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1. A deep catalogue of protein-coding variation in 983,578 individuals

Author

Sun, Kathie Y., Bai, Xiaodong, Chen, Siying, Bao, Suying, Zhang, Chuanyi, Kapoor, Manav, Backman, Joshua, Joseph, Tyler, Maxwell, Evan, Mitra, George, Gorovits, Alexander, Mansfield, Adam, Boutkov, Boris, Gokhale, Sujit, Habegger, Lukas, Marcketta, Anthony, Locke, Adam E., Ganel, Liron, Hawes, Alicia, Kessler, Michael D., Sharma, Deepika, Staples, Jeffrey, Bovijn, Jonas, Gelfman, Sahar, Di Gioia, Alessandro, Rajagopal, Veera M., Lopez, Alexander, Varela, Jennifer Rico, Alegre-Díaz, Jesús, Berumen, Jaime, Tapia-Conyer, Roberto, Kuri-Morales, Pablo, Torres, Jason, Emberson, Jonathan, Collins, Rory, Cantor, Michael, Thornton, Timothy, Kang, Hyun Min, Overton, John D., Shuldiner, Alan R., Cremona, M. Laura, Nafde, Mona, Baras, Aris, Abecasis, Gonçalo, Marchini, Jonathan, Reid, Jeffrey G., Salerno, William, and Balasubramanian, Suganthi

Published
2024
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3. NOTCH3 p.Arg1231Cys is markedly enriched in South Asians and associated with stroke

Author

Rodriguez-Flores, Juan Lorenzo, Khalid, Shareef, Parikshak, Neelroop, Rasheed, Asif, Ye, Bin, Kapoor, Manav, Backman, Joshua, Sepehrband, Farshid, Gioia, Silvio Alessandro Di, Gelfman, Sahar, De, Tanima, Banerjee, Nilanjana, Sharma, Deepika, Martinez, Hector, Castaneda, Sofia, D’Ambrosio, David, Zhang, Xingmin A., Xun, Pengcheng, Tsai, Ellen, Tsai, I-Chun, Khan, Maleeha Zaman, Jahanzaib, Muhammad, Mian, Muhammad Rehan, Liaqat, Muhammad Bilal, Mahmood, Khalid, Salam, Tanvir Us, Hussain, Muhammad, Iqbal, Javed, Aslam, Faizan, Cantor, Michael N., Tzoneva, Gannie, Overton, John, Marchini, Jonathan, Reid, Jeffrey G., Baras, Aris, Verweij, Niek, Lotta, Luca A., Coppola, Giovanni, Karalis, Katia, Economides, Aris, Fazio, Sergio, Liedtke, Wolfgang, Danesh, John, Kamal, Ayeesha, Frossard, Philippe, Coleman, Thomas, Shuldiner, Alan R., and Saleheen, Danish

Published
2024
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4. Rare coding variants in CHRNB2 reduce the likelihood of smoking

Author

Rajagopal, Veera M., Watanabe, Kyoko, Mbatchou, Joelle, Ayer, Ariane, Quon, Peter, Sharma, Deepika, Kessler, Michael D., Praveen, Kavita, Gelfman, Sahar, Parikshak, Neelroop, Otto, Jacqueline M., Bao, Suying, Chim, Shek Man, Pavlopoulos, Elias, Avbersek, Andreja, Kapoor, Manav, Chen, Esteban, Jones, Marcus B., Leblanc, Michelle, Emberson, Jonathan, Collins, Rory, Torres, Jason, Morales, Pablo Kuri, Tapia-Conyer, Roberto, Alegre, Jesus, Berumen, Jaime, Shuldiner, Alan R., Balasubramanian, Suganthi, Abecasis, Gonçalo R., Kang, Hyun M., Marchini, Jonathan, Stahl, Eli A., Jorgenson, Eric, Sanchez, Robert, Liedtke, Wolfgang, Anderson, Matthew, Cantor, Michael, Lederer, David, Baras, Aris, and Coppola, Giovanni

