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1. Genotyping, sequencing and analysis of 140,000 adults from Mexico City

Author

Ziyatdinov, Andrey, Torres, Jason, Alegre-Díaz, Jesús, Backman, Joshua, Mbatchou, Joelle, Turner, Michael, Gaynor, Sheila M., Joseph, Tyler, Zou, Yuxin, Liu, Daren, Wade, Rachel, Staples, Jeffrey, Panea, Razvan, Popov, Alex, Bai, Xiaodong, Balasubramanian, Suganthi, Habegger, Lukas, Lanche, Rouel, Lopez, Alex, Maxwell, Evan, Jones, Marcus, García-Ortiz, Humberto, Ramirez-Reyes, Raul, Santacruz-Benítez, Rogelio, Nag, Abhishek, Smith, Katherine R., Damask, Amy, Lin, Nan, Paulding, Charles, Reppell, Mark, Zöllner, Sebastian, Jorgenson, Eric, Salerno, William, Petrovski, Slavé, Overton, John, Reid, Jeffrey, Thornton, Timothy A., Abecasis, Gonçalo, Berumen, Jaime, Orozco-Orozco, Lorena, Collins, Rory, Baras, Aris, Hill, Michael R., Emberson, Jonathan R., Marchini, Jonathan, Kuri-Morales, Pablo, and Tapia-Conyer, Roberto

Published
2023
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2. Author Correction: Genotyping, sequencing and analysis of 140,000 adults from Mexico City

Author

Ziyatdinov, Andrey, Torres, Jason, Alegre-Díaz, Jesús, Backman, Joshua, Mbatchou, Joelle, Turner, Michael, Gaynor, Sheila M., Joseph, Tyler, Zou, Yuxin, Liu, Daren, Wade, Rachel, Staples, Jeffrey, Panea, Razvan, Popov, Alex, Bai, Xiaodong, Balasubramanian, Suganthi, Habegger, Lukas, Lanche, Rouel, Lopez, Alex, Maxwell, Evan, Jones, Marcus, García-Ortiz, Humberto, Ramirez-Reyes, Raul, Santacruz-Benítez, Rogelio, Nag, Abhishek, Smith, Katherine R., Damask, Amy, Lin, Nan, Paulding, Charles, Reppell, Mark, Zöllner, Sebastian, Jorgenson, Eric, Salerno, William, Petrovski, Slavé, Overton, John, Reid, Jeffrey, Thornton, Timothy A., Abecasis, Gonçalo, Berumen, Jaime, Orozco-Orozco, Lorena, Collins, Rory, Baras, Aris, Hill, Michael R., Emberson, Jonathan R., Marchini, Jonathan, Kuri-Morales, Pablo, and Tapia-Conyer, Roberto

Published
2024
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3. “PMS2CL interference leading to erroneous identification of a pathogenic PMS2 variant in Black Patients”

Author

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

Published
2024
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4. Joint testing of rare variant burden scores using non-negative least squares.

Author

Ziyatdinov, Andrey, Mbatchou, Joelle, Marcketta, Anthony, Backman, Joshua, Gaynor, Sheila, Zou, Yuxin, Joseph, Tyler, Geraghty, Benjamin, Herman, Joseph, Watanabe, Kyoko, Ghosh, Arkopravo, Kosmicki, Jack, Locke, Adam, Thornton, Timothy, Kang, Hyun Min, Ferreira, Manuel, Baras, Aris, Abecasis, Goncalo, and Marchini, Jonathan

Subjects
*GENETIC variation, *QUANTITATIVE research, *BINS, *ANNOTATIONS, *COMPUTER software
Abstract

