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Raffield, Laura M.

https://orcid.org/0000-0002-7892-193X

Mayo Clinic

University of North Carolina at Chapel Hill

Citations 10

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed
Blackwell, Thomas W., Kachuri, Linda , Castaldi, Peter J. , Cho, Michael H. , Christenson, Stephanie A. , Durda, Peter , Gabriel, Stacey , Hersh, Craig P. , Huntsman, Scott , Hwang, S.-J., Joehanes, Roby, Johnson, Mari , Li, Xingnan , Lin, Honghuang , Liu, Ching‐Ti , Liu, Yongmei , Mak, Angel C. Y. , Manichaikul, Ani , Paik, David T. , Saferali, Aabida , Smith, Joshua D. , Taylor, Kent D. , Wang, Jiongming , Weinstock, Joshua S. , Weiss, Jeffrey, Wheeler, Heather E. , Zhou, Ying , Zoellner, Sebastian, Mestroni, Luisa , Graw, Sharon , Taylor, Matthew R.G. , Ortega, Victor E. , Johnson, Craig , Gan, Weiniu , Abecasis, Gonçalo , Nickerson, Deborah A. , Ardlie, Kristin G., Woodruff, Prescott G. , Bowler, Russell P. , Meyers, Deborah A. , Reiner, Alex, Kooperberg, Charles , Ziv, Elad , Vasan, Ramachandran S. , Silverman, Edwin K. , Rich, Stephen S., Heard‐Costa, Nancy L. , Tang, Hua, Rotter, Jerome I. , Smith, Albert V. , Aguet, François, Scott, Laura J. , Raffield, Laura M. , Parker, Stephen C.J. , Abe, Namiko, Almasy, Laura, Ament, Seth, Anugu, Pramod, Auer, Paul, Avramopoulos, Dimitrios, Balasubramanian, Adithya, Barr, R. Graham, Barwick, Lucas, Beaty, Terri, Becker, Diane, Becker, Lewis, Beitelshees, Amber, Benos, Takis, Bezerra, Marcos, Bis, Joshua, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Buth, Erin, Carey, Vincent, Carty, Cara, Casaburi, Richard, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Ren-Hua, Conomos, Matthew, Cornell, Elaine, Crandall, Carolyn, Crapo, James, Curtis, Jeffrey, Damcott, Coleen, David, Sean, Fuentes, Lisa de las, Vries, Paul de, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Duggirala, Ravi, Eaton, Charles, Ekunwe, Lynette, Boueiz, Adel El, Emery, Leslie, Farber, Charles, Farek, Jesse, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Ghosh, Auyon, Gignoux, Chris, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Grine, Daniel, Gu, C. Charles, Guan, Yue, Hall, Michael, Han, Yi, Harris, Daniel, Heavner, Ben, Herrington, David, Hobbs, Brian, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Jackson, Rebecca, Jain, Deepti, Johnsen, Jill, Johnston, Rich, Jones, Kimberly, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Kramer, Holly, Lange, Christoph, Lange, Ethan, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Sandra, Lee, Wen-Jane, Levine, David, Lewis, Joshua, Li, Yun, Lin, Xihong, Liu, Simin, Liu, Yu, Make, Barry, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGoldrick, Daniel, McHugh, Caitlin, Mei, Hao, Meigs, James, Menon, Vipin, Min, Nancy, Moll, Matt, Momin, Zeineen, Montasser, May, Mychaleckyj, Josyf C, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nkechinyere, Osuji, O'Connell, Jeff, O’Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pankow, James, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Phillips, Lawrence S, Pollin, Toni, Becker, Julia Powers, Boorgula, Meher Preethi, Psaty, Bruce, Qiao, Dandi, Rafaels, Nicholas, Rajendran, Mahitha, Rasmussen-Torvik, Laura, Ratan, Aakrosh, Reed, Robert, Regan, Elizabeth, Reupena, Muagututi‘a Sefuiva, Robillard, Rebecca, Roselli, Carolina, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ryan, Kathleen, Sabino, Ester Cerdeira, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Santibanez, Jireh, Schwander, Karen, Sciurba, Frank, Sériès, Frédéric, Shetty, Amol, Shetty, Aniket, Silver, Brian, Skomro, Robert, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taub, Margaret, Taylor, Simeon, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tong, Catherine, Tsai, Michael, Vaidya, Dhananjay, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Watson, Karol, Watt, Jennifer, Weng, Lu-Chen, Wessel, Jennifer, Williams, Kayleen, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Zekavat, Seyedeh