Published
2023
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5. A large meta-analysis identifies genes associated with anterior uveitis

Author

Gelfman, Sahar, Moscati, Arden, Huergo, Santiago Mendez, Wang, Rujin, Rajagopal, Veera, Parikshak, Neelroop, Pounraja, Vijay Kumar, Chen, Esteban, Leblanc, Michelle, Hazlewood, Ralph, Freudenberg, Jan, Cooper, Blerta, Ligocki, Ann J., Miller, Charles G., Van Zyl, Tavé, Weyne, Jonathan, Romano, Carmelo, Sagdullaev, Botir, Melander, Olle, Baras, Aris, Stahl, Eli A., and Coppola, Giovanni

Published
2023
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6. The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders

Author

Alkelai, Anna, Greenbaum, Lior, Docherty, Anna R., Shabalin, Andrey A., Povysil, Gundula, Malakar, Ayan, Hughes, Daniel, Delaney, Shannon L., Peabody, Emma P., McNamara, James, Gelfman, Sahar, Baugh, Evan H., Zoghbi, Anthony W., Harms, Matthew B., Hwang, Hann-Shyan, Grossman-Jonish, Anat, Aggarwal, Vimla, Heinzen, Erin L., Jobanputra, Vaidehi, Pulver, Ann E., Lerer, Bernard, and Goldstein, David B.

Published
2022
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8. “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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9. 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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10. NOTCH3p.Arg1231Cys is Markedly Enriched in South Asians and Associated with Stroke

Author

Rodriguez-Flores, Juan L., primary, Khalid, Shareef, additional, Parikshak, Neelroop, additional, Rasheed, Asif, additional, Ye, Bin, additional, Kapoor, Manav, additional, Backman, Joshua, additional, Sepehrband, Farshid, additional, DiGioia, Silvio Alessandro, additional, Gelfman, Sahar, additional, De, Tanima, additional, Banerjee, Nilanjana, additional, Sharma, Deepika, additional, Martinez, Hector, additional, Castaneda, Sofia, additional, D’Ambrosio, David, additional, Zhang, Xingmin A., additional, Xun, Pengcheng, additional, Tsai, Ellen, additional, Tsai, I-Chun, additional, Center, Regeneron Genetics, additional, Jahanzaib, Muhammad, additional, Khan, Maleeha Zaman, additional, Mian, Muhammad Rehan, additional, Liaqat, Muhammad Bilal, additional, Mahmood, Khalid, additional, Salam, Tanvir Us, additional, Hussain, Muhammad, additional, Iqbal, Javed, additional, Aslam, Faizan, additional, Cantor, Michael N., additional, Tzoneva, Gannie, additional, Overton, John, additional, Marchini, Jonathan, additional, Reid, Jeff, additional, Baras, Aris, additional, Verweij, Niek, additional, Lotta, Luca A., additional, Coppola, Giovanni, additional, Karalis, Katia, additional, Economides, Aris, additional, Fazio, Sergio, additional, Liedtke, Wolfgang, additional, Danesh, John, additional, Kamal, Ayeesha, additional, Frossard, Philippe, additional, Coleman, Thomas, additional, Shuldiner, Alan R., additional, and Saleheen, Danish, additional

Published
2023
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11. 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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12. Epilepsy in a mouse model of GNB1 encephalopathy arises from altered potassium (GIRK) channel signaling and is alleviated by a GIRK inhibitor

Author

Colombo, Sophie, primary, Reddy, Haritha P., additional, Petri, Sabrina, additional, Williams, Damian J., additional, Shalomov, Boris, additional, Dhindsa, Ryan S., additional, Gelfman, Sahar, additional, Krizay, Daniel, additional, Bera, Amal K., additional, Yang, Mu, additional, Peng, Yueqing, additional, Makinson, Christopher D., additional, Boland, Michael J., additional, Frankel, Wayne N., additional, Goldstein, David B., additional, and Dascal, Nathan, additional

