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1. Accurate genome-wide phasing from IBD data

Author

Keith Noto and Luong Ruiz

Subjects
Genotype phasing, Long-range phasing, IBD, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract As genotype databases increase in size, so too do the number of detectable segments of identity by descent (IBD): segments of the genome where two individuals share an identical copy of one of their two parental haplotypes, due to shared ancestry. We show that given a large enough genotype database, these segments of IBD collectively overlap entire chromosomes, including instances of IBD that span multiple chromosomes, and can be used to accurately separate the alleles inherited from each parent across the entire genome. The resulting phase is not an improvement over state-of-the-art local phasing methods, but provides accurate long-range phasing that indicates which of two haplotypes in different regions of the genome, including different chromosomes, was inherited from the same parent. We are able to separate the DNA inherited from each parent completely, across the entire genome, with 98% median accuracy in a test set of 30,000 individuals. We estimate the IBD data requirements for accurate genome-wide phasing, and we propose a method for estimating confidence in the resulting phase. We show that our methods do not require the genotypes of close family, and that they are robust to genotype errors and missing data. In fact, our method can impute missing data accurately and correct genotype errors.

Published
2022
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2. Ancestry inference using reference labeled clusters of haplotypes

Author

Yong Wang, Shiya Song, Joshua G. Schraiber, Alisa Sedghifar, Jake K. Byrnes, David A. Turissini, Eurie L. Hong, Catherine A. Ball, and Keith Noto

Subjects
ARCHes, Ancestry inference, Haplotype modeling, Local ancestry, HMM, RFMix, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual’s ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. Results The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture. Conclusions Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.

Published
2021
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3. COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults

Author

Yong Wang, Robert Burton, Cecily Vaughn, Miao Zhang, Brooke Rhead, Heather Harris, Spencer C Knight, Shannon R McCurdy, Marie V Coignet, Danny S Park, Genevieve H L Roberts, Nathan D Berkowitz, David Turissini, Karen Delgado, Milos Pavlovic, Asher K Haug Baltzell, Harendra Guturu, Kristin A Rand, Ahna R Girshick, Eurie L Hong, Catherine A Ball, Yambazi Banda, Ke Bi, Marjan Champine, Ross Curtis, Abby Drokhlyansky, Ashley Elrick, Cat Foo, Michael Gaddis, Jialiang Gu, Shannon Hateley, Shea King, Christine Maldonado, Evan McCartney-Melstad, Alexandra McFarland, Patty Miller, Luong Nguyen, Keith Noto, Jingwen Pei, Jenna Petersen, Scott Pew, Chodon Sass, Josh Schraiber, Alisa Sedghifar, Andrey Smelter, Sarah South, and Barry Starr

Subjects
Medicine
Abstract

Objectives The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.Design A cross-sectional study.Setting AncestryDNA customers in the USA who consented to research.Participants The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test.Results We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study.Conclusions The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Published
2022
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4. A Prospective Analysis of Genetic Variants Associated with Human Lifespan

Author

Kevin M. Wright, Kristin A. Rand, Amir Kermany, Keith Noto, Don Curtis, Daniel Garrigan, Dmitri Slinkov, Ilya Dorfman, Julie M. Granka, Jake Byrnes, Natalie Myres, Catherine A. Ball, and J. Graham Ruby

Subjects
GWAS, human, lifespan, Genetics, QH426-470
Abstract

We present a massive investigation into the genetic basis of human lifespan. Beginning with a genome-wide association (GWA) study using a de-identified snapshot of the unique AncestryDNA database – more than 300,000 genotyped individuals linked to pedigrees of over 400,000,000 people – we mapped six genome-wide significant loci associated with parental lifespan. We compared these results to a GWA analysis of the traditional lifespan proxy trait, age, and found only one locus, APOE, to be associated with both age and lifespan. By combining the AncestryDNA results with those of an independent UK Biobank dataset, we conducted a meta-analysis of more than 650,000 individuals and identified fifteen parental lifespan-associated loci. Beyond just those significant loci, our genome-wide set of polymorphisms accounts for up to 8% of the variance in human lifespan; this value represents a large fraction of the heritability estimated from phenotypic correlations between relatives.

Published
2019
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5. Clustering of 770,000 genomes reveals post-colonial population structure of North America

Author

Eunjung Han, Peter Carbonetto, Ross E. Curtis, Yong Wang, Julie M. Granka, Jake Byrnes, Keith Noto, Amir R. Kermany, Natalie M. Myres, Mathew J. Barber, Kristin A. Rand, Shiya Song, Theodore Roman, Erin Battat, Eyal Elyashiv, Harendra Guturu, Eurie L. Hong, Kenneth G. Chahine, and Catherine A. Ball

Subjects
Science
Abstract

Genetic data has led to great advances in our understanding of human evolution and dispersal, but information on more recent events is limited. Here, the authors analyse genotypes from 770,000 US individuals to map the fine-scale population structure of North America after European settlement.