Maryam, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Rich, Stephen S. , Pankow, James S. , Reiner, Alex P. , Aguet, François , Johnson, W. Craig , Ardlie, Kristin , Orchard, Peter , Kachuri, Linda , Castaldi, Peter J , Cho, Michael H , Christenson, Stephanie A , Durda, Peter , Hersh, Craig P , Huntsman, Scott , Hwang, Seungyong , Joehanes, Roby , Johnson, Mari , Li, Xingnan , Lin, Honghuang , Liu, Ching-Ti , Liu, Yongmei , Mak, Angel C Y , Manichaikul, Ani W , Paik, David T , Smith, Joshua D , Taylor, Kent D , Tracy, Russell P , Wang, Jiongming , Wang, Mingqiang , Weinstock, Joshua S , Weiss, Jeffrey , Wheeler, Heather E , Zhou, Ying , Zöllner, Sebastian , Wu, Joseph C , Mestroni, Luisa , Graw, Sharon , Taylor, Matthew R G , Ortega, Victor E , Johnson, W Craig , Gan, Weiniu , Gupta, Namrata , Ardlie, Kristin , Woodruff, Prescott G , Bowler, Russell P , Reiner, Alexander P , Kooperberg, Charles , Ziv, Elad , Vasan, Ramachandran S , Silverman, Edwin K , Rich, Stephen S , Heard-Costa, Nancy , Tang, Hua , Rotter, Jerome I , Smith, Albert V , Levy, Daniel , Aguet, François , Scott, Laura J , Raffield, Laura M , Parker, Stephen C J , Pankow, James S , Tracy, Russell P. , Wu, Joseph C. , Larson, Martin G. , Cupples, L. Adrienne , Abe, Namiko, Almasy, Laura, Ament, Seth, Anugu, Pramod, Auer, Paul, Avramopoulos, Dimitrios, Balasubramanian, Adithya, Barr, R. Graham, Barwick, Lucas, Beaty, Terri, Becker, Diane, Becker, Lewis, Beitelshees, Amber, Benos, Takis, Bezerra, Marcos, Bis, Joshua, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Buth, Erin, Carey, Vincent, Carty, Cara, Casaburi, Richard, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Choi, Seung Hoan , Chuang, Lee-Ming, Chung, Ren-Hua, Conomos, Matthew, Cornell, Elaine, Crandall, Carolyn, Crapo, James, Curtis, Jeffrey, Damcott, Coleen, David, Sean, Fuentes, Lisa de las, Vries, Paul de, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Duggirala, Ravi, Eaton, Charles, Ekunwe, Lynette, Boueiz, Adel El, Emery, Leslie, Farber, Charles, Farek, Jesse, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Ghosh, Auyon, Gignoux, Chris, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Grine, Daniel, Gu, C. Charles, Guan, Yue, Hall, Michael, Han, Yi, Harris, Daniel, Heavner, Ben, Herrington, David, Hobbs, Brian, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Jackson, Rebecca, Jain, Deepti, Johnsen, Jill, Johnston, Rich, Jones, Kimberly, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Kramer, Holly, Lange, Christoph, Lange, Ethan, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Sandra, Lee, Wen-Jane, Levine, David, Lewis, Joshua, Li, Yun, Lin, Xihong, Liu, Simin, Liu, Yu, Make, Barry, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGoldrick, Daniel, McHugh, Caitlin, Mei, Hao, Meigs, James, Menon, Vipin, Min, Nancy, Moll, Matt, Momin, Zeineen, Montasser, May, Mychaleckyj, Josyf C, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nkechinyere, Osuji, O'Connell, Jeff, O’Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Phillips, Lawrence S, Pollin, Toni, Becker, Julia Powers, Boorgula, Meher Preethi, Psaty, Bruce, Qiao, Dandi, Rafaels, Nicholas, Rajendran, Mahitha, Rasmussen-Torvik, Laura, Ratan, Aakrosh, Reed, Robert, Regan, Elizabeth, Reupena, Muagututi‘a Sefuiva, Robillard, Rebecca, Roselli, Carolina, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ryan, Kathleen, Sabino, Ester Cerdeira, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Santibanez, Jireh, Schwander, Karen, Sciurba, Frank, Sériès, Frédéric , Shetty, Amol, Shetty, Aniket, Silver, Brian, Skomro, Robert, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taub, Margaret, Taylor, Simeon, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tong, Catherine, Tsai, Michael, Vaidya, Dhananjay, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Watson, Karol, Watt, Jennifer, Weng, Lu-Chen, Wessel, Jennifer, Williams, Kayleen, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Zekavat, Seyedeh Maryam, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng

