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利用等位基因折叠变化量化 - 作用遗传变异的调控效应大小。

Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.

机构信息

New York Genome Center, New York, New York 10013, USA.

Department of Systems Biology, Columbia University, New York, New York 10032, USA.

出版信息

Genome Res. 2017 Nov;27(11):1872-1884. doi: 10.1101/gr.216747.116. Epub 2017 Oct 11.

DOI:10.1101/gr.216747.116
PMID:29021289
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5668944/
Abstract

Mapping -acting expression quantitative trait loci (-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 -eQTLs and a mathematically convenient approach for systematic modeling of -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 -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.

摘要

定位效应表达数量性状(eQTL)已经成为一种研究近端遗传调控变体的流行方法。在本文中,我们描述并描述了对数等位基因折叠变化(aFC),这是与给定遗传变体相关的表达变化的幅度,作为量化 eQTL 效应大小的生物学可解释单位和系统建模的数学便利方法 - 调控。该度量与表达水平和等位基因频率、加性、适用于多等位基因变体以及可推广到多个独立变体在数学上是独立的。我们提供了从 eQTL 和等位基因表达数据集估计 aFC 的有效工具和指南,并将其应用于基因型组织表达(GTEx)数据。我们表明,独立于 eQTL 和等位基因表达数据推断的 aFC 估计值高度一致,并确定了 eQTL 效应大小的技术和生物学相关性。我们将 aFC 推广到 GTEx 中分析具有两个 eQTL 的基因,并表明在几乎所有情况下,两个 eQTL 独立调节基因表达。总之,aFC 是 - 调控效应大小的可靠衡量标准,允许从群体数据中对细胞调控事件进行定量解释,并且是研究 eQTL 数据集新方面的有价值的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/92643e0a0bd0/1872f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/5d0c625fb995/1872f01.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/2c4dbf26ab9a/1872f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/2120322e784d/1872f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/92643e0a0bd0/1872f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/5d0c625fb995/1872f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/0e015992701e/1872f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/ae7cb9d2aa73/1872f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/2c4dbf26ab9a/1872f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/2120322e784d/1872f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/5668944/92643e0a0bd0/1872f06.jpg

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