Characterizing the Relationship Between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

Overview

Journal Cancers (Basel)

Publisher MDPI

Specialty Oncology

Date 2024 Jun 19

PMID 38893190

Authors

Peh Joo Ho

Alexis Khng

Benita Kiat-Tee Tan

Chiea Chuen Khor

Ern Yu Tan

Geok Hoon Lim

Jian-Min Yuan

Su-Ming Tan

Xuling Chang

Veronique Kiak Mien Tan

Xueling Sim

Rajkumar Dorajoo

Woon-Puay Koh

Mikael Hartman

Jingmei Li

Affiliations

Soon will be listed here.

Abstract

Purpose: To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer.

Methods: Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study ( = 10,213) comprising 418 cases.

Results: We identified 149 variants showing pleiotropic association with breast cancer risk (eQTL > 0.05 = 9, mQTL > 0.05 = 165). The discriminatory ability of the functional PRS (AUC [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUC [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUC, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS.

Conclusions: While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

Citing Articles

Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study.

Liu J, Sun W, Li N, Li H, Wu L, Yi H Breast Cancer Res. 2024; 26(1):172.

PMID: 39614330 PMC: 11606077. DOI: 10.1186/s13058-024-01928-0.

References

Gel B, Serra E . karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics. 2017; 33(19):3088-3090. PMC: 5870550. DOI: 10.1093/bioinformatics/btx346. View

Hou L, Zhao H . A review of post-GWAS prioritization approaches. Front Genet. 2013; 4:280. PMC: 3856625. DOI: 10.3389/fgene.2013.00280. View

Maxim L, Niebo R, Utell M . Screening tests: a review with examples. Inhal Toxicol. 2014; 26(13):811-28. PMC: 4389712. DOI: 10.3109/08958378.2014.955932. View

Tan K, Tan L, Sim X, Tai E, Lee J, Chia K . Cohort Profile: The Singapore Multi-Ethnic Cohort (MEC) study. Int J Epidemiol. 2018; 47(3):699-699j. DOI: 10.1093/ije/dyy014. View

Schork A, Thompson W, Pham P, Torkamani A, Roddey J, Sullivan P . All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 2013; 9(4):e1003449. PMC: 3636284. DOI: 10.1371/journal.pgen.1003449. View

Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z . Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018; 9(1):918. PMC: 5834629. DOI: 10.1038/s41467-018-03371-0. View

Gallagher M, Chen-Plotkin A . The Post-GWAS Era: From Association to Function. Am J Hum Genet. 2018; 102(5):717-730. PMC: 5986732. DOI: 10.1016/j.ajhg.2018.04.002. View

Liu D, Wang Y, Jing H, Meng Q, Yang J . Mendelian randomization integrating GWAS and mQTL data identified novel pleiotropic DNA methylation loci for neuropathology of Alzheimer's disease. Neurobiol Aging. 2020; 97:18-27. PMC: 7736197. DOI: 10.1016/j.neurobiolaging.2020.09.019. View

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson M, Powell J . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5):481-7. DOI: 10.1038/ng.3538. View

10.

Tak Y, Farnham P . Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin. 2016; 8:57. PMC: 4696349. DOI: 10.1186/s13072-015-0050-4. View

11.

Cantor R, Lange K, Sinsheimer J . Prioritizing GWAS results: A review of statistical methods and recommendations for their application. Am J Hum Genet. 2010; 86(1):6-22. PMC: 2801749. DOI: 10.1016/j.ajhg.2009.11.017. View

12.

Ho W, Tan M, Mavaddat N, Tai M, Mariapun S, Li J . European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nat Commun. 2020; 11(1):3833. PMC: 7395776. DOI: 10.1038/s41467-020-17680-w. View

13.

Cano-Gamez E, Trynka G . From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front Genet. 2020; 11:424. PMC: 7237642. DOI: 10.3389/fgene.2020.00424. View

14.

Youden W . Index for rating diagnostic tests. Cancer. 1950; 3(1):32-5. DOI: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3. View

15.

Hannon E, Gorrie-Stone T, Smart M, Burrage J, Hughes A, Bao Y . Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits. Am J Hum Genet. 2018; 103(5):654-665. PMC: 6217758. DOI: 10.1016/j.ajhg.2018.09.007. View

16.

Wald N, Old R . The illusion of polygenic disease risk prediction. Genet Med. 2019; 21(8):1705-1707. DOI: 10.1038/s41436-018-0418-5. View

17.

Yang Z, Yang J, Liu D, Yu W . Mendelian randomization analysis identified genes pleiotropically associated with central corneal thickness. BMC Genomics. 2021; 22(1):517. PMC: 8263012. DOI: 10.1186/s12864-021-07860-3. View

18.

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View

19.

Keller P, Lin A, Arendt L, Klebba I, Jones A, Rudnick J . Mapping the cellular and molecular heterogeneity of normal and malignant breast tissues and cultured cell lines. Breast Cancer Res. 2010; 12(5):R87. PMC: 3096980. DOI: 10.1186/bcr2755. View

20.

Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A . Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet. 2018; 104(1):21-34. PMC: 6323553. DOI: 10.1016/j.ajhg.2018.11.002. View