Science | July 16, 2026 | Volume: 393 | Issue: 6808 | Pages: No page available

10.1126/science.adx2989 Get It

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.
Associations of Prepandemic Chronic Kidney Disease Stages With Severe COVID-19 Incidence: The C4R Study
Choi, Yuni , Reiner, Alex P. , Sun, Yifei , Lash, James P., Umans, Jason G. , Oelsner, Elizabeth C. , Oelsner, Elizabeth C , Jacobs, David R. , Kramer, Holly , Coresh, Josef , Cushman, Mary , Demmer, Ryan T. , Tracy, Russell P. , Balte, Pallavi , Raffield, Laura M. , Min, Yuan‐I , Vasan, Ramachandran S. , Xanthakis, Vanessa , Regan, Elizabeth Anne , Kanaya, Alka M , Lash, James P.

American Journal of Respiratory and Critical Care Medicine | May 11, 2025 | Volume: 211 | Issue: Supplement_1 | Pages: A6330-A6330

10.1164/ajrccm.2025.211.abstracts.a6330 Get It

Abstract RATIONALE: Chronic kidney disease (CKD) is common in the general population and is associated with inflammation and compromised immune function. Since these mechanisms are hypothesized to promote severe COVID-19, we tested associations of pre-pandemic CKD with incident COVID-19 hospitalization or death in a large, multi-ethnic, US general population-based meta-cohort of adults. METHODS: Data were harmonized and pooled from 9 NIH-funded longitudinal cohorts participating in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R). CKD was classified by KDIGO 2024 CKD guideline (Low Risk, Moderately Increased Risk, High Risk, Very High Risk) using pre-pandemic urinary albumin-to-creatinine ratio (uACR) and estimated glomerular filtration rate using creatinine (eGFRcr). Incident COVID-19 hospitalization or death was ascertained over 2020−2023 via self-report and active surveillance, with medical record adjudication in a subset. In our primary analysis, we assessed associations between CKD stages and incident COVID-19 hospitalization or death. A secondary analysis examined joint association of CKD stages and the number of comorbidities (obesity, hypertension, diabetes, and cardiovascular disease) with the severe COVID-19. Multivariable hazard ratios (HRs) were estimated by cause-specific Cox regression, adjusting for socio-demographics and smoking. RESULTS: Among 26,039 multi-ethnic adult participants (mean age 66.1±14.8, 60.6 % female, 28.6% Whites, 3.2% Asians, 20.6% Blacks, 41.3% Hispanics, 6.3% American Indians) with a median follow-up of 550 days since the onset of the pandemic (baseline: March 1, 2020), there were 734 incident COVID-19 hospitalizations or deaths. The mean time since CKD markers were measured before the baseline was 6.5 years. The cumulative incidence of severe COVID-19 was 2.3, 3.5, 6.5, and 7.3% across CKD risk stages, and similar patterns were observed within variant types (Figure 1). Compared to participants with Low Risk of CKD, there was a graded increased hazards of severe COVID-19 across CKD stages, with HRs (95% CIs) of 1.27 (1.03−1.55) for Moderately Increased Risk, 2.33 (1.77−3.06) for High Risk, and 2.72 (1.89−3.92) for Very High Risk. In a joint analysis, both increased CKD risk and a higher number of comorbidities were associated with a higher risk of severe COVID-19, showing the highest HR of 12.29 (5.75−26.27) in Very High Risk individuals with four comorbidities compared to Low Risk individuals with none. CONCLUSIONS: Higher CKD stages were associated with a greater risk of COVID-19 hospitalization or death and the risk is substantially heightened with additional comorbidities. These results suggest that adults with CKD and related comorbidities may merit enhanced COVID-19 risk mitigation.
Abstract P1063: Association of Demographic and Clinical Factors with SARS-CoV-2 Anti-Nucleocapsid Antibody Response Among Previously Infected US Adults: The C4R Study
Wu, Christina , Sun, Yifei , Cushman, Mary , Boyle, Rebekah, Styer, Linda M. , Bell, Taison D., Anderson, Michaela R. , Bowler, Russell, Rock, Jean M., Bievenue, Rachel, Kandula, Namratha R. , Morganroth, Sally E, Coresh, Joe, Elkind, Mitchell S.V. , Ortega, Victor E. , Woodruff, Prescott G. , Oelsner, Elizabeth C. , Oelsner, Elizabeth C , Demmer, Ryan T. , Kim, John S. , Balte, Pallavi , Tracy, Russell P. , Allen, Norrina B. , Schreiner, Pamela J. , Schwartz, David A. , Lee, Joyce , Xanthakis, Vanessa , Pirzada, Amber , Doyle, Margaret F. , Regan, Elizabeth Anne , Make, Barry J. , Kanaya, Alka M , Isasi, Carmen R. , Raffield, Laura M. , Howard, Virginia J. , Cole, Shelley A. , Henderson, Joel , Mantis, Nicholas J

Circulation | April 17, 2025 | Volume: 151 | Issue: Suppl_1 | Pages: No page available

10.1161/cir.151.suppl_1.p1063 Get It

Background: Despite the availability of effective vaccines and a recent decrease in annual deaths, coronavirus disease-2019 (COVID-19) remains a leading cause of death. Serological studies of anti-nucleocapsid (N) antibody response to infection provide insights into host immunobiology of adaptive immune response, which holds promise for identifying high-risk individuals for adverse acute and chronic COVID-19 outcomes. Hypothesis: Among participants previously infected with SARS-CoV-2, traditional vascular disease risk factors are associated with higher anti-N antibodies while vaccination is associated with lower anti-N antibodies. Methods: Among previously infected participants, anti-N antibodies were measured from dried blood spots collected between February 2021-February 2023 among 1,419 Collaborative Cohort of Cohorts for COVID-19 Research (C4R) participants with prior SARS-CoV-2 infection. We measured anti-N IgG antibodies reported as median fluorescence intensity (MFI) units; reactivity (i.e., seropositivity) was defined based on MFI above a validated threshold. Vascular disease risk factors were assessed via pre-pandemic in-person examination, questionnaires, and medical record review. Multivariable generalized linear models regressed anti-N reactivity (modified Poisson) or LN transformed MFI (linear). Results: Among 1,419 participants with prior infection, mean age (standard deviation) was 65.8(12.1) years, 61% were women, and 42.8% were self-reported from a race/ethnicity minority group. Participant reactivity to nucleocapsid peaked at 69% by 4 months post-infection and waned to only 44% ≥12 months after infection. After multivariable adjustment, higher anti-N antibody response was associated with older age, Hispanic (vs. White) or American Indian (vs. White) race/ethnicity, lower income and education, former smoking, and higher anti-spike antibody levels. Asian race (vs. White) and vaccination ( even after infection ) were associated with lower nucleocapsid reactivity; common cardiometabolic co-morbidities were not associated with anti-N reactivity or levels (Figures 1 and 2). Conclusions: Among participants previously infected with SARS-CoV-2, select sociodemographic and behavioral risk factors were associated with higher anti-N antibody levels while vaccination was associated with lower anti-N antibody levels. The observation that vaccination, even after infection, is related to lower anti-N antibody levels merits further investigation.
Abstract P1062: Pre-Pandemic Thrombo-inflammatory Biomarkers and Risk of Severe Covid-19: the Collaborative Cohort of Cohorts for Covid-19 Research (C4R)
Cushman, Mary , Choi, Yuni , Reiner, Alex P. , Mukaz, Debora Kamin , Xanthakis, Vanessa, Johnson, Andrew D. , Umans, Jason G. , Kandula, Namratha R. , Oelsner, Elizabeth C. , Oelsner, Elizabeth C , Jacobs, David R. , Demmer, Ryan T. , Tracy, Russell P. , Olson, Nels C. , Doyle, Margaret F. , Raffield, Laura M. , Min, Yuan‐I , Vasan, Ramachandran S. , Xanthakis, Vanessa , Make, Barry J. , Regan, Elizabeth Anne , Bowler, Russell P. , Thyagarajan, Bharat

