Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk

Data From 228,951 Women of European Descent

Yaohua Yang; Lang Wu; Xiao-Ou Shu; Qiuyin Cai; Xiang Shu; Bingshan Li; Xingyi Guo; Fei Ye; Kyriaki Michailidou; Manjeet K. Bolla; Qin Wang; Joe Dennis; Irene L. Andrulis; Hermann Brenner; Georgia Chenevix-Trench; Daniele Campa; Jose E. Castelao; Manuela Gago-Dominguez; Thilo Dörk; Antoinette Hollestelle; Artitaya Lophatananon; Kenneth Muir; Susan L. Neuhausen; Håkan Olsson; Dale P. Sandler; Jacques Simard; Peter Kraft; Paul D. P. Pharoah; Douglas F. Easton; Wei Zheng; Jirong Long

Disclosures

J Natl Cancer Inst. 2020;112(3):295-304.

Abstract and Introduction

Abstract

Background: DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk.

Methods: Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women's Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided.

Results: Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10⁻⁷, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes.

Conclusion: Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

Introduction

Breast cancer is the most common cancer for women in the United States as well as many countries around the world.^[1] DNA methylation plays critical roles in cancer development, including breast cancer.^[2]

DNA methylation of several genes in white blood cells had been associated with breast cancer risk; however, inconsistent results were found.^[3–7] Most of these studies had a retrospective design. Two prospective studies found that overall DNA hypomethylation in white blood cells was associated with increased breast cancer risk.^[8,9] In addition, a panel of 250 CpGs sites (CpGs) in white blood cell DNA was identified to be predictive of breast cancer risk.^[10] However, none of these CpGs were consistently observed in a later study.^[9] These studies were limited by small sample size, lack of replication, and/or reverse causation. Furthermore, the repeatability of DNA methylation measurements at some CpGs using the HumanMethylation450 BeadChip (Illumina, San Diego, CA) was not optimal,^[11] which may have contributed to the inconsistency across studies.

A recent study indicated the epigenetic supersimilarity of monozygotic twin pairs.^[12] More recently, 24 heritable CpGs were associated with breast cancer risk.^[13] Multiple genetic variants had been identified as DNA methylation quantitative trait loci (meQTL),^[14–16] suggesting that DNA methylation at some CpGs are genetically determined and thus can be predicted using genetic variants. Studies using cis (500 kilobase [kb] flanking regions) meQTL single-nucleotide polymorphisms (SNPs) had discovered novel CpGs for diseases.^[17,18] However, the proportion of variance explained by a single-meQTL SNP for most CpGs is typically small. Herein, we propose a new methodology to build statistical models to predict DNA methylation in white blood cells via multiple SNPs in a reference dataset and then apply the models to large genomewide association study (GWAS) datasets to evaluate genetically predicted DNA methylation in association with disease risk. We tested this methodology by investigating the association of genetically predicted DNA methylation with breast cancer risk using data from 122 977 breast cancer patients and 105 974 controls.

