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

ABSTRACT: RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two inbred strains. In contrast, this task is much more challenging in outbred populations such as humans. To address this challenge, we propose a new framework to combine experimental strategies and novel statistical methods. Specifically, we propose to study genetic and imprinting effects in family trios with RNA-seq data from the children and genotype data from both parents and children, and quantify genetic effects by cis-eQTLs. Towards this end, we have extended our method that studies the eQTLs of RNA-seq data (Sun, Biometrics 2012, 68(1): 1-11) to model both cis-eQTL and parent-of-origin effects, and evaluated its performance using extensive simulations. Since sample size may be limited in family trios, we have developed a data analysis pipeline that borrows information from external data of unrelated individuals for cis-eQTL mapping. We have also collected RNA-seq data from the children of 30 family trios, applied our method to analyze this dataset, and identified some previously reported imprinted genes as well as some new candidates of imprinted genes.

SUBMITTER: Zhabotynsky V

PROVIDER: S-EPMC6646089 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Zhabotynsky Vasyl V Inoue Kaoru K Magnuson Terry T Mauro Calabrese J J Sun Wei W

Biometrics 20190401 3

RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two in ...[more]

PMID: 30666629

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Project description:Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

Dataset Information

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

Publications

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

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