Genome-wide association study of breast density among women of African ancestry

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 i

Global Biobank Meta-analysis Initiative : Powering genetic discovery across human disease

Funding Information: The work of the contributing biobanks was supported by numerous grants from governmental and charitable bodies. Biobank-specific acknowledgments and more detailed acknowledgments are included in Data S2. Initiative management, S.B.C. J.C. N.J.C. M.J.D. E.E.K. A.R.M. B.M.N. Y.O. A.V.P. D.A.v.H. R.G.W. C.J.W. W.Z. and S.Z.; individual biobank analysis, A.B. Y.B. B.M.B. C.D.B. S.C. T.-T.C. K.C. S.M.D. M.D. G.H.d.B. Y.D. N.J.D. M.-J.F. Y.-C.A.F. S.F. V.L.F. L.G.F. E.R.G. T.R.G. D.H.G. C.R.G. G.G.-A. S.E.G. L.A.G. C.H. J.B.H. W.E.H. H.H. K.H. N.I. A.I. R.J. M. Kurki, J.K. N.K. E.E.K. J.T.K. M. Kanai, T.L. K.L. M.H.L. S.L. K.L. Y.-F.L. V.L.F. R.J.F.L. E.A.L.-M. A.R.-M. S.M.-G. R.M. R.E.M. H.C.M. A.R.M. Y.M. H.M. S.E.M. I.Y.M. B.M. S.M. K.N. S.N. M.A.N.-A. K.N. Y.O. P.P. A.L.-P. A.P. B.P. S.P. M.H.P. D.J.R. N.R. M.D.R. A.R. C.S. S.S. S.S.S. J.A.S. P.S. I.S. T.T. R.T. K.T. J.U. D.A.v.H. B.V. M.V. Y.V. J.M.V. R.G.W. Y.W. S.J.W. B.N.W. K.-H.H.W. M.Z. X.Z. and S.Z.; individual biobank management, N.A. A.A.T. K.M.A.-D. P.A. K.C.B. M. Boehnke, M. Boezen, C.D.B. A.C. Z.C. C.-Y.C. J.C. N.J.C. S.M.D. S.F. Y.-C.A.F. S.F. E.F. T.G. C.R.G. C.J.G. Y.G. H.H. K.A.H. K.H. S.I.I. N.M.J. N.K. E.E.K. J.T.K. C.L. M.H.L. M.T.M.L. L.L. K.L. Y.-F.L. R.J.F.L. J.L. S.M. Y.M. K.M. I.Y.M. Y.O. C.M.O. A.V.P. B.P. D.J.P. D.J.R. M.D.R. S.S. J.W.S. H.S. K.S. T.T. U.T. R.C.T. D.A.v.H. M.V. R.G.W. D.C.W. C.W. J.W. M.Z. X.Z. and S.Z.; study design and interpretation of results, A.B. M. Boehnke, M. Boezen, B.M.B. T.-T.C. C.-Y.C. M.J.D. G.D.S. N.J.D. S.F. M.-J.F. H.K.F. E.R.G. A.G. T.G. J.B.H. J.H. K.H. R.J. M.K. E.E.K. T.K. C.M.L. V.L.F. E.A.L.-M. A.R.M. S.N. B.M.N. C.M.O. J.J.P. B.P. N.R. H.R. J.A.S. I.S. K.T. D.A.v.H. R.G.W. Y.W. D.C.W. S.J.W. C.J.W. B.N.W. J.W. K.-H.H.W. M.Z. H.Z. J.Z. W.Z. X.Z. and S.Z.; drafted and edited the paper, A.B. M. Boehnke, M. Boezen, M.J.D. G.H.d.B. N.J.D. T.R.G. J.B.H. N.I. N.M.J. M.K. V.L.F. S.M. A.R.M. H.M. S.N. B.M.N. C.M.O. B.P. H.R. C.S. J.A.S. J.W.S. K.T. Y.W. D.C.W. C.J.W. K.-H.H.W. H.Z. J.Z. W.Z. and S.Z.; primary meta-analysis and quality control, M.J.D. H.K.F. M. Kanai, J.K. J.T.K. M. Kurki, M.M. B.M.N. C.J.W. K.-H.H.W. and W.Z.; drug discovery: S.N. T.K. K.-H.H.W. W.Z. and Y.O.; fine mapping, M. Kanai, W.Z. M.J.D. and H.K.F.; polygenic risk score, Y.W. S.N. E.A.L.-M. S.K. K.T. K.L. M. Kanai, W.Z. K.W. M.-J.F. L.B. P.A. P.D. V.L.F. R.M. Y.M. B.B. S.S. J.U. E.R.G. N.J.C. I.S. Y.O. A.R.M. and J.B.H.; proteome-wide Mendelian randomization, H.Z. H.R. A.B. G.H. G.D.S. B.M.B. W.Z. B.M.N. T.R.G. and J.Z.; transcriptome-wide association study, A.B. J.B.H. W.Z. J.Z. M. Kanai, B.P. E.R.G. and N.J.C.; asthma, K.T. W.Z. Y.W. M. Kanai, S.N. Y.O. B.M.N. M.J.D. and A.R.M.; heart failure, K.-H.H.W. N.J.D. B.N.W. I.S. S.E.G. J.B.H. N.J.C. M.P. R.J.F.L. M.J.D. B.M.N. W.Z. W.E.H. and C.J.W.; idiopathic pulmonary fibrosis, J.J.P. W.Z. M.J.D. J.T.K. N.J.C. and J.B.H.; primary open-angle glaucoma, V.L.F. A.B. W.Z. Y.W. K.L. M. Kanai, E.A.L.-M. P.S. R.T. X.Z. S.N. S.S. Y.O. N.I. S.M. H.S. I.S. C.W. A.R.M. E.R.G. N.M.J. N.J.C. and J.B.H.; stroke, I.S. K.-H.H.W. W.H. B.N.W. W.Z. J.E.H. A.P. B.B. A.H.S. M.E.G. R.G.W. K.H. C.K. S.Z. M.J.D. B.M.N. and C.J.W.; venous thromboembolism, B.N.W. I.S. K.-H.H.W. B.B. V.L.F. K.T. M.D. B.N. W.Z. J.A.S. and C.J.W. All authors reviewed the manuscript. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. The spouse of C.J.W. works at Regeneron Pharmaceuticals. C.-Y.C. is employed by Biogen. C.R.G. owns stock in 23andMe, Inc. T.R.G. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. E.E.K. has received speaker fees from Regeneron, Illumina, and 23andMe and is a member of the advisory board for Galateo Bio. R.E.M. has received speaker fees from Illumina and is a scientific advisor to the Epigenetic Clock Development Foundation. G.D.S. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. K.S. and U.T. are employed by deCODE Genetics/Amgen, Inc. J.Z. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. S.M. is a co-founder of and holds stock in Seonix Bio. Publisher Copyright: © 2022Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.Peer reviewe