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Next Section

Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Seventeen DNA methylation marks associated with breast cancer risk identified in genomic regions not yet reported for breast cancer risk
CpG	Chr	Position	Closest gene	Classification	Z score*	OR (95% CI)*	P*	R² _FHS†	R² _WHI†	Closest breast cancer risk SNP	Distance to risk SNP (kb)	P* adjusted for risk SNP
cg04794690	1	17 768 059	RCC2; ARHGEF10L	Intergenic	5.04	1.36 (1.20 to 1.53)	4.74 × 10⁻⁷	0.01	0.04	rs2992756	−1039	2.61 × 10⁻⁸
cg22221025	1	110 186 044	GSTM4	Upstream	5.36	1.15 (1.09 to 1.21)	8.53 × 10⁻⁸	0.05	0.03	rs11552449	−4262	3.04 × 10⁻⁷
cg26668989	1	110 186 163	GSTM4	Upstream	6.37	1.20 (1.13 to 1.27)	1.92 × 10⁻¹⁰	0.04	0.04	rs11552449	−4262	1.23 × 10⁻⁹
cg04411307	2	69 391 395	ANTXR1	Intronic	5.10	1.05 (1.03 to 1.07)	3.38 × 10⁻⁷	0.31	0.22	rs6725517	44 262	3.19 × 10⁻⁷
cg16190888	2	69 428 235	ANTXR1	Intronic	−5.20	0.87 (0.82 to 0.91)	1.99 × 10⁻⁷	0.04	0.03	rs6725517	44 299	2.06 × 10⁻⁷
cg03277049	3	156 534 076	LINC00886	ncRNA_intronic	−5.01	0.92 (0.90 to 0.95)	5.52 × 10⁻⁷	0.10	0.10	rs58058861	−15 751	5.01 × 10⁻⁷
cg11359771	5	131 558 794	P4HA2	5'UTR	5.00	1.21 (1.13 to 1.31)	5.59 × 10⁻⁷	0.01	0.02	rs6596100	−848	1.70 × 10⁻⁷
cg16647868	5	131 706 066	SLC22A5	Intronic	4.95	1.05 (1.03 to 1.08)	7.36 × 10⁻⁷	0.20	0.27	rs6596100	−701	2.02 × 10⁻⁷
cg19040266	5	131 723 239	SLC22A5	Intronic	−5.05	0.93 (0.91 to 0.96)	4.31 × 10⁻⁷	0.13	0.10	rs6596100	−684	1.32 × 10⁻⁷
cg11920449	6	36 645 608	CDKN1A	TSS1500	−5.04	0.97 (0.96 to 0.98)	4.56 × 10⁻⁷	0.69	0.67	rs9257408	7719	7.78 × 10⁻⁷
cg03714916	6	36 645 886	CDKN1A	TSS1500	−5.17	0.94 (0.92 to 0.96)	2.31 × 10⁻⁷	0.17	0.16	rs9257408	7720	3.95 × 10⁻⁷
cg03171419	8	37 700 802	GPR124	3'UTR	−5.26	0.87 (0.82 to 0.92)	1.46 × 10⁻⁷	0.04	0.04	rs13365225	842	7.86 × 10⁻⁸
cg07540652	8	81 805 956	ZNF704; PAG1	Intergenic	5.05	1.19 (1.11 to 1.27)	4.48 × 10⁻⁷	0.03	0.02	rs2943559	5388	2.71 × 10⁻⁷
cg25626611	12	115 102 065	TBX5-AS1; TBX3	Intergenic	6.01	1.12 (1.08 to 1.16)	1.91 × 10⁻⁹	0.09	0.05	rs1292011	−734	1.09 × 10⁻⁷
cg07211768	12	115 102 290	TBX5-AS1; TBX3	Intergenic	5.98	1.08 (1.06 to 1.11)	2.25 × 10⁻⁹	0.16	0.14	rs1292011	−734	1.52 × 10⁻⁷
cg25938347	15	75 639 163	NEIL1	TSS200	5.51	1.29 (1.18 to 1.42)	3.59 × 10⁻⁸	0.01	0.02	rs151090251	8231	1.84 × 10⁻⁷
cg25839482	15	75 931 953	IMP3	3'UTR	−5.57	0.94 (0.93 to 0.96)	2.59 × 10⁻⁸	0.21	0.25	rs151090251	8524	1.57 × 10⁻⁷

*MetaXcan was used to estimate association Z scores, ORs, 95% CIs, and P values. All statistical tests were two-sided. Chr = chromosome; CI = confidence interval; CpG = CpG sites; FHS = Framingham Heart Study; kb = kilobase; ncRNA = noncoding RNA; OR = odds ratio per SD increase in genetically predicted DNA methylation level (continuous variable); SNP = single-nucleotide polymorphisms; TSS = transcription start site; UTR = untranslated region; WHI = Women's Health Initiative.
†Correlation between predicted and measured DNA methylation levels.

Table 2. Eleven DNA methylation marks associated with breast cancer risk identified in genomic regions within 500 kb of known breast cancer risk variants but representing independent association signals
CpG	Chr	Position	Closest gene	Classification	z score*	OR (95% CI)*	P*	R² _FHS†	R² _WHI†	Breast cancer risk SNPs	Distance to risk SNPs (kb)	P* adjusted for risk SNPsf
cg18789177	2	217 729 408	TNP1;LOC105373876	Intergenic	5.73	1.28 (1.17 to 1.39)	1.01 × 10⁻⁸	0.02	0.01	rs4442975; rs34005590	191; 233	7.44 × 10⁻⁸
cg16971831	5	56 110 935	MAP3K1	5'UTR	−11.90	0.51 (0.46 to 0.57)	1.23 × 10⁻³²	0.01	0.06	rs16886397; rs16886113; rs2229882; rs7726354; rs16886034; rs16886181; rs12655019; rs889312	23; −115; 57; 145; −127; −81; 84; −79	7.42 × 10⁻⁸
cg20580673	14	91 735 665	GPR68; CCDC88C	Intergenic	−6.16	0.76 (0.69 to 0.83)	7.26 × 10⁻¹⁰	0.02	0.03	rs941764	105	8.03 × 10⁻⁹
cg00787180	14	91 751 731	CCDC88C	Intronic	−5.57	0.88 (0.84 to 0.92)	2.54 × 10⁻⁸	0.05	0.06	rs941764	89	6.72 × 10⁻⁷
cg09032423	16	4 015 231	ADCY9	3'UTR	−5.01	0.82 (0.77 to 0.89)	5.56 × 10⁻⁷	0.02	0.01	rs11076805	91	1.02 × 10⁻⁷
cg12776287	18	24 125 939	KCTD1	Intronic	−5.36	0.95 (0.93 to 0.97)	8.12 × 10⁻⁸	0.27	0.21	rs1436904; rs527616	444; 211	1.45 × 10⁻⁸
cg19738924	18	24 126 072	KCTD1	Intronic	−5.60	0.93 (0.91 to 0.96)	2.20 × 10⁻⁸	0.14	0.14	rs1436904; rs527616	444; 211	2.91 × 10⁻⁸
cg15073853	19	18 549 131	ISYNA1	TSS200	9.28	1.09 (1.07 to 1.11)	1.77 × 10⁻²⁰	0.26	0.21	rs4808801	22	9.24 × 10⁻²⁶
cg21962901	19	18 549 134	ISYNA1	TSS200	9.37	1.11 (1.09 to 1.13)	7.27 × 10⁻²¹	0.19	0.15	rs4808801	22	9.24 × 10⁻²⁶
cg11102782	19	18 549 136	ISYNA1	TSS200	8.80	1.08 (1.06 to 1.10)	1.38 × 10⁻¹⁸	0.32	0.26	rs4808801	22	9.24 × 10⁻²⁶
cg09232727	22	29 140 725	HSCB	Intronic	−6.23	0.76 (0.70 to 0.83)	4.60 × 10⁻¹⁰	0.02	0.03	rs17879961; rs132390	−19; 480	2.12 × 10⁻⁸

