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1. Temporal dynamics of the multi-omic response to endurance exercise training

Author

Bae, Dam, Dasari, Surendra, Dennis, Courtney, Evans, Charles R, Gaul, David A, Ilkayeva, Olga, Ivanova, Anna A, Kachman, Maureen T, Keshishian, Hasmik, Lanza, Ian R, Lira, Ana C, Muehlbauer, Michael J, Nair, Venugopalan D, Piehowski, Paul D, Rooney, Jessica L, Smith, Kevin S, Stowe, Cynthia L, Zhao, Bingqing, Clark, Natalie M, Jimenez-Morales, David, Lindholm, Malene E, Many, Gina M, Sanford, James A, Smith, Gregory R, Vetr, Nikolai G, Zhang, Tiantian, Almagro Armenteros, Jose J, Avila-Pacheco, Julian, Bararpour, Nasim, Ge, Yongchao, Hou, Zhenxin, Marwaha, Shruti, Presby, David M, Natarajan Raja, Archana, Savage, Evan M, Steep, Alec, Sun, Yifei, Wu, Si, Zhen, Jimmy, Bodine, Sue C, Esser, Karyn A, Goodyear, Laurie J, Schenk, Simon, Montgomery, Stephen B, Fernández, Facundo M, Sealfon, Stuart C, Snyder, Michael P, Adkins, Joshua N, Ashley, Euan, Burant, Charles F, Carr, Steven A, Clish, Clary B, Cutter, Gary, Gerszten, Robert E, Kraus, William E, Li, Jun Z, Miller, Michael E, Nair, K Sreekumaran, Newgard, Christopher, Ortlund, Eric A, Qian, Wei-Jun, Tracy, Russell, Walsh, Martin J, Wheeler, Matthew T, Dalton, Karen P, Hastie, Trevor, Hershman, Steven G, Samdarshi, Mihir, Teng, Christopher, Tibshirani, Rob, Cornell, Elaine, Gagne, Nicole, May, Sandy, Bouverat, Brian, Leeuwenburgh, Christiaan, Lu, Ching-ju, Pahor, Marco, Hsu, Fang-Chi, Rushing, Scott, Walkup, Michael P, Nicklas, Barbara, Rejeski, W Jack, Williams, John P, Xia, Ashley, Albertson, Brent G, Barton, Elisabeth R, Booth, Frank W, Caputo, Tiziana, Cicha, Michael, De Sousa, Luis Gustavo Oliveira, Farrar, Roger, Hevener, Andrea L, Hirshman, Michael F, Jackson, Bailey E, Ke, Benjamin G, Kramer, Kyle S, Lessard, Sarah J, Makarewicz, Nathan S, Marshall, Andrea G, and Nigro, Pasquale

Subjects
Health Sciences, Sports Science and Exercise, Prevention, Genetics, Human Genome, Physical Activity, Behavioral and Social Science, Cardiovascular, Aetiology, 1.1 Normal biological development and functioning, Underpinning research, 2.1 Biological and endogenous factors, Inflammatory and immune system, Generic health relevance, Good Health and Well Being, Animals, Female, Humans, Male, Rats, Acetylation, Blood, Cardiovascular Diseases, Databases, Factual, Endurance Training, Epigenome, Inflammatory Bowel Diseases, Internet, Lipidomics, Metabolome, Mitochondria, Multiomics, Non-alcoholic Fatty Liver Disease, Organ Specificity, Phosphorylation, Physical Conditioning, Animal, Physical Endurance, Proteome, Proteomics, Time Factors, Transcriptome, Ubiquitination, Wounds and Injuries, MoTrPAC Study Group, Lead Analysts, MoTrPAC Study Group, General Science & Technology
Abstract

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

Published
2024

2. De novo variants in the RNU4-2 snRNA cause a frequent neurodevelopmental syndrome

Author

Chen, Yuyang, Dawes, Ruebena, Kim, Hyung Chul, Ljungdahl, Alicia, Stenton, Sarah L., Walker, Susan, Lord, Jenny, Lemire, Gabrielle, Martin-Geary, Alexandra C., Ganesh, Vijay S., Ma, Jialan, Ellingford, Jamie M., Delage, Erwan, D’Souza, Elston N., Dong, Shan, Adams, David R., Allan, Kirsten, Bakshi, Madhura, Baldwin, Erin E., Berger, Seth I., Bernstein, Jonathan A., Bhatnagar, Ishita, Blair, Ed, Brown, Natasha J., Burrage, Lindsay C., Chapman, Kimberly, Coman, David J., Compton, Alison G., Cunningham, Chloe A., D’Souza, Precilla, Danecek, Petr, Délot, Emmanuèle C., Dias, Kerith-Rae, Elias, Ellen R., Elmslie, Frances, Evans, Care-Anne, Ewans, Lisa, Ezell, Kimberly, Fraser, Jamie L., Gallacher, Lyndon, Genetti, Casie A., Goriely, Anne, Grant, Christina L., Haack, Tobias, Higgs, Jenny E., Hinch, Anjali G., Hurles, Matthew E., Kuechler, Alma, Lachlan, Katherine L., Lalani, Seema R., Lecoquierre, François, Leitão, Elsa, Fevre, Anna Le, Leventer, Richard J., Liebelt, Jan E., Lindsay, Sarah, Lockhart, Paul J., Ma, Alan S., Macnamara, Ellen F., Mansour, Sahar, Maurer, Taylor M., Mendez, Hector R., Metcalfe, Kay, Montgomery, Stephen B., Moosajee, Mariya, Nassogne, Marie-Cécile, Neumann, Serena, O’Donoghue, Michael, O’Leary, Melanie, Palmer, Elizabeth E., Pattani, Nikhil, Phillips, John, Pitsava, Georgia, Pysar, Ryan, Rehm, Heidi L., Reuter, Chloe M., Revencu, Nicole, Riess, Angelika, Rius, Rocio, Rodan, Lance, Roscioli, Tony, Rosenfeld, Jill A., Sachdev, Rani, Shaw-Smith, Charles J., Simons, Cas, Sisodiya, Sanjay M., Snell, Penny, St Clair, Laura, Stark, Zornitza, Stewart, Helen S., Tan, Tiong Yang, Tan, Natalie B., Temple, Suzanna E. L., Thorburn, David R., Tifft, Cynthia J., Uebergang, Eloise, VanNoy, Grace E., Vasudevan, Pradeep, Vilain, Eric, Viskochil, David H., Wedd, Laura, Wheeler, Matthew T., White, Susan M., Wojcik, Monica, Wolfe, Lynne A., Wolfenson, Zoe, Wright, Caroline F., Xiao, Changrui, Zocche, David, Rubenstein, John L., Markenscoff-Papadimitriou, Eirene, Fica, Sebastian M., Baralle, Diana, Depienne, Christel, MacArthur, Daniel G., Howson, Joanna M. M., Sanders, Stephan J., O’Donnell-Luria, Anne, and Whiffin, Nicola

Published
2024
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3. The functional impact of rare variation across the regulatory cascade

