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19 results on '"Cassa, Christopher A."'

1. Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Kousi, Maria, Söylemez, Onuralp, Ozanturk, Aysegül, Mourtzi, Niki, Akle, Sebastian, Jungreis, Irwin, Muller, Jean, Cassa, Christopher A, Brand, Harrison, Mokry, Jill Anne, Wolf, Maxim Y., Sadeghpour, Azita, McFadden, Kelsey, Lewis, Richard A, Talkowski, Michael E, Dollfus, Hélène, Kellis, Manolis, Davis, Erica E, Sunyaev, Shamil R, Katsanis, Nicholas, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Kousi, Maria, Söylemez, Onuralp, Ozanturk, Aysegül, Mourtzi, Niki, Akle, Sebastian, Jungreis, Irwin, Muller, Jean, Cassa, Christopher A, Brand, Harrison, Mokry, Jill Anne, Wolf, Maxim Y., Sadeghpour, Azita, McFadden, Kelsey, Lewis, Richard A, Talkowski, Michael E, Dollfus, Hélène, Kellis, Manolis, Davis, Erica E, Sunyaev, Shamil R, and Katsanis, Nicholas

Abstract

© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet–Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes—a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

Published
2022

2. Machine learning based CRISPR gRNA design for therapeutic exon skipping

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Computational and Systems Biology Program, Massachusetts Institute of Technology. Department of Biological Engineering, Louie, Wilson, Shen, Max W, Tahiry, Zakir, Zhang, Sophia, Worstell, Daniel, Cassa, Christopher A, Sherwood, Richard I, Gifford, David K, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Computational and Systems Biology Program, Massachusetts Institute of Technology. Department of Biological Engineering, Louie, Wilson, Shen, Max W, Tahiry, Zakir, Zhang, Sophia, Worstell, Daniel, Cassa, Christopher A, Sherwood, Richard I, and Gifford, David K

Abstract

© 2021 Louie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.

Published
2022

3. Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy

Author

Kousi, Maria, Söylemez, Onuralp, Ozanturk, Aysegül, Mourtzi, Niki, Akle, Sebastian, Jungreis, Irwin, Muller, Jean, Cassa, Christopher A, Brand, Harrison, Mokry, Jill Anne, Wolf, Maxim Y, Sadeghpour, Azita, McFadden, Kelsey, Lewis, Richard A, Talkowski, Michael E, Dollfus, Hélène, Kellis, Manolis, Davis, Erica E, Sunyaev, Shamil R, Katsanis, Nicholas, Kousi, Maria, Söylemez, Onuralp, Ozanturk, Aysegül, Mourtzi, Niki, Akle, Sebastian, Jungreis, Irwin, Muller, Jean, Cassa, Christopher A, Brand, Harrison, Mokry, Jill Anne, Wolf, Maxim Y, Sadeghpour, Azita, McFadden, Kelsey, Lewis, Richard A, Talkowski, Michael E, Dollfus, Hélène, Kellis, Manolis, Davis, Erica E, Sunyaev, Shamil R, and Katsanis, Nicholas

Abstract

© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet–Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes—a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

Published
2021

4. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions

Author

Massachusetts Institute of Technology. Department of Biology, Fahed, Akl C, Wang, Minxian, Homburger, Julian R, Patel, Aniruddh P, Bick, Alexander G, Neben, Cynthia L, Lai, Carmen, Brockman, Deanna, Philippakis, Anthony, Ellinor, Patrick T, Cassa, Christopher A, Lebo, Matthew, Ng, Kenney, Lander, Eric S, Zhou, Alicia Y, Kathiresan, Sekar, Khera, Amit V, Massachusetts Institute of Technology. Department of Biology, Fahed, Akl C, Wang, Minxian, Homburger, Julian R, Patel, Aniruddh P, Bick, Alexander G, Neben, Cynthia L, Lai, Carmen, Brockman, Deanna, Philippakis, Anthony, Ellinor, Patrick T, Cassa, Christopher A, Lebo, Matthew, Ng, Kenney, Lander, Eric S, Zhou, Alicia Y, Kathiresan, Sekar, and Khera, Amit V

