Functional regression method for whole genome eQTL epistasis analysis with sequencing data

BMC Genomics
Kelin XuMomiao Xiong

Abstract

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis ...Continue Reading

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Citations

Jun 18, 2019·Annals of Human Genetics·Guanjie ChenCharles N Rotimi

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Methods Mentioned

BETA
RNA-seq
RNA-req
PCA
genotyping
the

Software Mentioned

R packge
varGamma
igraph
DESeq
BFGM
R package GOstats
displaystyle
LOESS
kern1em
mathrm

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