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Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change
Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic varian...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668944/ https://www.ncbi.nlm.nih.gov/pubmed/29021289 http://dx.doi.org/10.1101/gr.216747.116 |
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author | Mohammadi, Pejman Castel, Stephane E. Brown, Andrew A. Lappalainen, Tuuli |
author_facet | Mohammadi, Pejman Castel, Stephane E. Brown, Andrew A. Lappalainen, Tuuli |
author_sort | Mohammadi, Pejman |
collection | PubMed |
description | Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of cis-eQTLs and a mathematically convenient approach for systematic modeling of cis-regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets. |
format | Online Article Text |
id | pubmed-5668944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56689442017-11-13 Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change Mohammadi, Pejman Castel, Stephane E. Brown, Andrew A. Lappalainen, Tuuli Genome Res Method Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of cis-eQTLs and a mathematically convenient approach for systematic modeling of cis-regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets. Cold Spring Harbor Laboratory Press 2017-11 /pmc/articles/PMC5668944/ /pubmed/29021289 http://dx.doi.org/10.1101/gr.216747.116 Text en © 2017 Mohammadi et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Method Mohammadi, Pejman Castel, Stephane E. Brown, Andrew A. Lappalainen, Tuuli Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title | Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title_full | Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title_fullStr | Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title_full_unstemmed | Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title_short | Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
title_sort | quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668944/ https://www.ncbi.nlm.nih.gov/pubmed/29021289 http://dx.doi.org/10.1101/gr.216747.116 |
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