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1. PMS2CL interference leading to erroneous identification of a pathogenic PMS2 variant in Black patients

Author

Jacqueline Cappadocia, Lisa B. Aiello, Michael J. Kelley, Bryson W. Katona, Kara N. Maxwell, Anurag Verma, Ph.D., Shefali S. Verma, Ph.D., Yuki Bradford, M.S., Ashlei Brock, Stephanie DerOhannessian, Scott Dudek, M.S., Joseph Dunn, Theodore Drivas, M.D., Ph.D., Ned Haubein, Khadijah Hu-Sain, Renae Judy, Ashley Kloter, Yi-An Ko, Meghan Livingstone, Linda Morrel, Colleen Morse, M.S., Afiya Poindexter, Marjorie Risman, M.S., Teo Tran, Fred Vadivieso, JoEllen Weaver, Daniel J. Rader, M.D., Marylyn D. Ritchie, Ph.D., Michael D. Feldman, M.D., Ph.D., Christina Beechert, Caitlin Forsythe, M.S., Erin D. Fuller, Zhenhua Gu, M.S., Michael Lattari, Alexander Lopez, M.S., John D. Overton, Ph.D., Maria Sotiropoulos Padilla, M.S., Manasi Pradhan, M.S., Kia Manoochehri, B.S., Thomas D. Schleicher, M.S., Louis Widom, Sarah E. Wolf, M.S., Ricardo H. Ulloa, B.S., Amelia Averitt, Ph.D., Nilanjana Banerjee, Ph.D., Michael Cantor, M.D., Dadong Li, Ph.D., Sameer Malhotra, M.D., Deepika Sharma, MHI, Jeffrey Staples, Ph.D., Xiaodong Bai, Ph.D., Suganthi Balasubramanian, Ph.D., Suying Bao, Ph.D., Boris Boutkov, Ph.D., Siying Chen, Ph.D., Gisu Eom, B.S., Lukas Habegger, Ph.D., Alicia Hawes, B.S., Shareef Khalid, Olga Krasheninina, M.S., Rouel Lanche, B.S., Adam J. Mansfield, B.A., Evan K. Maxwell, Ph.D., George Mitra, B.A., Mona Nafde, M.S., Sean O’Keeffe, Ph.D., Max Orelus, B.B.A., Razvan Panea, Ph.D., Tommy Polanco, B.A., Ayesha Rasool, M.S., Jeffrey G. Reid, Ph.D., William Salerno, Ph.D., Jeffrey C. Staples, Ph.D., Kathie Sun, Ph.D., Goncalo Abecasis, D.Phil., Joshua Backman, Ph.D., Amy Damask, Ph.D., Lee Dobbyn, Ph.D., Manuel Allen Revez Ferreira, Ph.D., Arkopravo Ghosh, M.S., Christopher Gillies, Ph.D., Lauren Gurski, B.S., Eric Jorgenson, Ph.D., Hyun Min Kang, Ph.D., Michael Kessler, Ph.D., Jack Kosmicki, Ph.D., Alexander Li, Ph.D., Nan Lin, Ph.D., Daren Liu, M.S., Adam Locke, Ph.D., Jonathan Marchini, Ph.D., Anthony Marcketta, M.S., Joelle Mbatchou, Ph.D., Arden Moscati, Ph.D., Charles Paulding, Ph.D., Carlo Sidore, Ph.D., Eli Stahl, Ph.D., Kyoko Watanabe, Ph.D., Bin Ye, Ph.D., Blair Zhang, Ph.D., Andrey Ziyatdinov, Ph.D., Ariane Ayer, B.S., Aysegul Guvenek, Ph.D., George Hindy, Ph.D., Giovanni Coppola, M.D., Jan Freudenberg, M.D., Jonas Bovijn, M.D., Katherine Siminovitch, M.D., Kavita Praveen, Ph.D., Luca A. Lotta, M.D., Manav Kapoor, Ph.D., Mary Haas, Ph.D., Moeen Riaz, Ph.D., Niek Verweij, Ph.D., Olukayode Sosina, Ph.D., Parsa Akbari, Ph.D., Priyanka Nakka, Ph.D., Sahar Gelfman, Ph.D., Sujit Gokhale, B.E., Tanima De, Ph.D., Veera Rajagopal, Ph.D., Alan Shuldiner, M.D., Gannie Tzoneva, Ph.D., Juan Rodriguez-Flores, Ph.D., Esteban Chen, M.S., Marcus B. Jones, Ph.D., Michelle G. LeBlanc, Ph.D., Jason Mighty, Ph.D., Lyndon J. Mitnaul, Ph.D., Nirupama Nishtala, Ph.D., Nadia Rana, Ph.D., Jaimee Hernandez, Goncalo Abecasis, PhD, Aris Baras, M.D., Andrew Deubler, Aris Economides, Ph.D., and Luca A. Lotta, M.D., Ph.D.