Circulation | April 17, 2025 | Volume: 151 | Issue: Suppl_1 | Pages: No page available

10.1161/cir.151.suppl_1.p1062 Get It

Background: Thrombo-inflammation is a hallmark of acute Covid-19, with higher thrombo-inflammatory markers during infection associated with adverse outcomes. We hypothesized that higher pre-pandemic biomarkers, reflecting a higher thrombo-inflammatory setpoint, might identify people at greater risk of severe manifestations of Covid-19. Hypothesis: Pre-pandemic inflammation markers are associated with risk of hospitalized or fatal (hereafter, “severe”) Covid-19 in a multi-ethnic US sample. Methods: From 2020-2023, C4R harmonized pre-pandemic measures and ascertained Covid-19 across 14 longstanding cohort studies. In 12 cohorts, we assessed biomarker data and covariates from the study visit closest to the pandemic for 6 analytes shown in the Table. HRs of severe Covid-19 by pre-pandemic biomarkers were estimated using cause-specific Cox regression, treating non-severe Covid-19 as a competing risk, and adjusting for pre-pandemic sociodemographic and clinical characteristics. Results: Biomarkers were measured a mean of 20 years before the pandemic with different sample sizes for each (Table footnote). Mean age at the start of the pandemic was in the 60s or 70s depending on the biomarker. The most represented race or ethnic groups were White and Black. The largest sample was for C-reactive protein, and there were 1,001 severe Covid-19 cases among 38,784 participants. In the adjusted models, considered individually, higher CRP, leukocyte count, factor VIII and fibrinogen, but not other markers, were associated with greater risk of severe Covid-19 (Table).Associations were robust across subgroups and time since biomarker measurement (example for C-reactive protein in Figure). Conclusions: In this prospective multi-study analysis of a multi-ethnic population, higher basal levels of thrombo-inflammation markers were associated with greater risk of severe Covid-19. Since we considered non-severe Covid-19 as a competing risk, associations may reflect factors related to progression to more serious Covid-19 illness.
Demographic and Clinical Factors Associated With SARS-CoV-2 Anti-Nucleocapsid Antibody Response Among Previously Infected US Adults: The C4R Study
Demmer, Ryan T. , Wu, Christina , Sun, Yifei , Cushman, Mary , Boyle, Rebekah, Styer, Linda M. , Bell, Taison D., Anderson, Michaela R. , Schreiner, Pamela J. , Bowler, Russell, Rock, Jean M., Bievenue, Rachel, Kandula, Namratha R. , Raffield, Laura M. , Elkind, Mitchell S.V. , Ortega, Victor E. , Woodruff, Prescott G. , Oelsner, Elizabeth C. , Wu, Chaoqi , Anderson, Michaela R , Lee, Joyce S , Xanthakis, Vanessa , Pirzada, Amber , Mantis, Nicholas J , Raffield, Laura M , Oelsner, Elizabeth C , Demmer, Ryan T , Kim, John S. , Balte, Pallavi , Tracy, Russell P. , Allen, Norrina B. , Schwartz, David A. , Lee, Joyce , Xanthakis, Vanessa , Doyle, Margaret F. , Regan, Elizabeth Anne , Make, Barry J. , Kanaya, Alka M , Wenzel, Sally E. , Coresh, Josef , Isasi, Carmen R. , Howard, Virginia J. , Cole, Shelley A. , Henderson, Joel

Open Forum Infectious Diseases | March 20, 2025 | Volume: 12 | Issue: 3 | Pages: No page available

