Search

Your search keyword '"Hobbs, Brian"' showing total 1,243 results
Start Over Author "Hobbs, Brian"
1,243 results on '"Hobbs, Brian"'

1. A protein risk score for all-cause and respiratory-specific mortality in non-Hispanic white and African American individuals who smoke

Author

Moll, Matthew, Pratte, Katherine A, Debban, Catherine L, Liu, Congjian, Belinsky, Steven A, Picchi, Maria, Konigsberg, Iain, Tern, Courtney, Rijhwani, Heena, Hobbs, Brian D, Silverman, Edwin K, Tesfaigzi, Yohannes, Rich, Stephen S, Manichaikul, Ani, Rotter, Jerome I, Bowler, Russel P, and Cho, Michael H

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Epidemiology, Public Health, Health Sciences, Health Disparities, Clinical Research, Prevention, Precision Medicine, Minority Health, Lung, Respiratory, Good Health and Well Being, Aged, Female, Humans, Male, Middle Aged, Biomarkers, Black or African American, Cardiovascular Diseases, Proteomics, Risk Factors, Smoking, White, Mortality, Respiratory Tract Diseases
Abstract

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31-2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein-protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

Published
2024

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

Author

Abecasis, Gonçalo, Coppola, Giovanni, Deubler, Andrew, Economides, Aris, Ferrando, Adolfo, Lotta, Luca A, Shuldiner, Alan, Siminovitch, Katherine, Beechert, Christina, Brian, Erin D, Cremona, Laura M, Du, Hang, Forsythe, Caitlin, Gu, Zhenhua, Guevara, Kristy, Lattari, Michael, Manoochehri, Kia, Challa, Prathyusha, Pradhan, Manasi, Reynoso, Raymond, Schiavo, Ricardo, Padilla, Maria Sotiropoulos, Wang, Chenggu, Wolf, Sarah E, Averitt, Amelia, Banerjee, Nilanjana, Li, Dadong, Malhotra, Sameer, Mower, Justin, Sarwar, Mudasar, Staples, Jeffrey C, Yu, Sean, Zhang, Aaron, Bunyea, Andrew, Punuru, Krishna Pawan, Sreeram, Sanjay, Eom, Gisu, Sultan, Benjamin, Lanche, Rouel, Mahajan, Vrushali, Austin, Eliot, O’Keeffe, Sean, Panea, Razvan, Polanco, Tommy, Rasool, Ayesha, Zhang, Lance, Edelstein, Evan, Guan, Ju, Krasheninina, Olga, Zarate, Samantha, Mansfield, Adam J, Maxwell, Evan K, Sun, Kathie, Ferreira, Manuel Allen Revez, Burch, Kathy, Campos, Adrian, Chen, Lei, Choi, Sam, Damask, Amy, Gaynor, Sheila, Geraghty, Benjamin, Ghosh, Arkopravo, Martinez, Salvador Romero, Gillies, Christopher, Gurski, Lauren, Herman, Joseph, Jorgenson, Eric, Kessler, Michael, Kosmicki, Jack, Lin, Nan, Locke, Adam, Nakka, Priyanka, Landheer, Karl, Delaneau, Olivier, Ghoussaini, Maya, Mbatchou, Joelle, Moscati, Arden, Pandey, Aditeya, Pandit, Anita, Paulding, Charles, Ross, Jonathan, Sidore, Carlo, Stahl, Eli, Suciu, Maria, VandeHaar, Peter, Vedantam, Sailaja, Vrieze, Scott, Zhang, Jingning, Wang, Rujin, Wu, Kuan-Han, Ye, Bin, Zhang, Blair, Ziyatdinov, Andrey, Zou, Yuxin, Watanabe, Kyoko, Tang, Mira, Hobbs, Brian, Silver, Jon, Palmer, William, and Guerreiro, Rita

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Precision Medicine, Clinical Research, Minority Health, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Generic health relevance, Good Health and Well Being, Humans, Alleles, Exome, Exome Sequencing, Gene Frequency, Genetic Variation, Heterozygote, Loss of Function Mutation, Mutation, Missense, Open Reading Frames, Proteins, RNA Splice Sites, Regeneron Genetics Center, RGC-ME Cohort Partners, General Science & Technology
Abstract

Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

Published
2024

3. Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study

Author

Chen, Yongzhuo, Liang, Yixuan, Liu, Yiran, Hobbs, Brian, and Kane, Michael

Subjects
Quantitative Finance - Pricing of Securities
Abstract

This research paper addresses the critical challenge of accurately valuing post-revenue drug assets in the biotechnology and pharmaceutical sectors, a key factor influencing a wide range of strategic operations and investment decisions. Recognizing the importance of reliable valuations for stakeholders such as pharmaceutical companies, venture capitalists, and private equity firms, this study introduces a novel model for forecasting future sales of post-revenue biopharmaceutical assets. The proposed model leverages historical sales data, a resource known for its high quality and availability in company financial records, to produce distributional estimates of cumulative sales for individual assets. These estimates are instrumental in calculating the Net Present Value of each asset, thereby facilitating more informed and strategic investment decisions. A practical application of this model is demonstrated through its implementation in analyzing Pfizer's portfolio of post-revenue assets. This precision highlights the model's potential as a valuable tool in the financial assessment and decision-making processes within the biotech and pharmaceutical industries, offering a methodical approach to identifying investment opportunities and optimizing capital allocation., Comment: 14 pages, 17 figures

Published
2023

6. Network Based Approach to Gene Prioritization at Genome-Wide Association Study Loci

Author

Martini, Leonardo, Fazzone, Adriano, Gentili, Michele, Becchetti, Luca, and Hobbs, Brian

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Genomics
Abstract

Motivation: Genome-wide association studies (GWAS) have successfully identified thousands of genetic risk loci for complex traits and diseases. Most of these GWAS loci lie in regulatory regions of the genome and the gene through which each GWAS risk locus exerts its effects is not always clear. Many computational methods utilizing biological data sources have been proposed to identify putative casual genes at GWAS loci; however, these methods can be improved upon. Results: We present the Relations-Maximization Method, a dense module searching method to identify putative causal genes at GWAS loci through the generation of candidate sub-networks derived by integrating association signals from GWAS data into the gene co-regulation network. We employ our method in a chronic obstructive pulmonary disease GWAS. We perform an extensive, comparative study of Relations-Maximization Method's performance against well-established baselines.

Published
2022

7. Optimal predictive probability designs for randomized biomarker-guided oncology trials

Author

Zabor, Emily C., Kaizer, Alexander M., Pennell, Nathan A., and Hobbs, Brian P.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. Six antibodies acting on programmed death ligand 1 or programmed death 1 pathways were approved in 75 cancer indications between 2015 and 2021. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of single-arm phase II trials. And yet, in the absence of randomization, patient outcomes may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used in single arm studies often arise as population averages, lacking specificity to the targeted subgroup. For example, a recent phase III trial of atezolizumab in patients with metastatic urothelial carcinoma found a 21.6% response rate to standard of care chemotherapy in the biomarker subgbroup of interest, much higher than the historical control rate of 10% that had been used to declare success in the preceding phase II trial. Innovations in design methodology are needed to enable efficient implementation of randomized trials for agents that target biomarker subpopulations. This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. A simulation study motivated by the results reported in the atezolizumab trial is used to evaluate the operating characteristics of the various designs. Our findings suggest that efficient statistical design can be conducted with randomization and futility stopping to effectively acquire more evidence pertaining to comparative efficacy before deciding to conduct a phase III confirmatory trial., Comment: 12 pages, 2 figures, 2 tables, submitted to Frontiers in Oncology

Published
2022

11. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Author

Chen, Fang, Wang, Xingyan, Jang, Seon-Kyeong, Quach, Bryan C, Weissenkampen, J Dylan, Khunsriraksakul, Chachrit, Yang, Lina, Sauteraud, Renan, Albert, Christine M, Allred, Nicholette DD, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Barr, R Graham, Becker, Diane M, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boorgula, Meher Preethi, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der I, Chuang, Lee-Ming, Correa, Adolfo, Curran, Joanne E, David, Sean P, Fuentes, Lisa de las, Deka, Ranjan, Duggirala, Ravindranath, Faul, Jessica D, Garrett, Melanie E, Gharib, Sina A, Guo, Xiuqing, Hall, Michael E, Hawley, Nicola L, He, Jiang, Hobbs, Brian D, Hokanson, John E, Hsiung, Chao A, Hwang, Shih-Jen, Hyde, Thomas M, Irvin, Marguerite R, Jaffe, Andrew E, Johnson, Eric O, Kaplan, Robert, Kardia, Sharon LR, Kaufman, Joel D, Kelly, Tanika N, Kleinman, Joel E, Kooperberg, Charles, Lee, I-Te, Levy, Daniel, Lutz, Sharon M, Manichaikul, Ani W, Martin, Lisa W, Marx, Olivia, McGarvey, Stephen T, Minster, Ryan L, Moll, Matthew, Moussa, Karine A, Naseri, Take, North, Kari E, Oelsner, Elizabeth C, Peralta, Juan M, Peyser, Patricia A, Psaty, Bruce M, Rafaels, Nicholas, Raffield, Laura M, Reupena, Muagututi’a Sefuiva, Rich, Stephen S, Rotter, Jerome I, Schwartz, David A, Shadyab, Aladdin H, Sheu, Wayne H-H, Sims, Mario, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Telen, Marilyn J, Watson, Harold, Weeks, Daniel E, Weir, David R, Yanek, Lisa R, Young, Kendra A, Young, Kristin L, Zhao, Wei, Hancock, Dana B, Jiang, Bibo, Vrieze, Scott, and Liu, Dajiang J