Published
2023
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13. 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. Rare coding variants in CHRNB2reduce the likelihood of smoking

Author

Rajagopal, Veera M., Watanabe, Kyoko, Mbatchou, Joelle, Ayer, Ariane, Quon, Peter, Sharma, Deepika, Kessler, Michael D., Praveen, Kavita, Gelfman, Sahar, Parikshak, Neelroop, Otto, Jacqueline M., Bao, Suying, Chim, Shek Man, Pavlopoulos, Elias, Avbersek, Andreja, Kapoor, Manav, Chen, Esteban, Jones, Marcus B., Leblanc, Michelle, Emberson, Jonathan, Collins, Rory, Torres, Jason, Morales, Pablo Kuri, Tapia-Conyer, Roberto, Alegre, Jesus, Berumen, Jaime, Shuldiner, Alan R., Balasubramanian, Suganthi, Abecasis, Gonçalo R., Kang, Hyun M., Marchini, Jonathan, Stahl, Eli A., Jorgenson, Eric, Sanchez, Robert, Liedtke, Wolfgang, Anderson, Matthew, Cantor, Michael, Lederer, David, Baras, Aris, and Coppola, Giovanni

Abstract

Human genetic studies of smoking behavior have been thus far largely limited to common variants. Studying rare coding variants has the potential to identify drug targets. We performed an exome-wide association study of smoking phenotypes in up to 749,459 individuals and discovered a protective association in CHRNB2, encoding the β2 subunit of the α4β2 nicotine acetylcholine receptor. Rare predicted loss-of-function and likely deleterious missense variants in CHRNB2in aggregate were associated with a 35% decreased odds for smoking heavily (odds ratio (OR) = 0.65, confidence interval (CI) = 0.56–0.76, P= 1.9 × 10−8). An independent common variant association in the protective direction (rs2072659; OR = 0.96; CI = 0.94–0.98; P= 5.3 × 10−6) was also evident, suggesting an allelic series. Our findings in humans align with decades-old experimental observations in mice that β2 loss abolishes nicotine-mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targeting CHRNB2in the brain for the treatment of nicotine addiction.

Published
2023
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17. The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders

Author

Alkelai, Anna, primary, Greenbaum, Lior, additional, Docherty, Anna R., additional, Shabalin, Andrey A., additional, Povysil, Gundula, additional, Malakar, Ayan, additional, Hughes, Daniel, additional, Delaney, Shannon L., additional, Peabody, Emma P., additional, McNamara, James, additional, Gelfman, Sahar, additional, Baugh, Evan H., additional, Zoghbi, Anthony W., additional, Harms, Matthew B., additional, Hwang, Hann-Shyan, additional, Grossman-Jonish, Anat, additional, Aggarwal, Vimla, additional, Heinzen, Erin L., additional, Jobanputra, Vaidehi, additional, Pulver, Ann E., additional, Lerer, Bernard, additional, and Goldstein, David B., additional

Published
2021
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21. Focused goodness of fit tests for gene set analyses.

Author

Zhang, Mengqi, Gelfman, Sahar, Moreno, Cristiane Araujo Martins, McCarthy, Janice M, Harms, Matthew B, Goldstein, David B, and Allen, Andrew S

Subjects
*GOODNESS-of-fit tests, *SIGNAL detection, *GENES
Abstract

Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Exome-Based Rare-Variant Analyses in CKD

