Dataset Information

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

ABSTRACT: Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

SUBMITTER: Vosa U

PROVIDER: S-EPMC8432599 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Võsa Urmo U Claringbould Annique A Westra Harm-Jan HJ Bonder Marc Jan MJ Deelen Patrick P Zeng Biao B Kirsten Holger H Saha Ashis A Kreuzhuber Roman R Yazar Seyhan S Brugge Harm H Oelen Roy R de Vries Dylan H DH van der Wijst Monique G P MGP Kasela Silva S Pervjakova Natalia N Alves Isabel I Favé Marie-Julie MJ Agbessi Mawussé M Christiansen Mark W MW Jansen Rick R Seppälä Ilkka I Tong Lin L Teumer Alexander A Schramm Katharina K Hemani Gibran G Verlouw Joost J Yaghootkar Hanieh H Sönmez Flitman Reyhan R Brown Andrew A Kukushkina Viktorija V Kalnapenkis Anette A Rüeger Sina S Porcu Eleonora E Kronberg Jaanika J Kettunen Johannes J Lee Bernett B Zhang Futao F Qi Ting T Hernandez Jose Alquicira JA Arindrarto Wibowo W Beutner Frank F Dmitrieva Julia J Elansary Mahmoud M Fairfax Benjamin P BP Georges Michel M Heijmans Bastiaan T BT Hewitt Alex W AW Kähönen Mika M Kim Yungil Y Knight Julian C JC Kovacs Peter P Krohn Knut K Li Shuang S Loeffler Markus M Marigorta Urko M UM Mei Hailang H Momozawa Yukihide Y Müller-Nurasyid Martina M Nauck Matthias M Nivard Michel G MG Penninx Brenda W J H BWJH Pritchard Jonathan K JK Raitakari Olli T OT Rotzschke Olaf O Slagboom Eline P EP Stehouwer Coen D A CDA Stumvoll Michael M Sullivan Patrick P 't Hoen Peter A C PAC Thiery Joachim J Tönjes Anke A van Dongen Jenny J van Iterson Maarten M Veldink Jan H JH Völker Uwe U Warmerdam Robert R Wijmenga Cisca C Swertz Morris M Andiappan Anand A Montgomery Grant W GW Ripatti Samuli S Perola Markus M Kutalik Zoltan Z Dermitzakis Emmanouil E Bergmann Sven S Frayling Timothy T van Meurs Joyce J Prokisch Holger H Ahsan Habibul H Pierce Brandon L BL Lehtimäki Terho T Boomsma Dorret I DI Psaty Bruce M BM Gharib Sina A SA Awadalla Philip P Milani Lili L Ouwehand Willem H WH Downes Kate K Stegle Oliver O Battle Alexis A Visscher Peter M PM Yang Jian J Scholz Markus M Powell Joseph J Gibson Greg G Esko Tõnu T Franke Lude L

Nature genetics 20210902 9

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) s ...[more]

PMID: 34475573

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Project description:Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute's "Up for a Challenge" (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer.

Dataset Information

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Publications

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Similar Datasets

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