Subjects
Genetics, Tobacco, Drug Abuse (NIDA only), Tobacco Smoke and Health, Substance Misuse, Brain Disorders, Human Genome, Good Health and Well Being, Humans, Transcriptome, Drug Repositioning, Genome-Wide Association Study, Tobacco Use, Biology, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Published
2023

12. Large scale proteomic studies create novel privacy considerations

Author

Hill, Andrew C, Guo, Claire, Litkowski, Elizabeth M, Manichaikul, Ani W, Yu, Bing, Konigsberg, Iain R, Gorbet, Betty A, Lange, Leslie A, Pratte, Katherine A, Kechris, Katerina J, DeCamp, Matthew, Coors, Marilyn, Ortega, Victor E, Rich, Stephen S, Rotter, Jerome I, Gerzsten, Robert E, Clish, Clary B, Curtis, Jeffrey L, Hu, Xiaowei, Obeidat, Ma-en, Morris, Melody, Loureiro, Joseph, Ngo, Debby, O’Neal, Wanda K, Meyers, Deborah A, Bleecker, Eugene R, Hobbs, Brian D, Cho, Michael H, Banaei-Kashani, Farnoush, and Bowler, Russell P

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Atherosclerosis, Cardiovascular, Minority Health, Health Disparities, Human Genome, Biotechnology, Generic health relevance, Humans, Proteome, Bayes Theorem, Privacy, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Published
2023

13. Lung tissue shows divergent gene expression between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Author

Ghosh, Auyon J, Hobbs, Brian D, Yun, Jeong H, Saferali, Aabida, Moll, Matthew, Xu, Zhonghui, Chase, Robert P, Morrow, Jarrett, Ziniti, John, Sciurba, Frank, Barwick, Lucas, Limper, Andrew H, Flaherty, Kevin, Criner, Gerard, Brown, Kevin K, Wise, Robert, Martinez, Fernando J, McGoldrick, Daniel, Cho, Michael H, DeMeo, Dawn L, Silverman, Edwin K, Castaldi, Peter J, and Hersh, Craig P

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Genetics, Rare Diseases, Chronic Obstructive Pulmonary Disease, Orphan Drug, Autoimmune Disease, Lung, Aetiology, 2.1 Biological and endogenous factors, Respiratory, Humans, Idiopathic Pulmonary Fibrosis, Pulmonary Disease, Chronic Obstructive, Sequence Analysis, RNA, Transcriptome, COPD, IPF, RNA sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Cardiorespiratory Medicine and Haematology, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by shared exposures and clinical features, but distinct genetic and pathologic features exist. These features have not been well-studied using large-scale gene expression datasets. We hypothesized that there are divergent gene, pathway, and cellular signatures between COPD and IPF.MethodsWe performed RNA-sequencing on lung tissues from individuals with IPF (n = 231) and COPD (n = 377) compared to control (n = 267), defined as individuals with normal spirometry. We grouped the overlapping differential expression gene sets based on direction of expression and examined the resultant sets for genes of interest, pathway enrichment, and cell composition. Using gene set variation analysis, we validated the overlap group gene sets in independent COPD and IPF data sets.ResultsWe found 5010 genes differentially expressed between COPD and control, and 11,454 genes differentially expressed between IPF and control (1% false discovery rate). 3846 genes overlapped between IPF and COPD. Several pathways were enriched for genes upregulated in COPD and downregulated in IPF; however, no pathways were enriched for genes downregulated in COPD and upregulated in IPF. There were many myeloid cell genes with increased expression in COPD but decreased in IPF. We found that the genes upregulated in COPD but downregulated in IPF were associated with lower lung function in the independent validation cohorts.ConclusionsWe identified a divergent gene expression signature between COPD and IPF, with increased expression in COPD and decreased in IPF. This signature is associated with worse lung function in both COPD and IPF.

Published
2022

14. Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

Author

Moll, Matthew, Hecker, Julian, Platig, John, Zhang, Jingzhou, Ghosh, Auyon J., Pratte, Katherine A., Wang, Rui-Sheng, Hill, Davin, Konigsberg, Iain R., Chiles, Joe W., III, Hersh, Craig P., Castaldi, Peter J., Glass, Kimberly, Dy, Jennifer G., Sin, Don D., Tal-Singer, Ruth, Mouded, Majd, Rennard, Stephen I., Anderson, Gary P., Kinney, Gregory L., Bowler, Russell P., Curtis, Jeffrey L., McDonald, Merry-Lynn, Silverman, Edwin K., Hobbs, Brian D., and Cho, Michael H.

Published
2024
Full Text
View/download PDF

17. Augmenting control arms with Real-World Data for cancer trials: Hybrid control arm methods and considerations

Author

Tan, W. Katherine, Segal, Brian D., Curtis, Melissa D., Baxi, Shrujal S., Capra, William B., Garrett-Mayer, Elizabeth, Hobbs, Brian P., Hong, David S., Hubbard, Rebecca A., Zhu, Jiawen, Sarkar, Somnath, and Samant, Meghna

Subjects
Statistics - Methodology
Abstract

Randomized controlled trials (RCTs) are the gold standard for assessing drug safety and efficacy. However, RCTs have some drawbacks which have led to the use of single-arm studies to make certain internal drug development and regulatory decisions, particularly in oncology. Hybrid controlled trials with real-world data (RWD), in which the control arm is composed of both trial and real-world patients, have the potential to help address some of the shortcomings of both RCTs and single-arm studies in particular situations, such as when a disease has low prevalence or when the standard of care to be used in the control arm is ineffective or highly toxic and an experimental therapy shows early promise. This paper discusses why it may be beneficial to consider hybrid controlled trials with RWD, what such a design entails, when it may be appropriate, and how to conduct the analyses. We propose a novel two-step borrowing method for the construction of hybrid control arms. We use simulations to demonstrate the operating characteristics of dynamic and static borrowing methods, and highlight the trade-offs and analytic decisions that study teams will need to address when designing a hybrid study., Comment: 71 pgs (with supplemental) 3 Tables, 4 Figures

Published
2021

18. Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Author

Little, Amarise, Hu, Yao, Sun, Quan, Jain, Deepti, Broome, Jai, Chen, Ming-Huei, Thibord, Florian, McHugh, Caitlin, Surendran, Praveen, Blackwell, Thomas W, Brody, Jennifer A, Bhan, Arunoday, Chami, Nathalie, de Vries, Paul S, Ekunwe, Lynette, Heard-Costa, Nancy, Hobbs, Brian D, Manichaikul, Ani, Moon, Jee-Young, Preuss, Michael H, Ryan, Kathleen, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Abecasis, Goncalo R, Almasy, Laura, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Choquet, Hélène, Correa, Adolfo, Curran, Joanne E, Faraday, Nauder, Fornage, Myriam, Glahn, David C, Hou, Lifang, Jorgenson, Eric, Kooperberg, Charles, Lewis, Joshua P, Lloyd-Jones, Donald M, Loos, Ruth JF, Min, Yuan-I, Mitchell, Braxton D, Morrison, Alanna C, Nickerson, Deborah A, North, Kari E, O'Connell, Jeffrey R, Pankratz, Nathan, Psaty, Bruce M, Vasan, Ramachandran S, Rich, Stephen S, Rotter, Jerome I, Smith, Albert V, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Conomos, Matthew P, Laurie, Cecelia A, Mathias, Rasika A, Li, Yun, Auer, Paul L, Consortium, NHLBI Trans-Omics for Precision Medicine, Thornton, Timothy, Reiner, Alexander P, Johnson, Andrew D, and Raffield, Laura M

Subjects
Biological Sciences, Genetics, Precision Medicine, Biotechnology, Hematology, Clinical Research, Human Genome, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Good Health and Well Being, Blood Platelets, Genome-Wide Association Study, Humans, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Genetics & Heredity
Abstract