Published
2017
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6. Finding novel molecular connections between developmental processes and disease.

Author

Jisoo Park, Heather C Wick, Daniel E Kee, Keith Noto, Jill L Maron, and Donna K Slonim

Subjects
Biology (General), QH301-705.5
Abstract

Identifying molecular connections between developmental processes and disease can lead to new hypotheses about health risks at all stages of life. Here we introduce a new approach to identifying significant connections between gene sets and disease genes, and apply it to several gene sets related to human development. To overcome the limits of incomplete and imperfect information linking genes to disease, we pool genes within disease subtrees in the MeSH taxonomy, and we demonstrate that such pooling improves the power and accuracy of our approach. Significance is assessed through permutation. We created a web-based visualization tool to facilitate multi-scale exploration of this large collection of significant connections (http://gda.cs.tufts.edu/development). High-level analysis of the results reveals expected connections between tissue-specific developmental processes and diseases linked to those tissues, and widespread connections to developmental disorders and cancers. Yet interesting new hypotheses may be derived from examining the unexpected connections. We highlight and discuss the implications of three such connections, linking dementia with bone development, polycystic ovary syndrome with cardiovascular development, and retinopathy of prematurity with lung development. Our results provide additional evidence that TGFB lays a key role in the early pathogenesis of polycystic ovary syndrome. Our evidence also suggests that the VEGF pathway and downstream NFKB signaling may explain the complex relationship between bronchopulmonary dysplasia and retinopathy of prematurity, and may form a bridge between two currently-competing hypotheses about the molecular origins of bronchopulmonary dysplasia. Further data exploration and similar queries about other gene sets may generate a variety of new information about the molecular relationships between additional diseases.

Published
2014
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18. SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Author

