A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver

被引:0
|
作者
Tobias Strunz
Felix Grassmann
Javier Gayán
Satu Nahkuri
Debora Souza-Costa
Cyrille Maugeais
Sascha Fauser
Everson Nogoceke
Bernhard H. F. Weber
机构
[1] Roche Innovation Center Basel,
[2] F. Hoffmann-La Roche Ltd,undefined
[3] Institute of Human Genetics,undefined
[4] University of Regensburg,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Genome-wide association studies (GWAS) have identified numerous genetic variants in the human genome associated with diseases and traits. Nevertheless, for most loci the causative variant is still unknown. Expression quantitative trait loci (eQTL) in disease relevant tissues is an excellent approach to correlate genetic association with gene expression. While liver is the primary site of gene transcription for two pathways relevant to age-related macular degeneration (AMD), namely the complement system and cholesterol metabolism, we explored the contribution of AMD associated variants to modulate liver gene expression. We extracted publicly available data and computed the largest eQTL data set for liver tissue to date. Genotypes and expression data from all studies underwent rigorous quality control. Subsequently, Matrix eQTL was used to identify significant local eQTL. In total, liver samples from 588 individuals revealed 202,489 significant eQTL variants affecting 1,959 genes (Q-Value < 0.001). In addition, a further 101 independent eQTL signals were identified in 93 of the 1,959 eQTL genes. Importantly, our results independently reinforce the notion that high density lipoprotein metabolism plays a role in AMD pathogenesis. Taken together, our study generated a first comprehensive map reflecting the genetic regulatory landscape of gene expression in liver.
引用
收藏
相关论文
共 50 条
  • [31] Network-based group variable selection for detecting expression quantitative trait loci (eQTL)
    Weichen Wang
    Xuegong Zhang
    BMC Bioinformatics, 12
  • [32] Network-based group variable selection for detecting expression quantitative trait loci (eQTL)
    Wang, Weichen
    Zhang, Xuegong
    BMC BIOINFORMATICS, 2011, 12
  • [33] Genome-wide cis-expression-Quantitative Trait Loci (eQTL) in association with asthma
    Chua, Z.
    Sio, Y. Y.
    Chew, F. T.
    EUROPEAN RESPIRATORY JOURNAL, 2022, 60
  • [34] Genetic Susceptibility to Therapy-Related Leukemia - Role of Expression Quantitative Trait Loci (eQTL)
    Ding, Yan
    Sun, Can-Lan
    Francisco, Liton
    Li, Liang
    Li, Min
    Hahn, Brian
    Noe, Jennifer
    Larson, Garrett P.
    Forman, Stephen J.
    Bhatia, Ravi
    Bhatia, Smita
    BLOOD, 2011, 118 (21) : 1047 - 1048
  • [35] Expression quantitative trait loci analysis of nasopharyngeal carcinoma
    Su, W.
    Zhang, J.
    Chang, K.
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2019, 27 : 1017 - 1018
  • [36] Expression Quantitative Trait Loci Analysis Identifies Associations Between Genotype and Gene Expression in Human Intestine
    Kabakchiev, Boyko
    Silverberg, Mark S.
    GASTROENTEROLOGY, 2013, 144 (07) : 1488 - U284
  • [37] eQTL-seq: a Rapid Genome-Wide Integrative Genetical Genomics Strategy to Dissect Complex Regulatory Architecture of Gene Expression Underlying Quantitative Trait Variation in Crop Plants
    Mohanty, Jitendra K.
    Jha, Uday Chand
    Dixit, G. P.
    Bharadwaj, Chellapilla
    Parida, Swarup K.
    PLANT MOLECULAR BIOLOGY REPORTER, 2024, 42 (02) : 218 - 223
  • [38] Statistical Approach of Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Studies
    Das, Samarendra
    Rai, Shesh N.
    ENTROPY, 2021, 23 (08)
  • [39] Discovering Context-dependent Whole Blood Gene Expression Quantitative Trait Loci (eQTL) In Relapsing Remitting Multiple Sclerosis
    Partha, Raghavendran
    Bronson, Paola G.
    Sangurdekar, Dipen
    NEUROLOGY, 2019, 92 (15)
  • [40] Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs
    Deborah Velez-Irizarry
    Sebastian Casiro
    Kaitlyn R. Daza
    Ronald O. Bates
    Nancy E. Raney
    Juan P. Steibel
    Catherine W. Ernst
    BMC Genomics, 20