Author

Li, Taibo, Ferraro, Nicole, Strober, Benjamin J, Aguet, Francois, Kasela, Silva, Arvanitis, Marios, Ni, Bohan, Wiel, Laurens, Hershberg, Elliot, Ardlie, Kristin, Arking, Dan E, Beer, Rebecca L, Brody, Jennifer, Blackwell, Thomas W, Clish, Clary, Gabriel, Stacey, Gerszten, Robert, Guo, Xiuqing, Gupta, Namrata, Johnson, W Craig, Lappalainen, Tuuli, Lin, Henry J, Liu, Yongmei, Nickerson, Deborah A, Papanicolaou, George, Pritchard, Jonathan K, Qasba, Pankaj, Shojaie, Ali, Smith, Josh, Sotoodehnia, Nona, Taylor, Kent D, Tracy, Russell P, Van Den Berg, David, Wheeler, Matthew T, Rich, Stephen S, Rotter, Jerome I, Battle, Alexis, and Montgomery, Stephen B

Subjects
Biological Sciences, Health Sciences, Genetics, Human Genome, Brain Disorders, Schizophrenia, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, functional genomics, machine learning, methylome, multi-omics, proteome, rare variants, transcriptome
Abstract

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Published
2023

4. Genetic architecture of cardiac dynamic flow volumes

Author

Gomes, Bruna, Singh, Aditya, O’Sullivan, Jack W., Schnurr, Theresia M., Goddard, Pagé C., Loong, Shaun, Amar, David, Hughes, J. Weston, Kostur, Mykhailo, Haddad, Francois, Salerno, Michael, Foo, Roger, Montgomery, Stephen B., Parikh, Victoria N., Meder, Benjamin, and Ashley, Euan A.

Published
2024
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5. Organ aging signatures in the plasma proteome track health and disease

Author

Oh, Hamilton Se-Hwee, Rutledge, Jarod, Nachun, Daniel, Pálovics, Róbert, Abiose, Olamide, Moran-Losada, Patricia, Channappa, Divya, Urey, Deniz Yagmur, Kim, Kate, Sung, Yun Ju, Wang, Lihua, Timsina, Jigyasha, Western, Dan, Liu, Menghan, Kohlfeld, Pat, Budde, John, Wilson, Edward N., Guen, Yann, Maurer, Taylor M., Haney, Michael, Yang, Andrew C., He, Zihuai, Greicius, Michael D., Andreasson, Katrin I., Sathyan, Sanish, Weiss, Erica F., Milman, Sofiya, Barzilai, Nir, Cruchaga, Carlos, Wagner, Anthony D., Mormino, Elizabeth, Lehallier, Benoit, Henderson, Victor W., Longo, Frank M., Montgomery, Stephen B., and Wyss-Coray, Tony

Published
2023
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6. Beyond the exome: what's next in diagnostic testing for Mendelian conditions

Author

Wojcik, Monica H., Reuter, Chloe M., Marwaha, Shruti, Mahmoud, Medhat, Duyzend, Michael H., Barseghyan, Hayk, Yuan, Bo, Boone, Philip M., Groopman, Emily E., Délot, Emmanuèle C., Jain, Deepti, Sanchis-Juan, Alba, Diseases, Genomics Research to Elucidate the Genetics of Rare, Consortium, Starita, Lea M., Talkowski, Michael, Montgomery, Stephen B., Bamshad, Michael J., Chong, Jessica X., Wheeler, Matthew T., Berger, Seth I., O'Donnell-Luria, Anne, Sedlazeck, Fritz J., and Miller, Danny E.

Subjects
Quantitative Biology - Genomics
Abstract

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.

Published
2023
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7. Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases

Author

Guo, Margaret G., Reynolds, David L., Ang, Cheen E., Liu, Yingfei, Zhao, Yang, Donohue, Laura K. H., Siprashvili, Zurab, Yang, Xue, Yoo, Yongjin, Mondal, Smarajit, Hong, Audrey, Kain, Jessica, Meservey, Lindsey, Fabo, Tania, Elfaki, Ibtihal, Kellman, Laura N., Abell, Nathan S., Pershad, Yash, Bayat, Vafa, Etminani, Payam, Holodniy, Mark, Geschwind, Daniel H., Montgomery, Stephen B., Duncan, Laramie E., Urban, Alexander E., Altman, Russ B., Wernig, Marius, and Khavari, Paul A.

Published
2023
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10. The mitochondrial multi-omic response to exercise training across rat tissues

Author

Adkins, Joshua N., Armenteros, Jose Juan Almagro, Amper, Mary Anne S., Bae, Dam, Bamman, Marcas, Bararpour, Nasim, Barnes, Jerry, Bergman, Bryan C., Bessesen, Daniel H., Broskey, Nicholas T., Buford, Thomas W., Carr, Steven, Chambers, Toby L., Chavez, Clarisa, Chiu, Roxanne, Clark, Natalie, Cutter, Gary, Evans, Charles R., Franczak, Edziu, Gagne, Nicole, Ge, Yongchao, Hennig, Krista M., Houmard, Joseph A., Huffman, Kim M., Hung, Chia-Jui, Hutchinson-Bunch, Chelsea, Ilkayeva, Olga, Jackson, Bailey E., Jankowski, Catherine M., Jin, Christopher A., Johannsen, Neil M., Katz, Daniel H., Keshishian, Hasmik, Kohrt, Wendy M., Kramer, Kyle S., Kraus, William E., Lester, Bridget, Li, Jun Z., Lira, Ana K., Lowe, Adam, Mani, D.R., Many, Gina M., May, Sandy, Melanson, Edward L., Moore, Samuel G., Moreau, Kerrie L., Musi, Nicolas, Nachun, Daniel, Nair, Venugopalan D., Newgard, Christopher, Nudelman, German, Piehowski, Paul D., Pincas, Hanna, Qian, Wei-Jun, Rankinen, Tuomo, Rasmussen, Blake B., Ravussin, Eric, Rooney, Jessica L., Rushing, Scott, Samdarshi, Mihir, Sanford, James A., Schauer, Irene E., Sealfon, Stuart C., Smith, Kevin S., Smith, Gregory R., Snyder, Michael, Stowe, Cynthia L., Talton, Jennifer W., Teng, Christopher, Thalacker-Mercer, Anna, Tracy, Russell, Trappe, Scott, Trappe, Todd A., Vasoya, Mital, Vetr, Nikolai G., Volpi, Elena, Walkup, Michael P., Wiel, Laurens, Wu, Si, Yan, Zhen, Yu, Jiye, Zaslavsky, Elena, Zebarjadi, Navid, Zhen, Jimmy, Amar, David, Gay, Nicole R., Jimenez-Morales, David, Jean Beltran, Pierre M., Ramaker, Megan E., Raja, Archana Natarajan, Zhao, Bingqing, Sun, Yifei, Marwaha, Shruti, Gaul, David A., Hershman, Steven G., Ferrasse, Alexis, Xia, Ashley, Lanza, Ian, Fernández, Facundo M., Montgomery, Stephen B., Hevener, Andrea L., Ashley, Euan A., Walsh, Martin J., Sparks, Lauren M., Burant, Charles F., Rector, R. Scott, Thyfault, John, Wheeler, Matthew T., Goodpaster, Bret H., Coen, Paul M., Schenk, Simon, Bodine, Sue C., and Lindholm, Malene E.

