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Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40–85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available “LIBRA” software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithmDiseasE Trajectory fEature extraCTion (DETECT)for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between a specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks. There is a median of seven years of longitudinal data in the EHR available on participants, who also consent to permission to recontact. Herein, we describe the operations and infrastructure of the PMBB, summarize the phenotypic architecture of the enrolled participants, and use body mass index (BMI) as a proof-of-concept quantitative phenotype for PheWAS, LabWAS, and GWAS. The major representation of African-American participants in the PMBB addresses the essential need to expand the diversity in genetic and translational research. There is a critical need for a “medical biobank consortium” to facilitate replication, increase power for rare phenotypes and variants, and promote harmonized collaboration to optimize the potential for biological discovery and precision medicine.

Introduction & Purpose: Breast density, the relative amount of fibroglandular versus fatty tissue in the breast, is one of the strongest breast cancer risk factors. Understanding genetic factors associated with breast density may help clarify mechanisms by which breast density increases breast cancer risk. To date, 46 genetic loci have been associated with breast density, however, these studies have been performed among predominantly European ancestry populations. This study sought to identify genetic factors associated with breast density among women of African ancestry. Methods: We utilized a cohort of women aged 40-85 years who underwent screening mammography at the Hospital of the University of Pennsylvania from 2010-2014. The cohort was linked to the Penn Medicine BioBank (PMBB), a repository of DNA samples from consenting patients. We conducted a genome-wide association study (GWAS) of breast density among women with available genotypes who either self-reported their race as Black/African American, or had an African ancestry genotype (N = 1,323). For each mammogram, the publicly available “LIBRA” software (v1.0.4) was used to quantify dense area and area percent density (dense area divided by breast area) from raw (i.e., FOR PROCESSING) full-field digital mammograms (Selenia Dimensions, Hologic Inc.). Women completed a breast cancer risk factor questionnaire at the time of mammography and BMI was obtained from electronic health records. We tested for associations of dense area and area percent density with ~8.5M common variants, adjusting for age, BMI, menopausal status, and two principle components of ancestry as covariates. Results: We identified three novel loci associated with dense area at genome wide significance (p < 5e-08), including KCNA3 (rs61803939, p =3.40e-08, beta=3.26, SE=0.59) and two nearby loci on chromosome 19, ZNF486 (rs6511094, p =1.41e-08, beta=2.44, SE=0.43) and LOC100129265 (rs1428621382 p = 5.13E-08, beta=2.40, SE=0.44). Our results also replicated three previously reported signals near genes TET3 associated with dense area, TNS1 and SMIM25 associated with area percent density at nominally significant p < 1e-06. Conclusions: To our knowledge, this is the first breast density GWAS in women of African ancestry. Although the study had a small sample size, the analyses replicated associations of three known genes and also identified three novel breast density loci. Notably, variant rs61803939 mapping to KCNA3 gene is present in samples of African ancestry at a higher frequency (MAF=0.14) than in samples of European ancestry (MAF=0.09). Additionally, this variant also has a high oncogenecity score (Phred Score=21.9) and is estimated as likely driver of cancer. Further investigation of the role of variants in KCNA3 in breast density and in influencing the breast cancer risk are warranted, as are larger GWAS evaluating genetic variants for breast density among African ancestry populations. Citation Format: Shefali Verma, Lindsay Guare, Aimilia Gastounioti, Sarah Ehsan, Emily F. Conant, Marylyn Ritchie, Despina Kontos, Anne Marie McCarthy. Genome wide association study of breast density among women of African ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5871.