Gene-based burden tests are a popular and powerful approach for analysis of exome-wide association studies. These approaches combine sets of variants within a gene into a single burden score that is then tested for association. Typically, a range of burden scores are calculated and tested across a range of annotation classes and frequency bins. Correlation between these tests can complicate the multiple testing correction and hamper interpretation of the results. We introduce a method called the sparse burden association test (SBAT) that tests the joint set of burden scores under the assumption that causal burden scores act in the same effect direction. The method simultaneously assesses the significance of the model fit and selects the set of burden scores that best explain the association at the same time. Using simulated data, we show that the method is well calibrated and highlight scenarios where the test outperforms existing gene-based tests. We apply the method to 73 quantitative traits from the UK Biobank, showing that SBAT is a valuable additional gene-based test when combined with other existing approaches. This test is implemented in the REGENIE software. Gene-based burden tests are commonly used in exome-wide association studies. We introduce SBAT (sparse burden association test), which jointly models a set of burden scores under the assumption that the causal burden scores act in the same effect direction. We apply SBAT to 73 quantitative traits in the UK Biobank. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Biallelic BRCA Loss and Homologous Recombination Deficiency in Nonbreast/Ovarian Tumors in Germline BRCA1/2 Carriers

Author

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

Published
2023
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8. Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma

Author

Sarin, Kavita Y., Lin, Yuan, Daneshjou, Roxana, Ziyatdinov, Andrey, Thorleifsson, Gudmar, Rubin, Adam, Pardo, Luba M., Wu, Wenting, Khavari, Paul A., Uitterlinden, Andre, Nijsten, Tamar, Toland, Amanda E., Olafsson, Jon H., Sigurgeirsson, Bardur, Thorisdottir, Kristin, Jorgensen, Eric, Whittemore, Alice S., Kraft, Peter, Stacey, Simon N., Stefansson, Kari, Asgari, Maryam M., and Han, Jiali

Published
2020
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9. PMS2CL interference leading to erroneous identification of a pathogenic PMS2 variant in Black patients

Author

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

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

Author

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

Published
2023
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15. Identifying COPD subtypes using multi-trait genetics

Author

Ziyatdinov, Andrey, primary, Hobbs, Brian D., additional, Kanaan-Izquierdo, Samir, additional, Moll, Matthew, additional, Sakornsakolpat, Phuwanat, additional, Shrine, Nick, additional, Chen, Jing, additional, Song, Kijoung, additional, Bowler, Russell P., additional, Castaldi, Peter J., additional, Tobin, Martin D., additional, Kraft, Peter, additional, Silverman, Edwin K., additional, Julienne, Hanna, additional, Aschard, Hugues, additional, and Cho, Michael H., additional

Published
2023
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18. Multitrait GWAS to connect disease variants and biological mechanisms

Author

Julienne, Hanna, Laville, Vincent, Mccaw, Zachary, He, Zihuai, Guillemot, Vincent, Lasry, Carla, Ziyatdinov, Andrey, Nerin, Cyril, Vaysse, Amaury, Lechat, Pierre, Ménager, Hervé, Le Goff, Wilfried, Dube, Marie-Pierre, Kraft, Peter, Ionita-Laza, Iuliana, Vilhjálmsson, Bjarni, Aschard, Hugues, Département de Biologie Computationnelle - Department of Computational Biology, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Harvard T.H. Chan School of Public Health, Stanford University School of Medicine [CA, USA], Stanford University, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Montreal Heart Institute - Institut de Cardiologie de Montréal, Columbia University [New York], Aarhus University [Aarhus], This work has been conducted as part of the INCEPTION program (ANR-16-CONV-0005) (HA). It was also supported by NIH grant R03DE025665 to HA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), Aschard, hugues, and Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs - - INCEPTION2016 - ANR-16-CONV-0005 - CONV - VALID

