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Comment
. 2024 Jun 6;111(6):1084-1099.
doi: 10.1016/j.ajhg.2024.04.012. Epub 2024 May 8.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

S Taylor Head  1 , Felipe Dezem  2 , Andrei Todor  3 , Jingjing Yang  3 , Jasmine Plummer  2 , Simon Gayther  4 , Siddhartha Kar  5 , Joellen Schildkraut  6 , Michael P Epstein  7
Affiliations
Comment

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

S Taylor Head et al. Am J Hum Genet. .

Abstract

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

Keywords: GReX; TWAS; breast cancer; cis-eQTL; eQTL architecture; gene expression; genetic epidemiology; ovarian cancer; trans-eQTL; transcriptome.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1
Figure 1
Ideogram of BCAC-derived BGW-TWAS results for overall breast cancer and breast cancer subtypes 101 genes shown meet transcriptome-wide Bonferroni-adjusted p value threshold for one or more phenotypes. Gray lines indicate position of genetic variants with BCAC GWAS p < 5E−08 for association with overall breast cancer risk.
Figure 2
Figure 2
Comparison of estimated eQTL PP and GWAS results for ACAP3 BGW SNPs Top plot shows estimated posterior probability (PP) of non-zero eQTL effects sizes from BGW-TWAS-selected SNPs for ACAP3 on chromosome 1 in breast tissue, and bottom plot shows negative logarithm of the overall breast cancer GWAS p values for these selected SNPs. Blue dotted line indicates genome-wide significance threshold for GWAS (5E−08).
Figure 3
Figure 3
Comparison of estimated eQTL PP and GWAS results for CCDC106 BGW SNPs Top plot shows estimated posterior probability (PP) of non-zero eQTL effects sizes from BGW-TWAS-selected SNPs for CCDC106 on chromosome 19 in ovarian tissue, and bottom plot shows negative logarithm of the non-mucinous ovarian cancer GWAS p values for these selected SNPs. Blue dotted line indicates genome-wide significance threshold for GWAS (5E−08).
See this image and copyright information in PMC

Update of

Comment on

  • Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.
    Dareng EO, Coetzee SG, Tyrer JP, Peng PC, Rosenow W, Chen S, Davis BD, Dezem FS, Seo JH, Nameki R, Reyes AL, Aben KKH, Anton-Culver H, Antonenkova NN, Aravantinos G, Bandera EV, Beane Freeman LE, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bernardini MQ, Bjorge L, Black A, Bogdanova NV, Bolton KL, Brenton JD, Budzilowska A, Butzow R, Cai H, Campbell I, Cannioto R, Chang-Claude J, Chanock SJ, Chen K, Chenevix-Trench G; AOCS Group; Chiew YE, Cook LS, DeFazio A, Dennis J, Doherty JA, Dörk T, du Bois A, Dürst M, Eccles DM, Ene G, Fasching PA, Flanagan JM, Fortner RT, Fostira F, Gentry-Maharaj A, Giles GG, Goodman MT, Gronwald J, Haiman CA, Håkansson N, Heitz F, Hildebrandt MAT, Høgdall E, Høgdall CK, Huang RY, Jensen A, Jones ME, Kang D, Karlan BY, Karnezis AN, Kelemen LE, Kennedy CJ, Khusnutdinova EK, Kiemeney LA, Kjaer SK, Kupryjanczyk J, Labrie M, Lambrechts D, Larson MC, Le ND, Lester J, Li L, Lubiński J, Lush M, Marks JR, Matsuo K, May T, McLaughlin JR, McNeish IA, Menon U, Missmer S, Modugno F, Moffitt M, Monteiro AN, Moysich KB, Narod SA, Nguyen-Dumont T, Odunsi K, Olsson H, Onland-Moret NC, Park SK, Pejovic T, Permuth JB, Piskorz A, Prokofyeva D, Riggan MJ, Risch HA, Rodríguez-A… See abstract for full author list ➔ Dareng EO, et al. Am J Hum Genet. 2024 Jun 6;111(6):1061-1083. doi: 10.1016/j.ajhg.2024.04.011. Epub 2024 May 8. Am J Hum Genet. 2024. PMID: 38723632 Free PMC article.

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