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JEPEGMIX2:改进的全球队列 eQTL 基因水平联合分析。

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

机构信息

Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.

The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

出版信息

Bioinformatics. 2018 Jan 15;34(2):286-288. doi: 10.1093/bioinformatics/btx509.

Abstract

MOTIVATION

To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.

RESULTS

We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Chatzinakos/JEPEGMIX2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

为了提高检测能力,研究人员使用基因水平分析方法来聚合弱标记信号。由于基因表达控制着生物过程,研究人员提出了聚合表达数量性状基因座(eQTL)的信号。大多数基因水平的 eQTL 方法基于(i)来自全基因组关联研究(GWAS)的汇总统计数据和(ii)来自相关参考面板的连锁不平衡模式进行统计推断。虽然大多数此类工具假设是同质队列,但我们的基因水平联合分析功能性 SNP 于世界性队列(JEPEGMIX)方法通过使用异质面板来适应世界性队列。然而,JEPGMIX 依赖于来自较旧基因表达研究的脑 eQTL,并且不调整 GWAS 信号中的背景富集。

结果

我们提出了 JEPEGMIX2,这是 JEPEGMIX 的扩展。与 JPEGMIX 相比,它使用了(i)来自最新表达研究的顺式-eQTL SNPs,以及(ii)大脑特异(亚)组织和除大脑以外的组织。JEPEGMIX2 还(i)通过调整背景富集来避免累积平均富集的多基因信息,并且(ii)为避免具有大量高度富集(高于背景)基因的研究的假阳性率增加,它根据 Holm 调整 P 值输出基因 q 值。

可用性和实现

https://github.com/Chatzinakos/JEPEGMIX2。

补充信息

补充数据可在生物信息学在线获得。

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