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1,095 results on '"SMITH, JOSH"'

1. Galaxy Zoo DESI: large-scale bars as a secular mechanism for triggering AGN

Author

Garland, Izzy L., Walmsley, Mike, Silcock, Maddie S., Potts, Leah M., Smith, Josh, Simmons, Brooke D., Lintott, Chris J., Smethurst, Rebecca J., Dawson, James M., Keel, William C., Kruk, Sandor, Mantha, Kameswara Bharadwaj, Masters, Karen L., O'Ryan, David, Popp, Jürgen J., and Thorne, Matthew R.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena
Abstract

Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and that most of the growth of these SMBHs occurs via merger-free processes, the underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate the role that both strong and weak large-scale galactic bars play in mediating this relationship. Using 72,940 disc galaxies in a volume-limited sample from Galaxy Zoo DESI, we analyse the active galactic nucleus (AGN) fraction in strongly barred, weakly barred, and unbarred galaxies up to z = 0.1 over a range of stellar masses and colours. After controlling for stellar mass and colour, we find that the optically selected AGN fraction is 31.6 +/- 0.9 per cent in strongly barred galaxies, 23.3 +/- 0.8 per cent in weakly barred galaxies, and 14.2 +/- 0.6 per cent in unbarred disc galaxies. These are highly statistically robust results, strengthening the tantalising results in earlier works. Strongly barred galaxies have a higher fraction of AGNs than weakly barred galaxies, which in turn have a higher fraction than unbarred galaxies. Thus, while bars are not required in order to grow a SMBH in a disc galaxy, large-scale galactic bars appear to facilitate AGN fuelling, and the presence of a strong bar makes a disc galaxy more than twice as likely to host an AGN than an unbarred galaxy at all galaxy stellar masses and colours., Comment: 11 pages, 8 figures, accepted for publication in MNRAS

Published
2024

2. Workload Estimation for Unknown Tasks: A Survey of Machine Learning Under Distribution Shift

Author

Smith, Josh Bhagat and Adams, Julie A.

Subjects
Computer Science - Robotics, Computer Science - Human-Computer Interaction
Abstract

Human-robot teams involve humans and robots collaborating to achieve tasks under various environmental conditions. Successful teaming will require robots to adapt autonomously to a human teammate's internal state. An important element of such adaptation is the ability to estimate the human teammates' workload in unknown situations. Existing workload models use machine learning to model the relationships between physiological metrics and workload; however, these methods are susceptible to individual differences and are heavily influenced by other factors. These methods cannot generalize to unknown tasks, as they rely on standard machine learning approaches that assume data consists of independent and identically distributed (IID) samples. This assumption does not necessarily hold for estimating workload for new tasks. A survey of non-IID machine learning techniques is presented, where commonly used techniques are evaluated using three criteria: portability, model complexity, and adaptability. These criteria are used to argue which techniques are most applicable for estimating workload for unknown tasks in dynamic, real-time environments.

Published
2024

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

Author

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

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

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

Published
2023

6. Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Author

Jiang, Min-Zhi, Aguet, François, Ardlie, Kristin, Chen, Jiawen, Cornell, Elaine, Cruz, Dan, Durda, Peter, Gabriel, Stacey B, Gerszten, Robert E, Guo, Xiuqing, Johnson, Craig W, Kasela, Silva, Lange, Leslie A, Lappalainen, Tuuli, Liu, Yongmei, Reiner, Alex P, Smith, Josh, Sofer, Tamar, Taylor, Kent D, Tracy, Russell P, VanDenBerg, David J, Wilson, James G, Rich, Stephen S, Rotter, Jerome I, Love, Michael I, Raffield, Laura M, Li, Yun, and Consortium, TOPMed Analysis Working Group NHLBI Trans-Omics for Precision Medicine

Subjects
Biological Sciences, Genetics, Atherosclerosis, Cardiovascular, Generic health relevance, Humans, Proteomics, Canonical Correlation Analysis, Multiomics, Cohort Studies, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group, Developmental Biology
Abstract

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Published
2023

7. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Author

Buschur, Kristina L, Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G, Durda, Peter, Craig Johnson, W, Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S, Rotter, Jerome I, Smith, Josh, Taylor, Kent D, Tracy, Russell P, Lappalainen, Tuuli, Graham Barr, R, Sciurba, Frank, Hersh, Craig P, and Benos, Panayiotis V

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Chronic Obstructive Pulmonary Disease, Genetics, Lung, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Good Health and Well Being, Humans, Gene Regulatory Networks, Smokers, Genome-Wide Association Study, Pulmonary Disease, Chronic Obstructive, Prognosis, COPD, Graphical models, Gene expression, Disease subtypes, Cardiorespiratory Medicine and Haematology, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.MethodsBlood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.ResultsThe discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).ConclusionsThe identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Published
2023

9. Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Author

Patel, Roshni A, Musharoff, Shaila A, Spence, Jeffrey P, Pimentel, Harold, Tcheandjieu, Catherine, Mostafavi, Hakhamanesh, Sinnott-Armstrong, Nasa, Clarke, Shoa L, Smith, Courtney J, Program, VA Million Veteran, Durda, Peter P, Taylor, Kent D, Tracy, Russell, Liu, Yongmei, Johnson, W Craig, Aguet, Francois, Ardlie, Kristin G, Gabriel, Stacey, Smith, Josh, Nickerson, Deborah A, Rich, Stephen S, Rotter, Jerome I, Tsao, Philip S, Assimes, Themistocles L, and Pritchard, Jonathan K

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Atherosclerosis, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Cholesterol, LDL, Gene Expression, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, White People, V.A. Million Veteran Program, complex traits, genetic correlation, genome-wide association, population genetics, statistical genetics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Published
2022

10. Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Author

Abouhala, Siwaar, Albert, Jessica, Almalvez, Miguel, Alvarez, Raquel, Amin, Mutaz, Anderson, Peter, Aradhya, Swaroop, Ashley, Euan, Assimes, Themistocles, Auriga, Light, Austin-Tse, Christina, Bamshad, Mike, Barseghyan, Hayk, Baxter, Samantha, Behera, Sairam, Beheshti, Shaghayegh, Bejerano, Gill, Berger, Seth, Bernstein, Jon, Best, Sabrina, Blankenmeister, Benjamin, Blue, Elizabeth, Boerwinkle, Eric, Bonkowski, Emily, Bonner, Devon, Boone, Philip, Bornhorst, Miriam, Brand, Harrison, Buckingham, Kati, Calame, Daniel, Carter, Jennefer, Casadei, Silvia, Chadwick, Lisa, Chavez, Clarisa, Chen, Ziwei, Chinn, Ivan, Chong, Jessica, Coban-Akdemir, Zeynep, Cohen, Andrea J., Conner, Sarah, Conomos, Matthew, Coveler, Karen, Cui, Ya Allen, Currin, Sara, Daber, Robert, Dardas, Zain, Davis, Colleen, Dawood, Moez, de Dios, Ivan, de Esch, Celine, Delaney, Meghan, Delot, Emmanuele, DiTroia, Stephanie, Doddapaneni, Harsha, Du, Haowei, Duan, Ruizhi, Dugan-Perez, Shannon, Duong, Nhat, Duyzend, Michael, Eichler, Evan, Emami, Sara, Fraser, Jamie, Fusaro, Vincent, Galey, Miranda, Ganesh, Vijay, Garcia, Brandon, Garimella, Kiran, Gibbs, Richard, Gifford, Casey, Ginsburg, Amy, Goddard, Page, Gogarten, Stephanie, Gogate, Nikhita, Gordon, William, Gorzynski, John E., Greenleaf, William, Grochowski, Christopher, Groopman, Emily, Sousa, Rodrigo Guarischi, Gudmundsson, Sanna, Gulati, Ashima, Hall, Stacey, Harvey, William, Hawley, Megan, Heavner, Ben, Horike-Pyne, Martha, Hu, Jianhong, Huang, Yongqing, Hwang, James, Jarvik, Gail, Jensen, Tanner, Jhangiani, Shalini, Jimenez-Morales, David, Jin, Christopher, Saad, Ahmed K., Kahn-Kirby, Amanda, Kain, Jessica, Kaur, Parneet, Keehan, Laura, Knoblach, Susan, Ko, Arthur, Kundaje, Anshul, Kundu, Soumya, Lancaster, Samuel M., Larsson, Katie, Lee, Arthur, Lemire, Gabrielle, Lewis, Richard, Li, Wei, Li, Yidan, Liu, Pengfei, LoTempio, Jonathan, Lupski, James (Jim), Ma, Jialan, MacArthur, Daniel, Mahmoud, Medhat, Malani, Nirav, Mangilog, Brian, Marafi, Dana, Marmolejos, Sofia, Marten, Daniel, Martinez, Eva, Marvin, Colby, Marwaha, Shruti, Mastrorosa, Francesco Kumara, Matalon, Dena, May, Susanne, McGee, Sean, Meador, Lauren, Mefford, Heather, Mendez, Hector Rodrigo, Miller, Alexander, Miller, Danny E., Mitani, Tadahiro, Montgomery, Stephen, Moyses, Mariana, Munderloh, Chloe, Muzny, Donna, Nelson, Sarah, Nguyen, Thuy-mi P., Nguyen, Jonathan, Nussbaum, Robert, Nykamp, Keith, O'Callaghan, William, O'Heir, Emily, O'Leary, Melanie, Olsen, Jeren, Osei-Owusu, Ikeoluwa, O'Donnell-Luria, Anne, Padhi, Evin, Pais, Lynn, Pan, Miao, Panchal, Piyush, Patterson, Karynne, Payne, Sheryl, Pehlivan, Davut, Petrowski, Paul, Pham, Alicia, Pitsava, Georgia, Podesta, Astaria`Sara, Ponce, Sarah, Porter, Elizabeth, Posey, Jennifer, Prosser, Jaime, Quertermous, Thomas, Rai, Archana, Ramani, Arun, Rehm, Heidi, Reuter, Chloe, Reuter, Jason, Richardson, Matthew, Rivera-Munoz, Andres, Rubio, Oriane, Sabo, Aniko, Salani, Monica, Samocha, Kaitlin, Sanchis-Juan, Alba, Savage, Sarah, Scott, Evette, Scott, Stuart, Sedlazeck, Fritz, Shah, Gulalai, Shojaie, Ali, Singh, Mugdha, Smith, Kevin, Smith, Josh, Snow, Hana, Snyder, Michael, Socarras, Kayla, Starita, Lea, Stark, Brigitte, Stenton, Sarah, Stergachis, Andrew, Stilp, Adrienne, Sutton, V. Reid, Tai, Jui-Cheng, Talkowski, Michael (Mike), Tise, Christina, Tong, Catherine (Cat), Tsao, Philip, Ungar, Rachel, VanNoy, Grace, Vilain, Eric, Voutos, Isabella, Walker, Kim, Wei, Chia-Lin, Weisburd, Ben, Weiss, Jeff, Wellington, Chris, Weng, Ziming, Westheimer, Emily, Wheeler, Marsha, Wheeler, Matthew, Wiel, Laurens, Wilson, Michael, Wojcik, Monica, Wong, Quenna, Xiao, Changrui, Yadav, Rachita, Yi, Qian, Yuan, Bo, Zhao, Jianhua, Zhen, Jimmy, Zhou, Harry, Chong, Jessica X., Berger, Seth I., Smith, Erica, Calame, Daniel G., Hawley, Megan H., Rivera-Munoz, E. Andres, Bamshad, Michael J., and Rehm, Heidi L.