*MetaXcan was used to estimate association z scores, ORs, 95% CIs, and P values. All statistical tests were two-sided. Chr = chromosome; CI = confidence interval; CpG = CpG sites; FHS = Framingham Heart Study; kb = kilobase; ncRNA = noncoding RNA; OR = odds ratio per SD increase in genetically predicted DNA methylation level (continuous variable); SNP = single-nucleotide polymorphisms; TSS = transcription start site; UTR = untranslated region; WHI = Women's Health Initiative.
†Correlation between predicted and measured DNA methylation levels.

Table 3. Selected* consistent directions of associations across DNA methylation, gene expression, and breast cancer risk
CpG	Chr	Position	Gene	Classification	CpG vs breast cancer risk		CpG vs Gex		Gex vs breast cancer risk		Gex vs breast cancer risk adjusted for DNA methylation†
CpG	Chr	Position	Gene	Classification	Dir	P‡	Dir	P§	Dir	P‡	Dir	P‡
cg26668989	1	110 186 163	GSTM4	Upstream	Positive	1.92 × 10⁻¹⁰	Negative	5.37 × 10⁻⁵	Negative	2.04 × 10⁻⁵	Negative	.52
cg08614201	1	145 715 134	CD160	5'UTR	Positive	5.00 × 10⁻⁷	Negative	3.26 × 10⁻⁵¹	Negative	9.35 × 10⁻⁴	Negative	.93
cg20311333	1	155 197 753	GBAP1	TSS1500	Positive	2.54 × 10⁻¹¹	Negative	4.55 × 10⁻¹²	Negative	2.22 × 10⁻⁹	Positive	.86
cg02834765	1	155 214 859	GBA	TSS1500	Negative	1.16 × 10⁻⁷	Negative	2.44 × 10⁻⁵	Negative	5.77 × 10⁻⁸	Negative	.62
cg16030869	4	38 867 304	FAM114A1	Upstream	Positive	1.72 × 10⁻⁸	Positive	6.47 × 10⁻⁵	Positive	1.72 × 10⁻⁸	Positive	.22
cg17942617	5	81 327 376	ATG10	Intronic	Negative	6.81 × 10⁻¹³	Positive	9.66 × 10⁻¹¹	Negative	6.84 × 10⁻¹¹	Negative	.75
cg16647868	5	131 706 066	SLC22A5	Intronic	Positive	7.36 × 10⁻⁷	Negative	9.84 × 10⁻⁹	Negative	1.81 × 10⁻⁶	Negative	.90
cg12078157	6	13 612 218	SIRT5	3'UTR	Negative	1.44 × 10⁻⁷	Negative	1.62 × 10⁻⁵	Positive	1.74 × 10⁻⁴	Positive	.51
cg05216056	6	28 887 836	TRIM27	Exonic	Negative	3.71 × 10⁻⁷	Negative	1.45 × 10⁻²⁹	Positive	7.34 × 10⁻⁴	Negative	.99
cg14701867	10	64 193 068	ZNF365	Intronic	Negative	5.92 × 10⁻¹⁰	Negative	1.15 × 10⁻⁶	Positive	.02	Positive	.26
cg23754390	11	835 074	CD151	Intronic	Positive	2.51 × 10⁻⁷	Positive	9.14 × 10⁻²²	Positive	2.03 × 10⁻³	Negative	.81
cg04111478	11	1 991 677	MRPL23	Downstream	Positive	2.20 × 10⁻⁸	Positive	.008	Positive	3.63 × 10⁻⁶	Positive	.57
cg15531562	11	65 601 754	SNX32	Intronic	Positive	1.41 × 10⁻¹³	Positive	.002	Positive	6.15 × 10⁻⁸	Positive	.74
cg06065225	11	65 640 137	EFEMP2	Intronic	Negative	2.37 × 10⁻⁹	Negative	.009	Positive	1.93 × 10⁻¹¹	Positive	.42
cg23526087	14	68 973 466	RAD51B	Intronic	Negative	4.21 × 10⁻²¹	Negative	5.62 × 10⁻¹³	Positive	4.55 × 10⁻⁴	Positive	1.00
cg25839482	15	75 931 953	IMP3	3'UTR	Negative	2.59 × 10⁻⁸	Negative	1.27 × 10⁻⁴	Positive	6.18 × 10⁻⁶	Negative	.78
cg18878992	17	43 974 344	MAPT	TSS1500	Negative	7.07 × 10⁻¹⁰	Negative	.003	Positive	8.78 × 10⁻⁶	Positive	.09
cg21757127	19	18 525 886	LRRC25	Downstream	Negative	1.33 × 10⁻¹⁵	Negative	1.71 × 10⁻⁵	Positive	4.25 × 10⁻¹⁶	Positive	2.21 × 10⁻⁵
cg09516349	19	18 529 339	SSBP4	TSS1500	Positive	4.95 × 10⁻²⁵	Positive	9.70 × 10⁻⁴	Positive	2.55 × 10⁻²³	Positive	.49
cg22161383	19	18 545 441	ISYNA1	3'UTR	Positive	3.85 × 10⁻²⁷	Negative	.003	Negative	9.62 × 10⁻¹⁰	Negative	.01
cg14066757	19	44 285 568	KCNN4	TSS200	Negative	3.55 × 10⁻¹⁶	Negative	.01	Positive	6.12 × 10⁻¹⁵	Positive	.23