Gene burden analysis identifies genes associated with increased risk and severity of adult-onset hearing loss in a diverse hospital-based cohort.

Loss or absence of hearing is common at both extremes of human lifespan, in the forms of congenital deafness and age-related hearing loss. While these are often studied separately, there is increasing evidence that their genetic basis is at least partially overlapping. In particular, both common and rare variants in genes associated with monogenic forms of hearing loss also contribute to the more polygenic basis of age-related hearing loss. Here, we directly test this model in the Penn Medicine BioBank-a healthcare system cohort of around 40,000 individuals with linked genetic and electronic health record data. We show that increased burden of predicted deleterious variants in Mendelian hearing loss genes is associated with increased risk and severity of adult-onset hearing loss. As a specific example, we identify one gene-TCOF1, responsible for a syndromic form of congenital hearing loss-in which deleterious variants are also associated with adult-onset hearing loss. We also identify four additional novel candidate genes (COL5A1, HMMR, RAPGEF3, and NNT) in which rare variant burden may be associated with hearing loss. Our results confirm that rare variants in Mendelian hearing loss genes contribute to polygenic risk of hearing loss, and emphasize the utility of healthcare system cohorts to study common complex traits and diseases

The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

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

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Funder: BiogenBiobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits

Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.

BACKGROUND: Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. RESULTS: Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. CONCLUSION: We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern

Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.

BACKGROUND: Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. RESULTS: Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation. CONCLUSION: We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern