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

Overview of attention for article published in BMC Genomics, May 2017
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Title
Functional regression method for whole genome eQTL epistasis analysis with sequencing data
Published in
BMC Genomics, May 2017
DOI 10.1186/s12864-017-3777-4
Pubmed ID
Authors

Kelin Xu, Li Jin, Momiao 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 with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples. The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Ph. D. Student 5 17%
Student > Doctoral Student 4 13%
Student > Master 3 10%
Other 2 7%
Other 6 20%
Unknown 3 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 37%
Agricultural and Biological Sciences 6 20%
Computer Science 3 10%
Environmental Science 1 3%
Immunology and Microbiology 1 3%
Other 3 10%
Unknown 5 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 December 2017.
All research outputs
#17,893,544
of 22,973,051 outputs
Outputs from BMC Genomics
#7,606
of 10,686 outputs
Outputs of similar age
#224,056
of 313,770 outputs
Outputs of similar age from BMC Genomics
#158
of 215 outputs
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