Subjects
Single Nucleotide Polymorphisms, QH426-470, Biochemistry, [STAT.AP] Statistics [stat]/Applications [stat.AP], Medicine and Health Sciences, Genetics of the Immune System, Cluster Analysis, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], RISK, [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ME] Statistics [stat]/Methodology [stat.ME], Covariance, MESH: Polymorphism, Single Nucleotide, MESH: Genetic Predisposition to Disease, Genomics, Lipids, STATISTICS, Phenotypes, Cholesterol, Phenotype, DENSITY-LIPOPROTEIN CHOLESTEROL, Physical Sciences, [STAT.ME]Statistics [stat]/Methodology [stat.ME], MESH: Computational Biology, TRIGLYCERIDES, Research Article, Immunology, Quantitative Trait Loci, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, MESH: Phenotype, Polymorphism, Single Nucleotide, ESTER TRANSFER PROTEIN, Genetics, Genome-Wide Association Studies, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, METAANALYSIS, MESH: Humans, CORRELATED TRAITS, MORTALITY, Biology and Life Sciences, Computational Biology, Human Genetics, Random Variables, Genome Analysis, Probability Theory, MESH: Cluster Analysis, MESH: Quantitative Trait Loci, MODEL, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, MESH: Genome-Wide Association Study, Genetics of Disease, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Clinical Immunology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Clinical Medicine, Mathematics, Genome-Wide Association Study
Abstract

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials., Author summary Genome-wide association studies (GWAS) established numerous associations between genetic variants and human traits. The anonymized summary of GWAS results is generally made publicly available to the scientific community and can be explored further. Amongst the many possible secondary analyses, one is to study the effect of a genetic variant on several traits (multi-trait GWAS) rather than a unique trait. We compared several tests to conduct multi-trait GWAS on simulated and real data. We detected 322 new associations that were not previously reported by standard univariate GWAS. We then detected clusters of genetic variants having a similar effect across several traits. Focusing on two subsets of immunity and metabolism traits, we demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster and success of drug targets in randomized controlled trials. We propose this method for improving the functional interpretation of GWAS results.

Published
2021

21. The Synthetic Moth

Author

Vouloutsi, Vasiliki, primary, Lopez-Serrano, Lucas, additional, Mathews, Zenon, additional, Chimeno, Alex, additional, Ziyatdinov, Andrey, additional, Lluna, Alexandre, additional, Badia, Sergi, additional, and Verschure, Paul, additional

Published
2013
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23. Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

Author

Julienne, Hanna, primary, Laville, Vincent, additional, McCaw, Zachary R., additional, He, Zihuai, additional, Guillemot, Vincent, additional, Lasry, Carla, additional, Ziyatdinov, Andrey, additional, Vaysse, Amaury, additional, Lechat, Pierre, additional, Ménager, Hervé, additional, Goff, Wilfried Le, additional, Dube, Marie-Pierre, additional, Kraft, Peter, additional, Ionita-Laza, Iuliana, additional, Vilhjálmsson, Bjarni J., additional, and Aschard, Hugues, additional

Published
2020
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24. Computationally efficient whole genome regression for quantitative and binary traits

Author

Mbatchou, Joelle, primary, Barnard, Leland, additional, Backman, Joshua, additional, Marcketta, Anthony, additional, Kosmicki, Jack A., additional, Ziyatdinov, Andrey, additional, Benner, Christian, additional, O’Dushlaine, Colm, additional, Barber, Mathew, additional, Boutkov, Boris, additional, Habegger, Lukas, additional, Ferreira, Manuel, additional, Baras, Aris, additional, Reid, Jeffrey, additional, Abecasis, Gonçalo, additional, Maxwell, Evan, additional, and Marchini, Jonathan, additional

Published
2020
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28. Additional file 1 of lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

Author

Ziyatdinov, Andrey, Vรกzquez-Santiago, Miquel, Brunel, Helena, Martinez-Perez, Angel, Aschard, Hugues, and Soria, Jose Manuel

Abstract

Supplementary Tables and Figures. Supplementary Note 1: R code to compare lme4qtl and pedigreemm R packages. Supplementary Note 2: Multi-trait and multi-environment linear mixed models. Supplementary Note 3: R code applied to the GAIT2 data. (PDF 1341 kb)