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Published
2022

19. Alpha-1 Antitrypsin MZ Heterozygosity Is an Endotype of Chronic Obstructive Pulmonary Disease.

Author

Ghosh, Auyon J, Hobbs, Brian D, Moll, Matthew, Saferali, Aabida, Boueiz, Adel, Yun, Jeong H, Sciurba, Frank, Barwick, Lucas, Limper, Andrew H, Flaherty, Kevin, Criner, Gerard, Brown, Kevin K, Wise, Robert, Martinez, Fernando J, Lomas, David, Castaldi, Peter J, Carey, Vincent J, DeMeo, Dawn L, Cho, Michael H, Silverman, Edwin K, Hersh, Craig P, Crapo, James D, Make, Barry J, Regan, Elizabeth A, Beaty, Terri H, El-Boueiz, Adel, Foreman, Marilyn G, Hayden, Lystra P, Hetmanski, Jacqueline, Hokanson, John E, Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Prokopenko, Dmitry, Morrow, Jarrett, Qiao, Dandi, Sakornsakolpat, Phuwanat, Wan, Emily S, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Pernicano, Perry G, and Hanania, Nicola

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Chronic Obstructive Pulmonary Disease, Lung, Emphysema, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Respiratory, Adult, Aged, Aged, 80 and over, Case-Control Studies, Female, Genetic Markers, Genotype, Heterozygote, Humans, Longitudinal Studies, Male, Middle Aged, Phenotype, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, Survival Analysis, Whole Genome Sequencing, alpha 1-Antitrypsin, COPDGene Investigators, RNA sequencing, alpha-1 antitrypsin, chronic obstructive pulmonary disease, meta-analysis, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Rationale: Multiple studies have demonstrated an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygous carriers of the AAT (alpha-1 antitrypsin) Z allele. However, it is not known if MZ subjects with COPD are phenotypically different from noncarriers (MM genotype) with COPD. Objectives: To assess if MZ subjects with COPD have different clinical features compared with MM subjects with COPD. Methods: Genotypes of SERPINA1 were ascertained by using whole-genome sequencing data in three independent studies. We compared outcomes between MM subjects with COPD and MZ subjects with COPD in each study and combined the results in a meta-analysis. We performed longitudinal and survival analyses to compare outcomes in MM and MZ subjects with COPD over time. Measurements and Main Results: We included 290 MZ subjects with COPD and 6,184 MM subjects with COPD across the three studies. MZ subjects had a lower FEV1% predicted and greater quantitative emphysema on chest computed tomography scans compared with MM subjects. In a meta-analysis, the FEV1 was 3.9% lower (95% confidence interval [CI], -6.55% to -1.26%) and emphysema (the percentage of lung attenuation areas

Published
2022

20. Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Author

Shrine, Nick, Guyatt, Anna L., Erzurumluoglu, A. Mesut, Jackson, Victoria E., Hobbs, Brian D., Melbourne, Carl A., Batini, Chiara, Fawcett, Katherine A., Song, Kijoung, Sakornsakolpat, Phuwanat, Li, Xingnan, Boxall, Ruth, Reeve, Nicola F., Obeidat, Ma’en, Zhao, Jing Hua, Wielscher, Matthias, Weiss, Stefan, Kentistou, Katherine A., Cook, James P., Sun, Benjamin B., Zhou, Jian, Hui, Jennie, Karrasch, Stefan, Imboden, Medea, Harris, Sarah E., Marten, Jonathan, Enroth, Stefan, Kerr, Shona M., Surakka, Ida, Vitart, Veronique, Lehtimäki, Terho, Allen, Richard J., Bakke, Per S., Beaty, Terri H., Bleecker, Eugene R., Bossé, Yohan, Brandsma, Corry-Anke, Chen, Zhengming, Crapo, James D., Danesh, John, DeMeo, Dawn L., Dudbridge, Frank, Ewert, Ralf, Gieger, Christian, Gulsvik, Amund, Hansell, Anna L., Hao, Ke, Hoffman, Joshua D., Hokanson, John E., Homuth, Georg, Joshi, Peter K., Joubert, Philippe, Langenberg, Claudia, Li, Xuan, Li, Liming, Lin, Kuang, Lind, Lars, Locantore, Nicholas, Luan, Jian’an, Mahajan, Anubha, Maranville, Joseph C., Murray, Alison, Nickle, David C., Packer, Richard, Parker, Margaret M., Paynton, Megan L., Porteous, David J., Prokopenko, Dmitry, Qiao, Dandi, Rawal, Rajesh, Runz, Heiko, Sayers, Ian, Sin, Don D., Smith, Blair H., Artigas, María Soler, Sparrow, David, Tal-Singer, Ruth, Timmers, Paul R. H. J., Van den Berge, Maarten, Whittaker, John C., Woodruff, Prescott G., Yerges-Armstrong, Laura M., Troyanskaya, Olga G., Raitakari, Olli T., Kähönen, Mika, Polašek, Ozren, Gyllensten, Ulf, Rudan, Igor, Deary, Ian J., Probst-Hensch, Nicole M., Schulz, Holger, James, Alan L., Wilson, James F., Stubbe, Beate, Zeggini, Eleftheria, Jarvelin, Marjo-Riitta, Wareham, Nick, Silverman, Edwin K., Hayward, Caroline, Morris, Andrew P., Butterworth, Adam S., Scott, Robert A., Walters, Robin G., Meyers, Deborah A., Cho, Michael H., Strachan, David P., Hall, Ian P., Tobin, Martin D., and Wain, Louise V.

Published
2024
Full Text
View/download PDF

21. 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
Full Text
View/download PDF

22. A Bayesian Nonparametric model for textural pattern heterogeneity

Author

Li, Xiao, Guindani, Michele, Ng, Chaan S., and Hobbs, Brian P.

Subjects
Statistics - Applications
Abstract

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies., Comment: 45 pages, 7 figures, 1 Table

Published
2020

23. A Survival Mediation Model with Bayesian Model Averaging

Author

Zhou, Jie, Jiang, Xun, Xia, H. Amy, Wei, Peng, and Hobbs, Brian P.

Subjects
Statistics - Methodology
Abstract

Determining the extent to which a patient is benefiting from cancer therapy is challenging. Criteria for quantifying the extent of "tumor response" observed within a few cycles of treatment have been established for various types of solid as well as hematologic malignancies. These measures comprise the primary endpoints of phase II trials. Regulatory approvals of new cancer therapies, however, are usually contingent upon the demonstration of superior overall survival with randomized evidence acquired with a phase III trial comparing the novel therapy to an appropriate standard of care treatment. With nearly two thirds of phase III oncology trials failing to achieve statistically significant results, researchers continue to refine and propose new surrogate endpoints. This article presents a Bayesian framework for studying relationships among treatment, patient subgroups, tumor response and survival. Combining classical components of mediation analysis with Bayesian model averaging (BMA), the methodology is robust to model mis-specification among various possible relationships among the observable entities. Posterior inference is demonstrated via application to a randomized controlled phase III trial in metastatic colorectal cancer. Moreover, the article details posterior predictive distributions of survival and statistical metrics for quantifying the extent of direct and indirect, or tumor response mediated, treatment effects., Comment: 25 pages, 3 figures and 3 tables in the main manuscript. Supplementary materials included

Published
2020

24. Statistical design considerations for trials that study multiple indications

Author

Kaizer, Alexander M., Koopmeiners, Joseph S., Chen, Nan, and Hobbs, Brian P.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios, however there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. This article proposes novel optimization criteria for calibrating and evaluating candidate statistical designs of master protocols in the presence of the potential for treatment effect heterogeneity among enrolled patient subpopulations. The framework is applied to demonstrate the statistical properties of conventional study designs when treatments offer heterogeneous benefit as well as identify optimal designs devised to monitor the potential for heterogeneity among patients with differing clinical indications using Bayesian modeling., Comment: 27 pages, 10 figures

Published
2020

25. Genetic variation in genes regulating skeletal muscle regeneration and tissue remodelling associated with weight loss in chronic obstructive pulmonary disease

Author

Kumar, Preeti Lakshman, Wilson, Ava C, Rocco, Alison, Cho, Michael H, Wan, Emily, Hobbs, Brian D, Washko, George R, Ortega, Victor E, Christenson, Stephanie A, Li, Xingnan, Wells, J Michael, Bhatt, Surya P, DeMeo, Dawn L, Lutz, Sharon M, Rossiter, Harry, Casaburi, Richard, Rennard, Stephen I, Lomas, David A, Labaki, Wassim W, Tal‐Singer, Ruth, Bowler, Russel P, Hersh, Craig P, Tiwari, Hemant K, Dransfield, Mark, Thalacker‐Mercer, Anna, Meyers, Deborah A, Silverman, Edwin K, McDonald, Merry‐Lynn N, and COPDGene, ECLIPSE and SPIROMICS investigators

Subjects
Epidemiology, Health Sciences, Nutrition, Lung, Genetics, Prevention, Chronic Obstructive Pulmonary Disease, Human Genome, Clinical Research, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Respiratory, Adult, Bayes Theorem, Genetic Variation, Genome-Wide Association Study, Humans, Muscle, Skeletal, Nerve Tissue Proteins, Pulmonary Disease, Chronic Obstructive, Regeneration, Weight Loss, GWAS, Cachexia, Weight loss, COPD, Biomarkers, Skeletal muscle regeneration, Tissue remodelling, COPDGene, ECLIPSE and SPIROMICS investigators, Physiology, Clinical Sciences, Human Movement and Sports Sciences, Clinical sciences, Allied health and rehabilitation science, Sports science and exercise
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. COPD patients with cachexia or weight loss have increased risk of death independent of body mass index (BMI) and lung function. We tested the hypothesis genetic variation is associated with weight loss in COPD using a genome-wide association study approach.MethodsParticipants with COPD (N = 4308) from three studies (COPDGene, ECLIPSE, and SPIROMICS) were analysed. Discovery analyses were performed in COPDGene with replication in SPIROMICS and ECLIPSE. In COPDGene, weight loss was defined as self-reported unintentional weight loss > 5% in the past year or low BMI (BMI

Published
2021

26. Emphysema Progression and Lung Function Decline Among Angiotensin Converting Enzyme Inhibitors and Angiotensin-Receptor Blockade Users in the COPDGene Cohort

Author

Tejwani, Vickram, Fawzy, Ashraf, Putcha, Nirupama, Castaldi, Peter J, Cho, Michael H, Pratte, Katherine A, Bhatt, Surya P, Lynch, David A, Humphries, Stephen M, Kinney, Gregory L, D’Alessio, Franco R, Hansel, Nadia N, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Regan, Elizabeth, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, Ginneken, Bramvan, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Kinney, Gregory, Young, Kendra A, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Pernicano, Perry G, Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, and Guy, Elizabeth

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Emphysema, Lung, Tobacco, Chronic Obstructive Pulmonary Disease, Tobacco Smoke and Health, Cancer, Respiratory, Aged, Angiotensin Receptor Antagonists, Angiotensin-Converting Enzyme Inhibitors, Cohort Studies, Disease Progression, Female, Forced Expiratory Volume, Humans, Lung Volume Measurements, Male, Middle Aged, Prospective Studies, Protective Factors, Pulmonary Disease, Chronic Obstructive, Pulmonary Emphysema, Spirometry, Tomography, X-Ray Computed, Vital Capacity, Walk Test, angiotensin II, COPD, emphysema progression, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundAttenuation of transforming growth factor β by blocking angiotensin II has been shown to reduce emphysema in a murine model. General population studies have demonstrated that the use of angiotensin converting enzyme inhibitors (ACEis) and angiotensin-receptor blockers (ARBs) is associated with reduction of emphysema progression in former smokers and that the use of ACEis is associated with reduction of FEV1 progression in current smokers.Research questionIs use of ACEi and ARB associated with less progression of emphysema and FEV1 decline among individuals with COPD or baseline emphysema?MethodsFormer and current smokers from the Genetic Epidemiology of COPD Study who attended baseline and 5-year follow-up visits, did not change smoking status, and underwent chest CT imaging were included. Adjusted linear mixed models were used to evaluate progression of adjusted lung density (ALD), percent emphysema (%total lung volume

Published
2021

27. Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program

Author

Mikhaylova, Anna V, McHugh, Caitlin P, Polfus, Linda M, Raffield, Laura M, Boorgula, Meher Preethi, Blackwell, Thomas W, Brody, Jennifer A, Broome, Jai, Chami, Nathalie, Chen, Ming-Huei, Conomos, Matthew P, Cox, Corey, Curran, Joanne E, Daya, Michelle, Ekunwe, Lynette, Glahn, David C, Heard-Costa, Nancy, Highland, Heather M, Hobbs, Brian D, Ilboudo, Yann, Jain, Deepti, Lange, Leslie A, Miller-Fleming, Tyne W, Min, Nancy, Moon, Jee-Young, Preuss, Michael H, Rosen, Jonathon, Ryan, Kathleen, Smith, Albert V, Sun, Quan, Surendran, Praveen, de Vries, Paul S, Walter, Klaudia, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Zhong, Xue, Abecasis, Goncalo R, Almasy, Laura, Barnes, Kathleen C, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Chavan, Sameer, Cho, Michael H, Choquet, Hélène, Correa, Adolfo, Cox, Nancy, DeMeo, Dawn L, Faraday, Nauder, Fornage, Myriam, Gerszten, Robert E, Hou, Lifang, Johnson, Andrew D, Jorgenson, Eric, Kaplan, Robert, Kooperberg, Charles, Kundu, Kousik, Laurie, Cecelia A, Lettre, Guillaume, Lewis, Joshua P, Li, Bingshan, Li, Yun, Lloyd-Jones, Donald M, Loos, Ruth JF, Manichaikul, Ani, Meyers, Deborah A, Mitchell, Braxton D, Morrison, Alanna C, Ngo, Debby, Nickerson, Deborah A, Nongmaithem, Suraj, North, Kari E, O’Connell, Jeffrey R, Ortega, Victor E, Pankratz, Nathan, Perry, James A, Psaty, Bruce M, Rich, Stephen S, Soranzo, Nicole, Rotter, Jerome I, Silverman, Edwin K, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Thornton, Timothy A, Vasan, Ramachandran S, Zein, Joe, Mathias, Rasika A, Consortium, NHLBI Trans-Omics for Precision Medicine, Reiner, Alexander P, and Auer, Paul L

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Good Health and Well Being, Asthma, Biomarkers, Dermatitis, Atopic, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Leukocytes, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Prognosis, Proteome, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, United Kingdom, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, blood-cell counts, whole-genome sequencing, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Published
2021

29. The Association Between Lung Hyperinflation and Coronary Artery Disease in Smokers

Author

Chandra, Divay, Gupta, Aman, Kinney, Gregory L, Fuhrman, Carl R, Leader, Joseph K, Diaz, Alejandro A, Bon, Jessica, Barr, R Graham, Washko, George, Budoff, Matthew, Hokanson, John, Sciurba, Frank C, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Boueiz, Adel R, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Newell, John D, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Wilson, Carla G, Jensen, Robert, Crooks, Jim, Everett, Douglas, Moore, Camille, Strand, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Martinez, Carlos, Murray, Susan, Soler, Xavier, Banaei-Kashani, Farnoush, Bowler, Russell P, Kechris, Katerina, Curtis, Jeffrey L, Pernicano, Perry G, Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, and Parulekar, Amit

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Lung, Heart Disease, Prevention, Atherosclerosis, Heart Disease - Coronary Heart Disease, Chronic Obstructive Pulmonary Disease, Emphysema, Tobacco Smoke and Health, Biomedical Imaging, Tobacco, Cardiovascular, Clinical Research, Respiratory, Good Health and Well Being, Airway Obstruction, Airway Remodeling, Asymptomatic Diseases, Biological Variation, Population, Coronary Artery Disease, Coronary Vessels, Female, Humans, Male, Middle Aged, Organ Size, Plethysmography, Pulmonary Emphysema, Respiratory Function Tests, Risk Factors, Smoking, Tomography, X-Ray Computed, United States, COPD, coronary artery disease, lung hyperinflation, smoking, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundSmokers manifest varied phenotypes of pulmonary impairment.Research questionWhich pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers?Study design and methodsWe analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV1 to FVC ratio, < 0.70).ResultsPulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV1 and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts.InterpretationLung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV1 and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

Published
2021

30. Pulmonary Arterial Pruning and Longitudinal Change in Percent Emphysema and Lung Function The Genetic Epidemiology of COPD Study

Author

Pistenmaa, Carrie L, Nardelli, P, Ash, SY, Come, CE, Diaz, AA, Rahaghi, FN, Barr, RG, Young, KA, Kinney, GL, Simmons, JP, Wade, RC, Wells, JM, Hokanson, JE, Washko, GR, San José Estépar, R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri H, Castaldi, Peter J, Cho, Michael H, DeMeo, Dawn L, Boueiz, Adel El, Foreman, Marilyn G, Ghosh, Auyon, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Regan, Elizabeth, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S, Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, Ginneken, Bramvan, van Rikxoort, Eva, Ferrero, Gonzalo Vegas Sanchez-, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, and Curtis, Jeffrey L

Subjects
Emphysema, Tobacco, Tobacco Smoke and Health, Lung, Chronic Obstructive Pulmonary Disease, Clinical Research, Biomedical Imaging, Respiratory, Disease Progression, Endothelium, Vascular, Female, Humans, Longitudinal Studies, Male, Middle Aged, Pulmonary Artery, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, Smokers, Tomography, X-Ray Computed, emphysema, imaging, longitudinal, lung function, pulmonary circulation, COPDGene Investigators, Clinical Sciences, Respiratory System
Abstract