Author

Cameron-Christie, Sophia, Wolock, Charles J., Groopman, Emily, Petrovski, Slave, Kamalakaran, Sitharthan, Povysil, Gundula, Vitsios, Dimitrios, Zhang, Mengqi, Fleckner, Jan, March, Ruth E., Gelfman, Sahar, Marasa, Maddalena, Li, Yifu, Sanna-Cherchi, Simone, Kiryluk, Krzysztof, Allen, Andrew S., Fellström, Bengt, Haefliger, Carolina, Platt, Adam, Goldstein, David B., Gharavi, Ali G., Cameron-Christie, Sophia, Wolock, Charles J., Groopman, Emily, Petrovski, Slave, Kamalakaran, Sitharthan, Povysil, Gundula, Vitsios, Dimitrios, Zhang, Mengqi, Fleckner, Jan, March, Ruth E., Gelfman, Sahar, Marasa, Maddalena, Li, Yifu, Sanna-Cherchi, Simone, Kiryluk, Krzysztof, Allen, Andrew S., Fellström, Bengt, Haefliger, Carolina, Platt, Adam, Goldstein, David B., and Gharavi, Ali G.

Abstract

Background Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined. Methods We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene. Results The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two-to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD. Conclusions This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.

Published
2019
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23. G protein-coupled potassium channels implicated in mouse and cellular models of GNB1 Encephalopathy

Author

Colombo, Sophie, primary, Petri, Sabrina, additional, Shalomov, Boris, additional, Reddy, Haritha P., additional, Tabak, Galit, additional, Dhindsa, Ryan S., additional, Gelfman, Sahar, additional, Teng, Sasa, additional, Krizay, Daniel, additional, Rafikian, Elizabeth E., additional, Bera, Amal K., additional, Yang, Mu, additional, Boland, Michael J., additional, Peng, Yueqing, additional, Frankel, Wayne N., additional, Dascal, Nathan, additional, and Goldstein, David B., additional

Published
2019
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24. Exome-Based Rare-Variant Analyses in CKD

Author

Cameron-Christie, Sophia, primary, Wolock, Charles J., additional, Groopman, Emily, additional, Petrovski, Slavé, additional, Kamalakaran, Sitharthan, additional, Povysil, Gundula, additional, Vitsios, Dimitrios, additional, Zhang, Mengqi, additional, Fleckner, Jan, additional, March, Ruth E., additional, Gelfman, Sahar, additional, Marasa, Maddalena, additional, Li, Yifu, additional, Sanna-Cherchi, Simone, additional, Kiryluk, Krzysztof, additional, Allen, Andrew S., additional, Fellström, Bengt C., additional, Haefliger, Carolina, additional, Platt, Adam, additional, Goldstein, David B., additional, and Gharavi, Ali G., additional

Published
2019
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25. A new approach for rare variation collapsing on functional protein domains implicates specific genic regions in ALS

Author

Gelfman, Sahar, primary, Dugger, Sarah, additional, de Araujo Martins Moreno, Cristiane, additional, Ren, Zhong, additional, Wolock, Charles J., additional, Shneider, Neil A., additional, Phatnani, Hemali, additional, Cirulli, Elizabeth T., additional, Lasseigne, Brittany N., additional, Harris, Tim, additional, Maniatis, Tom, additional, Rouleau, Guy A., additional, Brown, Robert H., additional, Gitler, Aaron D., additional, Myers, Richard M., additional, Petrovski, Slavé, additional, Allen, Andrew, additional, Goldstein, David B., additional, and Harms, Matthew B., additional

Published
2019
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27. meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays

Author

Gelfman, Sahar, primary, Wang, Quanli, additional, Lu, Yi-Fan, additional, Hall, Diana, additional, Bostick, Christopher D., additional, Dhindsa, Ryan, additional, Halvorsen, Matt, additional, McSweeney, K. Melodi, additional, Cotterill, Ellese, additional, Edinburgh, Tom, additional, Beaumont, Michael A., additional, Frankel, Wayne N., additional, Petrovski, Slavé, additional, Allen, Andrew S., additional, Boland, Michael J., additional, Goldstein, David B., additional, and Eglen, Stephen J., additional

Published
2018
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28. Regional Collapsing of Rare Variation Implicates Specific Genic Regions in ALS