Abstract

© 2020, The Author(s). Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions — familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background — the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

Published
2021

5. Predictable and precise template-free CRISPR editing of pathogenic variants

Author

Massachusetts Institute of Technology. Computational and Systems Biology Program, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Broad Institute of MIT and Harvard, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Shen, Max Walt, Arbab, Mandana, Hsu, Jonathan Yee-Ting, Worstell, Daniel, Culbertson, Sannie J., Krabbe, Olga, Cassa, Christopher A., Liu, David R., Gifford, David K., Sherwood, Richard I., Massachusetts Institute of Technology. Computational and Systems Biology Program, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Broad Institute of MIT and Harvard, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Shen, Max Walt, Arbab, Mandana, Hsu, Jonathan Yee-Ting, Worstell, Daniel, Culbertson, Sannie J., Krabbe, Olga, Cassa, Christopher A., Liu, David R., Gifford, David K., and Sherwood, Richard I.

Abstract

Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5–11% of Cas9 guide RNAs targeting the human genome are ‘precise-50’, yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky–Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing. Keywords: Functional genomics; Genome informatics

Published
2020

6. An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.

Author

Haghighi, Alireza, Haghighi, Alireza, Krier, Joel B, Toth-Petroczy, Agnes, Cassa, Christopher A, Frank, Natasha Y, Carmichael, Nikkola, Fieg, Elizabeth, Bjonnes, Andrew, Mohanty, Anwoy, Briere, Lauren C, Lincoln, Sharyn, Lucia, Stephanie, Gupta, Vandana A, Söylemez, Onuralp, Sutti, Sheila, Kooshesh, Kameron, Qiu, Haiyan, Fay, Christopher J, Perroni, Victoria, Valerius, Jamie, Hanna, Meredith, Frank, Alexander, Ouahed, Jodie, Snapper, Scott B, Pantazi, Angeliki, Chopra, Sameer S, Leshchiner, Ignaty, Stitziel, Nathan O, Feldweg, Anna, Mannstadt, Michael, Loscalzo, Joseph, Sweetser, David A, Liao, Eric, Stoler, Joan M, Nowak, Catherine B, Sanchez-Lara, Pedro A, Klein, Ophir D, Perry, Hazel, Patsopoulos, Nikolaos A, Raychaudhuri, Soumya, Goessling, Wolfram, Green, Robert C, Seidman, Christine E, MacRae, Calum A, Sunyaev, Shamil R, Maas, Richard L, Vuzman, Dana, Undiagnosed Diseases Network, Brigham and Women’s Hospital FaceBase Project, Brigham Genomic Medicine (BGM), Haghighi, Alireza, Haghighi, Alireza, Krier, Joel B, Toth-Petroczy, Agnes, Cassa, Christopher A, Frank, Natasha Y, Carmichael, Nikkola, Fieg, Elizabeth, Bjonnes, Andrew, Mohanty, Anwoy, Briere, Lauren C, Lincoln, Sharyn, Lucia, Stephanie, Gupta, Vandana A, Söylemez, Onuralp, Sutti, Sheila, Kooshesh, Kameron, Qiu, Haiyan, Fay, Christopher J, Perroni, Victoria, Valerius, Jamie, Hanna, Meredith, Frank, Alexander, Ouahed, Jodie, Snapper, Scott B, Pantazi, Angeliki, Chopra, Sameer S, Leshchiner, Ignaty, Stitziel, Nathan O, Feldweg, Anna, Mannstadt, Michael, Loscalzo, Joseph, Sweetser, David A, Liao, Eric, Stoler, Joan M, Nowak, Catherine B, Sanchez-Lara, Pedro A, Klein, Ophir D, Perry, Hazel, Patsopoulos, Nikolaos A, Raychaudhuri, Soumya, Goessling, Wolfram, Green, Robert C, Seidman, Christine E, MacRae, Calum A, Sunyaev, Shamil R, Maas, Richard L, Vuzman, Dana, and Undiagnosed Diseases Network, Brigham and Women’s Hospital FaceBase Project, Brigham Genomic Medicine (BGM)

Abstract

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs.