BackgroundPulmonary endothelial damage has been shown to precede the development of emphysema in animals, and vascular changes in humans have been observed in COPD and emphysema.Research questionIs intraparenchymal vascular pruning associated with longitudinal progression of emphysema on CT imaging or decline in lung function over 5 years?Study design and methodsThe Genetic Epidemiology of COPD Study enrolled ever smokers with and without COPD from 2008 through 2011. The percentage of emphysema-like lung, or "percent emphysema," was assessed at baseline and after 5 years on noncontrast CT imaging as the percentage of lung voxels < -950 Hounsfield units. An automated CT imaging-based tool assessed and classified intrapulmonary arteries and veins. Spirometry measures are postbronchodilator. Pulmonary arterial pruning was defined as a lower ratio of small artery volume (< 5 mm2 cross-sectional area) to total lung artery volume. Mixed linear models included demographics, anthropomorphics, smoking, and COPD, with emphysema models also adjusting for CT imaging scanner and lung function models adjusting for clinical center and baseline percent emphysema.ResultsAt baseline, the 4,227 participants were 60 ± 9 years of age, 50% were women, 28% were Black, 47% were current smokers, and 41% had COPD. Median percent emphysema was 2.1 (interquartile range, 0.6-6.3) and progressed 0.24 percentage points/y (95% CI, 0.22-0.26 percentage points/y) over 5.6 years. Mean FEV1 to FVC ratio was 68.5 ± 14.2% and declined 0.26%/y (95% CI, -0.30 to -0.23%/y). Greater pulmonary arterial pruning was associated with more rapid progression of percent emphysema (0.11 percentage points/y per 1-SD increase in arterial pruning; 95% CI, 0.09-0.16 percentage points/y), including after adjusting for baseline percent emphysema and FEV1. Arterial pruning also was associated with a faster decline in FEV1 to FVC ratio (-0.04%/y per 1-SD increase in arterial pruning; 95% CI, -0.008 to -0.001%/y).InterpretationPulmonary arterial pruning was associated with faster progression of percent emphysema and more rapid decline in FEV1 to FVC ratio over 5 years in ever smokers, suggesting that pulmonary vascular differences may be relevant in disease progression.Trial registryClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Published
2021

31. Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

Author

Hu, Yao, Stilp, Adrienne M, McHugh, Caitlin P, Rao, Shuquan, Jain, Deepti, Zheng, Xiuwen, Lane, John, de Bellefon, Sébastian Méric, Raffield, Laura M, Chen, Ming-Huei, Yanek, Lisa R, Wheeler, Marsha, Yao, Yao, Ren, Chunyan, Broome, Jai, Moon, Jee-Young, de Vries, Paul S, Hobbs, Brian D, Sun, Quan, Surendran, Praveen, Brody, Jennifer A, Blackwell, Thomas W, Choquet, Hélène, Ryan, Kathleen, Duggirala, Ravindranath, Heard-Costa, Nancy, Wang, Zhe, Chami, Nathalie, Preuss, Michael H, Min, Nancy, Ekunwe, Lynette, Lange, Leslie A, Cushman, Mary, Faraday, Nauder, Curran, Joanne E, Almasy, Laura, Kundu, Kousik, Smith, Albert V, Gabriel, Stacey, Rotter, Jerome I, Fornage, Myriam, Lloyd-Jones, Donald M, Vasan, Ramachandran S, Smith, Nicholas L, North, Kari E, Boerwinkle, Eric, Becker, Lewis C, Lewis, Joshua P, Abecasis, Goncalo R, Hou, Lifang, O’Connell, Jeffrey R, Morrison, Alanna C, Beaty, Terri H, Kaplan, Robert, Correa, Adolfo, Blangero, John, Jorgenson, Eric, Psaty, Bruce M, Kooperberg, Charles, Walton, Russell T, Kleinstiver, Benjamin P, Tang, Hua, Loos, Ruth JF, Soranzo, Nicole, Butterworth, Adam S, Nickerson, Debbie, Rich, Stephen S, Mitchell, Braxton D, Johnson, Andrew D, Auer, Paul L, Li, Yun, Mathias, Rasika A, Lettre, Guillaume, Pankratz, Nathan, Laurie, Cathy C, Laurie, Cecelia A, Bauer, Daniel E, Conomos, Matthew P, Reiner, Alexander P, and Consortium, NHLBI Trans-Omics for Precision Medicine

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Biotechnology, Clinical Research, Hematology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Aged, Chromosomes, Human, Pair 16, Datasets as Topic, Erythrocytes, Female, Gene Editing, Genetic Variation, Genome-Wide Association Study, HEK293 Cells, Humans, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Quality Control, Reproducibility of Results, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, base editing, red blood cell traits, whole-genome sequencing, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

Published
2021

33. Longitudinal Association Between Muscle Loss and Mortality in Ever Smokers

Author

Crapo, James D., Silverman, Edwin K., Cummings, Sara, Madden, Kelley, Make, Barry J., Nabbosa, Juliet, Port, Emily, Rashdi, Serine, Regan, Elizabeth A., Stepp, Lori, Watts, Shandi, Weaver, Michael, Beaty, Terri, Bowler, Russell P., Curtis, Jeffrey L., Han, MeiLan K., Hokanson, John E., Lynch, David A., Strand, Matthew J., Anderson, Gary, Bleecker, Eugene R., Coxson, Harvey O., Crystal, Ronald G., Hogg, James C., Province, Michael A., Rennard, Stephen I., Croxton, Thomas, Gan, Weiniu, Postow, Lisa A., Viviano, Lisa M., Costa-Davis, Corinne, Malanga, Elisha, Prieto, Delia, Tal-Singer, Ruth, Farzadegan, Homayoon, Hadji, Akila, Sathe, Leena, Baraghoshi, David, Chen, Grace, Crooks, James, Knowles, Ruthie, Pratte, Katherine, Wilson, Carla, Zelarney, Pearlanne T., Kechris, Katerina J., Leach, Sonia, Austin, Erin E., Czizik, Annika, Kinney, Gregory, Li, Yisha, Lutz, Sharon M., Ragland, Margaret F., Richmond, Nicole, Young, Kendra A., Cho, Michael, Castaldi, Peter J., Glass, Kimberly, Hersh, Craig, Kim, Wonji, Liu, Yang-Yu, Hersh, Craig P., Bidinger, Jacqueline, Cho, Michael H., Conrad, Douglas, DeMeo, Dawn L., El-Boueiz, Adel R., Foreman, Marilyn G., Ghosh, Auyon, Hahn, Georg, Hansel, Nadia N., Hayden, Lystra P., Hobbs, Brian, Kim, Woori, Lange, Christoph, McDonald, Merry- Lynn, McGeachie, Michael, Moll, Matthew, Morris, Melody, Patsopoulos, Nikolaos A., Qiao, Dandi, Ruczinski, Ingo, Wan, Emily S., Dy, Jennifer G., Fain, Sean B., Ginsburg, Shoshana, Hoffman, Eric A., Humphries, Stephen, Judy, Philip F., Stefanie Mason, Alex Kluiber, Oh, Andrea, Poynton, Clare, Reinhardt, Joseph M., Ross, James, San Jose Estepar, Raul, Schroeder, Joyce D., Sitek, Arkadiusz, Steiner, Robert M., van Beek, Edwin, Ginneken, Bram van, van Rikxoort, Eva, Washko, George R., Jensen, Robert, John E. Hokanson, Co-Chair, Bhatt, Surya P., Casaburi, Richard, Kim, Victor, Putcha, Nirupama, Han, MeiLan, Bon, Jessica, Diaz, Alejandro A., Regan, Elizabeth, Anzueto, Antonio, Bailey, William C., Criner, Gerard J., Dransfield, Mark T., Kinney, Greg, Sprenger, Kim, Benos, Takis, Hanania, Nicola A., Hoth, Karin F., Lambert, Allison, Lowe, Katherine, Oates, Gabriela, Parekh, Trisha, Westney, Gloria, Young, Kendra, Balasubramanian, Aparna, Boriek, Aladin, Fawzy, Ashraf, Jacobson, Francine, LaFon, David C., MacIntyre, Neil, Maselli-Caceres, Diego, McCormack, Meredith C., McDonald, Merry-Lynn, Sciurba, Frank, Soler, Xavier, Tejwani, Vickram, van Beek, Edwin JR., Wade, Raymond C., Wells, Mike, Wendt, Chris H., Yun, Jeong H., Zhang, Jingzhou, Gillenwater, Lucas, Lowe, Katherine E., Pratte, Katherine A., Ragland, Margaret, Attaway, Amy, Mason, Stefanie, Rossiter, Harry B., Saha, Punam Kumar, Wilson, Ava, Amaza, Hannatu, Baldomero, Adrienne, Mamary, A. James, O’Brien, James, Wise, Robert A., Eakin, Michelle, Fiedorowicz, Jess G., Henkle, Ben, Holm, Kristen, Iyer, Anand, Kunisaki, Ken M., McEvoy, Charlene, Mkorombindo, Takudzwa, Shinozaki, Gen, Yohannes, Abebaw, Hobbs, Brian D., Miller, Bruce E., Retson, Tara, McCloskey, Lisa, Pernicano, Perry G., Atik, Mustafa, Bertrand, Laura, Monaco, Thomas, Narendra, Dharani, Lenge de Rosen, Veronica V., Badu-Danso, Kwame, Jacobson, Francine L., Kaufman, Laura, Maguire, Cherie, Struble, Sophie, Wilson, Seth, Barr, R. Graham, Almonte, Casandra, Austin, John H.M., Gomez Blum, Maria Lorena, D’Souza, Belinda M., Florez, Emilay, Martinez, Rodney, MacIntyre, Neil, Jr., Curry, Wendy, McAdams, H. Page, Reikofski, Charlotte V., Washington, Lacey, Brown, Robert, Clare, Cheryl, Daniel, Marie, Horton, Karen, Ting “Tony” Lin, Cheng, Mirza, Tahira, Scott, Meagan, Shade, Becky, Budoff, Matt, Calmelat, Robert, Cavanaugh, Deborah, Dailing, Chris, Diaz, Leticia, Fischer, Hans, Indelicato, Renee Love, Porszasz, Janos, Soriano, April, Stringer, William, Urrutia, Miriam, Baldomero, Arianne, Bell, Brian, Deconcini, Miranda, Loes, Linda, Phelan, Jonathan, Robichaux, Camille, Sasse, Cheryl, Tashjian, Joseph H., Flenaugh, Eric L., Abson, Kema, Gebrekristos, Hirut, Johnson, Priscilla, Jordan, Jessica, Ponce, Mario, Terpenning, Silanath, Wilson, Derrick, Broadhurst, Grace, Dyer, Debra, Engel, Elena, Finigan, Jay, Hill, Andrew, Jones, Alex, Jones, Ryan, Owen, Jordan, Rosiello, Richard, Andries, Nicole, Charpentier, Mary, Kirk, Diane, Pace, David, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Davis, Valena, Desai, Parag, Fehrle, Dee, Grabianowski, Carla, Jacobs, Michael, Jameson, Laurie, Jones, Gayle M., Kelsen, Steven, Marchetti, Nathaniel, McGonagle, Francine, Satti, Aditi, Shenoy, Kartik, Sheridan, Regina, Vega-Sanchez, Maria, Wallace, Samantha, Akinseye-kolapo, Samuel, Baker, Matthew, Goggins, Arnissa, McClain, Anny, Nath, Hrudaya, Singh, Satinder P., Sonavane, Sushil K., Westfall, Elizabeth, Gil, Marissa, El Hajjaoui, Tarek, Hsiao, Albert, Martineau, Amber, Mielke, Jenna, Perez, Karl, Querido, Gabriel, Reston, Tara, Yen, Andrew, Comellas, Alejandro, Fortis, Spyridon, Galizia, Mauricio, Garcia, Eric, Keating, Janet, Laroia, Archana, Lee, Changhyun, Meyer, Amber, Mullan, Brian, Nagpal, Prashant, Ofori, Oloigbe, Suiter, Sierra, Mason, Stefanie E., Moreta-Martinez, Rafael, Labaki, Wassim W., San Jose Estepar, Ruben, Make, Barry, and Stringer, Kathleen