Matteo D’Antonio, Jennifer P. Nguyen, Timothy D. Arthur, Hiroko Matsui, Agnieszka D’Antonio-Chronowska, Kelly A. Frazer, Benjamin M. Neale, Mark Daly, Andrea Ganna, Christine Stevens, Gita A. Pathak, Shea J. Andrews, Masahiro Kanai, Mattia Cordioli, Juha Karjalainen, Renato Polimanti, Matti Pirinen, Nadia Harerimana, Kumar Veerapen, Brooke Wolford, Huy Nguyen, Matthew Solomonson, Rachel G. Liao, Karolina Chwialkowska, Amy Trankiem, Mary K. Balaconis, Caroline Hayward, Anne Richmond, Archie Campbell, Marcela Morris, Chloe Fawns-Ritchie, Joseph T. Glessner, Douglas M. Shaw, Xiao Chang, Hannah Polikowski, Petty E. Lauren, Hung-Hsin Chen, Zhu Wanying, Hakon Hakonarson, David J. Porteous, Jennifer Below, Kari North, Joseph B. McCormick, Paul R.H.J. Timmers, James F. Wilson, Albert Tenesa, Kenton D’Mellow, Shona M. Kerr, Mari E.K. Niemi, Lindokuhle Nkambul, Kathrin Aprile von Hohenstaufen, Ali Sobh, Madonna M. Eltoukhy, Amr M. Yassen, Mohamed A.F. Hegazy, Kamal Okasha, Mohammed A. Eid, Hanteera S. Moahmed, Doaa Shahin, Yasser M. El-Sherbiny, Tamer A. Elhadidy, Mohamed S. Abd Elghafar, Jehan J. El-Jawhari, Attia A.S. Mohamed, Marwa H. Elnagdy, Amr Samir, Mahmoud Abdel-Aziz, Walid T. Khafaga, Walaa M. El-Lawaty, Mohamed S. Torky, Mohamed R. El-shanshory, Chiara Batini, Paul H. Lee, Nick Shrine, Alexander T. Williams, Martin D. Tobin, Anna L. Guyatt, Catherine John, Richard J. Packer, Altaf Ali, Robert C. Free, Xueyang Wang, Louise V. Wain, Edward J. Hollox, Laura D. Venn, Catherine E. Bee, Emma L. Adams, Ahmadreza Niavarani, Bahareh Sharififard, Rasoul Aliannejad, Ali Amirsavadkouhi, Zeinab Naderpour, Hengameh Ansari Tadi, Afshar Etemadi Aleagha, Saeideh Ahmadi, Seyed Behrooz Mohseni Moghaddam, Alireza Adamsara, Morteza Saeedi, Hamed Abdollahi, Abdolmajid Hosseini, Pajaree Chariyavilaskul, Monpat Chamnanphon, Thitima B. Suttichet, Vorasuk Shotelersuk, Monnat Pongpanich, Chureerat Phokaew, Wanna Chetruengchai, Watsamon Jantarabenjakul, Opass Putchareon, Pattama Torvorapanit, Thanyawee Puthanakit, Pintip Suchartlikitwong, Nattiya Hirankarn, Voraphoj Nilaratanakul, Pimpayao Sodsai, Ben M. Brumpton, Kristian Hveem, Cristen Willer, Wei Zhou, Tormod Rogne, Erik Solligard, Bjørn Olav Åsvold, Malak Abedalthagafi, Manal Alaamery, Saleh Alqahtani, Dona Baraka, Fawz Al Harthi, Ebtehal Alsolm, Leen Abu Safieh, Albandary M. Alowayn, Fatimah Alqubaishi, Amal Al Mutairi, Serghei Mangul, Abdulraheem Alshareef, Mona Sawaji, Mansour Almutairi, Nora Aljawini, Nour Albesher, Yaseen M. Arabi, Ebrahim S. Mahmoud, Amin K. Khattab, Roaa T. Halawani, Ziab Z. Alahmadey, Jehad K. Albakri, Walaa A. Felemban, Bandar A. Suliman, Rana Hasanato, Laila Al-Awdah, Jahad Alghamdi, Deema AlZahrani, Sameera AlJohani, Hani Al-Afghani, May Alrashed, Nouf AlDhawi, Hadeel AlBardis, Sarah Alkwai, Moneera Alswailm, Faisal Almalki, Maha Albeladi, Iman Almohammed, Eman Barhoush, Anoud Albader, Salam Massadeh, Abdulaziz AlMalik, Sara Alotaibi, Bader Alghamdi, Junghyun Jung, Mohammad S. Fawzy, Yunsung Lee, Per Magnus, Lill-Iren S. Trogstad, Øyvind Helgeland, Jennifer R. Harris, Massimo Mangino, Tim D. Spector, Duncan Emma, Sandra P. Smieszek, Bartlomiej P. Przychodzen, Christos Polymeropoulos, Vasilios Polymeropoulos, Mihael H. Polymeropoulos, Israel Fernandez-Cadenas, Jordi Perez-Tur, Laia Llucià-Carol, Natalia Cullell, Elena Muiño, Jara Cárcel-Márquez, Marta L. DeDiego, Lara Lloret Iglesias, Anna M. Planas, Alex Soriano, Veronica Rico, Daiana Agüero, Josep L. Bedini, Francisco Lozano, Carlos Domingo, Veronica Robles, Francisca Ruiz-Jaén, Leonardo Márquez, Juan Gomez, Eliecer Coto, Guillermo M. Albaiceta, Marta García-Clemente, David Dalmau, Maria J. Arranz, Beatriz Dietl, Alex Serra-Llovich, Pere Soler, Roger Colobrán, Andrea Martín-Nalda, Alba Parra Martínez, David Bernardo, Silvia Rojo, Aida Fiz-López, Elisa Arribas, Paloma de la Cal-Sabater, Tomás Segura, Esther González-Villa, Gemma Serrano-Heras, Joan Martí-Fàbregas, Elena Jiménez-Xarrié, Alicia de Felipe Mimbrera, Jaime Masjuan, Sebastian García-Madrona, Anna Domínguez-Mayoral, Joan Montaner Villalonga, Paloma Menéndez-Valladares, Daniel I. Chasman, Julie E. Buring, Paul M. Ridker, Giulianini Franco, Howard D. Sesso, JoAnn E. Manson, Joseph R. Glessner, Carolina Medina-Gomez, Andre G. Uitterlinden, M. Arfan Ikram, Kati Kristiansson, Sami Koskelainen, Markus Perola, Kati Donner, Katja Kivinen, Aarno