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.

Statistical dissection of cyto-nuclear epistasis subject to genomic imprinting in line crosses

Cytoplasm contains important metabolism reaction organelles such as mitochondria and chloroplast (in plant). In particular, mitochondria contains special DNA information which can be passed to offsprings through maternal gametes, and has been confirmed to play a pivotal role in nuclear activities. Experimental evidences have documented the importance of cyto-nuclear interactions in affecting important biological traits. While studies have also pointed out the role of interaction between imprinting nuclear DNA and cytoplasm, no statistical method has been developed to efficiently model such effect and further quantify its effect size. In this work, we developed an efficient statistical model for genome-wide estimating and testing the cytoplasmic effect, nuclear DNA imprinting effect as well as the interaction between them under reciprocal backcross and F2 designs derived from inbred lines. Parameters are estimated under maximum likelihood framework implemented with the EM algorithm. Extensive simulations show good performance in a variety of scenarios. The utility of the method is demonstrated by analyzing a published data set in an F2 family derived from C3H/HeJBir and C57BL/6 J mouse strains. Important cyto-nuclear interactions were identified. Our approach provides a quantitative framework for identifying and estimating cyto-nuclear interactions subject to genomic imprinting involved in the genetic control of complex traits.

Characterization of genomic imprinting effects and patterns with parametric accelerated failure time model

Genomic imprinting, a genetic phenomenon of non-equivalent allele expression that depends on parental origins, has been ubiquitously observed in nature. It does not only control the traits of growth and development but also may be responsible for survival traits. Based on the accelerated failure time model, we construct a general parametric model for mapping the imprinted QTL (iQTL). Within the framework of interval mapping, maximum likelihood estimation of iQTL parameters is implemented via EM algorithm. The imprinting patterns of the detected iQTL are statistically tested according to a series of null hypotheses. BIC model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameters. Simulations are used to validate the proposed mapping procedure. A published dataset from a mouse model system was used to illustrate the proposed framework. Results show that among the five commonly used survival distributions, Log-logistic distribution is the optimal baseline hazard function for mapping QTL of hyperoxic acute lung injury (HALI) survival; under the log-logistic distribution, four QTLs were identified, in which only one QTL was inherited in Mendelian fashion, whereas others were imprinted in different imprinting patterns.