Published
2024
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11. Africa-specific human genetic variation near CHD1L associates with HIV-1 load

Author

McLaren, Paul J., Porreca, Immacolata, Iaconis, Gennaro, Mok, Hoi Ping, Mukhopadhyay, Subhankar, Karakoc, Emre, Cristinelli, Sara, Pomilla, Cristina, Bartha, István, Thorball, Christian W., Tough, Riley H., Angelino, Paolo, Kiar, Cher S., Carstensen, Tommy, Fatumo, Segun, Porter, Tarryn, Jarvis, Isobel, Skarnes, William C., Bassett, Andrew, DeGorter, Marianne K., Sathya Moorthy, Mohana Prasad, Tuff, Jeffrey F., Kim, Eun-Young, Walter, Miriam, Simons, Lacy M., Bashirova, Arman, Buchbinder, Susan, Carrington, Mary, Cossarizza, Andrea, De Luca, Andrea, Goedert, James J., Goldstein, David B., Haas, David W., Herbeck, Joshua T., Johnson, Eric O., Kaleebu, Pontiano, Kilembe, William, Kirk, Gregory D., Kootstra, Neeltje A., Kral, Alex H., Lambotte, Olivier, Luo, Ma, Mallal, Simon, Martinez-Picado, Javier, Meyer, Laurence, Miro, José M., Moodley, Pravi, Motala, Ayesha A., Mullins, James I., Nam, Kireem, Obel, Niels, Pirie, Fraser, Plummer, Francis A., Poli, Guido, Price, Matthew A., Rauch, Andri, Theodorou, Ioannis, Trkola, Alexandra, Walker, Bruce D., Winkler, Cheryl A., Zagury, Jean-François, Montgomery, Stephen B., Ciuffi, Angela, Hultquist, Judd F., Wolinsky, Steven M., Dougan, Gordon, Lever, Andrew M. L., Gurdasani, Deepti, Groom, Harriet, Sandhu, Manjinder S., and Fellay, Jacques

Published
2023
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12. Loss of function of FAM177A1, a Golgi complex localized protein, causes a novel neurodevelopmental disorder

Author

Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Sessions Cole, F., Colley, Heather A., Cope, Heidi, Corner, Brian, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell’Angelica, Esteban C., Dickson, Patricia, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Ezell, Kimberly, Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Lawrence Merritt, J., Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Neumann, Serena, Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Carl Pallais, J., Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Ron Scott, C., Seto, Elaine, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Legro, Nicole R., Bowman, Angela, Ugur, Berrak, Jackstadt, Madelyn M., Shriver, Leah P., Patti, Gary J., Zhang, Bo, Feng, Wenjia, McAdow, Anthony R., Goddard, Pagé, Jensen, Tanner, Fresard, Laure, Alvarez, Raquel, McCormack, Colleen, Holt, James M., Worthey, Elizabeth A., Montgomery, Stephen B., Postlethwait, John, De Camilli, Pietro, and Solnica-Krezel, Lila

Published
2024
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13. Methylation differences in Alzheimer’s disease neuropathologic change in the aged human brain

Author

Lang, Anna-Lena, Eulalio, Tiffany, Fox, Eddie, Yakabi, Koya, Bukhari, Syed A, Kawas, Claudia H, Corrada, Maria M, Montgomery, Stephen B, Heppner, Frank L, Capper, David, Nachun, Daniel, and Montine, Thomas J

Subjects
Biological Sciences, Genetics, Neurodegenerative, Neurosciences, Aging, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Dementia, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aetiology, Neurological, Aged, 80 and over, Humans, Middle Aged, Aged, Alzheimer Disease, Amyloid beta-Peptides, Neuropathology, Brain, Plaque, Amyloid, DNA Methylation, Methylation, Deconvolution, plus, Old age, Alzheimer's disease, AD, PEN-2, Gamma secretase, Alzheimer’s disease, Biochemistry and Cell Biology, Clinical Sciences, Biochemistry and cell biology
Abstract

Alzheimer's disease (AD) is the most common cause of dementia with advancing age as its strongest risk factor. AD neuropathologic change (ADNC) is known to be associated with numerous DNA methylation changes in the human brain, but the oldest old (> 90 years) have so far been underrepresented in epigenetic studies of ADNC. Our study participants were individuals aged over 90 years (n = 47) from The 90+ Study. We analyzed DNA methylation from bulk samples in eight precisely dissected regions of the human brain: middle frontal gyrus, cingulate gyrus, entorhinal cortex, dentate gyrus, CA1, substantia nigra, locus coeruleus and cerebellar cortex. We deconvolved our bulk data into cell-type-specific (CTS) signals using computational methods. CTS methylation differences were analyzed across different levels of ADNC. The highest amount of ADNC related methylation differences was found in the dentate gyrus, a region that has so far been underrepresented in large scale multi-omic studies. In neurons of the dentate gyrus, DNA methylation significantly differed with increased burden of amyloid beta (Aβ) plaques at 5897 promoter regions of protein-coding genes. Amongst these, higher Aβ plaque burden was associated with promoter hypomethylation of the Presenilin enhancer 2 (PEN-2) gene, one of the rate limiting genes in the formation of gamma-secretase, a multicomponent complex that is responsible in part for the endoproteolytic cleavage of amyloid precursor protein into Aβ peptides. In addition to novel ADNC related DNA methylation changes, we present the most detailed array-based methylation survey of the old aged human brain to date. Our open-sourced dataset can serve as a brain region reference panel for future studies and help advance research in aging and neurodegenerative diseases.

Published
2022

14. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.

Author

Bonder, Marc Jan, Smail, Craig, Gloudemans, Michael J, Frésard, Laure, Jakubosky, David, D'Antonio, Matteo, Li, Xin, Ferraro, Nicole M, Carcamo-Orive, Ivan, Mirauta, Bogdan, Seaton, Daniel D, Cai, Na, Vakili, Dara, Horta, Danilo, Zhao, Chunli, Zastrow, Diane B, Bonner, Devon E, HipSci Consortium, iPSCORE consortium, Undiagnosed Diseases Network, PhLiPS consortium, Wheeler, Matthew T, Kilpinen, Helena, Knowles, Joshua W, Smith, Erin N, Frazer, Kelly A, Montgomery, Stephen B, and Stegle, Oliver

Subjects
HipSci Consortium, iPSCORE consortium, Undiagnosed Diseases Network, PhLiPS consortium, Cell Line, Humans, Bardet-Biedl Syndrome, Cerebellar Ataxia, Rare Diseases, Proteins, Calcium Channels, Sequence Analysis, RNA, DNA Methylation, Gene Expression, Regulatory Sequences, Nucleic Acid, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genetic Variation, Induced Pluripotent Stem Cells, Whole Genome Sequencing, Stem Cell Research - Induced Pluripotent Stem Cell, Stem Cell Research - Induced Pluripotent Stem Cell - Human, Human Genome, Stem Cell Research - Embryonic - Human, Regenerative Medicine, Genetics, Stem Cell Research - Induced Pluripotent Stem Cell - Non-Human, Stem Cell Research, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Generic health relevance, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