Published
2024
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11. Making the Most of Parallel Composition in Differential Privacy

Author

Smith, Josh, Asghar, Hassan Jameel, Gioiosa, Gianpaolo, Mrabet, Sirine, Gaspers, Serge, and Tyler, Paul

Subjects
Computer Science - Cryptography and Security, Computer Science - Databases
Abstract

We show that the `optimal' use of the parallel composition theorem corresponds to finding the size of the largest subset of queries that `overlap' on the data domain, a quantity we call the \emph{maximum overlap} of the queries. It has previously been shown that a certain instance of this problem, formulated in terms of determining the sensitivity of the queries, is NP-hard, but also that it is possible to use graph-theoretic algorithms, such as finding the maximum clique, to approximate query sensitivity. In this paper, we consider a significant generalization of the aforementioned instance which encompasses both a wider range of differentially private mechanisms and a broader class of queries. We show that for a particular class of predicate queries, determining if they are disjoint can be done in time polynomial in the number of attributes. For this class, we show that the maximum overlap problem remains NP-hard as a function of the number of queries. However, we show that efficient approximate solutions exist by relating maximum overlap to the clique and chromatic numbers of a certain graph determined by the queries. The link to chromatic number allows us to use more efficient approximate algorithms, which cannot be done for the clique number as it may underestimate the privacy budget. Our approach is defined in the general setting of $f$-differential privacy, which subsumes standard pure differential privacy and Gaussian differential privacy. We prove the parallel composition theorem for $f$-differential privacy. We evaluate our approach on synthetic and real-world data sets of queries. We show that the approach can scale to large domain sizes (up to $10^{20000}$), and that its application can reduce the noise added to query answers by up to 60\%., Comment: This is the full version of the paper with the same title to appear in the proceedings on the 22nd Privacy Enhancing Technologies Symposium (PETS 2022)

Published
2021

12. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

Published
2024
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17. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P., Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Daniel, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J.F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A., Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Pleiness, Jacob, Pollin, Toni, Post, Wendy, Powers Becker, Julia, Preethi Boorgula, Meher, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T., Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Williams, Scott, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Recto, Kathryn, Kachroo, Priyadarshini, Huan, Tianxiao, Lee, Gha Young, Bui, Helena, Lee, Dong Heon, Gereige, Jessica, Yao, Chen, Hwang, Shih-Jen, Joehanes, Roby, O’Connor, George T., and DeMeo, Dawn L.

Published
2023
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18. Beyond the exome: What’s next in diagnostic testing for Mendelian conditions

Author

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

Published
2023
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21. Galaxy Zoo DESI: large-scale bars as a secular mechanism for triggering AGNs.

