.A.
Shili
S
Lin
D002874:Chromosome Mapping / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods; D018392:Genomic Imprinting / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D000072741:Maternal Inheritance / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D005819:Genetic Markers
Ascertainment; Association study; Imprinting effect; Maternal effect; Partial likelihood; Study design