Joint detection of association, imprinting and maternal effects using all children and their parents

Abstract

Genomic imprinting and maternal effects have been increasingly explored for their contributions to complex diseases. Statistical methods have been proposed to detect both imprinting and maternal effects simultaneously based on nuclear families. However, these methods only make use of case-parents triads and possibly control-parents triads, thus wasting valuable information contained in the siblings. More seriously, most existing methods are full-likelihood based and have to make strong assumptions concerning mating-type probabilities (nuisance parameters) to avoid over-parametrization. In this paper, we develop a partial Likelihood approach for detecting Imprinting and Maternal Effects (LIME), using nuclear families with an arbitrary number of affected and unaffected children. By matching affected children with unaffected ones (within or across families) having the same triad/pair familial genotype combination, we derive a partial likelihood that is free of nuisance parameters. This alleviates the need to make strong, yet unrealistic assumptions about the population, leading to a procedure that is robust to departure from Hardy-Weinberg equilibrium. Power gain by including siblings and robustness of LIME under a variety of settings are demonstrated. Our simulation study also indicates that it is more profitable to recruit additional siblings than additional families when the total number of individuals is kept the same. We applied LIME to the Framingham Heart Study data to demonstrate its utility in analyzing real data. Many of our findings are consistent with results in the literature; potentially novel genes for hypertension have also emerged.

Optimum Study Design for Detecting Imprinting and Maternal Effects Based on Partial Likelihood

“…There are a number of existing methods that do model imprinting and maternal effects simultaneously to avoid potential confounding. Such approaches include a L ikelihood inference method for detecting I mprinting and M aternal E ffects (LIME), which can utilize nuclear families with an arbitrary number of affected and unaffected children, no matter whether the father's genotype is missing or not (Yang and Lin, ; Han et al, ). LIME uses only part of the full likelihood—partial likelihood—by exploiting the fact that the part of the likelihood containing the parameters of interest can be separated from that containing the nuisance parameters.…”

“…Such a question is of great interest in genetic epidemiology in general, but the conclusions in the literature are mixed. There are studies showing that recruiting a smaller number of larger families is better than a larger number of smaller families (Zhou et al, ; Han et al, ), but there are also studies arguing for the reverse (He et al, ; Li et al, ). There are yet another set of articles that show both may result depending on the underlying settings (Li and Cui, ; Sung and Rao, ).…”

“…There are yet another set of articles that show both may result depending on the underlying settings (Li and Cui, ; Sung and Rao, ). For LIME, in particular, Han et al () carried out a limited simulation study to investigate relative power for detecting association, imprinting, and maternal effects for several case‐control family‐based study designs having the same total number of individuals. They concluded that the results “suggest that collecting more siblings rather than more families is a more effective way to increase statistics power.” However, the conclusion is far from settled as the evidence is weak and the conclusion is based on a limited simulation.…”

A Family-Based Rare Haplotype Association Method for Quantitative Traits

“…In particular, we consider haplotype blocks that span the SNP rs2229188 at position 86,371,063 bp of the CYP51A1 gene on chromosome 7. This region has been shown earlier to be associated with the qualitative trait hypertension [15, 40]. Thus, it is of interest to study if blood pressure as a quantitative trait is associated with any haplotype, especially rHTV, in this region.…”

Role of Protein-Protein Interactions in Metabolism: Genetics, Structure, Function

“…Several are associated with a rare variant rs2229188 causing a change of Val19 to Ala. This SNP was found associated with HDL-C level, hypertension, and lifespan (Charlesworth et al, 2009;Han et al, 2013;Yashin et al, 2015). This missense mutation lies at the N-terminal part of the protein and is responsible for interaction with the membrane; therefore, it is not directly involved in enzymatic activity (Pikuleva and Waterman, 2013).…”