Author

Garland, Izzy L, Walmsley, Mike, Silcock, Maddie S, Potts, Leah M, Smith, Josh, Simmons, Brooke D, Lintott, Chris J, Smethurst, Rebecca J, Dawson, James M, Keel, William C, Kruk, Sandor, Mantha, Kameswara Bharadwaj, Masters, Karen L, O'Ryan, David, Popp, Jürgen J, and Thorne, Matthew R

Subjects
DISK galaxies, ACTIVE galactic nuclei, SUPERMASSIVE black holes, GALAXIES, STELLAR mass, ACTIVE galaxies, SEYFERT galaxies
Abstract

Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and that most of the growth of these SMBHs occurs via merger-free processes, the underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate the role that both strong and weak large-scale galactic bars play in mediating this relationship. Using 48 871 disc galaxies in a volume-limited sample from Galaxy Zoo DESI, we analyse the active galactic nucleus (AGN) fraction in strongly barred, weakly barred, and unbarred galaxies up to |$z = 0.1$| over a range of stellar masses and colours. After controlling for stellar mass and colour, we find that the optically selected AGN fraction is |$31.6 \pm 0.9$| per cent in strongly barred galaxies, |$23.3 \pm 0.8$| per cent in weakly barred galaxies, and |$14.2 \pm 0.6$| per cent in unbarred disc galaxies. These are highly statistically robust results, strengthening the tantalizing results in earlier works. Strongly barred galaxies have a higher fraction of AGNs than weakly barred galaxies, which in turn have a higher fraction than unbarred galaxies. Thus, while bars are not required in order to grow an SMBH in a disc galaxy, large-scale galactic bars appear to facilitate AGN fuelling, and the presence of a strong bar makes a disc galaxy more than twice as likely to host an AGN than an unbarred galaxy at all galaxy stellar masses and colours. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

Author

Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Palmer Allred, Nicholette (Nichole), Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Chen, Yii-Der Ida, Cho, Michael H., Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, Das, Sayantan, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Do, Ron, Duan, Qing, Duggirala, Ravi, Durda, Peter, Dutcher, Susan, Eaton, Charles, Ekunwe, Lynette, Ellinor, Patrick, Emery, Leslie, Farber, Charles, Farnam, Leanna, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Yan, Gass, Margery, Gelb, Bruce, Geng, Xiaoqi (Priscilla), Germer, Soren, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Haessler, Jeff, Hall, Michael, Harris, Daniel, Hawley, Nicola, He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hokanson, John, Holly, Kramer, Hong, Elliott, Hoth, Karin, (Agnes) Hsiung, Chao, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Jhun, Min A., Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kachroo, Priyadarshini, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kathiresan, Sekar, Kaufman, Laura, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Krauter, Stephanie, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Seunggeun Shawn, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Yun, Lin, Honghuang, Lin, Keng Han, Liu, Simin, Liu, Yongmei, Loos, Ruth, Lubitz, Steven, Lunetta, Kathryn, Luo, James, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manson, JoAnn, Margolin, Lauren, Martin, Lisa, Mathai, Susan, Mathias, Rasika, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, Mei, Hao, Meyers, Deborah A., Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton, Montasser, May E., Musani, Solomon, Mwasongwe, Stanford, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Natarajan, Pradeep, Nekhai, Sergei, Nickerson, Deborah, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Pankow, James, Papanicolaou, George, Parker, Margaret, Parsa, Afshin, Penchev, Sara, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Phillips, Sam, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Prokopenko, Dmitry, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Ramachandran, Vasan, Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Regan, Elizabeth, Reiner, Alex, Rice, Ken, Rich, Stephen, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sakornsakolpat, Phuwanat, Salimi, Shabnam, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay, Scheller, Christopher, Schmidt, Ellen, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sheehan, Vivien, Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne, Streeten, Elizabeth, Sung, Yun Ju, Su-Lasky, Jessica, Sylvia, Jody, Szpiro, Adam, Sztalryd, Carole, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wan, Emily, Wang, Fei Fei, Watson, Karol, Weeks, Daniel E., Weir, Bruce, Weiss, Scott, Weng, Lu-Chen, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Wong, Quenna, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yang, Rongze, Zaghloul, Norann, Zekavat, Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zheng, Xiuwen, Zhi, Degui, Zhou, Xiang, Zody, Michael, Zoellner, Sebastian, Hecker, Julian, Chawes, Bo L., Ahluwalia, Tarunveer S., Kelly, Rachel S., Chu, Su H., Virkud, Yamini V., Huang, Mengna, Barnes, Kathleen C., Burchard, Esteban G., Eng, Celeste, Hu, Donglei, Celedón, Juan C., Levin, Albert M., Gui, Hongsheng, Forno, Erick, Mak, Angel C.Y., Avila, Lydiana, Soto-Quiros, Manuel E., Cloutier, Michelle M., Acosta-Pérez, Edna, Canino, Glorisa, Bønnelykke, Klaus, Bisgaard, Hans, Raby, Benjamin A., Weiss, Scott T., and Lasky-Su, Jessica A.

