Literature DB >> 31849013

High-Order Association Mapping for Expression Quantitative Trait Loci.

Huaizhen Qin1, Weiwei Ouyang2, Jinying Zhao3.   

Abstract

Mapping expression quantitative trait loci (eQTLs) is an important avenue to identify putative genetic variants in regulatory regions. Famed eQTL mapping methods exploit the mean effects of locus-wise genetic variants on expression quantitative traits. Despite their successes, such methods are suboptimal because they neglect high-order heterogeneity inherent in genetic variants and covariates. High-order effects of observed loci are common due to their connections to various latent factors, i.e., latent interactions among genes and environmental factors. In this chapter, we introduce a new scheme to harmoniously integrate mean and high-order effects of genetic variants on expression quantitative trait. We rigorously evaluate its validity and utility of signal augmentation.

Entities:  

Keywords:  High-order heterogeneity; Latent gene-by-environment interactions; Latent genetic and nongenetic factors

Mesh:

Year:  2020        PMID: 31849013      PMCID: PMC8936396          DOI: 10.1007/978-1-0716-0026-9_10

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

Review 1.  Genetics of global gene expression.

Authors:  Matthew V Rockman; Leonid Kruglyak
Journal:  Nat Rev Genet       Date:  2006-11       Impact factor: 53.242

2.  Genetic variants contribute to gene expression variability in humans.

Authors:  Amanda M Hulse; James J Cai
Journal:  Genetics       Date:  2012-11-12       Impact factor: 4.562

Review 3.  Expression quantitative trait loci: present and future.

Authors:  Alexandra C Nica; Emmanouil T Dermitzakis
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2013-05-06       Impact factor: 6.237

4.  A versatile omnibus test for detecting mean and variance heterogeneity.

Authors:  Ying Cao; Peng Wei; Matthew Bailey; John S K Kauwe; Taylor J Maxwell
Journal:  Genet Epidemiol       Date:  2014-01       Impact factor: 2.135

5.  A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways.

Authors:  David Soave; Harriet Corvol; Naim Panjwani; Jiafen Gong; Weili Li; Pierre-Yves Boëlle; Peter R Durie; Andrew D Paterson; Johanna M Rommens; Lisa J Strug; Lei Sun
Journal:  Am J Hum Genet       Date:  2015-07-02       Impact factor: 11.025

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.