10.1093/ofid/ofaf123 Get It

Abstract Despite the availability of effective vaccines and a recent decrease in annual deaths, COVID-19 remains a leading cause of death. Serological studies provide insights into host immunobiology of adaptive immune response to infection, which holds promise for identifying high-risk individuals for adverse COVID-19 outcomes. We investigated correlates of anti-nucleocapsid antibody responses following SARS-CoV-2 infection in a US population-based meta-cohort of adults participating in longstanding National Institutes of Health–funded cohort studies. Anti-nucleocapsid antibodies were measured from dried blood spots collected between February 2021 and February 2023. Among 1419 Collaborative Cohort of Cohorts for COVID-19 Research participants with prior SARS-CoV-2 infection, the mean age (standard deviation) was 65.8 (12.1), 61% were women, and 42.8% self-reported membership in a race/ethnicity minority group. The proportion of participants reactive to nucleocapsid peaked at 69% by 4 months after infection and waned to only 44% ≥12 months after infection. Higher anti-nucleocapsid antibody response was associated with older age, Hispanic or American Indian Alaskan Native (vs White) race/ethnicity, lower income, lower education, former smoking, and higher anti-spike antibody levels. Asian race (vs White) and vaccination (even after infection) were associated with lower nucleocapsid reactivity. Neither vaccine manufacturer nor common cardiometabolic comorbidities were not associated with anti-nucleocapsid response. These findings inform the underlying immunobiology of adaptive immune response to infection, as well as the potential utility of anti-nucleocapsid antibody response for clinical practice and COVID-19 serosurveillance.
Association Between Whole Blood–Derived Mitochondrial DNA Copy Number, Low‐Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk
Liu, Xue , Sun, Xianbang, Jiang, Wenqing , Lai, M M , Wiggins, Kerri L. , Raffield, Laura M. , Bielak, Lawrence F. , Zhao, Wei , Pitsillides, Achilleas , Haessler, Jeffrey, Blackwell, Thomas W. , Yao, Jie , Qian, Yong , Thyagarajan, Bharat , Peyser, Patricia A. , Heckbert, Susan R. , Seshadri, Sudha , Boerwinkle, Eric , Grove, Megan L. , Larson, Nicholas B. , Smith, Jennifer A. , Fitzpatrick, Annette L., Fornage, Myriam , Ding, Jun , Carson, April P. , Reiner, Alex P. , Hou, Lifang , Wilson, James G. , Rotter, Jerome I. , Bis, Joshua C. , Arking, Dan E. , Zhao, Wei , Wiggins, Kerri L , Raffield, Laura M , Bielak, Lawrence F , Zhao, Wei , Pitsillides, Achilleas , Zheng, Yinan , Yao, Jie , Guo, Xiuqing , Thyagarajan, Bharat , Pankratz, Nathan , Rich, Stephen S , Taylor, Kent D , Peyser, Patricia A , Heckbert, Susan R , Boerwinkle, Eric , Larson, Nicholas B , Smith, Jennifer A , Vasan, Ramachandran S , Fornage, Myriam , Carson, April P , Dupuis, Josée , Reiner, Alexander P , Hou, Lifang , Psaty, Bruce M , Levy, Daniel , Rotter, Jerome I , Bis, Joshua C , Satizabal, Claudia L , Arking, Dan E , Liu, Chunyu , Seshadri, Sudha , Zhang, Yuankai , Zheng, Yinan , Guo, Xiuqing , Pankratz, Nathan , Rich, Stephen S. , Taylor, Kent D. , Vasan, Ramachandran S. , Abecasis, Gonçalo R. , Kooperberg, Charles , Psaty, Bruce M. , Levy, Daniel , Satizábal, Claudia L.

Journal of the American Heart Association | October 7, 2023 | Volume: 12 | Issue: 20 | Pages: No page available