Published
2019
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38. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

Author

Recto, Kathryn, primary, Kachroo, Priyadarshini, additional, Huan, Tianxiao, additional, Van Den Berg, David, additional, Lee, Gha Young, additional, Bui, Helena, additional, Lee, Dong Heon, additional, Gereige, Jessica, additional, Yao, Chen, additional, Hwang, Shih-Jen, additional, Joehanes, Roby, additional, Weiss, Scott T., additional, O’Connor, George T., additional, Levy, Daniel, additional, DeMeo, Dawn L., additional, Abe, Namiko, additional, Abecasis, Gonçalo, additional, Aguet, Francois, additional, Albert, Christine, additional, Almasy, Laura, additional, Alonso, Alvaro, additional, Ament, Seth, additional, Anderson, Peter, additional, Anugu, Pramod, additional, Applebaum-Bowden, Deborah, additional, Ardlie, Kristin, additional, Arking, Dan, additional, Arnett, Donna K., additional, Ashley-Koch, Allison, additional, Aslibekyan, Stella, additional, Assimes, Tim, additional, Auer, Paul, additional, Avramopoulos, Dimitrios, additional, Ayas, Najib, additional, Balasubramanian, Adithya, additional, Barnard, John, additional, Barnes, Kathleen, additional, Barr, R. Graham, additional, Barron-Casella, Emily, additional, Barwick, Lucas, additional, Beaty, Terri, additional, Beck, Gerald, additional, Becker, Diane, additional, Becker, Lewis, additional, Beer, Rebecca, additional, Beitelshees, Amber, additional, Benjamin, Emelia, additional, Benos, Takis, additional, Bezerra, Marcos, additional, Bielak, Larry, additional, Bis, Joshua, additional, Blackwell, Thomas, additional, Blangero, John, additional, Blue, Nathan, additional, Boerwinkle, Eric, additional, Bowden, Donald W., additional, Bowler, Russell, additional, Brody, Jennifer, additional, Broeckel, Ulrich, additional, Broome, Jai, additional, Brown, Deborah, additional, Bunting, Karen, additional, Burchard, Esteban, additional, Bustamante, Carlos, additional, Buth, Erin, additional, Cade, Brian, additional, Cardwell, Jonathan, additional, Carey, Vincent, additional, Carrier, Julie, additional, Carson, April P., additional, Carty, Cara, additional, Casaburi, Richard, additional, Casas Romero, Juan P., additional, Casella, James, additional, Castaldi, Peter, additional, Chaffin, Mark, additional, Chang, Christy, additional, Chang, Yi-Cheng, additional, Chasman, Daniel, additional, Chavan, Sameer, additional, Chen, Bo-Juen, additional, Chen, Wei-Min, additional, Ida Chen, Yii-Der, additional, Cho, Michael, additional, Choi, Seung Hoan, additional, Chuang, Lee-Ming, additional, Chung, Mina, additional, Chung, Ren-Hua, additional, Clish, Clary, additional, Comhair, Suzy, additional, Conomos, Matthew, additional, Cornell, Elaine, additional, Correa, Adolfo, additional, Crandall, Carolyn, additional, Crapo, James, additional, Cupples, L. Adrienne, additional, Curran, Joanne, additional, Curtis, Jeffrey, additional, Custer, Brian, additional, Damcott, Coleen, additional, Darbar, Dawood, additional, David, Sean, additional, Davis, Colleen, additional, Daya, Michelle, additional, de Andrade, Mariza, additional, de las Fuentes, Lisa, additional, de Vries, Paul, additional, DeBaun, Michael, additional, Deka, Ranjan, additional, DeMeo, Dawn, additional, Devine, Scott, additional, Dinh, Huyen, additional, Doddapaneni, Harsha, additional, Duan, Qing, additional, Dugan-Perez, Shannon, additional, Duggirala, Ravi, additional, Durda, Jon Peter, additional, Dutcher, Susan K., additional, Eaton, Charles, additional, Ekunwe, Lynette, additional, El Boueiz, Adel, additional, Ellinor, Patrick, additional, Emery, Leslie, additional, Erzurum, Serpil, additional, Farber, Charles, additional, Farek, Jesse, additional, Fingerlin, Tasha, additional, Flickinger, Matthew, additional, Fornage, Myriam, additional, Franceschini, Nora, additional, Frazar, Chris, additional, Fu, Mao, additional, Fullerton, Stephanie