Published
2018
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31. Multiview and multifeature spectral clustering using common eigenvectors

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Kanaan Izquierdo, Samir, Ziyatdinov, Andrey, Perera Lluna, Alexandre, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Kanaan Izquierdo, Samir, Ziyatdinov, Andrey, and Perera Lluna, Alexandre

Abstract

An ever-increasing number of data analysis problems include more than one view of the data, i.e. differ- ent measurement approaches to the population under study. In consequence, pattern analysis methods that deal appropriately with multiview data are becoming increasingly useful. In this paper, a novel mul- tiview spectral clustering algorithm is presented (multiview spectral clustering by common eigenvectors, or MVSC-CEV), based on computing the common eigenvectors of the Laplacian matrices derived from the similarity matrices of the input data. This algorithm maintains the features of spectral clustering, while allowing the use of an arbitrary number of input views, possibly of a different nature (feature or graph space) and with different dimensions. The method has been tested on four standard multiview data sets (UCI’s Handwritten, BBC segmented news, Max Planck Institute’s Animal With Attributes and Reuters multilingual), and compared with seven methods in the state of the art. Seven standard clus- tering evaluation metrics have been used in the experiments. The quality of the clustering produced by MVSC-CEV is above those obtained by other state-of-the-art methods in the majority of evaluation met- rics and dataset combinations. The computation times of this method are approximately twice those of the baseline spectral clustering of the concatenated data views., Postprint (author's final draft)

Published
2018

32. Multiview: a software package for multiview pattern recognition methods

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. B2SLab - Bioinformatics and Biomedical Signals Laboratory, Kanaan Izquierdo, Samir, Ziyatdinov, Andrey, Burgueño, Maria Araceli, Perera Lluna, Alexandre, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. B2SLab - Bioinformatics and Biomedical Signals Laboratory, Kanaan Izquierdo, Samir, Ziyatdinov, Andrey, Burgueño, Maria Araceli, and Perera Lluna, Alexandre

Abstract

Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data is gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling, and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets., Peer Reviewed, Postprint (author's final draft)

Published
2018

36. Platelet count and plateletcrit are associated with an increased risk of venous thrombosis in females. Results from the RETROVE study

Author

Vázquez-Santiago, Miquel, primary, Vilalta, Noelia, additional, Ziyatdinov, Andrey, additional, Cuevas, Biel, additional, Macho, Raquel, additional, Pujol-Moix, Núria, additional, Carrasco, Marina, additional, Mateo, José, additional, Fontcuberta, Jordi, additional, Soria, José Manuel, additional, and Souto, Juan Carlos, additional

Published
2017
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39. Biomimetic set up for chemosensor-based machine olfaction

Author

Ziyatdinov, Andrey, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, and Perera Lluna, Alexandre

Subjects
Quimioreceptors, Informàtica [Àrees temàtiques de la UPC], Nas electrònic
Abstract