Subjects
PMS2, germline genetic testing, Lynch syndrome, PMS2CL, pseudogene interference, Genetics, QH426-470, Medicine
Abstract

This study investigates the frequency of a clinically reported variant in PMS2, NM_000535.7:c.2523G>A p.(W841∗), from next-generation sequencing studies in 2 racially diverse cohorts. We identified clinical reports of the PMS2 c.2523G>A p.(W841∗) variant in the National Precision Oncology Program’s somatic testing database (n = 25,168). We determined frequency of the variant in germline exome sequencing from the Penn Medicine BioBank (n = 44,256) and in gnomAD. The PMS2 c.2523G>A p.(W841∗) was identified as a homozygous variant on tumor testing in an adult patient of self-identified Black race/ethnicity with no evidence of constitutional mismatch repair deficiency. The variant was clinically reported on 35 total tumor and liquid biopsy tests (0.1%), and all individuals with the variant were of self-identified Black race/ethnicity (0.6% of n = 5787). In individuals of African genetic ancestry (AFR), the variant's germline frequency was reported to be 0.2% and 1.3% in the Penn Medicine BioBank (PMBB) and gnomAD, respectively. The variant cannot be found in any individuals of European genetic ancestry (EUR) from either of the databases. The variant is found in a region of PMS2 with 100% homology to the PMS2CL pseudogene. PMS2 c.2523G>A p.(W841∗), when identified, is typically an African-ancestry-specific PMS2CL pseudogene variant, which should be recognized to prevent misdiagnosis of Lynch syndrome in Blacks.

Published
2024
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2. Inference of Causal Relationships Between Genetic Risk Factors for Cardiometabolic Phenotypes and Female‐Specific Health Conditions

Author

Brenda Xiao, Digna R. Velez Edwards, Anastasia Lucas, Theodore Drivas, Kathryn Gray, Brendan Keating, Chunhua Weng, Gail P. Jarvik, Hakon Hakonarson, Leah Kottyan, Noemie Elhadad, Wei‐Qi Wei, Yuan Luo, Dokyoon Kim, Marylyn Ritchie, Shefali Setia Verma, Goncalo Abecasis, Aris Baras, Michael Cantor, Giovanni Coppola, Andrew Deubler, Aris Economides, Katia Karalis, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Katherine Siminovitch, Alan Shuldiner, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander Lopez, Maria Sotiropoulos Padilla, Manasi Pradhan, Kia Manoochehri, Thomas D. Schleicher, Louis Widom, Sarah E. Wolf, Ricardo H. Ulloa, Amelia Averitt, Nilanjana Banerjee, Dadong Li, Sameer Malhotra, Deepika Sharma, Jeffrey Staples, Xiaodong Bai, Suganthi Balasubramanian, Suying Bao, Boris Boutkov, Siying Chen, Gisu Eom, Lukas Habegger, Alicia Hawes, Shareef Khalid, Olga Krasheninina, Rouel Lanche, Adam J. Mansfield, Evan K. Maxwell, George Mitra, Mona Nafde, Sean O’Keeffe, Max Orelus, Razvan Panea, Tommy Polanco, Ayesha Rasool, William Salerno, Jeffrey C. Staples, Kathie Sun, Joshua Backman, Amy Damask, Lee Dobbyn, Manuel Allen Revez Ferreira, Arkopravo Ghosh, Christopher Gillies, Lauren Gurski, Eric Jorgenson, Hyun Min Kang, Michael Kessler, Jack Kosmicki, Alexander Li, Nan Lin, Daren Liu, Adam Locke, Jonathan Marchini, Anthony Marcketta, Joelle Mbatchou, Arden Moscati, Charles Paulding, Carlo Sidore, Eli Stahl, Kyoko Watanabe, Bin Ye, Blair Zhang, Andrey Ziyatdinov, Ariane Ayer, Aysegul Guvenek, George Hindy, Jan Freudenberg, Jonas Bovijn, Kavita Praveen, Manav Kapoor, Mary Haas, Moeen Riaz, Niek Verweij, Olukayode Sosina, Parsa Akbari, Priyanka Nakka, Sahar Gelfman, Sujit Gokhale, Tanima De, Veera Rajagopal, Gannie Tzoneva, Juan Rodriguez‐Flores, Shek Man Chim, Valerio Donato, Daniel Fernandez, Giusy Della Gatta, Alessandro Di Gioia, Kristen Howell, Lori Khrimian, Minhee Kim, Hector Martinez, Lawrence Miloscio, Sheilyn Nunez, Elias Pavlopoulos, Trikaldarshi Persaud, Esteban Chen, Marcus B. Jones, Michelle G. LeBlanc, Jason Mighty, Lyndon J. Mitnaul, Nirupama Nishtala, and Nadia Rana

Subjects
Cardiology and Cardiovascular Medicine
Abstract

Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

Published
2023
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3. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Author