10.1161/jaha.122.029090 Get It

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross‐sectional and prospective association analyses of blood‐derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole‐genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1‐SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04–1.12; P <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; P =0.11) or in the reverse direction (β=−0.012; P =0.076). Additional bidirectional Mendelian randomization analyses revealed that low‐density lipoprotein cholesterol had a causal effect on mtDNA CN (β=−0.084; P <0.001), but the reverse direction was not significant ( P =0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low‐density lipoprotein cholesterol level ( P =0.52), whereas there was a strong direct causal effect of higher low‐density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=−0.092; P <0.001). Conclusions Our findings indicate that high low‐density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.
The genetic determinants of recurrent somatic mutations in 43,693 blood genomes
Weinstock, Joshua S. , Laurie, Cecelia , Broome, Jai , Taylor, Kent D. , Guo, Xiuqing , Shuldiner, Alan R. , O’Connell, Jeffrey R. , Lewis, Joshua P. , Boerwinkle, Eric , Barnes, Kathleen C. , Chami, Nathalie , Loos, Ruth J. F. , Fornage, Myriam , Redline, Susan , Cade, Brian E. , Chen, Zhanghua , Gauderman, W. James , Schleimer, Robert P. , Bis, Joshua C. , Brody, Jennifer A. , Silverman, Edwin K. , Qiao, Dandi , Lasky‐Su, Jessica , DeMeo, Dawn L. , Palmer, Nicholette D. , Bowden, Donald W. , Johnson, Andrew D. , Yanek, Lisa R. , Becker, Lewis C. , Kardia, Sharon L.R., Kaplan, Robert C. , Heckbert, Susan R. , Smith, Nicholas L. , Arnett, Donna K. , Irvin, Marguerite R. , Tiwari, Hemant K., Raffield, Laura M. , Gao, Yan , Rotter, Jerome I. , Wheeler, Marsha M. , Duggirala, Ravindranath , Xiao, Shujie, Williams, L. Keoki , Meyers, Deborah A. , Ortega, Victor E. , McGarvey, Stephen T. , Chen, Yii‐Der Ida , Lee, Wen‐Jane , Shoemaker, M. Benjamin , Roden, Dan M. , Blackwell, Thomas W. , Smith, Albert V. , Kang, Hyun Min , Mathias, Rasika A. , Natarajan, Pradeep , Jaiswal, Siddhartha , Reiner, Alex P. , Bick, Alexander G. , Kaplan, Robert C. , Broome, Jai , Kenny, Eimear E , Loos, Ruth J F , Fornage, Myriam , Redline, Susan , Cade, Brian E , Gauderman, W James , Grammer, Leslie , Schleimer, Robert P , Bis, Joshua C , Brody, Jennifer A , Silverman, Edwin K , Yun, Jeong H , Qiao, Dandi , Weiss, Scott T , Palmer, Nicholette D , Freedman, Barry I , Cho, Michael H , Vasan, Ramachandran S , Johnson, Andrew D , Yanek, Lisa R , Becker, Lewis C , He, Jiang , Heckbert, Susan R , Wiggins, Kerri L , Arnett, Donna K , Correa, Adolfo , Raffield, Laura M , Gao, Yan , Rotter, Jerome I , Rich, Stephen S , Manichaikul, Ani W , Konkle, Barbara A , Johnsen, Jill M , Custer, Brian S , Duggirala, Ravindranath , Curran, Joanne E , Li, Xingnan , McGarvey, Stephen T , Gu, C Charles , Lee, Wen-Jane , Darbar, Dawood , Roden, Dan M. , Kooperberg, Charles , Desai, Pinkal M , Smith, Albert V , Kang, Hyun M , Mathias, Rasika A , Natarajan, Pradeep , Jaiswal, Siddhartha , Bick, Alexander G , Weinstock, Joshua S , Shuldiner, Alan R , Taylor, Kent D , Guo, Xiuqing , Lasky-Su, Jessica , Gilliland, Frank D. , Kumar, Rajesh , Psaty, Bruce M. , Yun, Jeong H. , Weiss, Scott T. , Freedman, Barry I. , Cho, Michael H. , Vasan, Ramachandran S. , He, Jiang , Wiggins, Kerri L. , Andrade, Mariza de , Rich, Stephen S. , Manichaikul, Ani , Konkle, Barbara A. , Custer, Brian , Curran, Joanne E. , Blangero, John , Gui, Hongsheng , Li, Xingnan , Darbar, Dawood , Albert, Christine M. , Kooperberg, Charles , Desai, Pinkal , Abecasis, Gonçalo R.

Science Advances | April 26, 2023 | Volume: 9 | Issue: 17 | Pages: No page available

10.1126/sciadv.abm4945 Get It

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.
Prolonged, physiologically relevant nicotine concentrations in the airways of smokers
Esther, Charles R. , O’Neal, Wanda K. , Alexis, Neil E. , Koch, Abigail L. , Cooper, Christopher B. , Raffield, Laura M. , Bowler, Russel P., Comellas, Alejandro P. , Peters, Stephen P. , Hastie, Annette T. , Ronish, Bonnie , Ortega, Victor E. , Wells, J. Michael , Halper‐Stromberg, Eitan , Rennard, Stephen I. , Boucher, Richard C. , SPIROMICS, on behalf of, Hastie, Annette T , Curtis, Jeffrey L , Ronish, Bonnie , Boucher, Richard C , Raffield, Laura M , O'Neal, Wanda K , Comellas, Alejandro P , Wells, J Michael , Barjaktarević, Igor , Curtis, Jeffrey L. , SPIROMICS, on behalf of

American Journal of Physiology-Lung Cellular and Molecular Physiology | November 7, 2022 | Volume: 324 | Issue: 1 | Pages: L32-L37