Author

Gelfman, Sahar, primary, Dugger, Sarah, additional, Martins Moreno, Cristiane de Araujo, additional, Ren, Zhong, additional, Wolock, Charles J., additional, Shneider, Neil A., additional, Phatnani, Hemali, additional, Cirulli, Elizabeth T., additional, Lasseigne, Brittany N., additional, Harris, Tim, additional, Maniatis, Tom, additional, Rouleau, Guy A., additional, Brown, Robert H., additional, Gitler, Aaron D., additional, Myers, Richard M., additional, Petrovski, Slavé, additional, Allen, Andrew, additional, Harms, Matthew B., additional, and Goldstein, David B., additional

Published
2018
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29. T2. IDENTIFICATION OF PATHOGENIC VARIANTS IN PROTEIN CODING GENES

Author

Gelfman, Sahar, primary, Wang, Quanli, additional, Melodi McSweeney, K, additional, Ren, Zhong, additional, La Carpia, Francesca, additional, Halvorsen, Matt, additional, Schoch, Kelly, additional, Heinzen, Erin L., additional, Boland, Michael J., additional, Petrovski, Slavé, additional, and Goldstein, David, additional

Published
2017
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36. Changes in exon"intron structure during vertebrate evolution affect the splicing pattern of exons.

Author

Gelfman, Sahar, Burstein, David, Penn, Osnat, Savchenko, Anna, Amit, Maayan, Schwartz, Schraga, Pupko, Tal, and Ast, Gil

Subjects
*EXONS (Genetics), *INTRONS, *GENOMES, *GENOMICS, *VERTEBRATES, *MAMMALS
Abstract

Exon"intron architecture is one of the major features directing the splicing machinery to the short exons that are located within long flanking introns. However, the evolutionary dynamics of exon"intron architecture and its impact on splicing is largely unknown. Using a comparative genomic approach, we analyzed 17 vertebrate genomes and reconstructed the ancestral motifs of both 3' and 5' splice sites, as also the ancestral length of exons and introns. Our analyses suggest that vertebrate introns increased in length from the shortest ancestral introns to the longest primate introns. An evolutionary analysis of splice sites revealed that weak splice sites act as a restrictive force keeping introns short. In contrast, strong splice sites allow recognition of exons flanked by long introns. Reconstruction of the ancestral state suggests these phenomena were not prevalent in the vertebrate ancestor, but appeared during vertebrate evolution. By calculating evolutionary rate shifts in exons, we identified cis-acting regulatory sequences that became fixed during the transition from early vertebrates to mammals. Experimental validations performed on a selection of these hexamers confirmed their regulatory function. We additionally revealed many features of exons that can discriminate alternative from constitutive exons. These features were integrated into a machine-learning approach to predict whether an exon is alternative. Our algorithm obtains very high predictive power (AUC of 0.91), and using these predictions we have identified and successfully validated novel alternatively spliced exons. Overall, we provide novel insights regarding the evolutionary constraints acting upon exons and their recognition by the splicing machinery. [ABSTRACT FROM AUTHOR]

Published
2012
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37. meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays

Author

Tom Edinburgh, David Goldstein, Andrew S. Allen, Ellesse Cotterill, Diana Hall, Matt Halvorsen, Ryan S. Dhindsa, Michael A. Beaumont, Quanli Wang, Sahar Gelfman, Wayne N. Frankel, Christopher D. Bostick, Stephen J. Eglen, K. Melodi McSweeney, Slavé Petrovski, Yi-Fan Lu, Michael J. Boland, Gelfman, Sahar [0000-0002-4727-7862], Hall, Diana [0000-0001-5462-4407], Dhindsa, Ryan [0000-0002-8965-0813], Halvorsen, Matt [0000-0002-6707-2418], Cotterill, Ellese [0000-0003-4760-9748], Petrovski, Slavé [0000-0002-1527-961X], Eglen, Stephen J [0000-0001-8607-8025], and Apollo - University of Cambridge Repository