Palotie, Samuli Ripatti, Sanni Ruotsalainen, Mari Kaunisto, null FinnGen, Tomoko Nakanishi, Guillaume Butler-Laporte, Vincenzo Forgetta, David R. Morrison, Biswarup Ghosh, Laetitia Laurent, Alexandre Belisle, Danielle Henry, Tala Abdullah, Olumide Adeleye, Noor Mamlouk, Nofar Kimchi, Zaman Afrasiabi, Nardin Rezk Branka Vulesevic, Meriem Bouab, Charlotte Guzman, Louis Petitjean, Chris Tselios, Xiaoqing Xue, Erwin Schurr, Jonathan Afilalo, Marc Afilalo, Maureen Oliveira, Bluma Brenner, Pierre Lepage, Jiannis Ragoussis, Daniel Auld, Nathalie Brassard, Madeleine Durand, Michaël Chassé, Daniel E. Kaufmann, G. Mark Lathrop, Vincent Mooser, J. Brent Richards, Rui Li, Darin Adra, Souad Rahmouni, Michel Georges, Michel Moutschen, Benoit Misset, Gilles Darcis, Julien Guiot, Julien Guntz, Samira Azarzar, Stéphanie Gofflot, Yves Beguin, Sabine Claassen, Olivier Malaise, Pascale Huynen, Christelle Meuris, Marie Thys, Jessica Jacques, Philippe Léonard, Frederic Frippiat, Jean-Baptiste Giot, Anne-Sophie Sauvage, Christian Von Frenckell, Yasmine Belhaj, Bernard Lambermont, Sara Pigazzini, Lindokuhle Nkambule, Michelle Daya, Jonathan Shortt, Nicholas Rafaels, Stephen J. Wicks, Kristy Crooks, Kathleen C. Barnes, Christopher R. Gignoux, Sameer Chavan, Triin Laisk, Kristi Läll, Maarja Lepamets, Reedik Mägi, Tõnu Esko, Ene Reimann, Lili Milani, Helene Alavere, Kristjan Metsalu, Mairo Puusepp, Andres Metspalu, Paul Naaber, Edward Laane, Jaana Pesukova, Pärt Peterson, Kai Kisand, Jekaterina Tabri, Raili Allos, Kati Hensen, Joel Starkopf, Inge Ringmets, Anu Tamm, Anne Kallaste, Pierre-Yves Bochud, Carlo Rivolta, Stéphanie Bibert, Mathieu Quinodoz, Dhryata Kamdar, Noémie Boillat, Semira Gonseth Nussle, Werner Albrich, Noémie Suh, Dionysios Neofytos, Véronique Erard, Cathy Voide, null FHoGID, null RegCOVID, null P-PredictUs, null SeroCOVID, null CRiPSI, Rafael de Cid, Iván Galván-Femenía, Natalia Blay, Anna Carreras, Beatriz Cortés, Xavier Farré, Lauro Sumoy, Victor Moreno, Josep Maria Mercader, Marta Guindo-Martinez, David Torrents, Manolis Kogevinas, Judith Garcia-Aymerich, Gemma Castaño-Vinyals, Carlota Dobaño, Alessandra Renieri, Francesca Mari, Chiara Fallerini, Sergio Daga, Elisa Benetti, Margherita Baldassarri, Francesca Fava, Elisa Frullanti, Floriana Valentino, Gabriella Doddato, Annarita Giliberti, Rossella Tita, Sara Amitrano, Mirella Bruttini, Susanna Croci, Ilaria Meloni, Maria Antonietta Mencarelli, Caterina Lo Rizzo, Anna Maria Pinto, Giada Beligni, Andrea Tommasi, Laura Di Sarno, Maria Palmieri, Miriam Lucia Carriero, Diana Alaverdian, Stefano Busani, Raffaele Bruno, Marco Vecchia, Mary Ann Belli, Nicola Picchiotti, Maurizio Sanarico, Marco Gori, Simone Furini, Stefania Mantovani, Serena Ludovisi, Mario Umberto Mondelli, Francesco Castelli, Eugenia Quiros-Roldan, Melania Degli Antoni, Isabella Zanella, Massimo Vaghi, Stefano Rusconi, Matteo Siano, Francesca Montagnani, Arianna Emiliozzi, Massimiliano Fabbiani, Barbara Rossetti, Elena Bargagli, Laura Bergantini, Miriana D’Alessandro, Paolo Cameli, David Bennett, Federico Anedda, Simona Marcantonio, Sabino Scolletta, Federico Franchi, Maria Antonietta Mazzei, Susanna Guerrini, Edoardo Conticini, Luca Cantarini, Bruno Frediani, Danilo Tacconi, Chiara Spertilli, Marco Feri, Alice Donati, Raffaele Scala, Luca Guidelli, Genni Spargi, Marta Corridi, Cesira Nencioni, Leonardo Croci, Maria Bandini, Gian Piero Caldarelli, Paolo Piacentini, Elena Desanctis, Silvia Cappelli, Anna Canaccini, Agnese Verzuri, Valentina Anemoli, Agostino Ognibene, Alessandro Pancrazzi, Maria Lorubbio, Antonella D’Arminio Monforte, Federica Gaia Miraglia, Massimo Girardis, Sophie Venturelli, Andrea Cossarizza, Andrea Antinori, Alessandra Vergori, Arianna Gabrieli, Agostino Riva, Daniela Francisci, Elisabetta Schiaroli, Francesco Paciosi, Pier Giorgio Scotton, Francesca Andretta, Sandro Panese, Renzo Scaggiante, Francesca Gatti, Saverio Giuseppe Parisi, Stefano Baratti, Matteo Della Monica, Carmelo Piscopo, Mario Capasso, Roberta Russo, Immacolata Andolfo, Achille Iolascon, Giuseppe Fiorentino, Massimo Carella, Marco Castori, Giuseppe Merla, Gabriella Maria Squeo, Filippo Aucella, Pamela Raggi, Carmen Marciano, Rita Perna, Matteo Bassetti, Antonio Di Biagio, Maurizio Sanguinetti, Luca Masucci, Serafina Valente, Marco Mandalà, Alessia Giorli, Lorenzo Salerni, Patrizia Zucchi, Pierpaolo Parravicini, Elisabetta