The thesis falls into the field of machine olfaction and accompanying experimental set up for chemical gas sensing. Perhaps more than any other sensory modality, chemical sensing faces with major technical and conceptual challenges: low specificity, slow response time, long term instability, power consumption, portability, coding capacity and robustness. There is an important trend of the last decade pushing artificial olfaction to mimic the biological olfaction system of insects and mammalians. The designers of machine olfaction devices take inspiration from the biological olfactory system, because animals effortlessly accomplish some of the unsolved problems in machine olfaction. In a remarkable example of an olfactory guided behavior, male moths navigate over large distances in order to locate calling females by detecting pheromone signals both rapidly and robustly. The biomimetic chemical sensing aims to identify the key blocks in the olfactory pathways at all levels from the olfactory receptors to the central nervous system, and simulate to some extent the operation of these blocks, that would allow to approach the sensing performance known in biological olfactory system of animals. New technical requirements arise to the hardware and software equipment used in such machine olfaction experiments. This work explores the bioinspired approach to machine olfaction in depth on the technological side. At the hardware level, the embedded computer is assembled, being the core part of the experimental set up. The embedded computer is interfaced with two main biomimetic modules designed by the collaborators: a large-scale sensor array for emulation of the population of the olfactory receptors, and a mobile robotic platform for autonomous experiments for guiding olfactory behaviour. At the software level, the software development kit is designed to host the neuromorphic models of the collaborators for processing the sensory inputs as in the olfactory pathway. Virtualization of the set up was one of the key engineering solutions in the development. Being a device, the set up is transformed to a virtual system for running data simulations, where the software environment is essentially the same, and the real sensors are replaced by the virtual sensors coming from especially designed data simulation tool. The proposed abstraction of the set up results in an ecosystem containing both the models of the olfactory system and the virtual array. This ecosystem can loaded from the developed system image on any personal computer. In addition to the engineering products released in the course of thesis, the scientific results have been published in three journal articles, two book chapters and conference proceedings. The main results on validation of the set up under the scenario of robotic odour localization are reported in the book chapters. The series of three journal articles covers the work on the data simulation tool for machine olfaction: the novel model of drift, the models to simulate the sensor array data based on the reference data set, and the parametrized simulated data and benchmarks proposed for the first time in machine olfaction. This thesis ends up with a solid foundation for the research in biomimetic simulations and algorithms on machine olfaction. The results achieved in the thesis are expected to give rise to new bioinspired applications in machine olfaction, which could have a significant impact in the biomedical engineering research area., Esta tesis se enmarca en el campo de bioingeneria, mas particularmente en la configuración de un sistema experimental de sensores de gases químicos. Quizás más que en cualquier otra modalidad de sensores, los sensores químicos representan un conjunto de retos técnicos y conceptuales ya que deben lidiar con problemas como su baja especificidad, su respuesta temporal lenta, su inestabilidad a largo plazo, su alto consumo enérgético, su portabilidad, así como la necesidad de un sistema de datos y código robusto. En la última década, se ha observado una clara tendencia por parte de los sistemas de machine olfaction hacia la imitación del sistema de olfato biológico de insectos y mamíferos. Los diseñadores de estos sistemas se inspiran del sistema olfativo biológico, ya que los animales cumplen, sin apenas esfuerzo, algunos de los escenarios no resueltos en machine olfaction. Por ejemplo, las polillas machos recorren largas distancias para localizar las polillas hembra, detectando sus feromonas de forma rápida y robusta. La detección biomimética de gases químicos tiene como objetivo identificar los elementos fundamentales de la vía olfativa a todos los niveles, desde los receptores olfativos hasta el sistema nervioso central, y simular, en cierta medida, el funcionamiento de estos bloques, lo que permitiría acercar el rendimiento de la detección al rendimiento de los sistemas olfativos conociodos de los animales. Esto conlleva nuevos requisitos técnicos a nivel de equipamiento tanto hardware como software utilizado en este tipo de experimentos de machine olfaction. Este trabajo propone un enfoque bioinspirado para la ¿machine olfaction¿, explorando a fondo la parte tecnológica. A nivel hardware, un ordenador embedido se ha ensamblado, siendo ésta la parte más importante de la configuración experimental. Este ordenador integrado está interconectado con dos módulos principales biomiméticos diseñados por los colaboradores: una matriz de sensores a gran escala y una plataforma móvil robotizada para experimentos autónomos. A nivel software, el kit de desarrollo software se ha diseñado para recoger los modelos neuromórficos de los colaboradores para el procesamiento de las entradas sensoriales como en la vía olfativa biológica. La virtualización del sistema fue una de las soluciones ingenieriles clave de su desarrollo. Al ser un dispositivo, el sistema se ha transformado en un sistema virtual para la realización de simulaciones de datos, donde el entorno de software es esencialmente el mismo, y donde los sensores reales se sustituyen por sensores virtuales procedentes de una herramienta de simulación de datos especialmente diseñada. La propuesta de abstracción del sistema resulta en un ecosistema que contiene tanto los modelos del sistema olfativo como la matriz virtual . Este ecosistema se puede cargar en cualquier ordenador personal como una imagen del sistema desarrollado. Además de los productos de ingeniería entregados en esta tesis, los resultados científicos se han publicado en tres artículos en revistas, dos capítulos de libros y los proceedings de dos conferencias internacionales. Los principales resultados en la validación del sistema en el escenario de la localización robótica de olores se presentan en los capítulos del libro. Los tres artículos de revistas abarcan el trabajo en la herramienta de simulación de datos para machine olfaction: el novedoso modelo de drift, los modelos para simular la matriz de sensores basado en el conjunto de datos de referencia, y la parametrización de los datos simulados y los benchmarks propuestos por primera vez en machine olfaction. Esta tesis ofrece una base sólida para la investigación en simulaciones biomiméticas y en algoritmos en machine olfaction. Los resultados obtenidos en la tesis pretenden dar lugar a nuevas aplicaciones bioinspiradas en machine olfaction, lo que podría tener un significativo impacto en el área de investigación en ingeniería biomédica