M., additional, Fulton, Lucinda, additional, Gabriel, Stacey, additional, Gan, Weiniu, additional, Gao, Shanshan, additional, Gao, Yan, additional, Gass, Margery, additional, Geiger, Heather, additional, Gelb, Bruce, additional, Geraci, Mark, additional, Germer, Soren, additional, Gerszten, Robert, additional, Ghosh, Auyon, additional, Gibbs, Richard, additional, Gignoux, Chris, additional, Gladwin, Mark, additional, Glahn, David, additional, Gogarten, Stephanie, additional, Gong, Da-Wei, additional, Goring, Harald, additional, Graw, Sharon, additional, Gray, Kathryn J., additional, Grine, Daniel, additional, Gross, Colin, additional, Gu, C. Charles, additional, Guan, Yue, additional, Guo, Xiuqing, additional, Gupta, Namrata, additional, Haessler, Jeff, additional, Hall, Michael, additional, Han, Yi, additional, Hanly, Patrick, additional, Harris, Daniel, additional, Hawley, Nicola L., additional, He, Jiang, additional, Heavner, Ben, additional, Heckbert, Susan, additional, Hernandez, Ryan, additional, Herrington, David, additional, Hersh, Craig, additional, Hidalgo, Bertha, additional, Hixson, James, additional, Hobbs, Brian, additional, Hokanson, John, additional, Hong, Elliott, additional, Hoth, Karin, additional, Hsiung, Chao (Agnes), additional, Hu, Jianhong, additional, Hung, Yi-Jen, additional, Huston, Haley, additional, Hwu, Chii Min, additional, Irvin, Marguerite Ryan, additional, Jackson, Rebecca, additional, Jain, Deepti, additional, Jaquish, Cashell, additional, Johnsen, Jill, additional, Johnson, Andrew, additional, Johnson, Craig, additional, Johnston, Rich, additional, Jones, Kimberly, additional, Kang, Hyun Min, additional, Kaplan, Robert, additional, Kardia, Sharon, additional, Kelly, Shannon, additional, Kenny, Eimear, additional, Kessler, Michael, additional, Khan, Alyna, additional, Khan, Ziad, additional, Kim, Wonji, additional, Kimoff, John, additional, Kinney, Greg, additional, Konkle, Barbara, additional, Kooperberg, Charles, additional, Kramer, Holly, additional, Lange, Christoph, additional, Lange, Ethan, additional, Lange, Leslie, additional, Laurie, Cathy, additional, Laurie, Cecelia, additional, LeBoff, Meryl, additional, Lee, Jiwon, additional, Lee, Sandra, additional, Lee, Wen-Jane, additional, LeFaive, Jonathon, additional, Levine, David, additional, Lewis, Joshua, additional, Li, Xiaohui, additional, Li, Yun, additional, Lin, Henry, additional, Lin, Honghuang, additional, Lin, Xihong, additional, Liu, Simin, additional, Liu, Yongmei, additional, Liu, Yu, additional, Loos, Ruth J.F., additional, Lubitz, Steven, additional, Lunetta, Kathryn, additional, Luo, James, additional, Magalang, Ulysses, additional, Mahaney, Michael, additional, Make, Barry, additional, Manichaikul, Ani, additional, Manning, Alisa, additional, Manson, JoAnn, additional, Martin, Lisa, additional, Marton, Melissa, additional, Mathai, Susan, additional, Mathias, Rasika, additional, May, Susanne, additional, McArdle, Patrick, additional, McDonald, Merry-Lynn, additional, McFarland, Sean, additional, McGarvey, Stephen, additional, McGoldrick, Daniel, additional, McHugh, Caitlin, additional, McNeil, Becky, additional, Mei, Hao, additional, Meigs, James, additional, Menon, Vipin, additional, Mestroni, Luisa, additional, Metcalf, Ginger, additional, Meyers, Deborah A., additional, Mignot, Emmanuel, additional, Mikulla, Julie, additional, Min, Nancy, additional, Minear, Mollie, additional, Minster, Ryan L., additional, Mitchell, Braxton D., additional, Moll, Matt, additional, Momin, Zeineen, additional, Montasser, May E., additional, Montgomery, Courtney, additional, Muzny, Donna, additional, Mychaleckyj, Josyf C., additional, Nadkarni, Girish, additional, Naik, Rakhi, additional, Naseri, Take, additional, Natarajan, Pradeep, additional, Nekhai, Sergei, additional, Nelson, Sarah C., additional, Neltner, Bonnie, additional, Nessner, Caitlin, additional, Nickerson, Deborah, additional, Nkechinyere, Osuji, additional, North, Kari, additional, O'Connell, Jeff, additional, O'Connor, Tim, additional, Ochs-Balcom, Heather, additional, Okwuonu, Geoffrey, additional, Pack, Allan, additional, Paik, David