Published
2021

16. Beyond the exome: What’s next in diagnostic testing for Mendelian conditions

Author

Abouhala, Siwaar, Albert, Jessica, Almalvez, Miguel, Alvarez, Raquel, Amin, Mutaz, Anderson, Peter, Aradhya, Swaroop, Ashley, Euan, Assimes, Themistocles, Auriga, Light, Austin-Tse, Christina, Bamshad, Mike, Barseghyan, Hayk, Baxter, Samantha, Behera, Sairam, Beheshti, Shaghayegh, Bejerano, Gill, Berger, Seth, Bernstein, Jon, Best, Sabrina, Blankenmeister, Benjamin, Blue, Elizabeth, Boerwinkle, Eric, Bonkowski, Emily, Bonner, Devon, Boone, Philip, Bornhorst, Miriam, Bozkurt-Yozgatli, Tugce, Brand, Harrison, Buckingham, Kati, Calame, Daniel, Casadei, Silvia, Chadwick, Lisa, Chavez, Clarisa, Chen, Ziwei, Chinn, Ivan, Chong, Jessica, Coban-Akdemir, Zeynep, Cohen, Andrea J., Conner, Sarah, Conomos, Matthew, Coveler, Karen, Cui, Ya Allen, Currin, Sara, Daber, Robert, Dardas, Zain, Davis, Colleen, Dawood, Moez, de Dios, Ivan, de Esch, Celine, Delaney, Meghan, Délot, Emmanuèle, DiTroia, Stephanie, Doddapaneni, Harsha, Du, Haowei, Duan, Ruizhi, Dugan-Perez, Shannon, Duong, Nhat, Duyzend, Michael, Eichler, Evan, Emami, Sara, Fatih, Jawid, Fraser, Jamie, Fusaro, Vincent, Galey, Miranda, Ganesh, Vijay, Garimella, Kiran, Gibbs, Richard, Gifford, Casey, Ginsburg, Amy, Goddard, Pagé, Gogarten, Stephanie, Gogate, Nikhita, Gordon, William, Gorzynski, John E., Greenleaf, William, Grochowski, Christopher, Groopman, Emily, Guarischi Sousa, Rodrigo, Gudmundsson, Sanna, Gulati, Ashima, Guo, Daniel, Hale, Walker, Hall, Stacey, Harvey, William, Hawley, Megan, Heavner, Ben, Herman, Isabella, Horike-Pyne, Martha, Hu, Jianhong, Huang, Yongqing, Hwang, James, Jarvik, Gail, Jensen, Tanner, Jhangiani, Shalini, Jimenez-Morales, David, Jin, Christopher, Saad, Ahmed K., Kahn-Kirby, Amanda, Kain, Jessica, Kaur, Parneet, Keehan, Laura, Knoblach, Susan, Ko, Arthur, Kohler, Jennefer, Kundaje, Anshul, Kundu, Soumya, Lancaster, Samuel M., Larsson, Katie, Lemire, Gabrielle, Lewis, Richard, Li, Wei, Li, Yidan, Liu, Pengfei, LoTempio, Jonathan, Lupski, James, Ma, Jialan, MacArthur, Daniel, Mahmoud, Medhat, Malani, Nirav, Mangilog, Brian, Marafi, Dana, Marmolejos, Sofia, Marten, Daniel, Martinez, Eva, Marvin, Colby, Marwaha, Shruti, Kumara Mastrorosa, Francesco, Matalon, Dena, May, Susanne, McGee, Sean, Meador, Lauren, Mefford, Heather, Rodrigo Mendez, Hector, Miller, Alexander, Miller, Danny E., Mitani, Tadahiro, Montgomery, Stephen, Moussa, Hala Mohamed, Moyses, Mariana, Munderloh, Chloe, Muzny, Donna, Nelson, Sarah, Neu, Matthew B., Nguyen, Jonathan, Nguyen, Thuy-mi P., Nussbaum, Robert, Nykamp, Keith, O'Callaghan, William, O'Heir, Emily, O'Leary, Melanie, Olsen, Jeren, Osei-Owusu, Ikeoluwa, O'Donnell-Luria, Anne, Padhi, Evin, Pais, Lynn, Pan, Miao, Panchal, Piyush, Patterson, Karynne, Payne, Sheryl, Pehlivan, Davut, Petrowski, Paul, Pham, Alicia, Pitsava, Georgia, Podesta, Astaria, Ponce, Sarah, Posey, Jennifer, Prosser, Jaime, Quertermous, Thomas, Rai, Archana, Ramani, Arun, Rehm, Heidi, Reuter, Chloe, Reuter, Jason, Richardson, Matthew, Rivera-Munoz, Andres, Rubio, Oriane, Sabo, Aniko, Salani, Monica, Samocha, Kaitlin, Sanchis-Juan, Alba, Savage, Sarah, Scott, Stuart, Scott, Evette, Sedlazeck, Fritz, Shah, Gulalai, Shojaie, Ali, Singh, Mugdha, Smith, Josh, Smith, Kevin, Snow, Hana, Snyder, Michael, Socarras, Kayla, Starita, Lea, Stark, Brigitte, Stenton, Sarah, Stergachis, Andrew, Stilp, Adrienne, Sundaram, Laksshman, Sutton, V. Reid, Tai, Jui-Cheng, Talkowski, Michael, Tise, Christina, Tong, Catherine, Tsao, Philip, Ungar, Rachel, VanNoy, Grace, Vilain, Eric, Voutos, Isabella, Walker, Kim, Weisburd, Ben, Weiss, Jeff, Wellington, Chris, Weng, Ziming, Westheimer, Emily, Wheeler, Marsha, Wheeler, Matthew, Wiel, Laurens, Wilson, Michael, Wojcik, Monica, Wong, Quenna, Wong, Issac, Xiao, Changrui, Yadav, Rachita, Yi, Qian, Yuan, Bo, Zhao, Jianhua, Zhen, Jimmy, Zhou, Harry, Wojcik, Monica H., Reuter, Chloe M., Duyzend, Michael H., Boone, Philip M., Groopman, Emily E., Délot, Emmanuèle C., Jain, Deepti, Starita, Lea M., Montgomery, Stephen B., Bamshad, Michael J., Chong, Jessica X., Wheeler, Matthew T., Berger, Seth I., and Sedlazeck, Fritz J.

Published
2023
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17. Author Correction: Africa-specific human genetic variation near CHD1L associates with HIV-1 load

Author

McLaren, Paul J., Porreca, Immacolata, Iaconis, Gennaro, Mok, Hoi Ping, Mukhopadhyay, Subhankar, Karakoc, Emre, Cristinelli, Sara, Pomilla, Cristina, Bartha, István, Thorball, Christian W., Tough, Riley H., Angelino, Paolo, Kiar, Cher S., Carstensen, Tommy, Fatumo, Segun, Porter, Tarryn, Jarvis, Isobel, Skarnes, William C., Bassett, Andrew, DeGorter, Marianne K., Sathya Moorthy, Mohana Prasad, Tuff, Jeffrey F., Kim, Eun-Young, Walter, Miriam, Simons, Lacy M., Bashirova, Arman, Buchbinder, Susan, Carrington, Mary, Cossarizza, Andrea, De Luca, Andrea, Goedert, James J., Goldstein, David B., Haas, David W., Herbeck, Joshua T., Johnson, Eric O., Kaleebu, Pontiano, Kilembe, William, Kirk, Gregory D., Kootstra, Neeltje A., Kral, Alex H., Lambotte, Olivier, Luo, Ma, Mallal, Simon, Martinez-Picado, Javier, Meyer, Laurence, Miro, José M., Moodley, Pravi, Motala, Ayesha A., Mullins, James I., Nam, Kireem, Obel, Niels, Pirie, Fraser, Plummer, Francis A., Poli, Guido, Price, Matthew A., Rauch, Andri, Theodorou, Ioannis, Trkola, Alexandra, Walker, Bruce D., Winkler, Cheryl A., Zagury, Jean-François, Montgomery, Stephen B., Ciuffi, Angela, Hultquist, Judd F., Wolinsky, Steven M., Dougan, Gordon, Lever, Andrew M. L., Gurdasani, Deepti, Groom, Harriet, Sandhu, Manjinder S., and Fellay, Jacques

