A genome-wide association study of mammographic texture variation.

Female Humans Genome-Wide Association Study Mammography Breast Density / genetics Polymorphism, Single Nucleotide Neoplasms Risk Factors Alpha-Ketoglutarate-Dependent Dioxygenase FTO / genetics
Breast cancer Breast parenchymal texture feature GWAS Genetic correlation Mammographic density Texture variation V measure

Journal

Breast cancer research : BCR
ISSN: 1465-542X
Titre abrégé: Breast Cancer Res
Pays: England
ID NLM: 100927353

Informations de publication

Date de publication:
07 Nov 2022
Historique:
received: 13 05 2022
accepted: 26 10 2022
entrez: 7 11 2022
pubmed: 8 11 2022
medline: 10 11 2022
Statut: epublish

Résumé

Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited. We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors. We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (r These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.

Sections du résumé

BACKGROUND
Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited.
METHODS
We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors.
RESULTS
We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (r
CONCLUSIONS
These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.

Identifiants

DOI: 10.1186/s13058-022-01570-8 PMID: 36344993 PMC: PMC9639267
pubmed: 36344993
doi: 10.1186/s13058-022-01570-8
pii: 10.1186/s13058-022-01570-8
pmc: PMC9639267
doi:

Substances chimiques

FTO protein, human EC 1.14.11.33
Alpha-Ketoglutarate-Dependent Dioxygenase FTO EC 1.14.11.33

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

76

Subventions

Organisme : NCI NIH HHS
ID : U19 CA148112
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA194393
Pays : United States
Organisme : NCI NIH HHS
ID : U19 CA148537
Pays : United States
Organisme : NIAID NIH HHS
ID : R43 AI128978
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA244670
Pays : United States
Organisme : NCI NIH HHS
ID : U19 CA148065
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Yuxi Liu (Y)

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2-249A, Boston, MA, 02115, USA.

Hongjie Chen (H)

Department of Epidemiology, University of Washington, Seattle, WA, USA.

John Heine (J)

Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Sara Lindstrom (S)

Department of Epidemiology, University of Washington, Seattle, WA, USA.
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Constance Turman (C)

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Erica T Warner (ET)

Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Stacey J Winham (SJ)

Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.

Celine M Vachon (CM)

Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.

Rulla M Tamimi (RM)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.

Peter Kraft (P)

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. pkraft@hsph.harvard.edu.
Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2-249A, Boston, MA, 02115, USA. pkraft@hsph.harvard.edu.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. pkraft@hsph.harvard.edu.

Xia Jiang (X)

Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Visionsgatan 18, 171 77, Solna, Stockholm, Sweden. xia.jiang@ki.se.
West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China. xia.jiang@ki.se.

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