T., additional, Palmer, Nicholette, additional, Pankow, James, additional, Papanicolaou, George, additional, Parker, Cora, additional, Peloso, Gina, additional, Peralta, Juan Manuel, additional, Perez, Marco, additional, Perry, James, additional, Peters, Ulrike, additional, Peyser, Patricia, additional, Phillips, Lawrence S., additional, Pleiness, Jacob, additional, Pollin, Toni, additional, Post, Wendy, additional, Powers Becker, Julia, additional, Preethi Boorgula, Meher, additional, Preuss, Michael, additional, Psaty, Bruce, additional, Qasba, Pankaj, additional, Qiao, Dandi, additional, Qin, Zhaohui, additional, Rafaels, Nicholas, additional, Raffield, Laura, additional, Rajendran, Mahitha, additional, Ramachandran, Vasan S., additional, Rao, D.C., additional, Rasmussen-Torvik, Laura, additional, Ratan, Aakrosh, additional, Redline, Susan, additional, Reed, Robert, additional, Reeves, Catherine, additional, Regan, Elizabeth, additional, Reiner, Alex, additional, Reupena, Muagututi‘a Sefuiva, additional, Rice, Ken, additional, Rich, Stephen, additional, Robillard, Rebecca, additional, Robine, Nicolas, additional, Roden, Dan, additional, Roselli, Carolina, additional, Rotter, Jerome, additional, Ruczinski, Ingo, additional, Runnels, Alexi, additional, Russell, Pamela, additional, Ruuska, Sarah, additional, Ryan, Kathleen, additional, Sabino, Ester Cerdeira, additional, Saleheen, Danish, additional, Salimi, Shabnam, additional, Salvi, Sejal, additional, Salzberg, Steven, additional, Sandow, Kevin, additional, Sankaran, Vijay G., additional, Santibanez, Jireh, additional, Schwander, Karen, additional, Schwartz, David, additional, Sciurba, Frank, additional, Seidman, Christine, additional, Seidman, Jonathan, additional, Sériès, Frédéric, additional, Sheehan, Vivien, additional, Sherman, Stephanie L., additional, Shetty, Amol, additional, Shetty, Aniket, additional, Sheu, Wayne Hui-Heng, additional, Shoemaker, M. Benjamin, additional, Silver, Brian, additional, Silverman, Edwin, additional, Skomro, Robert, additional, Smith, Albert Vernon, additional, Smith, Jennifer, additional, Smith, Josh, additional, Smith, Nicholas, additional, Smith, Tanja, additional, Smoller, Sylvia, additional, Snively, Beverly, additional, Snyder, Michael, additional, Sofer, Tamar, additional, Sotoodehnia, Nona, additional, Stilp, Adrienne M., additional, Storm, Garrett, additional, Streeten, Elizabeth, additional, Su, Jessica Lasky, additional, Sung, Yun Ju, additional, Sylvia, Jody, additional, Szpiro, Adam, additional, Taliun, Daniel, additional, Tang, Hua, additional, Taub, Margaret, additional, Taylor, Kent, additional, Taylor, Matthew, additional, Taylor, Simeon, additional, Telen, Marilyn, additional, Thornton, Timothy A., additional, Threlkeld, Machiko, additional, Tinker, Lesley, additional, Tirschwell, David, additional, Tishkoff, Sarah, additional, Tiwari, Hemant, additional, Tong, Catherine, additional, Tracy, Russell, additional, Tsai, Michael, additional, Vaidya, Dhananjay, additional, VandeHaar, Peter, additional, Vrieze, Scott, additional, Walker, Tarik, additional, Wallace, Robert, additional, Walts, Avram, additional, Wang, Fei Fei, additional, Wang, Heming, additional, Wang, Jiongming, additional, Watson, Karol, additional, Watt, Jennifer, additional, Weeks, Daniel E., additional, Weinstock, Joshua, additional, Weir, Bruce, additional, Weng, Lu-Chen, additional, Wessel, Jennifer, additional, Willer, Cristen, additional, Williams, Kayleen, additional, Williams, L. Keoki, additional, Williams, Scott, additional, Wilson, Carla, additional, Wilson, James, additional, Winterkorn, Lara, additional, Wong, Quenna, additional, Wu, Baojun, additional, Wu, Joseph, additional, Xu, Huichun, additional, Yanek, Lisa, additional, Yang, Ivana, additional, Yu, Ketian, additional, Zekavat, Seyedeh Maryam, additional, Zhang, Yingze, additional, Zhao, Snow Xueyan, additional, Zhao, Wei, additional, Zhu, Xiaofeng, additional, Ziv, Elad, additional, Zody, Michael, additional, and Zoellner, Sebastian, additional