Published
2023
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18. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases

Author

Corces, M Ryan, Shcherbina, Anna, Kundu, Soumya, Gloudemans, Michael J, Frésard, Laure, Granja, Jeffrey M, Louie, Bryan H, Eulalio, Tiffany, Shams, Shadi, Bagdatli, S Tansu, Mumbach, Maxwell R, Liu, Boxiang, Montine, Kathleen S, Greenleaf, William J, Kundaje, Anshul, Montgomery, Stephen B, Chang, Howard Y, and Montine, Thomas J

Subjects
Human Genome, Prevention, Dementia, Aging, Brain Disorders, Alzheimer's Disease, Clinical Research, Genetics, Neurodegenerative, Neurosciences, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aetiology, 2.1 Biological and endogenous factors, Neurological, Adult, Alzheimer Disease, Atlases as Topic, Biological Variation, Population, Brain, Chromatin Assembly and Disassembly, Cohort Studies, Enhancer Elements, Genetic, Epigenomics, Genetic Heterogeneity, Genetic Predisposition to Disease, Genome-Wide Association Study, Haplotypes, Humans, Machine Learning, Neurons, Parkinson Disease, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, tau Proteins, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies of neurological diseases have identified thousands of variants associated with disease phenotypes. However, most of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional SNPs for Alzheimer's and Parkinson's diseases, nominating target genes and cell types for previously orphaned loci from genome-wide association studies. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) Parkinson's disease risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

Published
2020

19. Properties of structural variants and short tandem repeats associated with gene expression and complex traits.

Author

Jakubosky, David, D'Antonio, Matteo, Bonder, Marc Jan, Smail, Craig, Donovan, Margaret KR, Young Greenwald, William W, Matsui, Hiroko, i2QTL Consortium, D'Antonio-Chronowska, Agnieszka, Stegle, Oliver, Smith, Erin N, Montgomery, Stephen B, DeBoever, Christopher, and Frazer, Kelly A

Subjects
i2QTL Consortium, Cell Line, Humans, Microsatellite Repeats, Multifactorial Inheritance, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genetic Variation, Genome-Wide Association Study, Genetics, Human Genome, Generic health relevance
Abstract

Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we identify genomic features of SV classes and STRs that are associated with gene expression and complex traits, including their locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We identify a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and show that they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that are associated with gene expression and human traits.

Published
2020

20. Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats.

Author

Jakubosky, David, Smith, Erin N, D'Antonio, Matteo, Jan Bonder, Marc, Young Greenwald, William W, D'Antonio-Chronowska, Agnieszka, Matsui, Hiroko, i2QTL Consortium, Stegle, Oliver, Montgomery, Stephen B, DeBoever, Christopher, and Frazer, Kelly A

Subjects
i2QTL Consortium, Humans, Computational Biology, Microsatellite Repeats, Genotype, Haplotypes, Algorithms, High-Throughput Nucleotide Sequencing, Whole Genome Sequencing, Human Genome, Genetics
Abstract

Structural variants (SVs) and short tandem repeats (STRs) are important sources of genetic diversity but are not routinely analyzed in genetic studies because they are difficult to accurately identify and genotype. Because SVs and STRs range in size and type, it is necessary to apply multiple algorithms that incorporate different types of evidence from sequencing data and employ complex filtering strategies to discover a comprehensive set of high-quality and reproducible variants. Here we assemble a set of 719 deep whole genome sequencing (WGS) samples (mean 42×) from 477 distinct individuals which we use to discover and genotype a wide spectrum of SV and STR variants using five algorithms. We use 177 unique pairs of genetic replicates to identify factors that affect variant call reproducibility and develop a systematic filtering strategy to create of one of the most complete and well characterized maps of SVs and STRs to date.

Published
2020

21. FAM13A affects body fat distribution and adipocyte function.

Author

Fathzadeh, Mohsen, Li, Jiehan, Rao, Abhiram, Cook, Naomi, Chennamsetty, Indumathi, Seldin, Marcus, Zhou, Xiang, Sangwung, Panjamaporn, Gloudemans, Michael J, Keller, Mark, Attie, Allan, Yang, Jing, Wabitsch, Martin, Carcamo-Orive, Ivan, Tada, Yuko, Lusis, Aldons J, Shin, Myung Kyun, Molony, Cliona M, McLaughlin, Tracey, Reaven, Gerald, Montgomery, Stephen B, Reilly, Dermot, Quertermous, Thomas, Ingelsson, Erik, and Knowles, Joshua W

Abstract

Genetic variation in the FAM13A (Family with Sequence Similarity 13 Member A) locus has been associated with several glycemic and metabolic traits in genome-wide association studies (GWAS). Here, we demonstrate that in humans, FAM13A alleles are associated with increased FAM13A expression in subcutaneous adipose tissue (SAT) and an insulin resistance-related phenotype (e.g. higher waist-to-hip ratio and fasting insulin levels, but lower body fat). In human adipocyte models, knockdown of FAM13A in preadipocytes accelerates adipocyte differentiation. In mice, Fam13a knockout (KO) have a lower visceral to subcutaneous fat (VAT/SAT) ratio after high-fat diet challenge, in comparison to their wild-type counterparts. Subcutaneous adipocytes in KO mice show a size distribution shift toward an increased number of smaller adipocytes, along with an improved adipogenic potential. Our results indicate that GWAS-associated variants within the FAM13A locus alter adipose FAM13A expression, which in turn, regulates adipocyte differentiation and contribute to changes in body fat distribution.

Published
2020

24. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

Author

Frésard, Laure, Smail, Craig, Ferraro, Nicole M, Teran, Nicole A, Li, Xin, Smith, Kevin S, Bonner, Devon, Kernohan, Kristin D, Marwaha, Shruti, Zappala, Zachary, Balliu, Brunilda, Davis, Joe R, Liu, Boxiang, Prybol, Cameron J, Kohler, Jennefer N, Zastrow, Diane B, Reuter, Chloe M, Fisk, Dianna G, Grove, Megan E, Davidson, Jean M, Hartley, Taila, Joshi, Ruchi, Strober, Benjamin J, Utiramerur, Sowmithri, Undiagnosed Diseases Network, Care4Rare Canada Consortium, Lind, Lars, Ingelsson, Erik, Battle, Alexis, Bejerano, Gill, Bernstein, Jonathan A, Ashley, Euan A, Boycott, Kym M, Merker, Jason D, Wheeler, Matthew T, and Montgomery, Stephen B

Subjects
Undiagnosed Diseases Network, Care4Rare Canada Consortium, Humans, Rare Diseases, Oxidoreductases Acting on CH-CH Group Donors, Potassium Channels, RNA, Case-Control Studies, Cohort Studies, Sequence Analysis, RNA, RNA Splicing, Mutation, Models, Genetic, Child, Child, Preschool, Female, Male, Genetic Variation, Acid Ceramidase, Whole Exome Sequencing, Human Genome, Clinical Research, Genetics, Biotechnology, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Immunology, Medical and Health Sciences
Abstract

