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Gharib, Sina A.

0000-0002-2480-4367

Mayo Clinic

University of Washington

Citations 3

Whole Genome Sequence Analysis of Weight Loss in 16 972 Participants With COPD Reveals Novel Risk Loci in DRAIC and RFX3
Chiles, J.W. , Rocco, Alison, Srinivasasainagendra, Vinodh, Rossiter, Harry B. , Casaburi, Richard, Thalacker-Mercer, Anna, Wells, J Michael, Wan, Emily S., Silverman, Edwin K., Cho, Michael H., Hersh, Craig P., PSATY, B, Gharib, Sina A., Gao, Yan, O’Connor, George T., Lange, Leslie A., Rich, Stephen S., Manichaikul, Ani W., Barr, R. Graham, Ortega, V E, Meyers, Deborah A., Smith, Albert V., Tiwari, Hemant K., McDonald, Merry-Lynn N. , McDonald, Merry‐Lynn , Chiles, Joe W , Casaburi, Richard , Thalacker‐Mercer, Anna Elizabeth , Wells, J. Michael , Wan, Emily S. , Silverman, Edwin K. , Cho, Michael H. , Hersh, Craig P. , Psaty, Bruce M , Gharib, Sina A. , Lange, Leslie A., Rich, Stephen S. , Manichaikul, Ani , Barr, R. Graham, Meyers, Deborah A. , Tiwari, Hemant K.

Journal of Cachexia, Sarcopenia and Muscle | April 24, 2026 | Volume: 17 | Issue: 3 | Pages: No page available

10.1002/jcsm.70293 Get It

ABSTRACT Background Chronic obstructive pulmonary disease (COPD) is associated with musculoskeletal comorbidities, including cachexia. Weight loss (WL) is the major criterion for cachexia and increases risk for mortality in COPD. Risk factors for WL in COPD are incompletely understood. We performed this whole genome sequencing (WGS) analysis to identify genetic risk variants for WL in COPD. Methods We studied 16 972 participants from the Trans‐Omics for Precision Medicine (TOPMed) Initiative and All of Us Research Program. COPD was diagnosed using spirometry in TOPMed, while diagnosis codes were used in All of Us. WL was defined as WL ≥ 5% or a final body mass index (BMI) < 20 kg/m 2 . WGS data came from white blood cells in all cohorts. Single‐variant testing was conducted on both race‐ and study‐stratified cohorts and in a cosmopolitan, ancestry‐independent manner using GENESIS in TOPMed and SAIGE in All of Us. SAIGE‐GENE+ gene‐based analyses were performed on race‐stratified and cosmopolitan cohorts. Single variant meta‐analyses were conducted using METAL within (B/AA and NHW analyses of Black/African–American and non‐Hispanic white participants, respectively) and across racial groups (COSMO). Rare variant gene‐based results were combined using Fisher's method. Transcriptomic effects were predicted using MetaXcan. We used the GWAS Catalogue to analyse for colocalization with other related traits. Results Two single variants were associated with WL in COPD among All of Us participants: one intronic variant in HCN1 in Black/African–American participants (chr5:45271359:TACACAC:T, odds ratio with 95% confidence interval (OR (CI 95 )) = 2.43(1.78–3.31), p = 1.95 × 10 −8 ) and one intergenic variant between PPP4R2 and PDZRN3 in the cosmopolitan and NHW cohorts (chr3:73345901:A:G, OR (CI 95 ) = 0.21(0.12–0.35), p = 8.84 × 10 −9 in cosmopolitan and OR (CI 95 ) = 0.18(0.10–0.33), p = 9.44 × 10 −9 in NHW). Single‐variant meta‐analysis identified two loci associated with WL in COPD: five variants within DRAIC in the B/AA meta‐analysis (lead variant chr15:69571341:A:G, OR (CI 95 ) = 1.37(1.23–1.51), p = 1.29 × 10 −9 ) and two intronic variants within RFX3 in the cosmopolitan meta‐analysis (lead variant chr9:3390983:T:C, OR (CI 95 ) = 1.50(1.31–1.73), p = 1.06 × 10 −8 ). Rare variants within RNU6‐565P (NHW analysis in All of Us; p = 2.83 × 10 −7 ) and LOC339298 (B/AA analysis in All of Us, p = 1.85 × 10 −6 ) were associated with WL in COPD. The RNU‐565P signal remained significant after combination with TOPMed results (p combined = 1.23 × 10 −6 ). MetaXcan predicted differential expression of LNC00959 in visceral adipose tissue ( p = 1.16 × 10 −6 in COSMO analysis). Colocalization analyses identified genomic associations between BMI and variants in or near DRAIC , RFX3 , PDZRN3 , and LINC00959 . Conclusions In the first WGS analysis of WL in COPD, we have identified seven novel loci. Further characterization of these loci will validate our findings and improve our understanding of the molecular pathophysiology of this condition.
Associations of Pulmonary Function with MRI Brain Volumes: A Coordinated Multi-Study Analysis
Frenzel, Stefan , Bis, Joshua C. , O’Donnell, Adrienne , Simino, Jeannette , Yaqub, Amber , Bartz, Traci M. , DeCarli, Charles , Ghosh, Saptaparni , Gireud, Monica , London, Stephanie J. , Longstreth, W. T., Lent, Debora Melo van , Satizábal, Claudia L. , Shrestha, Srishti , Sigurðsson, Sigurður , Stubbe, Beate, Vernooij, Meike W. , Völzke, Henry , Yu, Bing , Guðnason, Vilmundur , Wolters, Frank J. , DeCarli, Charles , Satizabal, Claudia L , Gudmundsson, Elias Freyr , Brusselle, Guy , Bülow, Robin , Ewert, Ralf , Gharib, Sina A. , Gottesman, Rebecca F. , Ikram, M. Arfan , Knopman, David S. , Launer, Lenore J. , López, Oscar L. , O’Connor, George T. , Stubbe, Beate , Talluri, Rajesh , Vasan, Ramachandran S. , Wiggins, Kerri L. , Beiser, Alexa , Mosley, Thomas H. , Psaty, Bruce M. , Grabe, Hans Jörgen , Seshadri, Sudha