Published
2014

40. Joint Analysis of Multiple Interaction Parameters in Genetic Association Studies.

Author

Jihye Kim, Ziyatdinov, Andrey, Laville, Vincent, Hu, Frank B., Rimm, Eric, Kraft, Peter, and Aschard, Hugues

Subjects
*CHRONIC diseases, *ECOLOGY, *GENETIC polymorphisms, *GENETICS, *MEDICAL protocols, *MEDICAL research, *REGRESSION analysis, *RISK assessment, *LOGISTIC regression analysis, *STATISTICAL models, *SEQUENCE analysis
Abstract

With growing human genetic and epidemiologic data, there has been increased interest for the study of gene-byenvironment (G-E) interaction effects. Still, major questions remain on how to test jointly a large number of interactions between multiple SNPs and multiple exposures. In this study, we first compared the relative performance of four fixed-effect joint analysis approaches using simulated data, considering up to 10 exposures and 300 SNPs: (1) omnibus test, (2) multi-exposure and genetic risk score (GRS) test, (3) multi-SNP and environmental risk score (ERS) test, and (4) GRS-ERS test. Our simulations explored both linear and logistic regression while considering three statistics: the Wald test, the Score test, and the likelihood ratio test (LRT). We further applied the approaches to three large sets of human cohort data (n = 37,664), focusing on type 2 diabetes (T2D), obesity, hypertension, and coronary heart disease with smoking, physical activity, diets, and total energy intake. Overall, GRS-based approaches were the most robust, and had the highest power, especially when the G-E interaction effects were correlated with the marginal genetic and environmental effects. We also observed severe miscalibration of joint statistics in logistic models when the number of events per variable was too low when using either the Wald test or LRT test. Finally, our real data application detected nominally significant interaction effects for three outcomes (T2D, obesity, and hypertension), mainly from the GRS-ERS approach. In conclusion, this study provides guidelines for testing multiple interaction parameters in modern human cohorts including extensive genetic and environmental data. [ABSTRACT FROM AUTHOR]

Published
2019
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41. The Central role of KNG1 gene as a genetic determinant of coagulation pathway-related traits: Exploring metaphenotypes

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Brunel, Helena, Massanet, Raimon, Martinez Perez, Angel, Ziyatdinov, Andrey, Martin-Fernandez, Laura, Souto, Juan Carlos, Perera Lluna, Alexandre, Soria, José Manuel, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Brunel, Helena, Massanet, Raimon, Martinez Perez, Angel, Ziyatdinov, Andrey, Martin-Fernandez, Laura, Souto, Juan Carlos, Perera Lluna, Alexandre, and Soria, José Manuel

Abstract

Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway–related proteins). The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases., Postprint (published version)