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

Published
2019

25. Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure

Author

Cordero, Pablo, Parikh, Victoria N, Chin, Elizabeth T, Erbilgin, Ayca, Gloudemans, Michael J, Shang, Ching, Huang, Yong, Chang, Alex C, Smith, Kevin S, Dewey, Frederick, Zaleta, Kathia, Morley, Michael, Brandimarto, Jeff, Glazer, Nicole, Waggott, Daryl, Pavlovic, Aleksandra, Zhao, Mingming, Moravec, Christine S, Tang, WH Wilson, Skreen, Jamie, Malloy, Christine, Hannenhalli, Sridhar, Li, Hongzhe, Ritter, Scott, Li, Mingyao, Bernstein, Daniel, Connolly, Andrew, Hakonarson, Hakon, Lusis, Aldons J, Margulies, Kenneth B, Depaoli-Roach, Anna A, Montgomery, Stephen B, Wheeler, Matthew T, Cappola, Thomas, and Ashley, Euan A

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, Heart Disease, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Animals, Benzeneacetamides, Cells, Cultured, Datasets as Topic, Disease Models, Animal, Female, Gene Expression Profiling, Gene Expression Regulation, Gene Knockdown Techniques, Gene Regulatory Networks, Genome-Wide Association Study, Heart Failure, Humans, Male, Metabolic Networks and Pathways, Mice, Mice, Knockout, Middle Aged, Myocytes, Cardiac, Phosphoprotein Phosphatases, Primary Cell Culture, Pyridines, Quantitative Trait Loci, Rats, Rats, Sprague-Dawley, Sequence Analysis, RNA
Abstract

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure.

Published
2019

26. Integration of rare expression outlier-associated variants improves polygenic risk prediction

Author

Smail, Craig, Ferraro, Nicole M., Hui, Qin, Durrant, Matthew G., Aguirre, Matthew, Tanigawa, Yosuke, Keever-Keigher, Marissa R., Rao, Abhiram S., Justesen, Johanne M., Li, Xin, Gloudemans, Michael J., Assimes, Themistocles L., Kooperberg, Charles, Reiner, Alexander P., Huang, Jie, O'Donnell, Christopher J., Sun, Yan V., Rivas, Manuel A., and Montgomery, Stephen B.

Published
2022
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27. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

Author

Yang, Fan, Wang, Jiebiao, Consortium, The GTEx, Pierce, Brandon L, Chen, Lin S, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Jo, Brian, Kang, Eun Yong, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, and Wang, Gao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, Generic health relevance, Good Health and Well Being, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Selection, Genetic, Tissue Distribution, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Published
2017

28. Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Author

Saha, Ashis, Kim, Yungil, Gewirtz, Ariel DH, Jo, Brian, Gao, Chuan, McDowell, Ian C, Consortium, The GTEx, Engelhardt, Barbara E, Battle, Alexis, Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Im, Hae Kyung, Kang, Eun Yong, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, and Sul, Jae Hoon

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Biotechnology, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Bayes Theorem, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genotyping Techniques, Humans, Organ Specificity, Polymorphism, Single Nucleotide, RNA Splicing, Sequence Analysis, RNA, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Published
2017

29. Dynamic landscape and regulation of RNA editing in mammals

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Adenosine Deaminase, Animals, Female, Genotype, HEK293 Cells, Humans, Male, Mice, Muscles, Nuclear Proteins, Organ Specificity, Primates, Proteolysis, RNA Editing, RNA-Binding Proteins, Spatio-Temporal Analysis, Species Specificity, Transcriptome, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.

Published
2017

30. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Author

McAllister, Kimberly, Mechanic, Leah E, Amos, Christopher, Aschard, Hugues, Blair, Ian A, Chatterjee, Nilanjan, Conti, David, Gauderman, W James, Hsu, Li, Hutter, Carolyn M, Jankowska, Marta M, Kerr, Jacqueline, Kraft, Peter, Montgomery, Stephen B, Mukherjee, Bhramar, Papanicolaou, George J, Patel, Chirag J, Ritchie, Marylyn D, Ritz, Beate R, Thomas, Duncan C, Wei, Peng, Witte, John S, and participants, on behalf of workshop

Subjects
Epidemiology, Health Sciences, Cancer, Human Genome, Genetics, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Aetiology, Generic health relevance, Good Health and Well Being, Disease, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Software, environmental exposure, gene-environment interaction, genome-wide association study, Mathematical Sciences, Medical and Health Sciences
Abstract

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Published
2017

31. Landscape of X chromosome inactivation across human tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Genetics, Clinical Research, Human Genome, Generic health relevance, Good Health and Well Being, Chromosomes, Human, X, Female, Genes, X-Linked, Genome, Human, Genomics, Humans, Male, Organ Specificity, Phenotype, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, X Chromosome Inactivation, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.

Published
2017

32. The impact of rare variation on gene expression across tissues

Author

Aguet, François, Ardlie, Kristin G, Cummings, Beryl B, Gelfand, Ellen T, Getz, Gad, Hadley, Kane, Handsaker, Robert E, Huang, Katherine H, Kashin, Seva, Karczewski, Konrad J, Lek, Monkol, Li, Xiao, MacArthur, Daniel G, Nedzel, Jared L, Nguyen, Duyen T, Noble, Michael S, Segrè, Ayellet V, Trowbridge, Casandra A, Tukiainen, Taru, Abell, Nathan S, Balliu, Brunilda, Barshir, Ruth, Basha, Omer, Battle, Alexis, Bogu, Gireesh K, Brown, Andrew, Brown, Christopher D, Castel, Stephane E, Chen, Lin S, Chiang, Colby, Conrad, Donald F, Cox, Nancy J, Damani, Farhan N, Davis, Joe R, Delaneau, Olivier, Dermitzakis, Emmanouil T, Engelhardt, Barbara E, Eskin, Eleazar, Ferreira, Pedro G, Frésard, Laure, Gamazon, Eric R, Garrido-Martín, Diego, Gewirtz, Ariel DH, Gliner, Genna, Gloudemans, Michael J, Guigo, Roderic, Hall, Ira M, Han, Buhm, He, Yuan, Hormozdiari, Farhad, Howald, Cedric, Kyung Im, Hae, Jo, Brian, Yong Kang, Eun, Kim, Yungil, Kim-Hellmuth, Sarah, Lappalainen, Tuuli, Li, Gen, Li, Xin, Liu, Boxiang, Mangul, Serghei, McCarthy, Mark I, McDowell, Ian C, Mohammadi, Pejman, Monlong, Jean, Montgomery, Stephen B, Muñoz-Aguirre, Manuel, Ndungu, Anne W, Nicolae, Dan L, Nobel, Andrew B, Oliva, Meritxell, Ongen, Halit, Palowitch, John J, Panousis, Nikolaos, Papasaikas, Panagiotis, Park, YoSon, Parsana, Princy, Payne, Anthony J, Peterson, Christine B, Quan, Jie, Reverter, Ferran, Sabatti, Chiara, Saha, Ashis, Sammeth, Michael, Scott, Alexandra J, Shabalin, Andrey A, Sodaei, Reza, Stephens, Matthew, Stranger, Barbara E, Strober, Benjamin J, Sul, Jae Hoon, Tsang, Emily K, Urbut, Sarah, van de Bunt, Martijn, Wang, Gao, Wen, Xiaoquan, Wright, Fred A, Xi, Hualin S, Yeger-Lotem, Esti, and Zappala, Zachary

Subjects
Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, General Science & Technology
Abstract

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Published
2017

33. Genetic effects on gene expression across human tissues.

Author

GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, Battle, Alexis, Brown, Christopher D, Engelhardt, Barbara E, and Montgomery, Stephen B