Published
2022
Full Text
View/download PDF

34. Polygenic risk scores identify heterogeneity in asthma and chronic obstructive pulmonary disease

Author

Moll, Matthew, Sordillo, Joanne E., Ghosh, Auyon J., Hayden, Lystra P., McDermott, Gregory, McGeachie, Michael J., Dahlin, Amber, Tiwari, Anshul, Manmadkar, Monica G., Abston, Eric D., Pavuluri, Chandan, Saferali, Aabida, Begum, Sofina, Ziniti, John P., Gulsvik, Amund, Bakke, Per S., Aschard, Hugues, Iribarren, Carlos, Hersh, Craig P., Sparks, Jeffrey A., Hobbs, Brian D., Lasky-Su, Jessica A., Silverman, Edwin K., Weiss, Scott T., Wu, Ann Chen, and Cho, Michael H.

Published
2023
Full Text
View/download PDF

35. A Bayesian nonparametric model for textural pattern heterogeneity

Author

Li, Xiao, Guindani, Michele, Ng, Chaan S, and Hobbs, Brian P

Subjects
Cancer, Biomedical Imaging, Bayesian nonparametrics, cancer radiomics, grey&#8208, level co&#8208, occurrence matrix, multivariate count data, stat.AP, Statistics, Statistics & Probability
Abstract

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of grey-level co-occurrence matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine learning approaches currently used in cancer radiomics with simulation studies.

Published
2021

36. Analyzing Basket Trials under Multisource Exchangeability Assumptions

Author

Kane, Michael J., Chen, Nan, Kaizer, Alexander M., Jiang, Xun, Xia, H. Amy, and Hobbs, Brian P.

Subjects
Statistics - Computation, G.3
Abstract

Basket designs are prospective clinical trials that are devised with the hypothesis that the presence of selected molecular features determine a patient's subsequent response to a particular "targeted" treatment strategy. Basket trials are designed to enroll multiple clinical subpopulations to which it is assumed that the therapy in question offers beneficial efficacy in the presence of the targeted molecular profile. The treatment, however, may not offer acceptable efficacy to all subpopulations enrolled. Moreover, for rare disease settings, such as oncology wherein these trials have become popular, marginal measures of statistical evidence are difficult to interpret for sparsely enrolled subpopulations. Consequently, basket trials pose challenges to the traditional paradigm for trial design, which assumes inter-patient exchangeability. The R-package \pkg{basket} facilitates the analysis of basket trials by implementing multi-source exchangeability models. By evaluating all possible pairwise exchangeability relationships, this hierarchical modeling framework facilitates Bayesian posterior shrinkage among a collection of discrete and pre-specified subpopulations. Analysis functions are provided to implement posterior inference of the response rates and all possible exchangeability relationships between subpopulations. In addition, the package can identify "poolable" subsets of and report their response characteristics. The functionality of the package is demonstrated using data from an oncology study with subpopulations defined by tumor histology., Comment: 18 pages, 4 figures, 3 tables, submitted to the Journal of Open Source Software

Published
2019

40. Machine Learning Characterization of COPD Subtypes Insights From the COPDGene Study

Author

Castaldi, Peter J, Boueiz, Adel, Yun, Jeong, San Jose Estepar, Raul, Ross, James C, Washko, George, Cho, Michael H, Hersh, Craig P, Kinney, Gregory L, Young, Kendra A, Regan, Elizabeth A, Lynch, David A, Criner, Gerald J, Dy, Jennifer G, Rennard, Stephen I, Casaburi, Richard, Make, Barry J, Crapo, James, Silverman, Edwin K, Hokanson, John E, Crapo, James D, Beaty, Terri, Begum, Ferdouse, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hetmanski, Jacqueline, Hobbs, Brian D, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Regan, Elizabeth, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Kinney, Gregory, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, and Kechris, Katerina

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Genetics, Chronic Obstructive Pulmonary Disease, Lung, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Cluster Analysis, Diagnostic Imaging, Disease Progression, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Molecular Epidemiology, Phenotype, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, COPD, emphysema, machine learning, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

Published
2020

41. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Author

Shrine, Nick, Izquierdo, Abril G., Chen, Jing, Packer, Richard, Hall, Robert J., Guyatt, Anna L., Batini, Chiara, Thompson, Rebecca J., Pavuluri, Chandan, Malik, Vidhi, Hobbs, Brian D., Moll, Matthew, Kim, Wonji, Tal-Singer, Ruth, Bakke, Per, Fawcett, Katherine A., John, Catherine, Coley, Kayesha, Piga, Noemi Nicole, Pozarickij, Alfred, Lin, Kuang, Millwood, Iona Y., Chen, Zhengming, Li, Liming, Wijnant, Sara R. A., Lahousse, Lies, Brusselle, Guy, Uitterlinden, Andre G., Manichaikul, Ani, Oelsner, Elizabeth C., Rich, Stephen S., Barr, R. Graham, Kerr, Shona M., Vitart, Veronique, Brown, Michael R., Wielscher, Matthias, Imboden, Medea, Jeong, Ayoung, Bartz, Traci M., Gharib, Sina A., Flexeder, Claudia, Karrasch, Stefan, Gieger, Christian, Peters, Annette, Stubbe, Beate, Hu, Xiaowei, Ortega, Victor E., Meyers, Deborah A., Bleecker, Eugene R., Gabriel, Stacey B., Gupta, Namrata, Smith, Albert Vernon, Luan, Jian’an, Zhao, Jing-Hua, Hansen, Ailin F., Langhammer, Arnulf, Willer, Cristen, Bhatta, Laxmi, Porteous, David, Smith, Blair H., Campbell, Archie, Sofer, Tamar, Lee, Jiwon, Daviglus, Martha L., Yu, Bing, Lim, Elise, Xu, Hanfei, O’Connor, George T., Thareja, Gaurav, Albagha, Omar M. E., Suhre, Karsten, Granell, Raquel, Faquih, Tariq O., Hiemstra, Pieter S., Slats, Annelies M., Mullin, Benjamin H., Hui, Jennie, James, Alan, Beilby, John, Patasova, Karina, Hysi, Pirro, Koskela, Jukka T., Wyss, Annah B., Jin, Jianping, Sikdar, Sinjini, Lee, Mikyeong, May-Wilson, Sebastian, Pirastu, Nicola, Kentistou, Katherine A., Joshi, Peter K., Timmers, Paul R. H. J., Williams, Alexander T., Free, Robert C., Wang, Xueyang, Morrison, John L., Gilliland, Frank D., Chen, Zhanghua, Wang, Carol A., Foong, Rachel E., Harris, Sarah E., Taylor, Adele, Redmond, Paul, Cook, James P., Mahajan, Anubha, Lind, Lars, Palviainen, Teemu, Lehtimäki, Terho, Raitakari, Olli T., Kaprio, Jaakko, Rantanen, Taina, Pietiläinen, Kirsi H., Cox, Simon R., Pennell, Craig E., Hall, Graham L., Gauderman, W. James, Brightling, Chris, Wilson, James F., Vasankari, Tuula, Laitinen, Tarja, Salomaa, Veikko, Mook-Kanamori, Dennis O., Timpson, Nicholas J., Zeggini, Eleftheria, Dupuis, Josée, Hayward, Caroline, Brumpton, Ben, Langenberg, Claudia, Weiss, Stefan, Homuth, Georg, Schmidt, Carsten Oliver, Probst-Hensch, Nicole, Jarvelin, Marjo-Riitta, Morrison, Alanna C., Polasek, Ozren, Rudan, Igor, Lee, Joo-Hyeon, Sayers, Ian, Rawlins, Emma L., Dudbridge, Frank, Silverman, Edwin K., Strachan, David P., Walters, Robin G., Morris, Andrew P., London, Stephanie J., Cho, Michael H., Wain, Louise V., Hall, Ian P., and Tobin, Martin D.