Published
2018

7. An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.

Author

Haghighi, Alireza, Haghighi, Alireza, Krier, Joel B, Toth-Petroczy, Agnes, Cassa, Christopher A, Frank, Natasha Y, Carmichael, Nikkola, Fieg, Elizabeth, Bjonnes, Andrew, Mohanty, Anwoy, Briere, Lauren C, Lincoln, Sharyn, Lucia, Stephanie, Gupta, Vandana A, Söylemez, Onuralp, Sutti, Sheila, Kooshesh, Kameron, Qiu, Haiyan, Fay, Christopher J, Perroni, Victoria, Valerius, Jamie, Hanna, Meredith, Frank, Alexander, Ouahed, Jodie, Snapper, Scott B, Pantazi, Angeliki, Chopra, Sameer S, Leshchiner, Ignaty, Stitziel, Nathan O, Feldweg, Anna, Mannstadt, Michael, Loscalzo, Joseph, Sweetser, David A, Liao, Eric, Stoler, Joan M, Nowak, Catherine B, Sanchez-Lara, Pedro A, Klein, Ophir D, Perry, Hazel, Patsopoulos, Nikolaos A, Raychaudhuri, Soumya, Goessling, Wolfram, Green, Robert C, Seidman, Christine E, MacRae, Calum A, Sunyaev, Shamil R, Maas, Richard L, Vuzman, Dana, Undiagnosed Diseases Network, Brigham and Women’s Hospital FaceBase Project, Brigham Genomic Medicine (BGM), Haghighi, Alireza, Haghighi, Alireza, Krier, Joel B, Toth-Petroczy, Agnes, Cassa, Christopher A, Frank, Natasha Y, Carmichael, Nikkola, Fieg, Elizabeth, Bjonnes, Andrew, Mohanty, Anwoy, Briere, Lauren C, Lincoln, Sharyn, Lucia, Stephanie, Gupta, Vandana A, Söylemez, Onuralp, Sutti, Sheila, Kooshesh, Kameron, Qiu, Haiyan, Fay, Christopher J, Perroni, Victoria, Valerius, Jamie, Hanna, Meredith, Frank, Alexander, Ouahed, Jodie, Snapper, Scott B, Pantazi, Angeliki, Chopra, Sameer S, Leshchiner, Ignaty, Stitziel, Nathan O, Feldweg, Anna, Mannstadt, Michael, Loscalzo, Joseph, Sweetser, David A, Liao, Eric, Stoler, Joan M, Nowak, Catherine B, Sanchez-Lara, Pedro A, Klein, Ophir D, Perry, Hazel, Patsopoulos, Nikolaos A, Raychaudhuri, Soumya, Goessling, Wolfram, Green, Robert C, Seidman, Christine E, MacRae, Calum A, Sunyaev, Shamil R, Maas, Richard L, Vuzman, Dana, and Undiagnosed Diseases Network, Brigham and Women’s Hospital FaceBase Project, Brigham Genomic Medicine (BGM)

Abstract

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs.

Published
2018

8. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., Margulies, David M., Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., and Margulies, David M.

Abstract

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., QC 20140819

Published
2014
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9. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., Margulies, David M., Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., and Margulies, David M.