Subjects
GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, Chromosomes, Human, Humans, Disease, Gene Expression Profiling, Organ Specificity, Gene Expression Regulation, Genotype, Alleles, Quantitative Trait Loci, Genome, Human, Female, Male, Genetic Variation, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Data Analysis &Coordinating Center (LDACC):, Chromosomes, Human, Genome, General Science & Technology
Abstract

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Published
2017

36. Genetic architecture of cardiac dynamic flow volumes

Author

Gomes, Bruna, primary, Singh, Aditya, additional, O’Sullivan, Jack W., additional, Schnurr, Theresia M., additional, Goddard, Pagé C., additional, Loong, Shaun, additional, Amar, David, additional, Hughes, J. Weston, additional, Kostur, Mykhailo, additional, Haddad, Francois, additional, Salerno, Michael, additional, Foo, Roger, additional, Montgomery, Stephen B., additional, Parikh, Victoria N., additional, Meder, Benjamin, additional, and Ashley, Euan A., additional

Published
2023
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38. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Barbeira, Alvaro N., Bonazzola, Rodrigo, Gamazon, Eric R., Liang, Yanyu, Park, YoSon, Kim-Hellmuth, Sarah, Wang, Gao, Jiang, Zhuoxun, Zhou, Dan, Hormozdiari, Farhad, Liu, Boxiang, Rao, Abhiram, Hamel, Andrew R., Pividori, Milton D., Aguet, François, Bastarache, Lisa, Jordan, Daniel M., Verbanck, Marie, Do, Ron, Stephens, Matthew, Ardlie, Kristin, McCarthy, Mark, Montgomery, Stephen B., Segrè, Ayellet V., Brown, Christopher D., Lappalainen, Tuuli, Wen, Xiaoquan, and Im, Hae Kyung

Published
2021
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39. Transcriptomics and chromatin accessibility in multiple African population samples

Author

DeGorter, Marianne K, primary, Goddard, Page C, additional, Karakoc, Emre, additional, Kundu, Soumya, additional, Yan, Stephanie M, additional, Nachun, Daniel, additional, Abell, Nathan, additional, Aguirre, Matthew, additional, Carstensen, Tommy, additional, Chen, Ziwei, additional, Durrant, Matthew, additional, Dwaracherla, Vikranth R, additional, Feng, Karen, additional, Gloudemans, Michael J, additional, Hunter, Naiomi, additional, Moorthy, Mohana P S, additional, Pomilla, Cristina, additional, Rodrigues, Kameron B, additional, Smith, Courtney J, additional, Smith, Kevin S, additional, Ungar, Rachel A, additional, Balliu, Brunilda, additional, Fellay, Jacques, additional, Flicek, Paul, additional, McLaren, Paul J, additional, Henn, Brenna, additional, McCoy, Rajiv C, additional, Sugden, Lauren, additional, Kundaje, Anshul, additional, Sandhu, Manjinder S, additional, Gurdasani, Deepti, additional, and Montgomery, Stephen B, additional

Published
2023
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40. Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Author

Rubenstein, Aliza B, primary, Smith, Gregory R, additional, Zhang, Zidong, additional, Chen, Xi, additional, Chambers, Toby L., additional, Ruf-Zamojski, Frederique, additional, Mendelev, Natalia, additional, Cheng, Wan Sze, additional, Zamojski, Michel, additional, Amper, Mary Anne S., additional, Nair, Venugopalan D., additional, Marderstein, Andrew R., additional, Montgomery, Stephen B., additional, Troyanskaya, Olga G., additional, Zaslavsky, Elena, additional, Trappe, Todd, additional, Trappe, Scott, additional, and Sealfon, Stuart C., additional

Published
2023
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42. High-resolution transcriptome analysis with long-read RNA sequencing

Author

Cho, Hyunghoon, Davis, Joe, Li, Xin, Smith, Kevin S., Battle, Alexis, and Montgomery, Stephen B.

Subjects
Quantitative Biology - Genomics
Abstract

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2x75 bp and 2x262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals., Comment: 29 pages, 8 figures, 11 supplementary figures

Published
2014
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44. The landscape of genomic imprinting across diverse adult human tissues

Author

Baran, Yael, Subramaniam, Meena, Biton, Anne, Tukiainen, Taru, Tsang, Emily K, Rivas, Manuel A, Pirinen, Matti, Gutierrez-Arcelus, Maria, Smith, Kevin S, Kukurba, Kim R, Zhang, Rui, Eng, Celeste, Torgerson, Dara G, Urbanek, Cydney, Consortium, the GTEx, Li, Jin Billy, Rodriguez-Santana, Jose R, Burchard, Esteban G, Seibold, Max A, MacArthur, Daniel G, Montgomery, Stephen B, Zaitlen, Noah A, and Lappalainen, Tuuli

Subjects
Biological Sciences, Genetics, Human Genome, Adult, Alleles, Cluster Analysis, DNA Methylation, Databases, Nucleic Acid, Female, Gene Expression Regulation, Genetic Variation, Genomic Imprinting, Genomics, Genotype, Humans, Male, Organ Specificity, Polymorphism, Single Nucleotide, Reproducibility of Results, Sex Factors, GTEx Consortium, Medical and Health Sciences, Bioinformatics
Abstract

Genomic imprinting is an important regulatory mechanism that silences one of the parental copies of a gene. To systematically characterize this phenomenon, we analyze tissue specificity of imprinting from allelic expression data in 1582 primary tissue samples from 178 individuals from the Genotype-Tissue Expression (GTEx) project. We characterize imprinting in 42 genes, including both novel and previously identified genes. Tissue specificity of imprinting is widespread, and gender-specific effects are revealed in a small number of genes in muscle with stronger imprinting in males. IGF2 shows maternal expression in the brain instead of the canonical paternal expression elsewhere. Imprinting appears to have only a subtle impact on tissue-specific expression levels, with genes lacking a systematic expression difference between tissues with imprinted and biallelic expression. In summary, our systematic characterization of imprinting in adult tissues highlights variation in imprinting between genes, individuals, and tissues.

Published
2015

45. Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture.

Author

Martin, Alicia R, Costa, Helio A, Lappalainen, Tuuli, Henn, Brenna M, Kidd, Jeffrey M, Yee, Muh-Ching, Grubert, Fabian, Cann, Howard M, Snyder, Michael, Montgomery, Stephen B, and Bustamante, Carlos D

Subjects
Humans, Gene Expression Profiling, Human Genome Project, Genetics, Population, Haplotypes, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome, Human, Gene Regulatory Networks, HapMap Project, Human Migration, Genetics, Population, Genome, Human, Polymorphism, Single Nucleotide, Developmental Biology
Abstract

Large-scale sequencing efforts have documented extensive genetic variation within the human genome. However, our understanding of the origins, global distribution, and functional consequences of this variation is far from complete. While regulatory variation influencing gene expression has been studied within a handful of populations, the breadth of transcriptome differences across diverse human populations has not been systematically analyzed. To better understand the spectrum of gene expression variation, alternative splicing, and the population genetics of regulatory variation in humans, we have sequenced the genomes, exomes, and transcriptomes of EBV transformed lymphoblastoid cell lines derived from 45 individuals in the Human Genome Diversity Panel (HGDP). The populations sampled span the geographic breadth of human migration history and include Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico. We discover that approximately 25.0% of the variation in gene expression found amongst individuals can be attributed to population differences. However, we find few genes that are systematically differentially expressed among populations. Of this population-specific variation, 75.5% is due to expression rather than splicing variability, and we find few genes with strong evidence for differential splicing across populations. Allelic expression analyses indicate that previously mapped common regulatory variants identified in eight populations from the International Haplotype Map Phase 3 project have similar effects in our seven sampled HGDP populations, suggesting that the cellular effects of common variants are shared across diverse populations. Together, these results provide a resource for studies analyzing functional differences across populations by estimating the degree of shared gene expression, alternative splicing, and regulatory genetics across populations from the broadest points of human migration history yet sampled.