Subjects
Computer science, QH301-705.5, Spike train, Feature extraction, Action Potentials, Neural circuitry, Mice, 03 medical and health sciences, 0302 clinical medicine, Neural circuitry--Mathematical models, Genetic model, Animals, Biology (General), Cells, Cultured, Cultured neuronal network, 030304 developmental biology, Mice, Knockout, Neurons, 0303 health sciences, business.industry, Computational Biology, food and beverages, Pattern recognition, Mutual information, Electrophysiology, R package, Microelectrode, Neurology, Nervous system--Diseases, Artificial intelligence, business, Microelectrodes, Software, Algorithms, 030217 neurology & neurosurgery
Abstract

Here we present an open-source R package ‘meaRtools’ that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL>=3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.Author summaryCultured neuronal networks are widely used to study and characterize neuronal network activity. Among the many uses of neuronal cultures are the capabilities to evaluate neurotoxicity and the effects of pharmacological compounds on cellular physiology. Multi-well microelectrode arrays (MEAs) can collect high-throughput data from multiple neuronal cultures simultaneously, and thereby make possible hypotheses-driven inquiries into neurobiology and neuropharmacology. The analysis of MEA-derived information presents many computational challenges. High frequency data recorded simultaneously from hundreds of electrodes can be difficult to handle. The need to compare network activity across various drug treatments or genotypes recorded on the same plate from experiments lasting several weeks presents another challenge. These challenges inspired us to develop meaRtools; an MEA data analysis package that contains new methods to characterize network activity patterns, which are illustrated here using examples from a genetic mouse model of epilepsy. Among the highlights of meaRtools are novel algorithms designed to characterize neuronal activity dynamics and network properties such as bursting and synchronization, options to combine multiple recordings and use a robust statistical framework to draw appropriate statistical inferences, and finally data visualizations and plots. In summary, meaRtools provides a platform for the analyses of singular and longitudinal MEA experiments.

Published
2018

38. The Impact of HLA-A29 Homozygosity and of the Second HLA-A Allele on Susceptibility and Severity of Birdshot Chorioretinitis.

Author

Loeliger J, Lhotte R, Gelfman S, Stahl EA, Monnet D, Clichet V, Imikirene L, Kecili S, Taupin JL, Tabary T, Cohen JHM, and Brézin AP

Subjects
Humans, Male, Female, Severity of Illness Index, Middle Aged, Adult, Gene Frequency, HLA-A Antigens genetics, Chorioretinitis genetics, Chorioretinitis diagnosis, Homozygote, Alleles, Genetic Predisposition to Disease, Birdshot Chorioretinopathy genetics
Abstract

Purpose: HLA-A29 is the main susceptibility factor for birdshot chorioretinitis (BSCR). Our study assessed the impact of the second HLA-A allele alongside HLA-A29 on BSCR severity and susceptibility, focusing on HLA-A29 homozygous patients and those with alleles from the HLA-Aw19 group., Methods: We included 120 additional cases to our previous analysis of 286 patients with BSCR, all HLA-A29 positive. Patients were categorized based on the second allele being also HLA-A29 (A29/nonA29 vs. A29/A29) or belonging to the HLA-Aw19 family, including A29, A30, A31, and A33 (A29/nonAw19 vs. A29/Aw19). HLA-A32 was analyzed separately (A29/nonA32 vs. A29/A32). The prevalence of these groups among patients with BSCR was compared with their frequencies in a sample of 151,997 French subjects. Disease severity was approximated by assessing disease onset and visual function at the last visit and was compared between patient groups., Results: When comparing the HLA-A29-positive patients with BSCR to HLA-A29-positive French subjects, we found an overrepresentation of HLA-A29 for the second HLA-A allele (χ² = 4.34; P = 0.037; odds ratio, 1.57; confidence interval, 1.01-2.44). Within the HLA-Aw19 broad antigen family, HLA-A32 was found to be under-represented (χ² = 6.15; P = 0.013; odds ratio, 0.40; confidence interval, 0.19-0.85). The nature of the second HLA-A allele did not impact disease severity., Conclusions: Homozygosity for HLA-A29 increased the risk of developing BSCR without affecting disease severity. The under-representation of HLA-A32 in patients with BSCR suggests a potential protective role.