Published
2023
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42. Rita Ackermann

Author

Ackermann, Rita and Smith, Josh

Published
2017

43. Success with ESG.

Author

SMITH, JOSH

Subjects
ENVIRONMENTAL, social, & governance factors, AIRLINE industry
Published
2024

45. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Author

Buschur, Kristina L., Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G., Durda, Peter, Craig Johnson, W., Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S., Rotter, Jerome I., Smith, Josh, Taylor, Kent D., Tracy, Russell P., Lappalainen, Tuuli, Graham Barr, R., Sciurba, Frank, Hersh, Craig P., Benos, Panayiotis V., Buschur, Kristina L., Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G., Durda, Peter, Craig Johnson, W., Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S., Rotter, Jerome I., Smith, Josh, Taylor, Kent D., Tracy, Russell P., Lappalainen, Tuuli, Graham Barr, R., Sciurba, Frank, Hersh, Craig P., and Benos, Panayiotis V.

Abstract

BackgroundChronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.MethodsBlood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.ResultsThe discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).ConclusionsThe identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis., QC 20230303

Published
2023
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46. Restoring the seagrass Zostera muelleri with transplants: small cores are as effective as larger plots.

Author

Matheson, Fleur E., Mackay, Graeme, Middleton, Crispin, Griffiths, Richard, Eyre, Ricky, Smith, Josh, and Ovenden, Ron

Subjects
SEAGRASSES, ZOSTERA, GROUND cover plants
Abstract

Seagrass extent has declined globally, including in New Zealand. Following the success of an initial trial to transplant and re-establish intertidal seagrass at a former site in Whangarei Harbour, a second trial was initiated. This trial aimed to restore seagrass at a more distant former site and tested the efficacy of transplant units ranging in size from small cores (5 or 9 × 0.01 m diameter cores placed within a 0.25 m2 plot) to larger entire plots (0.25 m2 or 0.5 m2). Seagrass was transplanted in winter when plants were dormant. Plant cover of transplanted and donor plots and light climate at both sites were monitored over a period of four years. All but one of the 24 units survived, and the plants began to spread after 12–18 months to eventually develop patches ranging in size from 5 to 68 m2. Seagrass recolonised all donor plots completely within 10 months. Results show that small cores and larger plots can both be used successfully to transplant and restore populations of Zostera muelleri with no long-lasting damage to donor meadows. However, use of small cores is recommended to minimise extractions and loss of transplanted material, and to simplify logistics. [ABSTRACT FROM AUTHOR]

Published
2023
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47. First observations and early life-history aspects of lake rearing galaxiid larvae in the lower Waikato River Basin, New Zealand.

Author

David, Bruno O., Jarvis, Matt, Özkundakci, Deniz, Smith, Josh, Duggan, Ian C., Koh, Sung Soo, Augspurger, Jason, and King, Tania M.

Subjects
LARVAE, LAKE management, LAKES, GASTROINTESTINAL contents, FRESHWATER fishes, WATERSHEDS
Abstract

Larval behaviour for many of New Zealand's diadromous freshwater fish is inadequately described. Diadromy for many amphidromous species is not obligatory however, and where conditions are suitable, freshwater larval rearing may be facilitated. Where this occurs in lakes, opportunities to document the composition and conditions supporting larval rearing exist. Boat trawling was undertaken across nine lowland lakes in the Lower Waikato over four consecutive winters with a focus on larval galaxiids. Galaxiid larvae were captured in surface water habitats in all but one lake, with banded kōkopu (Galaxias fasciatus) and giant kōkopu (Galaxias argenteus) the most common species detected. One lake, Lake Waahi, consistently resulted in the most galaxiid captures for effort expended. Analyses of larvae from this and other lakes indicated that two sizes predominated in catches and that larger, older larvae were predominantly G. fasciatus while smaller, younger larvae were predominantly G. argenteus. Stomach contents indicated that two non-native zooplankton species predominated in the diet of larvae, the Holarctic daphnia, Daphnia galeata, and the Australian calanoid copepod Boeckella symmetrica. This study provides new information regarding the timing, movement and predicted recruitment of native fish species in this river basin that has important implications for lake and river management. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Author

Tracy, Russell P., VanDenBerg, David J., Smith, Josh, Sofer, Tamar, Liu, Yongmei, Durda, Peter, Rich, Stephen S., Rotter, Jerome I., Kasela, Silva, Lappalainen, Tuuli, Love, Michael I., Gabriel, Stacey B., Chen, Jiawen, Cruz, Dan, Ardlie, Kristin, Taylor, Kent D., Gerszten, Robert E., Johnson, Craig W., Raffield, Laura M., Reiner, Alex P., Wilson, James G., Lange, Leslie A., Guo, Xiuqing, Cornell, Elaine, Aguet, François, Jiang, Min-Zhi, Li, Yun, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group, and Le Cao, Kim-Anh

Subjects
Cohort Studies, Proteomics, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Canonical Correlation Analysis, Human Genome, Genetics, TOPMed Analysis Working Group, Humans, Generic health relevance, Multiomics, Cardiovascular, Biotechnology, Developmental Biology
Abstract

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Published
2023

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