Published
2014

46. Beyond the exome: What’s next in diagnostic testing for Mendelian conditions

Author

Wojcik, Monica H., primary, Reuter, Chloe M., additional, Marwaha, Shruti, additional, Mahmoud, Medhat, additional, Duyzend, Michael H., additional, Barseghyan, Hayk, additional, Yuan, Bo, additional, Boone, Philip M., additional, Groopman, Emily E., additional, Délot, Emmanuèle C., additional, Jain, Deepti, additional, Sanchis-Juan, Alba, additional, Starita, Lea M., additional, Talkowski, Michael, additional, Montgomery, Stephen B., additional, Bamshad, Michael J., additional, Chong, Jessica X., additional, Wheeler, Matthew T., additional, Berger, Seth I., additional, O'Donnell-Luria, Anne, additional, Sedlazeck, Fritz J., additional, and Miller, Danny E., additional

Published
2023
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47. Proficiency Testing of Standardized Samples Shows Very High Interlaboratory Agreement for Clinical Next-Generation Sequencing-Based Oncology Assays

Author

Merker, Jason D., Devereaux, Kelly, Iafrate, John, Kamel-Reid, Suzanne, Kim, Annette S., Moncur, Joel T., Montgomery, Stephen B., Nagarajan, Rakesh, Portier, Bryce P., Routbort, Mark J., Smail, Craig, Surrey, Lea F., Vasalos, Patricia, Lazar, Alexander J., and Lindeman, Neal I.

Subjects
Cancer research, DNA sequencing -- Usage, Biological markers -- Research, Genomes, Polymerase chain reaction, Tumors, Medical societies, Health
Abstract

Context.--Next-generation sequencing-based assays are being increasingly used in the clinical setting for the detection of somatic variants in solid tumors, but limited data are available regarding the interlaboratory performance of these assays. Objective.--To examine proficiency testing data from the initial College of American Pathologists (CAP) Next-Generation Sequencing Solid Tumor survey to report on laboratory performance. Design.--CAP proficiency testing results from 111 laboratories were analyzed for accuracy and associated assay performance characteristics. Results.--The overall accuracy observed for all variants was 98.3%. Rare false-negative results could not be attributed to sequencing platform, selection method, or other assay characteristics. The median and average of the variant allele fractions reported by the laboratories were within 10% of those orthogonally determined by digital polymerase chain reaction for each variant. The median coverage reported at the variant sites ranged from 1922 to 3297. Conclusions.--Laboratories demonstrated an overall accuracy of greater than 98% with high specificity when examining 10 clinically relevant somatic single-nucleotide variants with a variant allele fraction of 15% or greater. These initial data suggest excellent performance, but further ongoing studies are needed to evaluate the performance of lower variant allele fractions and additional variant types. (Arch Pathol Lab Med. 2019;143:463-471; doi: 10.5858/arpa.2018-0336-CP), The past several years have seen the development or expansion of several national and international efforts that aim to accelerate the implementation of precision medicine. (1-4) One area of focus [...]

Published
2019
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48. Systematic functional regulatory assessment of disease-associated variants.

Author

Karczewski, Konrad J, Dudley, Joel T, Kukurba, Kimberly R, Chen, Rong, Butte, Atul J, Montgomery, Stephen B, and Snyder, Michael

Subjects
Humans, Genetic Diseases, Inborn, NF-kappa B, Transcription Factors, Gene Expression Profiling, Computational Biology, Systems Biology, Protein Binding, Polymorphism, Single Nucleotide, Genetic Variation, Genome-Wide Association Study, regulatory genomics, systems biology, translational bioinformatics, Arthritis, Lung, Human Genome, Atherosclerosis, Biotechnology, Asthma, Genetics, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Generic health relevance, Cardiovascular
Abstract

Genome-wide association studies have discovered many genetic loci associated with disease traits, but the functional molecular basis of these associations is often unresolved. Genome-wide regulatory and gene expression profiles measured across individuals and diseases reflect downstream effects of genetic variation and may allow for functional assessment of disease-associated loci. Here, we present a unique approach for systematic integration of genetic disease associations, transcription factor binding among individuals, and gene expression data to assess the functional consequences of variants associated with hundreds of human diseases. In an analysis of genome-wide binding profiles of NFκB, we find that disease-associated SNPs are enriched in NFκB binding regions overall, and specifically for inflammatory-mediated diseases, such as asthma, rheumatoid arthritis, and coronary artery disease. Using genome-wide variation in transcription factor-binding data, we find that NFκB binding is often correlated with disease-associated variants in a genotype-specific and allele-specific manner. Furthermore, we show that this binding variation is often related to expression of nearby genes, which are also found to have altered expression in independent profiling of the variant-associated disease condition. Thus, using this integrative approach, we provide a unique means to assign putative function to many disease-associated SNPs.

Published
2013

49. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis

Author

Wirka, Robert C., Wagh, Dhananjay, Paik, David T., Pjanic, Milos, Nguyen, Trieu, Miller, Clint L., Kundu, Ramen, Nagao, Manabu, Coller, John, Koyano, Tiffany K., Fong, Robyn, Woo, Y. Joseph, Liu, Boxiang, Montgomery, Stephen B., Wu, Joseph C., Zhu, Kuixi, Chang, Rui, Alamprese, Melissa, Tallquist, Michelle D., Kim, Juyong B., and Quertermous, Thomas

Published
2019
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50. The Genome Sequence of the SARS-Associated Coronavirus

Author

Marra, Marco A., Astell, Caroline R., Holt, Robert A., Brooks-Wilson, Angela, Khattra, Jaswinder, Asano, Jennifer K., Barber, Sarah A., Chan, Susanna Y., Cloutier, Alison, Coughlin, Shaun M., Freeman, Doug, Girn, Noreen, Griffith, Obi L., Leach, Stephen R., Mayo, Michael, McDonald, Helen, Montgomery, Stephen B., Pandoh, Pawan K., Petrescu, Anca S., Robertson, A. Gordon, Schein, Jacqueline E., Siddiqui, Asim, Smailus, Duane E., Stott, Jeff M., Yang, George S., Plummer, Francis, Andonov, Anton, Artsob, Harvey, Bastien, Nathalie, Bernard, Kathy, Booth, Timothy F., Bowness, Donnie, Czub, Martin, Drebot, Michael, Fernando, Lisa, Flick, Ramon, Garbutt, Michael, Gray, Michael, Grolla, Allen, Jones, Steven, Feldmann, Heinz, Meyers, Adrienne, Kabani, Amin, Li, Yan, Normand, Susan, Stroher, Ute, Tipples, Graham A., Tyler, Shaun, Vogrig, Robert, Ward, Diane, Watson, Brynn, Brunham, Robert C., Krajden, Mel, Petric, Martin, Skowronski, Danuta M., Upton, Chris, and Roper, Rachel L.

Published
2003

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