Published
2024
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39. A deep catalog of protein-coding variation in 985,830 individuals.

Author

Sun KY, Bai X, Chen S, Bao S, Kapoor M, Zhang C, Backman J, Joseph T, Maxwell E, Mitra G, Gorovits A, Mansfield A, Boutkov B, Gokhale S, Habegger L, Marcketta A, Locke A, Kessler MD, Sharma D, Staples J, Bovijn J, Gelfman S, Gioia AD, Rajagopal V, Lopez A, Varela JR, Alegre J, Berumen J, Tapia-Conyer R, Kuri-Morales P, Torres J, Emberson J, Collins R, Cantor M, Thornton T, Kang HM, Overton J, Shuldiner AR, Cremona ML, Nafde M, Baras A, Abecasis G, Marchini J, Reid JG, Salerno W, and Balasubramanian S

Abstract

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Published
2023
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40. ERAP1, ERAP2, and Two Copies of HLA-Aw19 Alleles Increase the Risk for Birdshot Chorioretinopathy in HLA-A29 Carriers.

Author

Gelfman S, Monnet D, Ligocki AJ, Tabary T, Moscati A, Bai X, Freudenberg J, Cooper B, Kosmicki JA, Wolf S, Ferreira MAR, Overton J, Weyne J, Stahl EA, Baras A, Romano C, Cohen JHM, Coppola G, and Brézin A

Subjects
Alleles, Birdshot Chorioretinopathy diagnosis, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Heterozygote, Humans, Multiplex Polymerase Chain Reaction, Odds Ratio, Risk Factors, Aminopeptidases genetics, Birdshot Chorioretinopathy genetics, HLA-A Antigens genetics, Minor Histocompatibility Antigens genetics, Polymorphism, Single Nucleotide
Abstract

Purpose: Birdshot chorioretinopathy (BSCR) is strongly associated with HLA-A29. This study was designed to elucidate the genetic modifiers of BSCR in HLA-A29 carriers., Methods: We sequenced the largest BSCR cohort to date, including 286 cases and 108 HLA-A29-positive controls to determine genome-wide common and rare variant associations. We further typed the HLA alleles of cases and 45,386 HLA-A29 controls of European ancestry to identify HLA alleles that associate with BSCR risk., Results: Carrying a second allele that belongs to the HLA-Aw19 broad antigen family (including HLA-A29, -A30, -A31, and -A33) increases the risk for BSCR (odds ratio [OR] = 4.44; P = 2.2e-03). This result was validated by comparing allele frequencies to large HLA-A29-controlled cohorts (n = 45,386; OR > 2.5; P < 1.3e-06). We also confirm that ERAP1 and ERAP2 haplotypes modulate disease risk. A meta-analysis with an independent dataset confirmed that ERAP1 and ERAP2 haplotypes modulate the risk for disease at a genome-wide significant level: ERAP1-rs27432 (OR = 2.46; 95% confidence interval [CI], 1.85-3.26; P = 4.07e-10), an expression quantitative trait locus (eQTL) decreasing ERAP1 expression; and ERAP2-rs10044354 (OR = 1.95; 95% CI, 1.55-2.44; P = 6.2e-09), an eQTL increasing ERAP2 expression. Furthermore, ERAP2-rs2248374 that disrupts ERAP2 expression is protective (OR = 0.56; 95% CI, 0.45-0.70; P = 2.39e-07). BSCR risk is additively increased when combining ERAP1/ERAP2 risk genotypes with two copies of HLA-Aw19 alleles (OR = 13.53; 95% CI, 3.79-54.77; P = 1.17e-05)., Conclusions: The genetic factors increasing BSCR risk demonstrate a pattern of increased processing, as well as increased presentation of ERAP2-specific peptides. This suggests a mechanism in which exceeding a peptide presentation threshold activates the immune response in choroids of A29 carriers.

Published
2021
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