Published
2023
Full Text
View/download PDF

42. 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
Full Text
View/download PDF

44. Prevalence of abnormal spirometry in individuals with a smoking history and no known obstructive lung disease

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri H., Castaldi, Peter J., Cho, Michael H., DeMeo, Dawn L., El Boueiz, Adel, Foreman, Marilyn G., Ghosh, Auyon, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S., Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O., Galban, Craig J., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Nardelli, Pietro, Newell, John D., Jr., Notary, Aleena, Oh, Andrea, Ross, James C., San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R., Wilson, Carla G., Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A., Bhatt, Surya P., Bon, Jessica, Diaz, Alejandro A., Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P., Kechris, Katerina, BanaeiKashani, Farnoush, Curtis, Jeffrey L., Pernicano, Perry G., Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, Hersh, Craig, Washko, George, Barr, R. Graham, Austin, John, D'Souza, Belinda, Thomashow, Byron, MacIntyre, Neil, Jr., McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Hansel, Nadia N., Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Kunisaki, Ken M., Flenaugh, Eric L., Gebrekristos, Hirut, Ponce, Mario, Terpenning, Silanath, Westney, Gloria, Bowler, Russell, Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D'Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Swift, Alex, Swift, Irene, Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Iyer, Anand, Nath, Hrudaya, Wells, J. Michael, Conrad, Douglas, Yen, Andrew, Comellas, Alejandro P., Hoth, Karin F., Newell, John, Jr., Thompson, Brad, Kazerooni, Ella, Labaki, Wassim, Galban, Craig, Vummidi, Dharshan, Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Chandra, Divay, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, Ruiz, Mario E., Singh, Harjinder, Tran, Thuonghien V., Kinney, Gregory L., Comellas, Alejandro, Baldomero, Arianne K., Hokanson, John, and Fortis, Spyridon

Published
2023
Full Text
View/download PDF

45. Clinical Markers Associated With Risk of Suicide or Drug Overdose Among Individuals With Smoking Exposure: A Longitudinal Follow-up Study of the COPDGene Cohort

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri H., Castaldi, Peter J., Cho, Michael H., DeMeo, Dawn L., El Boueiz, Adel, Foreman, Marilyn G., Ghosh, Auyon, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S., Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O., Galban, Craig J., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Nardelli, Pietro, Newell, John D., Jr., Notary, Aleena, Oh, Andrea, Ross, James C., San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R., Wilson, Carla G., Jensen, Robert, Strand, Matthew, Crooks, Jim, Pratte, Katherine, Khatiwada, Aastha, Austin, Erin, Kinney, Gregory, Young, Kendra A., Bhatt, Surya P., Bon, Jessica, Diaz, Alejandro A., Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P., Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L., Pernicano, Perry G., Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, Hersh, Craig, Washko, George, Barr, R. Graham, Austin, John, D’Souza, Belinda, Thomashow, Byron, MacIntyre, Neil, Jr., McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Hansel, Nadia N., Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Kunisaki, Ken M., Flenaugh, Eric L., Gebrekristos, Hirut, Ponce, Mario, Terpenning, Silanath, Westney, Gloria, Bowler, Russell, Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Swift, Alex, Swift, Irene, Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Iyer, Anand, Nath, Hrudaya, Wells, J. Michael, Conrad, Douglas, Yen, Andrew, Comellas, Alejandro P., Hoth, Karin F., Newell, John, Jr., Thompson, Brad, Kazerooni, Ella, Labaki, Wassim, Galban, Craig, Vummidi, Dharshan, Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Chandra, Divay, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, Ruiz, Mario E., Singh, Harjinder, Adviento, Brigid A., Iyer, Anand S., Kinney, Gregory L., Hanania, Nicola A., Lowe, Katherine E., Holm, Kristen E., Yohannes, Abebaw M., Shinozaki, Gen, and Fiedorowicz, Jess G.

Published
2023
Full Text
View/download PDF

46. A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors

Author

Paxton, Laramie, Cao, Yufeng, Vixie, Kevin R., Wang, Yuan, Hobbs, Brian, and Ng, Chaan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensity values in the time series data and propose a simplified perimeter cost term in the energy functional. We normalize the data and perimeter terms in the functional to expedite the graph cut without having to optimize the scaling parameter $\lambda$. In place of a training process, predetermined tissue means are computed based on sample regions identified by expert radiologists. The proposed method also has the advantage of being relatively simple to implement computationally. It was evaluated against the ground truth on a clinical CT dataset of 10 tumors and yielded segmentations with a mean Dice similarity coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The average processing time was 1.25 minutes per slice., Comment: Image processing; image analysis; medical imaging

Published
2018

47. Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Author

Shrine, Nick, Izquierdo, Abril G., Chen, Jing, Packer, Richard, Hall, Robert J., Guyatt, Anna L., Batini, Chiara, Thompson, Rebecca J., Pavuluri, Chandan, Malik, Vidhi, Hobbs, Brian D., Moll, Matthew, Kim, Wonji, Tal-Singer, Ruth, Bakke, Per, Fawcett, Katherine A., John, Catherine, Coley, Kayesha, Piga, Noemi Nicole, Pozarickij, Alfred, Lin, Kuang, Millwood, Iona Y., Chen, Zhengming, Li, Liming, Wijnant, Sara R. A., Lahousse, Lies, Brusselle, Guy, Uitterlinden, Andre G., Manichaikul, Ani, Oelsner, Elizabeth C., Rich, Stephen S., Barr, R. Graham, Kerr, Shona M., Vitart, Veronique, Brown, Michael R., Wielscher, Matthias, Imboden, Medea, Jeong, Ayoung, Bartz, Traci M., Gharib, Sina A., Flexeder, Claudia, Karrasch, Stefan, Gieger, Christian, Peters, Annette, Stubbe, Beate, Hu, Xiaowei, Ortega, Victor E., Meyers, Deborah A., Bleecker, Eugene R., Gabriel, Stacey B., Gupta, Namrata, Smith, Albert Vernon, Luan, Jian’an, Zhao, Jing-Hua, Hansen, Ailin F., Langhammer, Arnulf, Willer, Cristen, Bhatta, Laxmi, Porteous, David, Smith, Blair H., Campbell, Archie, Sofer, Tamar, Lee, Jiwon, Daviglus, Martha L., Yu, Bing, Lim, Elise, Xu, Hanfei, O’Connor, George T., Thareja, Gaurav, Albagha, Omar M. E., Suhre, Karsten, Granell, Raquel, Faquih, Tariq O., Hiemstra, Pieter S., Slats, Annelies M., Mullin, Benjamin H., Hui, Jennie, James, Alan, Beilby, John, Patasova, Karina, Hysi, Pirro, Koskela, Jukka T., Wyss, Annah B., Jin, Jianping, Sikdar, Sinjini, Lee, Mikyeong, May-Wilson, Sebastian, Pirastu, Nicola, Kentistou, Katherine A., Joshi, Peter K., Timmers, Paul R. H. J., Williams, Alexander T., Free, Robert C., Wang, Xueyang, Morrison, John L., Gilliland, Frank D., Chen, Zhanghua, Wang, Carol A., Foong, Rachel E., Harris, Sarah E., Taylor, Adele, Redmond, Paul, Cook, James P., Mahajan, Anubha, Lind, Lars, Palviainen, Teemu, Lehtimäki, Terho, Raitakari, Olli T., Kaprio, Jaakko, Rantanen, Taina, Pietiläinen, Kirsi H., Cox, Simon R., Pennell, Craig E., Hall, Graham L., Gauderman, W. James, Brightling, Chris, Wilson, James F., Vasankari, Tuula, Laitinen, Tarja, Salomaa, Veikko, Mook-Kanamori, Dennis O., Timpson, Nicholas J., Zeggini, Eleftheria, Dupuis, Josée, Hayward, Caroline, Brumpton, Ben, Langenberg, Claudia, Weiss, Stefan, Homuth, Georg, Schmidt, Carsten Oliver, Probst-Hensch, Nicole, Jarvelin, Marjo-Riitta, Morrison, Alanna C., Polasek, Ozren, Rudan, Igor, Lee, Joo-Hyeon, Sayers, Ian, Rawlins, Emma L., Dudbridge, Frank, Silverman, Edwin K., Strachan, David P., Walters, Robin G., Morris, Andrew P., London, Stephanie J., Cho, Michael H., Wain, Louise V., Hall, Ian P., and Tobin, Martin D.