Abstract

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., AuthorCount:198

Published
2014
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10. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Szolovits, Peter, Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie Jr., David, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzyński, Piotr, Fairbrother, William G., Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. A., Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrečić, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., Lucas, F Anthony S., Gonzalez-Garay, Manuel L., Caskey, C. T., Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., González-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De La Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. M., Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac, Margulies, David M., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Szolovits, Peter, Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie Jr., David, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzyński, Piotr, Fairbrother, William G., Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. A., Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrečić, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., Lucas, F Anthony S., Gonzalez-Garay, Manuel L., Caskey, C. T., Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., González-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De La Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. M., Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac, and Margulies, David M.

Abstract

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., Boston Children's Hospital (Manton Center for Orphan Disease Research), Harvard Medical School (Center for Biomedical Infomatics)

Published
2014

11. Disclosing pathogenic genetic variants to research participants: Quantifying an emerging ethical responsibility

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., McGuire, Amy L., Mandl, Kenneth D., Savage, Sarah K., Taylor, Patrick L., Green, Robert C., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., McGuire, Amy L., Mandl, Kenneth D., Savage, Sarah K., Taylor, Patrick L., and Green, Robert C.

Abstract

There is an emerging consensus that when investigators obtain genomic data from research participants, they may incur an ethical responsibility to inform at-risk individuals about clinically significant variants discovered during the course of their research. With whole-exome sequencing becoming commonplace and the falling costs of full-genome sequencing, there will be an increasingly large number of variants identified in research participants that may be of sufficient clinical relevance to share. An explicit approach to triaging and communicating these results has yet to be developed, and even the magnitude of the task is uncertain. To develop an estimate of the number of variants that might qualify for disclosure, we apply recently published recommendations for the return of results to a defined and representative set of variants and then extrapolate these estimates to genome scale. We find that the total number of variants meeting the threshold for recommended disclosure ranges from 3955–12,579 (3.79%–12.06%, 95% CI) in the most conservative estimate to 6998–17,189 (6.69%–16.48%, 95% CI) in an estimate including variants with variable disease expressivity. Additionally, if the growth rate from the previous 4 yr continues, we estimate that the total number of disease-associated variants will grow 37% over the next 4 yr., National Library of Medicine (U.S.) (grant LM010470-01), Boston Children's Hospital. Manton Center for Orphan Disease Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (training grant HD040128), Baylor College of Medicine (BCM Clinical and Translational Research program), Baylor College of Medicine (Baylor Annual Fund), Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (AG027841), Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (HG02213), Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (HG005092)

Published
2013

12. Re-identification of home addresses from spatial locations anonymized by Gaussian skew

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Wieland, Shannon Christine, Mandl, Kenneth D., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Wieland, Shannon Christine, and Mandl, Kenneth D.

Abstract

Background: Knowledge of the geographical locations of individuals is fundamental to the practice of spatial epidemiology. One approach to preserving the privacy of individual-level addresses in a data set is to de-identify the data using a non-deterministic blurring algorithm that shifts the geocoded values. We investigate a vulnerability in this approach which enables an adversary to re-identify individuals using multiple anonymized versions of the original data set. If several such versions are available, each can be used to incrementally refine estimates of the original geocoded location. Results: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy). With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address. With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km. Conclusion: We demonstrate that multiple versions of the same data, each anonymized by non-deterministic Gaussian skew, can be used to ascertain original geographic locations. We explore solutions to this problem that include infrastructure to support the safe disclosure of anonymized medical data to prevent inference or re-identification of original address data, and the use of a Markov-process based algorithm to mitigate this risk., National Institutes of Health. (U.S.). National Library of Medicine (1 R01 LM007677 )

Published
2010

13. A software tool for creating simulated outbreaks to benchmark surveillance systems

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Iancu, Karin, Olson, Karen L., Mandl, Kenneth D., Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Iancu, Karin, Olson, Karen L., and Mandl, Kenneth D.

Abstract

Background: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. Methods: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered. Results: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly. Conclusion: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms., National Institutes of Health (U.S.) (R01LM007970-01), Alfred P. Sloan Foundation (grant 2002-12-1)

Published
2010

14. My sister's keeper?: genomic research and the identifiability of siblings

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, Mandl, Kenneth D., Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, and Mandl, Kenneth D.