Menatti, Tullio Trotta, Ferdinando Giannattasio, Gabriella Coiro, Fabio Lena, Domenico A. Coviello, Cristina Mussini, Enrico Martinelli, Sandro Mancarella, Luisa Tavecchia, Lia Crotti, Chiara Gabbi, Marco Rizzi, Franco Maggiolo, Diego Ripamonti, Tiziana Bachetti, Maria Teresa La Rovere, Simona Sarzi-Braga, Maurizio Bussotti, Stefano Ceri, Pietro Pinoli, Francesco Raimondi, Filippo Biscarini, Alessandra Stella, Kristina Zguro, Katia Capitani, Claudia Suardi, Simona Dei, Gianfranco Parati, Sabrina Ravaglia, Rosangela Artuso, Giordano Bottà, Paolo Di Domenico, Ilaria Rancan, Antonio Perrella Francesco Bianchi, Davide Romani, Paola Bergomi, Emanuele Catena, Riccardo Colombo, Marco Tanfoni, Antonella Vincenti, Claudio Ferri, Davide Grassi, Gloria Pessina, Mario Tumbarello, Massimo Di Pietro, Ravaglia Sabrina, Sauro Luchi, Chiara Barbieri, Donatella Acquilini, Elena Andreucci, Francesco Vladimiro Segala, Giusy Tiseo, Marco Falcone, Mirjam Lista, Monica Poscente, Oreste De Vivo, Paola Petrocelli, Alessandra Guarnaccia, Silvia Baroni, Albert V. Smith, Andrew P. Boughton, Kevin W. Li, Jonathon LeFaive, Aubrey Annis, Anne E. Justice, Tooraj Mirshahi, Geetha Chittoor, Navya Shilpa Josyula, Jack A. Kosmicki, Manuel A.R. Ferreira, Joseph B. Leader, Dave J. Carey, Matthew C. Gass, Julie E. Horowitz, Michael N. Cantor, Ashish Yadav, Aris Baras, Goncalo R. Abecasis, David A. van Heel, Karen A. Hunt, Dan Mason, Qin Qin Huang, Sarah Finer, null Genes & Health Research Team, Bhavi Trivedi, Christopher J. Griffiths, Hilary C. Martin, John Wright, Richard C. Trembath, Nicole Soranzo, Jing Hua Zhao, Adam S. Butterworth, John Danesh, Emanuele Di Angelantonio, Lude Franke Marike Boezen, Patrick Deelen, Annique Claringbould, Esteban Lopera, Robert Warmerdam, Judith.M. Vonk, Irene van Blokland, Pauline Lanting, Anil P.S. Ori, Brooke Wolford Sebastian Zöllner, Jiongming Wang, Andrew Beck, Gina Peloso, Yuk-Lam Ho, Yan V. Sun, Jennifer E. Huffman, Christopher J. O’Donnell, Kelly Cho, Phil Tsao, J. Michael Gaziano, Michel (M.G.) Nivard, Eco (E.J.C.) de geus, Meike Bartels, Jouke Jan Hottenga, Scott T. Weiss, Elizabeth W. Karlson, Jordan W. Smoller, Robert C. Green, Yen-Chen Anne Feng, Josep Mercader, Shawn N. Murphy, James B. Meigs, Ann E. Woolley, Emma F. Perez, Daniel Rader, Anurag Verma, Marylyn D. Ritchie, Binglan Li, Shefali S. Verma, Anastasia Lucas, Yuki Bradford, Hugo Zeberg, Robert Frithiof, Michael Hultström, Miklos Lipcsey, Lindo Nkambul, Nicolas Tardif, Olav Rooyackers, Jonathan Grip, Tomislav Maricic, Konrad J. Karczewski, Elizabeth G. Atkinson, Kristin Tsuo, Nikolas Baya, Patrick Turley, Rahul Gupta, Shawneequa Callier, Raymond K. Walters, Duncan S. Palmer, Gopal Sarma, Nathan Cheng, Wenhan Lu, Sam Bryant, Claire Churchhouse, Caroline Cusick, Jacqueline I. Goldstein, Daniel King, Cotton Seed, Hilary Finucane, Alicia R. Martin, F. Kyle Satterstrom, Daniel J. Wilson, Jacob Armstrong, Justine K. Rudkin, Gavin Band, Sarah G. Earle, Shang-Kuan Lin, Nicolas Arning, Derrick W. Crook, David H. Wyllie, Anne Marie O’Connell, Chris C.A. Spencer, Nils Koelling, Mark J. Caulfield, Richard H. Scott, Tom Fowler, Loukas Moutsianas, Athanasios Kousathanas, Dorota Pasko, Susan Walker, Augusto Rendon, Alex Stuckey, Christopher A. Odhams, Daniel Rhodes, Georgia Chan, Prabhu Arumugam, Catherine A. Ball, Eurie L. Hong, Kristin Rand, Ahna Girshick, Harendra Guturu, Asher Haug Baltzell, Genevieve Roberts, Danny Park, Marie Coignet, Shannon McCurdy, Spencer Knight, Raghavendran Partha, Brooke Rhead, Miao Zhang, Nathan Berkowitz, Michael Gaddis, Keith Noto, Luong Ruiz, Milos Pavlovic, Laura G. Sloofman, Alexander W. Charney, Noam D. Beckmann, Eric E. Schadt, Daniel M. Jordan, Ryan C. Thompson, Kyle Gettler, Noura S. Abul-Husn, Steven Ascolillo, Joseph D. Buxbaum, Kumardeep Chaudhary, Judy H. Cho, Yuval Itan, Eimear E. Kenny, Gillian M. Belbin, Stuart C. Sealfon, Robert P. Sebra, Irene Salib, Brett L. Collins, Tess Levy, Bari Britvan, Katherine Keller, Lara Tang, Michael Peruggia, Liam L. Hiester, Kristi Niblo, Alexandra Aksentijevich, Alexander Labkowsky, Avromie Karp, Menachem Zlatopolsky, Michael Preuss, Ruth J.F. Loos, Girish N. Nadkarni, Ron Do, Clive Hoggart, Sam Choi, Slayton J. Underwood, Paul O’Reilly, Laura M. Huckins, Marissa Zyndorf, AII - Infectious diseases, Amsterdam Neuroscience - Neuroinfection & -inflammation, and Neurology

