A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design

Covariate adjusted inference of parent-of-origin effects using case-control mother-child paired multilocus genotype data

It is of great interest to identify parent-of-origin effects (POEs) since POEs play an important role in many human heritable disorders and human early life growth and development. POE is sometimes referred to as imprinting effect in the literature. Compared with the standard logistic regression analyses, retrospective likelihood-based statistical methods are more powerful in identifying POEs when data are collected from related individuals retrospectively. However, none of existing retrospective-based methods can appropriately incorporate covariates that should be adjusted for if they are confounding factors. In this paper, a novel semiparametric statistical method, M-HAP, is developed to detect POEs by fully exploring available information from multilocus genotypes of case-control mother-child pairs and covariates. Some large sample properties are established for M-HAP. Finite sample properties of M-HAP are illustrated by extensive simulation studies and real data applications to the Jerusalem Perinatal Study and the Danish National Birth Cohort study, which confirm the desired superiority of M-HAP over some existing methods. M-HAP has been implemented in the updated R package CCMO.

Joint modeling of eQTLs and parent-of-origin effects using an orthogonal framework with RNA-seq data

Extensive studies have been conducted on the analysis of genome function, especially on the expression quantitative trait loci (eQTL). These studies offered promising results for characterization of the functional sequencing variation and understanding of the basic processes of gene regulation. Parent of origin effect (POE) is an important epigenetic phenomenon describing that the expression of certain genes depends on their allelic parent-of-origin and it is known to play important roles in human complex diseases. However, traditional eQTL mapping approaches do not allow for the detection of imprinting, or they focus on modeling the additive genetic effect thereby ignoring the estimation of the dominance genetic effect. In this study, we proposed a statistical framework to test the additive and dominance genetic effects of the candidate eQTLs along with detection of the POE with a functional model and an orthogonal model for RNA-seq data. We demonstrated the desirable power and preserved Type I errors of the methods in most scenarios, especially the orthogonal model with un-biased estimation of the genetic effects and over-dispersion of the RNA-seq data. The application to a HapMap project trio dataset validated existing imprinting genes and discovered two novel imprinting genes with potential dominance genetic effect and RB1 and IGF1R genes. This study provides new insights into the next generation statistical modeling of eQTL mapping for better understanding of the genetic architecture underlying the mechanisms of gene expression regulation.