Abstract

Background Genomic sequencing of SNPs is increasingly prevalent, though the amount of familial information these data contain has not been quantified. Methods We provide a framework for measuring the risk to siblings of a patient's SNP genotype disclosure, and demonstrate that sibling SNP genotypes can be inferred with substantial accuracy. Results Extending this inference technique, we determine that a very low number of matches at commonly varying SNPs is sufficient to confirm sib-ship, demonstrating that published sequence data can reliably be used to derive sibling identities. Using HapMap trio data, at SNPs where one child is homozygotic major, with a minor allele frequency ≤ 0.20, (N = 452684, 65.1%) we achieve 91.9% inference accuracy for sibling genotypes. Conclusion These findings demonstrate that substantial discrimination and privacy risks arise from use of inferred familial genomic data., National Institutes of Health (grant R01-LM009375-01A1), National Library of Medicine

Published
2010

15. Privacy and identifiability in clinical research, personalized medicine, and public health surveillance

Author

Peter Szolovits., Harvard University--MIT Division of Health Sciences and Technology., Cassa, Christopher A, Peter Szolovits., Harvard University--MIT Division of Health Sciences and Technology., and Cassa, Christopher A

Abstract

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Includes bibliographical references (p. 191-200)., Electronic transmission of protected health information has become pervasive in research, clinical, and public health investigations, posing substantial risk to patient privacy. From clinical genetic screenings to publication of data in research studies, these activities have the potential to disclose identity, medical conditions, and hereditary data. To enable an era of personalized medicine, many research studies are attempting to correlate individual clinical outcomes with genomic data, leading to thousands of new investigations. Critical to the success of many of these studies is research participation by individuals who are willing to share their genotypic and clinical data with investigators, necessitating methods and policies that preserve privacy with such disclosures. We explore quantitative models that allow research participants, patients and investigators to fully understand these complex privacy risks when disclosing medical data. This modeling will improve the informed consent and risk assessment process, for both demographic and medical data, each with distinct domain-specific scenarios. We first discuss the disclosure risk for genomic data, investigating both the risk of re-identification for SNPs and mutations, as well as the disclosure impact on family members. Next, the deidentification and anonymization of geospatial datasets containing information about patient home addresses will be examined, using mathematical skewing algorithms as well as a linear programming approach. Finally, we consider the re-identification potential of geospatial data, commonly shared in both textual form and in printed maps in journals and public health practice. We also explore methods to quantify the anonymity afforded when using these anonymization techniques., by Christopher A. Cassa., Ph.D.

Published
2009

16. Privacy and identifiability in clinical research, personalized medicine, and public health surveillance

Author

Peter Szolovits., Harvard University--MIT Division of Health Sciences and Technology., Cassa, Christopher A, Peter Szolovits., Harvard University--MIT Division of Health Sciences and Technology., and Cassa, Christopher A

Abstract

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Includes bibliographical references (p. 191-200)., Electronic transmission of protected health information has become pervasive in research, clinical, and public health investigations, posing substantial risk to patient privacy. From clinical genetic screenings to publication of data in research studies, these activities have the potential to disclose identity, medical conditions, and hereditary data. To enable an era of personalized medicine, many research studies are attempting to correlate individual clinical outcomes with genomic data, leading to thousands of new investigations. Critical to the success of many of these studies is research participation by individuals who are willing to share their genotypic and clinical data with investigators, necessitating methods and policies that preserve privacy with such disclosures. We explore quantitative models that allow research participants, patients and investigators to fully understand these complex privacy risks when disclosing medical data. This modeling will improve the informed consent and risk assessment process, for both demographic and medical data, each with distinct domain-specific scenarios. We first discuss the disclosure risk for genomic data, investigating both the risk of re-identification for SNPs and mutations, as well as the disclosure impact on family members. Next, the deidentification and anonymization of geospatial datasets containing information about patient home addresses will be examined, using mathematical skewing algorithms as well as a linear programming approach. Finally, we consider the re-identification potential of geospatial data, commonly shared in both textual form and in printed maps in journals and public health practice. We also explore methods to quantify the anonymity afforded when using these anonymization techniques., by Christopher A. Cassa., Ph.D.