Subjects
Medical Physiology, Gene Expression, Genome-wide association study, Genome, Severity of Illness Index, colocalization, Gene expression, Databases, Genetic, Ethnicity, 2.1 Biological and endogenous factors, GWAS, Aetiology, Biology (General), Lung, Genetics, Chromosome Mapping, Single Nucleotide, Organ Specificity, Biotechnology, Cell type, QH301-705.5, Quantitative Trait Loci, Single-nucleotide polymorphism, Biology, eQTL, Polymorphism, Single Nucleotide, Article, General Biochemistry, Genetics and Molecular Biology, Databases, Genetic, SNP, Humans, Genetic Predisposition to Disease, COVID-19 Host Genetics Initiative, Polymorphism, Gene, COVID-19, SARS-CoV-2, Gene Expression Profiling, Prevention, Human Genome, Computational Biology, Genetic Variation, Good Health and Well Being, Expression quantitative trait loci, Biochemistry and Cell Biology, Transcriptome, Genome-Wide Association Study
Abstract

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

Published
2022
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20. Novel COVID-19 phenotype definitions reveal phenotypically distinct patterns of genetic association and protective effects

Author

Marie V. Coignet, Eurie L. Hong, Sarah South, Miao Zhang, Harendra Guturu, Pei Jingwen, Jialiang Gu, Brooke Rhead, Michael Gaddis, Cecily Vaughn, Luong Ruiz, Nathan Berkowitz, Cat Foo, Ashley Elrick, Christine Maldonado, Ahna R. Girshick, Heather H. Harris, Ke Bi, Marjan Champine, Evan McCartney-Melstad, Spencer C. Knight, Keith Noto, Barry Starr, Milos Pavlovic, David A. Turrisini, Robert Burton, Kristin A. Rand, Ross E. Curtis, Shea King, Andrey Smelter, Karen Delgado, Yambazi Banda, Asher K. Haug Baltzell, Alisa Sedghifar, Raghavendran Partha, Yong Wang, Abby Drokhlyansky, Genevieve H.L. Roberts, Chodon Sass, Jenna Petersen, Patty Miller, Danny S. Park, Catherine A. Ball, and Shannon R. McCurdy

Subjects
education.field_of_study, Coronavirus disease 2019 (COVID-19), business.industry, Population, Genome-wide association study, Bioinformatics, Phenotype, Intensive care unit, law.invention, law, ABO blood group system, Medicine, business, education, Genetic association, Subclinical infection
Abstract

INTRODUCTION PARAGRAPHMultiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable.1-5 Most of these studies ascertained COVID-19 cases in medical clinics and hospitals, which can lead to an overrepresentation of cases with severe outcomes, such as hospitalization, intensive care unit admission, or ventilation. Here, we demonstrate the utility and validity of deep phenotyping with self-reported outcomes in a population with a large proportion of mild and subclinical cases. Using these data, we defined eight different phenotypes related to COVID-19 outcomes: four that align with previously studied COVID-19 definitions and four novel definitions that focus on susceptibility given exposure, mild clinical manifestations, and an aggregate score of symptom severity. We assessed replication of 13 previously identified COVID-19 genetic associations with all eight phenotypes and found distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. We then performed a discovery GWAS, which suggested some novel phenotypes may better capture protective associations and also identified a novel association in chr11/GALNT18 that reproduced in two fully independent populations.