Journal of Alzheimer's Disease | October 4, 2022 | Volume: 90 | Issue: 3 | Pages: 1073-1083

10.3233/jad-220667 Get It

Background: Previous studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent. Objective: To study the cross-sectional associations of pulmonary function with structural brain variables. Methods: Data from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses. Results: FEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume. Conclusion: In cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.
Genetic architecture of smoking: Evaluating rare variant contribution from deep whole-genome sequencing of up to 26,000 individuals
Jang, Seon-Kyeong , Arnett, Donna K. , Becker, Diane M. , Bis, Joshua C. , Bleecker, Eugene R., Fingerlin, Tasha E. , Haessler, Jeffrey, Heckbert, Susan R. , Hokanson, John E. , Judy, Renae , Justice, Anne E. , Kaplan, Robert C. , Kim, Wonji , Gladwin, Mark T. , Melander, Olle , Meyers, Deborah A. , Peljto, Anna L. , Qiao, Dandi , Reed, Robert M. , Reiner, Alex P. , Silverman, Edwin K. , Smith, Nicholas L. , Smith, Gustav, Smith, Albert V. , Tang, Weihong, Gordeuk, Victor R. , Yanek, Lisa R. , Yang, Ivana V. , Young, Kendra A. , Vrieze, Scott , Martin, Lisa , Smith, Albert V , Arnett, Donna K , Evans, Luke M. , Fialkowski, Allison , Blangero, John , Brody, Jennifer A. , Cupples, L. Adrienne , Damrauer, Scott M , David, Sean P. , Andrade, Mariza de , Gharib, Sina A. , Glahn, David C , Hwang, Shih‐Jen , Hyman, Matthew Craig , Kooperberg, Charles , Levy, Dan , Loos, Ruth J. F. , Manichaikul, Ani , Martin, Lisa W. , Nouraie, Mehdi , North, Kari E. , Oelsner, Elizabeth C. , Preuss, Michael , Psaty, Bruce M. , Rader, Daniel J , Rich, Stephen S. , Rotter, Jerome I. , Schwartz, David A. , Shadyab, Aladdin H , Taylor, Kent D. , Vasan, Ramachandran S. , Wang, Zhe , Wiggins, Kerri L. , Young, Kristin L. , Zhang, Yingze , Liu, Dajiang J. , Keller, Matthew C.

No container title available | May 6, 2021 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.21203/rs.3.rs-475149/v1 Get It

Abstract Background Across complex traits, common variants explain only a modest amount of variance, with SNP-heritability consistently below heritability estimates from close relatives. Here, we examined the contribution of rare variant to tobacco use risk in up to 26,000 individuals of European ancestry in the Trans-Omics for Precision Medicine (TOPMed) program with whole genome sequence (WGS;~30X coverage).Method We grouped about 35 million genetic variants by their minor allele frequencies (MAF) and linkage disequilibrium (LD) and estimated SNP-heritability for age of smoking initiation (N = 14,747), cigarettes smoked per day (N = 15,425), smoking cessation (N = 17,871) and initiation (N = 26,340) using linear mixed model. Rare variant population structure is detected and adjusted for by permutation procedure. We estimated an upper bound for narrow-sense heritability for tobacco use using available pedigrees consisting of close relatives in TOPMed.Results Rare variants with MAF 0.1–0.01%, mostly from non-protein altering region, accounted for 26% of variation in age of initiation and 15% for cessation. Follow-up analysis indicated that about one-third of these rare variants contribtion is potentially confounded with rare variants structure even after adjusting for principal components. After further conservative adjustment of population structure, we estimated SNP-based heritability to be 0.21 (SE = 0.08) for age of initiation, 0.15 (0.06) for cigarettes per day, 0.21 (0.09) for cessation, and 0.24 (0.07) for initiation, 1.8–4.5 times higher than previous SNP-based estimates. Our pedigree-based upper-bound for SNP-based heritability ranged from 0.18–0.35.Conclusion The substantial contribution of rare variants for several smoking phenotypes sheds light on the missing heritability and genetic etiology of tobacco use. It also informs fine-mapping strategies since the majority of the rare variant contribution was located in non-coding regulatory regions.