10.1152/ajplung.00038.2022 Get It

Nicotine from cigarette smoke is a biologically active molecule that has pleiotropic effects in the airway, which could play a role in smoking-induced lung disease. However, whether nicotine and its metabolites reach sustained, physiologically relevant concentrations on airway surfaces of smokers is not well defined. To address these issues, concentrations of nicotine, cotinine, and hydroxycotinine were measured by mass spectrometry (MS) in supernatants of induced sputum obtained from participants in the subpopulations and intermediate outcome measures in COPD study (SPIROMICS), an ongoing observational study that included never smokers, former smokers, and current smokers with and without chronic obstructive pulmonary disease (COPD). A total of 980 sputum supernatants were analyzed from 77 healthy never smokers, 494 former smokers (233 with COPD), and 396 active smokers (151 with COPD). Sputum nicotine, cotinine, and hydroxycotinine concentrations corresponded to self-reported smoking status and were strongly correlated to urine measures. A cutoff of ∼8–10 ng/mL of sputum cotinine distinguished never smokers from active smokers. Accounting for sample dilution during processing, active smokers had airway nicotine concentrations in the 70–850 ng/mL (∼0.5–5 µM) range, and concentrations remained elevated even in current smokers who had not smoked within 24 h. This study demonstrates that airway nicotine and its metabolites are readily measured in sputum supernatants and can serve as biological markers of smoke exposure. In current smokers, nicotine is present at physiologically relevant concentrations for prolonged periods, supporting a contribution to cigarette-induced airway disease.
Genomic and phenomic landscape of clonal hematopoiesis in over a million ancestrally diverse participants
Uddin, Md Mesbah , Weinstock, Joshua S. , Niroula, Abhishek , Vlasschaert, Caitlyn , Koyama, Satoshi , Nakao, Tetsushi , Urbut, Sarah , Paruchuri, Kaavya , Silver, Alexander J. , Mack, Taralynn , Wong, Megan , Bhattacharya, Romit , Jorshery, Saman Doroodgar , Raddatz, Michael A. , Hornsby, Whitney , Kresge, Hailey A. , Cho, Kelly , Sun, Yan V. , Pyarajan, Saiju , Chen, Yii‐Der Ida , Sheu, Wayne H-H , Hung, Yi‐Jen , Hidalgo, Bertha , Fornage, Myriam , Kaplan, Robert C., Williams, L. Keoki , Province, Mike , Kardia, Sharon, Peyser, Patricia A., Yanek, Lisa R. , Becker, Lewis C. , Meyers, Deborah A. , Naseri, Take, Barnes, Kathleen C. , Redline, S S, Schwartz, D A, Shoemaker, M. Benjamin , Palmer, Nicholette D., Bowden, Donald W., Kumar, Rajesh, Nj, Smith, Floyd, James, Tracy, Russell, Bis, Joshua C. , SS, Rich, Boerwinkle, Eric, Wang, Zhe, Silverman, Edwin K. , Auer, Paul L. , Reiner, Alexander P. , Ebert, Benjamin L. , Natarajan, Pradeep , Uddin, Md M , Niroula, Abhishek , Weinstock, Joshua S , Bhattacharya, Romit , Ebert, Benjamin L , Natarajan, Pradeep , Yu, Zhi , Zekavat, Seyedeh M , Truong, Buu Minh Thanh , Haidermota, Sara , Honigberg, Michael C. , Zhang, Martin Jinye , Dron, Jacqueline S. , Heimlich, Jonathan Brett , Sankaran, Vijay G. , Griffin, Gabriel K. , Gibson, Christopher James , Wilson, Peter W.F. , Genovese, Giulio , Xu, Yaomin , Savona, Michael R. , Raffield, Laura M. , Arnett, Donna K. , Konkle, Barbara A. , Peyser, Patricia A. , Smith, Jennifer A. , Mathias, Rasika A. , Andrade, Mariza de , Darbar, Dawood , He, Jiang , Blangero, John , Weiss, Scott T. , Custer, Brian , Minster, Ryan L , Viali, Satupaitea , Mitchell, Braxton D. , Palmer, Nicholette D , Freedman, Barry I. , Bowden, Donald W. , Albert, Christine M. , Smith, Nicholas, Tracy, Russell P. , Psaty, Bruce M. , Taylor, Kent D. , Rotter, Jerome I. , Boerwinkle, Eric , Vasan, Ramachandran S. , Lubitz, Steven A. , Ellinor, Patrick T. , Loos, Ruth J. F. , Yun, Jeong H. , Cho, Michael H. , Abecasis, Gonçalo R. , Kooperberg, Charles , Reiner, Alex P. , Jaiswal, Siddhartha , Bick, Alexander G.

No container title available | July 31, 2022 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.1101/2022.07.29.22278015 Get It