Published
2021
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21. Ancestry Inference Using Reference Labeled Clusters of Haplotypes

Author

Jake K. Byrnes, Alisa Sedghifar, Catherine A. Ball, Eurie L. Hong, Joshua G. Schraiber, Shiya Song, Keith Noto, David A. Turissini, and Yong Wang

Subjects
QH301-705.5, Computer science, Computer applications to medicine. Medical informatics, Population, R858-859.7, Inference, Polymorphism, Single Nucleotide, RFMix, Biochemistry, Structural Biology, Humans, Biology (General), HMM, 1000 Genomes Project, education, Molecular Biology, education.field_of_study, Genome, Human, Ancestry inference, Methodology Article, Applied Mathematics, Haplotype, Local ancestry, ARCHes, Computer Science Applications, Running time, Genetics, Population, Haplotypes, Evolutionary biology, Human genome, Haplotype modeling
Abstract

We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual’s ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations to 1,001 sections of a genotype using 10 CPU). We test ARCHes on public data from the 1,000 Genomes Project and HGDP as well as simulated examples of known admixture. Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.Author SummaryHuman DNA is inherited from ancestors that come from different populations across the globe and across time. Being able to identify which of those populations make up an individual’s DNA, how much they contribute, and on which chromosomes, is currently an important open research problem with many applications in the study of human diversity and history. As DNA sequencing and genotyping technology has developed, we have greater and greater amounts of data, which allows for the development of new sophisticated machine learning methods to approach this problem, and presents a need to process large amounts of data efficiently. These methods learn from examples of DNA data from known populations, and must be robust to differences in size and diversity among those reference populations. We present a new approach to this problem called ARCHes (Ancestry inference usingReference labeledClusters ofHaplotypes), that models the global diversity of small segments of human DNA sequence (“haplotypes”), and the extent to which these haplotypes are associated with each of a set of population reference panels. It then computes the most likely population assignments and the points along the genome where the populations change. Our experiments show that ARCHes has superior accuracy compared to a state-of-the-art method in identifying source populations and their locations on the genome, regardless of the number of different populations present in the genome, how closely related those populations are. ARCHes is also able to model populations despite having a small amount of population reference DNA data.

Published
2020
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22. Estimates of the Heritability of Human Longevity Are Substantially Inflated due to Assortative Mating

Author

Don Curtis, Dmitri Slinkov, Natalie M. Myres, Catherine A. Ball, Neal Varner, J. Graham Ruby, Ilya Dorfman, Daniel Garrigan, Keith Noto, Jake K. Byrnes, Amir R. Kermany, Julie M. Granka, Kristin A. Rand, and Kevin M. Wright

Subjects
Male, 0301 basic medicine, Longevity, Investigations, heritability, Biology, Structural equation modeling, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, human, Population and Evolutionary Genetics, Models, Genetic, Life span, Reproduction, Assortative mating, Heritability, Pedigree, 030104 developmental biology, assortative mating, Human longevity, Life expectancy, Female, Birth cohort, life span, 030217 neurology & neurosurgery, Demography
Abstract

Here, Ruby et al. analyze an unprecedented amount of public family tree data from Ancestry and determine that the heritability of human longevity was well below 10%, lower than the widely-held belief that lifespan..., Human life span is a phenotype that integrates many aspects of health and environment into a single ultimate quantity: the elapsed time between birth and death. Though it is widely believed that long life runs in families for genetic reasons, estimates of life span “heritability” are consistently low (∼15–30%). Here, we used pedigree data from Ancestry public trees, including hundreds of millions of historical persons, to estimate the heritability of human longevity. Although “nominal heritability” estimates based on correlations among genetic relatives agreed with prior literature, the majority of that correlation was also captured by correlations among nongenetic (in-law) relatives, suggestive of highly assortative mating around life span-influencing factors (genetic and/or environmental). We used structural equation modeling to account for assortative mating, and concluded that the true heritability of human longevity for birth cohorts across the 1800s and early 1900s was well below 10%, and that it has been generally overestimated due to the effect of assortative mating.

Published
2018
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23. A Prospective Analysis of Genetic Variants Associated with Human Lifespan

Author

Amir R. Kermany, Kristin A. Rand, Catherine A. Ball, Dmitri Slinkov, Ilya Dorfman, J. Graham Ruby, Don Curtis, Keith Noto, Jake K. Byrnes, Julie M. Granka, Daniel Garrigan, Natalie M. Myres, and Kevin M. Wright

Subjects
Male, Longevity, Locus (genetics), Pedigree chart, Genome-wide association study, QH426-470, Biology, Investigations, Polymorphism, Single Nucleotide, 03 medical and health sciences, Prospective analysis, 0302 clinical medicine, Apolipoproteins E, Proto-Oncogene Proteins, Databases, Genetic, Genetics, Humans, GWAS, Prospective Studies, human, Molecular Biology, Genetics (clinical), 030304 developmental biology, Aged, Aged, 80 and over, 0303 health sciences, Genetic variants, Nuclear Proteins, Heritability, Biobank, Pedigree, Trait, Female, Carrier Proteins, 030217 neurology & neurosurgery, lifespan, Genome-Wide Association Study
Abstract

We present a massive investigation into the genetic basis of human lifespan. Beginning with a genome-wide association (GWA) study using a de-identified snapshot of the unique AncestryDNA database – more than 300,000 genotyped individuals linked to pedigrees of over 400,000,000 people – we mapped six genome-wide significant loci associated with parental lifespan. We compared these results to a GWA analysis of the traditional lifespan proxy trait, age, and found only one locus, APOE, to be associated with both age and lifespan. By combining the AncestryDNA results with those of an independent UK Biobank dataset, we conducted a meta-analysis of more than 650,000 individuals and identified fifteen parental lifespan-associated loci. Beyond just those significant loci, our genome-wide set of polymorphisms accounts for up to 8% of the variance in human lifespan; this value represents a large fraction of the heritability estimated from phenotypic correlations between relatives.