Published
2023
Full Text
View/download PDF

48. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease

Author

Young, Alexandra L, Bragman, Felix JS, Rangelov, Bojidar, Han, MeiLan K, Galbán, Craig J, Lynch, David A, Hawkes, David J, Alexander, Daniel C, Hurst, John R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Qiao, Dandi, Wan, Emily S, Won, Sungho, Sakornsakolpat, Phuwanat, Prokopenko, Dmitry, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Newell, John D, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Bon, Jessica, Martinez, Carlos, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Barr, R Graham, Austin, John, D’Souza, Belinda, Pearson, Gregory DN, Rozenshtein, Anna, Thomashow, Byron, MacIntyre, Neil, McAdams, H Page, Washington, Lacey, McEvoy, Charlene, and Tashjian, Joseph

Subjects
Biomedical Imaging, Lung, Clinical Research, Chronic Obstructive Pulmonary Disease, Aetiology, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Respiratory, Aged, Disease Progression, Female, Humans, Male, Middle Aged, Models, Theoretical, Pulmonary Disease, Chronic Obstructive, Tomography, X-Ray Computed, clustering, CT imaging, emphysema, bronchitis, chronic obstructive pulmonary disease, COPDGene Investigators, Medical and Health Sciences, Respiratory System
Abstract

Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype (n = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r = -0.16 [P

Published
2020

49. Overlap of Genetic Risk between Interstitial Lung Abnormalities and Idiopathic Pulmonary Fibrosis

Author

Hobbs, Brian D, Putman, Rachel K, Araki, Tetsuro, Nishino, Mizuki, Gudmundsson, Gunnar, Gudnason, Vilmundur, Eiriksdottir, Gudny, Nogueira, Nuno Rodrigues Zilhao, Dupuis, Josée, Xu, Hanfei, O’Connor, George T, Manichaikul, Ani, Nguyen, Jennifer, Podolanczuk, Anna J, Madahar, Purnema, Rotter, Jerome I, Lederer, David J, Barr, R Graham, Rich, Stephen S, Ampleford, Elizabeth J, Ortega, Victor E, Peters, Stephen P, O’Neal, Wanda K, Newell, John D, Bleecker, Eugene R, Meyers, Deborah A, Allen, Richard J, Oldham, Justin M, Ma, Shwu-Fan, Noth, Imre, Jenkins, R Gisli, Maher, Toby M, Hubbard, Richard B, Wain, Louise V, Fingerlin, Tasha E, Schwartz, David A, Washko, George R, Rosas, Ivan O, Silverman, Edwin K, Hatabu, Hiroto, Cho, Michael H, and Hunninghake, Gary M

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Biomedical Imaging, Chronic Obstructive Pulmonary Disease, Women's Health, Prevention, Lung, Clinical Research, Rare Diseases, Genetics, Autoimmune Disease, Human Genome, 2.1 Biological and endogenous factors, Respiratory, Aged, Case-Control Studies, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Idiopathic Pulmonary Fibrosis, Lung Diseases, Interstitial, Male, Middle Aged, Mucin-5B, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, TATA Box Binding Protein-Like Proteins, beta Karyopherins, genetics, genome-wide association study, interstitial lung abnormalities, idiopathic pulmonary fibrosis, SNP, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Rationale: Interstitial lung abnormalities (ILAs) are associated with the highest genetic risk locus for idiopathic pulmonary fibrosis (IPF); however, the extent to which there are unique associations among individuals with ILAs or additional overlap with IPF is not known.Objectives: To perform a genome-wide association study (GWAS) of ILAs.Methods: ILAs and a subpleural-predominant subtype were assessed on chest computed tomography (CT) scans in the AGES (Age Gene/Environment Susceptibility), COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]), Framingham Heart, ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points), MESA (Multi-Ethnic Study of Atherosclerosis), and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) studies. We performed a GWAS of ILAs in each cohort and combined the results using a meta-analysis. We assessed for overlapping associations in independent GWASs of IPF.Measurements and Main Results: Genome-wide genotyping data were available for 1,699 individuals with ILAs and 10,274 control subjects. The MUC5B (mucin 5B) promoter variant rs35705950 was significantly associated with both ILAs (P = 2.6 × 10-27) and subpleural ILAs (P = 1.6 × 10-29). We discovered novel genome-wide associations near IPO11 (rs6886640, P = 3.8 × 10-8) and FCF1P3 (rs73199442, P = 4.8 × 10-8) with ILAs, and near HTRE1 (rs7744971, P = 4.2 × 10-8) with subpleural-predominant ILAs. These novel associations were not associated with IPF. Among 12 previously reported IPF GWAS loci, five (DPP9, DSP, FAM13A, IVD, and MUC5B) were significantly associated (P

Published
2019

50. COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease.

Author

Lowe, Katherine E, Regan, Elizabeth A, Anzueto, Antonio, Austin, Erin, Austin, John HM, Beaty, Terri H, Benos, Panayiotis V, Benway, Christopher J, Bhatt, Surya P, Bleecker, Eugene R, Bodduluri, Sandeep, Bon, Jessica, Boriek, Aladin M, Boueiz, Adel Re, Bowler, Russell P, Budoff, Matthew, Casaburi, Richard, Castaldi, Peter J, Charbonnier, Jean-Paul, Cho, Michael H, Comellas, Alejandro, Conrad, Douglas, Costa Davis, Corinne, Criner, Gerard J, Curran-Everett, Douglas, Curtis, Jeffrey L, DeMeo, Dawn L, Diaz, Alejandro A, Dransfield, Mark T, Dy, Jennifer G, Fawzy, Ashraf, Fleming, Margaret, Flenaugh, Eric L, Foreman, Marilyn G, Fortis, Spyridon, Gebrekristos, Hirut, Grant, Sarah, Grenier, Philippe A, Gu, Tian, Gupta, Abhya, Han, MeiLan K, Hanania, Nicola A, Hansel, Nadia N, Hayden, Lystra P, Hersh, Craig P, Hobbs, Brian D, Hoffman, Eric A, Hogg, James C, Hokanson, John E, Hoth, Karin F, Hsiao, Albert, Humphries, Stephen, Jacobs, Kathleen, Jacobson, Francine L, Kazerooni, Ella A, Kim, Victor, Kim, Woo Jin, Kinney, Gregory L, Koegler, Harald, Lutz, Sharon M, Lynch, David A, MacIntye, Neil R, Make, Barry J, Marchetti, Nathaniel, Martinez, Fernando J, Maselli, Diego J, Mathews, Anne M, McCormack, Meredith C, McDonald, Merry-Lynn N, McEvoy, Charlene E, Moll, Matthew, Molye, Sarah S, Murray, Susan, Nath, Hrudaya, Newell, John D, Occhipinti, Mariaelena, Paoletti, Matteo, Parekh, Trisha, Pistolesi, Massimo, Pratte, Katherine A, Putcha, Nirupama, Ragland, Margaret, Reinhardt, Joseph M, Rennard, Stephen I, Rosiello, Richard A, Ross, James C, Rossiter, Harry B, Ruczinski, Ingo, San Jose Estepar, Raul, Sciurba, Frank C, Sieren, Jessica C, Singh, Harjinder, Soler, Xavier, Steiner, Robert M, Strand, Matthew J, Stringer, William W, Tal-Singer, Ruth, Thomashow, Byron, Vegas Sánchez-Ferrero, Gonzalo, and Walsh, John W

Subjects
COPD Genetic Epidemiology study, preserved ratio-impaired spirometry, COPD diagnosis, COPD diagnosis, COPDGene, GOLD, Global initiative for chronic Obstructive Lung Dis, PRISm, chronic obstructive pulmonary disease, copd, preserved ratio-impaired spirometry, spirometry, Prevention, Tobacco Smoke and Health, Tobacco, Lung, Chronic Obstructive Pulmonary Disease, Biomedical Imaging, Clinical Research, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Respiratory, Good Health and Well Being, COPD, COPD Genetic Epidemiology study, Global initiative for chronic Obstructive Lung Disease
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.MethodsFour key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.ResultsUsing smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.ConclusionsA substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results