Cindy G. Boer, Konstantinos Hatzikotoulas, Lorraine Southam, Lilja Stefánsdóttir, Yanfei Zhang, Rodrigo Coutinho de Almeida, Tian T. Wu, Jie Zheng, April Hartley, Maris Teder-Laving, Anne Heidi Skogholt, Chikashi Terao, Eleni Zengini, George Alexiadis, Andrei Barysenka, Gyda Bjornsdottir, Maiken E. Gabrielsen, Arthur Gilly, Thorvaldur Ingvarsson, Marianne B. Johnsen, Helgi Jonsson, Margreet Kloppenburg, Almut Luetge, Sigrun H. Lund, Reedik Mägi, Massimo Mangino, Rob R.G.H.H. Nelissen, Manu Shivakumar, Julia Steinberg, Hiroshi Takuwa, Laurent F. Thomas, Margo Tuerlings, George C. Babis, Jason Pui Yin Cheung, Jae Hee Kang, Peter Kraft, Steven A. Lietman, Dino Samartzis, P. Eline Slagboom, Kari Stefansson, Unnur Thorsteinsdottir, Jonathan H. Tobias, André G. Uitterlinden, Bendik Winsvold, John-Anker Zwart, George Davey Smith, Pak Chung Sham, Gudmar Thorleifsson, Tom R. Gaunt, Andrew P. Morris, Ana M. Valdes, Aspasia Tsezou, Kathryn S.E. Cheah, Shiro Ikegawa, Kristian Hveem, Tõnu Esko, J. Mark Wilkinson, Ingrid Meulenbelt, Ming Ta Michael Lee, Joyce B.J. van Meurs, Unnur Styrkársdóttir, Eleftheria Zeggini, John Loughlin, Nigel Arden, Fraser Birrell, Andrew Carr, Panos Deloukas, Michael Doherty, Andrew W. McCaskie, William E.R. Ollier, Ashok Rai, Stuart H. Ralston, Tim D. Spector, Gillian A. Wallis, Amy E. Martinsen, Cristen Willer, Egil Andreas Fors, Ingunn Mundal, Knut Hagen, Kristian Bernhard Nilsen, Marie Udnesseter Lie, Sigrid Børte, Ben Brumpton, Jonas Bille Nielsen, Lars G. Fritsche, Wei Zhou, Ingrid Heuch, Kjersti Storheim, Evangelos Tyrpenou, Athanasios Koukakis, Dimitrios Chytas, Dimitrios Stergios Evangelopoulos, Chronopoulos Efstathios, Spiros Pneumaticos, Vasileios S. Nikolaou, Konstantinos Malizos, Lydia Anastasopoulou, Goncalo Abecasis, Aris Baras, Michael Cantor, Giovanni Coppola, Andrew Deubler, Aris Economides, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Alan Shuldiner, Katia Karalis, Katherine Siminovitch, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander Lopez, Thomas D. Schleicher, Maria Sotiropoulos Padilla, Louis Widom, Sarah E. Wolf, Manasi Pradhan, Kia Manoochehri, Xiaodong Bai, Suganthi Balasubramanian, Boris Boutkov, Gisu Eom, Lukas Habegger, Alicia Hawes, Olga Krasheninina, Rouel Lanche, Adam J. Mansfield, Evan K. Maxwell, Mona Nafde, Sean O’Keeffe, Max Orelus, Razvan Panea, Tommy Polanco, Ayesha Rasool, William Salerno, Jeffrey C. Staples, Dadong Li, Deepika Sharma, Ilanjana Banerjee, Jonas Bovijn, Adam Locke, Niek Verweij, Mary Haas, George Hindy, Tanima De, Parsa Akbari, Olukayode Sosina, Manuel A.R. Ferreira, Marcus B. Jones, Jason Mighty, Michelle G. LeBlanc, Lyndon J. Mitnaul, and Internal Medicine

Subjects
Resource, genome-wide association meta-analysis, Disease, Osteoarthritis, effector genes, Biology, Bioinformatics, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Drug Targets, Effector Genes, Functional Genomics, Genetic Architecture, Genome-wide Association Meta-analysis, Spine osteoarthritis, Risk Factors, drug targets, medicine, Humans, Genetic Predisposition to Disease, Sex Characteristics, Cartilage, Correction, medicine.disease, Phenotype, genetic architecture, Genetic architecture, ddc, osteoarthritis, Genetics, Population, medicine.anatomical_structure, Subchondral bone, Female, Functional genomics, functional genomics, Genome-Wide Association Study, Signal Transduction
Abstract

Summary Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation., Graphical abstract, Highlights • A multicohort study identifies 52 previously unknown osteoarthritis genetic risk variants • Similarities and differences in osteoarthritis genetic risk depend on joint sites • Osteoarthritis genetic components are associated with pain-related phenotypes • High-confidence effector genes highlight potential targets for drug intervention, A multicohort genome-wide association meta-analysis of osteoarthritis highlights the impact of joint site types on the features of genetic risk variants and the link between osteoarthritis genetics and pain-related phenotypes, pointing toward potential targets for therapeutic intervention.

Published
2021
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