Published
2009

17. My sister's keeper?: genomic research and the identifiability of siblings

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, Mandl, Kenneth D., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, and Mandl, Kenneth D.

Abstract

Background: Genomic sequencing of SNPs is increasingly prevalent, though the amount of familial information these data contain has not been quantified. Methods: We provide a framework for measuring the risk to siblings of a patient's SNP genotype disclosure, and demonstrate that sibling SNP genotypes can be inferred with substantial accuracy. Results: Extending this inference technique, we determine that a very low number of matches at commonly varying SNPs is sufficient to confirm sib-ship, demonstrating that published sequence data can reliably be used to derive sibling identities. Using HapMap trio data, at SNPs where one child is homozygotic major, with a minor allele frequency ≤ 0.20, (N = 452684, 65.1%) we achieve 91.9% inference accuracy for sibling genotypes. Conclusion: These findings demonstrate that substantial discrimination and privacy risks arise from use of inferred familial genomic data.

Published
2009

18. My sister's keeper?: genomic research and the identifiability of siblings

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, Mandl, Kenneth D., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cassa, Christopher A., Schmidt, Brian, Kohane, Isaac, and Mandl, Kenneth D.

Abstract

Background: Genomic sequencing of SNPs is increasingly prevalent, though the amount of familial information these data contain has not been quantified. Methods: We provide a framework for measuring the risk to siblings of a patient's SNP genotype disclosure, and demonstrate that sibling SNP genotypes can be inferred with substantial accuracy. Results: Extending this inference technique, we determine that a very low number of matches at commonly varying SNPs is sufficient to confirm sib-ship, demonstrating that published sequence data can reliably be used to derive sibling identities. Using HapMap trio data, at SNPs where one child is homozygotic major, with a minor allele frequency ≤ 0.20, (N = 452684, 65.1%) we achieve 91.9% inference accuracy for sibling genotypes. Conclusion: These findings demonstrate that substantial discrimination and privacy risks arise from use of inferred familial genomic data.

Published
2009

19. Spatial outbreak detection analysis tool : a system to create sets of semi-synthetic geo-spatial clusters

Author

Peter Szolovits., Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science., Cassa, Christopher A, Peter Szolovits., Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science., and Cassa, Christopher A

Abstract

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004., Includes bibliographical references (leaves 55-57)., Syndromic surveillance systems, especially software systems, have emerged as the leading outbreak detection mechanisms. Early outbreak detection systems can assist with medical and logistic decision support. One important concern for effectively testing these systems in practice is the scarcity of authentic outbreak health data. Because of this shortage, creating suitable geotemporal test clusters for surveillance algorithm validation is essential. Described is an automated tool that creates artificial patient clusters by varying a large variety of realistic outbreak parameters. The cluster creation tool is an open-source program that accepts a set of outbreak parameters and creates artificial geospatial patient data for a single cluster or a series of similar clusters. This helps automate the process of rigorous testing and validation of outbreak detection algorithms. Using the cluster generator, single patient clusters and series of patient clusters were created - as files and series of files containing patient longitude and latitude coordinates. These clusters were then tested and validated using a publicly-available GIS visualization program. All generated clusters were properly created within the ranges that were entered as parameters at program execution. Sample semi-synthetic datasets from the cluster creation tool were then used to validate a popular spatial outbreak detection algorithm, the M-Statistic., by Christopher A. Casa., M.Eng.and S.B.

Published
2006

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