Published
2019

24. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text.

Author

Martin Krallinger, Miguel Vazquez, Florian Leitner, David Salgado, Andrew Chatr-aryamontri, Andrew G. Winter, Livia Perfetto, Leonardo Briganti, Luana Licata, Marta Iannuccelli, Luisa Castagnoli, Gianni Cesareni, Mike Tyers, Gerold Schneider, Fabio Rinaldi 0001, Robert Leaman, Graciela Gonzalez 0001, Sérgio Matos, Sun Kim, W. John Wilbur, Luis M. Rocha, Hagit Shatkay, Ashish V. Tendulkar, Shashank Agarwal, Feifan Liu, Xinglong Wang, Rafal Rak, Keith Noto, Charles Elkan, and Zhiyong Lu

Published
2011
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26. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

Author

Carla E. Brodley, Keith Noto, and Donna K. Slonim

Subjects
Computer Networks and Communications, business.industry, Computer science, Intrusion detection system, Machine learning, computer.software_genre, Class (biology), Article, Computer Science Applications, Task (project management), Support vector machine, Feature (computer vision), Unsupervised learning, Anomaly detection, Artificial intelligence, Feature modeling, business, computer, Information Systems
Abstract

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

Published
2012

27. Learning Hidden Markov Models for Regression using Path Aggregation

Author

Keith, Noto and Mark, Craven

Subjects
Computer Science::Machine Learning, Article
Abstract

We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regression model that maps features that characterize paths through the model to continuous responses. Our results, in both synthetic and biological domains, demonstrate the value of jointly learning the two components of our approach.

Published
2011

28. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

Author

Marta Iannuccelli, Alfonso Valencia, Zhiyong Lu, Andrew G. Winter, Charles Elkan, Shashank Agarwal, Ashish V. Tendulkar, Martin Krallinger, Robert Leaman, Rafal Rak, Florian Leitner, Keith Noto, Feifan Liu, Hagit Shatkay, Gerold Schneider, Sun Kim, Graciela Gonzalez, Miguel Vazquez, W. John Wilbur, Sérgio Matos, Rezarta Islamaj Dogan, Xinglong Wang, Livia Perfetto, Luis M. Rocha, David Salgado, Miguel A. Andrade-Navarro, Luisa Castagnoli, Fabio Rinaldi, Leonardo Briganti, Gianni Cesareni, Mike Tyers, Luana Licata, Jean-Fred Fontaine, Andrew Chatr-aryamontri, Génétique Médicale et Génomique Fonctionnelle (GMGF), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Montréal (UdeM), Università degli Studi di Roma Tor Vergata [Roma], Mount Sinai Hospital [Toronto, Canada] (MSH), Institute of Computational Linguistics, Universität Zürich [Zürich] = University of Zurich (UZH), Department of Electronics, Telecommunications and Informatics [Aveiro] (DETI), Universidade de Aveiro, Department of Pathology, Case Western Reserve University [Cleveland], Sorenson Molecular Genealogy Foundation, National Center for Biotechnology Information (NCBI), Mitochondrie : Régulations et Pathologie, Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM), National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Periodicals as Topic, Animals, Data Mining, Humans, Databases, Protein, Algorithms, Proteins, PubMed, Computer science, Ontology (information science), computer.software_genre, Biochemistry, Task (project management), 03 medical and health sciences, Databases, Text mining, Structural Biology, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, business.industry, Applied Mathematics, Document classification, Protein, Research, 030302 biochemistry & molecular biology, Biomedical text mining, Computer Science Applications, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Settore BIO/18 - Genetica, Ranking, Test set, Ontology, Data mining, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Natural language processing
Abstract

Background Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. Results A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%. Conclusions The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.

Published
2011
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29. Anomaly Detection Using an Ensemble of Feature Models

Author

Carly Brodley, Keith Noto, and Donna K. Slonim

Subjects
Training set, business.industry, Computer science, Entropy (statistical thermodynamics), Feature vector, Feature extraction, Pattern recognition, Feature selection, computer.software_genre, Article, Feature model, Support vector machine, Data set, Kernel (linear algebra), Data point, Entropy (information theory), Anomaly detection, Artificial intelligence, Data mining, Entropy (energy dispersal), business, computer
Abstract

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of ``normal'' training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches.

Published
2010
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