Published
2016

42. Solarius: an R interface to SOLAR for variance component analysis in pedigrees

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Brunel, Helena, Martinez Perez, Angel, Buil, Alfonso, Perera Lluna, Alexandre, Soria, José Manuel, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Brunel, Helena, Martinez Perez, Angel, Buil, Alfonso, Perera Lluna, Alexandre, and Soria, José Manuel

Abstract

The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is one of the standard software programs to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It parses also the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses that makes the calculation of genome-wide scans more efficient., Postprint (author's final draft)

Published
2016

44. Genetic Determinants of Thrombin Generation and Their Relation to Venous Thrombosis: Results from the GAIT-2 Project

Author

Martin-Fernandez, Laura, primary, Ziyatdinov, Andrey, additional, Carrasco, Marina, additional, Millon, Juan Antonio, additional, Martinez-Perez, Angel, additional, Vilalta, Noelia, additional, Brunel, Helena, additional, Font, Montserrat, additional, Hamsten, Anders, additional, Souto, Juan Carlos, additional, and Soria, José Manuel, additional

Published
2016
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45. Bioinspired early detection through gas flow modulation in chemo-sensory systems

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Fonollosa Magrinyà, Jordi, Valentín-Fernández Gallart, Luis, Gutierrez Galvez, Agustín, Marco Colas, Santiago, Perera Lluna, Alexandre, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Fonollosa Magrinyà, Jordi, Valentín-Fernández Gallart, Luis, Gutierrez Galvez, Agustín, Marco Colas, Santiago, and Perera Lluna, Alexandre

Abstract

The design of bioinspired systems for chemical sensing is an engaging line of research in machine olfaction. Developments in this line could increase the lifetime and sensitivity of artificial chemo-sensory systems. Such approach is based on the sensory systems known in live organisms, and the resulting developed artificial systems are targeted to reproduce the biological mechanisms to some extent. Sniffing behaviour, sampling odours actively, has been studied recently in neuroscience, and it has been suggested that the respiration frequency is an important parameter of the olfactory system, since the odour perception, especially in complex scenarios such as novel odourants exploration, depends on both the stimulus identity and the sampling method. In this work we propose a chemical sensing system based on an array of 16 metal-oxide gas sensors that we combined with an external mechanical ventilator to simulate the biological respiration cycle. The tested gas classes formed a relatively broad combination of two analytes, acetone and ethanol, in binary mixtures. Two sets of low-frequency and high-frequency features were extracted from the acquired signals to show that the high-frequency features contain information related to the gas class. In addition, such information is available at early stages of the measurement, which could make the technique suitable in early detection scenarios. The full data set is made publicly available to the community. (C) 2014 Elsevier B.V. All rights reserved., Postprint (author's final draft)

Published
2015

46. Data set from gas sensor array under flow modulation

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Fonollosa, Jordi, Fernández Romero, Lluís, Gutierrez Galvez, Agustín, Marco Colás, Santiago, Perera Lluna, Alexandre, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Fonollosa, Jordi, Fernández Romero, Lluís, Gutierrez Galvez, Agustín, Marco Colás, Santiago, and Perera Lluna, Alexandre

Abstract

Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point., Postprint (published version)

Published
2015

47. Synthetic benchmarks for machine olfaction: Classification, segmentation and sensor damage

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, Perera Lluna, Alexandre, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics, Ziyatdinov, Andrey, and Perera Lluna, Alexandre

Abstract

The design of the signal and data processing algorithms requires a validation stage and some data relevant for a validation procedure. While the practice to share public data sets and make use of them is a recent and still on-going activity in the community, the synthetic benchmarks presented here are an option for the researches, who need data for testing and comparing the algorithms under development. The collection of synthetic benchmark data sets were generated for classification, segmentation and sensor damage scenarios, each defined at 5 difficulty levels. The published data are related to the data simulation tool, which was used to create a virtual array of 1020 sensors with a default set of parameters, Postprint (published version)

Published
2015

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