Cargando…
eQTL Epistasis – Challenges and Computational Approaches
The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular p...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668133/ https://www.ncbi.nlm.nih.gov/pubmed/23755066 http://dx.doi.org/10.3389/fgene.2013.00051 |
_version_ | 1782271574082060288 |
---|---|
author | Huang, Yang Wuchty, Stefan Przytycka, Teresa M. |
author_facet | Huang, Yang Wuchty, Stefan Przytycka, Teresa M. |
author_sort | Huang, Yang |
collection | PubMed |
description | The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods. |
format | Online Article Text |
id | pubmed-3668133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-36681332013-06-10 eQTL Epistasis – Challenges and Computational Approaches Huang, Yang Wuchty, Stefan Przytycka, Teresa M. Front Genet Genetics The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods. Frontiers Media S.A. 2013-05-31 /pmc/articles/PMC3668133/ /pubmed/23755066 http://dx.doi.org/10.3389/fgene.2013.00051 Text en Copyright © 2013 Huang, Wuchty and Przytycka. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Genetics Huang, Yang Wuchty, Stefan Przytycka, Teresa M. eQTL Epistasis – Challenges and Computational Approaches |
title | eQTL Epistasis – Challenges and Computational Approaches |
title_full | eQTL Epistasis – Challenges and Computational Approaches |
title_fullStr | eQTL Epistasis – Challenges and Computational Approaches |
title_full_unstemmed | eQTL Epistasis – Challenges and Computational Approaches |
title_short | eQTL Epistasis – Challenges and Computational Approaches |
title_sort | eqtl epistasis – challenges and computational approaches |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668133/ https://www.ncbi.nlm.nih.gov/pubmed/23755066 http://dx.doi.org/10.3389/fgene.2013.00051 |
work_keys_str_mv | AT huangyang eqtlepistasischallengesandcomputationalapproaches AT wuchtystefan eqtlepistasischallengesandcomputationalapproaches AT przytyckateresam eqtlepistasischallengesandcomputationalapproaches |