Abstract With aging, somatic mutations in hematopoietic stem and progenitor cells (HSPC) can give rise to clonal hematopoiesis of indeterminate potential (CHIP), a premalignant state associated with diverse age-related diseases. Here we report the largest multi-ancestry genome-wide analysis of CHIP to date (N = 1,018,305), including individuals of African (N = 85,978), Admixed American (N = 191,371), East Asian (N = 13,532), European (N = 694,015), and South Asian (N = 13,193) ancestry. Multi-ancestry meta-analyses identified 72 genome-wide significant loci, including 44 novel associations implicating genes such as AFF1 , ATF7IP , ATP8B4 , BCL2 , CEBPA , CYRIA , DNM2 , ELF1 , NKX2-3 , PIK3CB , PRDM16 , RPN1 , TERC , and TRIM4 . Notably, variants at MECOM and PHF20L1 showed opposite allelic effects between DNMT3A - and non- DNMT3A -driven CHIP, highlighting driver-specific germline influences. Implicated loci converge on pathways regulating telomere maintenance, cell-cycle control, hematopoietic transcription, DNA damage response and immune signaling. These findings support a model in which germline variation both expands the HSPC pool and biases clonal selection in a driver-dependent manner, providing a mechanistic basis for inter-individual heterogeneity in CHIP. Phenome-wide association analyses further linked CHIP to hematologic, neoplastic, and circulatory traits, with enrichment across hematopoietic and non-hematopoietic cell types. Together, this work expands the genomic and phenomic landscape of CHIP and reveals germline–somatic interactions that shape clonal evolution during aging.
Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential
Nakao, Tetsushi , Bick, Alexander G. , Taub, Margaret A. , Uddin, Md Mesbah , Niroula, Abhishek , Carty, Cara L. , Lane, John , Honigberg, Michael C. , Weinstock, Joshua S. , Pampana, Akhil , Griffin, Gabriel K. , Bhattacharya, Romit , Assimes, Themistocles L. , Emery, Leslie S. , Wong, Quenna , Broome, Jai , Laurie, Cecelia , Khan, Alyna , Smith, Albert V. , Blackwell, Thomas W. , Nelson, Christopher P. , Yoneda, Zachary T. , Peralta, Juan M. , Bowden, Donald W. , Irvin, Marguerite R. , Boorgula, Meher Preethi, Zhao, Wei , Yanek, Lisa R. , Roden, Dan M. , Reupena, Muagututi‘a Sefuiva , DeMeo, Dawn L. , Kelly, Shannon , Musani, Solomon K., Bis, Joshua C. , Lloyd‐Jones, Donald M. , Peyser, Patricia A. , Qiao, Dandi , Tiwari, Hemant K., Smith, Jennifer A. , Smith, Nicholas L. , Kelly, Tanika N. , Hidalgo, Bertha , Weeks, Daniel E. , Group, The Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA), Deka, Ranjan , Naseri, Take, Fuentes, Lisa de las , Raffield, Laura M. , Morrison, Alanna C. , Vries, Paul S. de , Shoemaker, M. Benjamin , Palmer, Nicholette D. , Barnes, Kathleen C. , Redline, Susan , Kardia, Sharon L.R., Becker, Lewis C. , Heckbert, Susan R. , He, Jiang , Post, Wendy S. , Arnett, Donna K. , McGarvey, Stephen T. , Chen, Yii‐Der Ida , Kaplan, Robert C. , Meyers, Deborah A. , Fornage, Myriam , Boerwinkle, Eric , Loos, Ruth J. F. , Silverman, Edwin K. , Danesh, John , Samani, Nilesh J. , Jaiswal, Siddhartha , Ebert, Benjamin L. , Reiner, Alex P. , Mathias, Rasika A. , Do, Ron , Natarajan, Pradeep , Zhao, Wei , Zekavat, Seyedeh M , Uddin, Md M , Niroula, Abhishek , Lane, John , Honigberg, Michael C , Weinstock, Joshua S , Pampana, Akhil , Griffin, Gabriel K , Clarke, Shoa L , Bhattacharya, Romit , Assimes, Themistocles L , Emery, Leslie S , Stilp, Adrienne M , Wong, Quenna , Broome, Jai , Khan, Alyna T , Smith, Albert V , Codd, Veryan , Nelson, Christopher P , Yoneda, Zachary T , Peralta, Juan M , Yanek, Lisa R , Wiggins, Kerri L , Gu, C Charles , Peloso, Gina M , Roden, Dan M. , Hwu, Chii-Min , North, Kari E. , Bis, Joshua C , Lloyd-Jones, Donald M , Johnsen, Jill M , Preuss, Michael , Tracy, Russell P , Peyser, Patricia A , Qiao, Dandi , Curran, Joanne E , Freedman, Barry I , Chavan, Sameer , Smith, Jennifer A , Kelly, Tanika N , Hidalgo, Bertha , Weeks, Daniel E , Hawley, Nicola L , Minster, Ryan L , de las Fuentes, Lisa , Raffield, Laura M , Ballantyne, Christie M , Kenny, Eimear E , Rich, Stephen S , Whitsel, Eric A , Cho, Michael H , Palmer, Nicholette D , Mitchell, Braxton D , Shuldiner, Alan R , Redline, Susan , Heckbert, Susan R , He, Jiang , Post, Wendy , Arnett, Donna K , Vasan, Ramachandran S , Weiss, Scott T , McGarvey, Stephen T , Custer, Brian S , Correa, Adolfo , Manson, JoAnn E , Konkle, Barbara A , Loos, Ruth J F , Rotter, Jerome I , Silverman, Edwin K , Kooperberg, Charles , Samani, Nilesh J , Jaiswal, Siddhartha , Libby, Peter , Ellinor, Patrick T , Pankratz, Nathan , Ebert, Benjamin L , Mathias, Rasika A , Do, Ron , Natarajan, Pradeep , Morrison, Alanna C , Bick, Alexander G , Gibson, Christopher James , Clarke, Shoa L. , Codd, Veryan , Wiggins, Kerri L. , Hixson, James E. , Peloso, Gina M. , Tracy, Russell P. , Desai, Pinkal , Curran, Joanne E. , Freedman, Barry I. , Cupples, L. Adrienne , Group, The Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA), Naseri, Take K. , Ballantyne, Christie M. , Rich, Stephen S. , Cho, Michael H. , Pace, Betty S. , Blangero, John , Mitchell, Braxton D. , Shuldiner, Alan R. , Abecasis, Gonçalo R. , Vasan, Ramachandran S. , Darbar, Dawood , Weiss, Scott T. , Andrade, Mariza de , Custer, Brian , Psaty, Bruce M. , Manson, JoAnn E. , Konkle, Barbara A. , Rotter, Jerome I. , Kooperberg, Charles , Libby, Peter , Ellinor, Patrick T. , Pankratz, Nathan

Science Advances | April 6, 2022 | Volume: 8 | Issue: 14 | Pages: No page available

10.1126/sciadv.abl6579 Get It

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.