*Selected from 38 consistent directions of associations across DNA methylation, gene expression, and breast cancer risk. Complete list is available in Supplementary Table 8 (available online). Chr = chromosome; CpG = CpG sites; Dir = direction of association and/or correlation; Gex = gene expression; TSS = transcription start site; UTR = untranslated region.
†Adjusted for all predicting variants included in prediction models of corresponding CpGs.
‡P values were calculated using MetaXcan. All statistical tests were two-sided.
§P values were calculated using Spearman correlation test. All statistical tests were two-sided.

Authors and Disclosures

Yaohua Yang, Lang Wu, Xiao-Ou Shu, Qiuyin Cai, Xiang Shu, Bingshan Li, Xingyi Guo, Fei Ye, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Joe Dennis, Irene L. Andrulis, Hermann Brenner, Georgia Chenevix-Trench, Daniele Campa, Jose E. Castelao, Manuela Gago-Dominguez, Thilo Dörk, Antoinette Hollestelle, Artitaya Lophatananon, Kenneth Muir, Susan L. Neuhausen, Håkan Olsson, Dale P. Sandler, Jacques Simard, Peter Kraft, Paul D. P. Pharoah, Douglas F. Easton, Wei Zheng and Jirong Long

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN (YY, LW, XOS, QC, XS, XG, WZ, JL); Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN (BL); Division of Cancer Biostatistics, Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN (FY); Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (KM, MKB, QW, JD, PDPP, DFE); Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada (ILA); Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada (ILA); Division of Clinical Epidemiology and Aging Research (HB) and German Cancer Consortium (HB), German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany (HB); Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia (GCT); Department of Biology, University of Pisa, Pisa, Italy (DC); Oncology and Genetics Unit, Instituto de Investigación Biomedica Orense-Pontevedra-Vigo, Xerencia de Xestión Integrada de Vigo-SERGAS, Vigo, Spain (JEC); Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago De Compostela, Spain (MGD); Moores Cancer Center, University of California San Diego, La Jolla, CA (MGD); Gynaecology Research Unit, Hannover Medical School, Hannover, Germany (TD); Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands (AH); Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, UK (AL, KM); Institute of Population Health, University of Manchester, Manchester, UK (AL, KM); Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA (SLN); Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden (HO); Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC (DPS); Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, QC, Canada (JS); Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health (PK) and Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA (PK).

Correspondence to
Jirong Long, PhD, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2525 West End Ave, Suite 800, Nashville, TN 37203 (e-mail: jirong.long@vanderbilt.edu).