Search

Your search keyword '"Rich, Stephen"' showing total 4,125 results
Start Over Author "Rich, Stephen"
4,125 results on '"Rich, Stephen"'

1. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations

Author

Lundin, Jessica I, Peters, Ulrike, Hu, Yao, Ammous, Farah, Avery, Christy L, Benjamin, Emelia J, Bis, Joshua C, Brody, Jennifer A, Carlson, Chris, Cushman, Mary, Gignoux, Chris, Guo, Xiuqing, Haessler, Jeff, Haiman, Chris, Joehanes, Roby, Kasela, Silva, Kenny, Eimear, Lapalainien, Tuuli, Levy, Daniel, Liu, Chunyu, Liu, Yongmei, Loos, Ruth JF, Lu, Ake, Matise, Tara, North, Kari E, Park, Sungshim L, Ratliff, Scott M, Reiner, Alex, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Sotoodehnia, Nona, Tracy, Russell, Van den Berg, David, Xu, Huichun, Ye, Ting, Zhao, Wei, Raffield, Laura M, Kooperberg, Charles, and Study, On Behalf of the PAGE

Subjects
Biological Sciences, Genetics, Minority Health, American Indian or Alaska Native, Human Genome, Health Disparities, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Humans, DNA Methylation, C-Reactive Protein, Epigenesis, Genetic, DNA, Inflammation, Genome-Wide Association Study, CpG Islands, Intracellular Signaling Peptides and Proteins, C-reactive protein, methylation, epigenetics, EWAS, racial and ethnic diversity, Mendelian randomization, causal pathway, PAGE Study, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Developmental Biology, Biochemistry and cell biology
Abstract

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p

Published
2024

2. Rare variant contribution to the heritability of coronary artery disease.

Author

Rocheleau, Ghislain, Clarke, Shoa, Auguste, Gaëlle, Hasbani, Natalie, Morrison, Alanna, Heath, Adam, Bielak, Lawrence, Iyer, Kruthika, Young, Erica, Stitziel, Nathan, Jun, Goo, Laurie, Cecelia, Broome, Jai, Khan, Alyna, Arnett, Donna, Becker, Lewis, Bis, Joshua, Boerwinkle, Eric, Bowden, Donald, Carson, April, Ellinor, Patrick, Fornage, Myriam, Franceschini, Nora, Freedman, Barry, Heard-Costa, Nancy, Hou, Lifang, Chen, Yii-Der, Kenny, Eimear, Kooperberg, Charles, Kral, Brian, Loos, Ruth, Lutz, Sharon, Manson, JoAnn, Martin, Lisa, Mitchell, Braxton, Nassir, Rami, Palmer, Nicholette, Post, Wendy, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Regan, Elizabeth, Rich, Stephen, Smith, Jennifer, Taylor, Kent, Yanek, Lisa, Young, Kendra, Hilliard, Austin, Tcheandjieu, Catherine, Peyser, Patricia, Vasan, Ramachandran, Rotter, Jerome, Miller, Clint, Assimes, Themistocles, de Vries, Paul, and Do, Ron

Subjects
Humans, Coronary Artery Disease, Genetic Predisposition to Disease, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Male, Female, Gene Frequency, Genome-Wide Association Study, White People, Case-Control Studies, Whole Genome Sequencing, Genetic Variation, Middle Aged
Abstract

Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Published
2024

3. 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, Clinical Research, Minority Health, Prevention, Health Disparities, Precision Medicine, 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

4. Looking back at the TEDDY study: lessons and future directions

Author

Lernmark, Åke, Agardh, Daniel, Akolkar, Beena, Gesualdo, Patricia, Hagopian, William A., Haller, Michael J., Hyöty, Heikki, Johnson, Suzanne Bennett, Larsson, Helena Elding, Liu, Edwin, Lynch, Kristian F., McKinney, Eoin F., McIndoe, Richard, Melin, Jessica, Norris, Jill M., Rewers, Marian, Rich, Stephen S., Toppari, Jorma, Triplett, Eric, Vehik, Kendra, Virtanen, Suvi M., Ziegler, Anette-G., Schatz, Desmond A., and Krischer, Jeffrey

Published
2024
Full Text
View/download PDF

5. Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Author

Mack, Taralynn M, Raddatz, Michael A, Pershad, Yash, Nachun, Daniel C, Taylor, Kent D, Guo, Xiuqing, Shuldiner, Alan R, O’Connell, Jeffrey R, Kenny, Eimear E, Loos, Ruth JF, Redline, Susan, Cade, Brian E, Psaty, Bruce M, Bis, Joshua C, Brody, Jennifer A, Silverman, Edwin K, Yun, Jeong H, Cho, Michael H, DeMeo, Dawn L, Levy, Daniel, Johnson, Andrew D, Mathias, Rasika A, Yanek, Lisa R, Heckbert, Susan R, Smith, Nicholas L, Wiggins, Kerri L, Raffield, Laura M, Carson, April P, Rotter, Jerome I, Rich, Stephen S, Manichaikul, Ani W, Gu, C Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Shoemaker, M Benjamin, Roden, Dan M, Kooperberg, Charles, Auer, Paul L, Desai, Pinkal, Blackwell, Thomas W, Smith, Albert V, Reiner, Alexander P, Jaiswal, Siddhartha, Weinstock, Joshua S, and Bick, Alexander G

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Hematology, Genetics, Biotechnology, Aging, Human Genome, Stem Cell Research, Precision Medicine, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Clinical sciences
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Published
2024

6. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

Author

Hrytsenko, Yana, Shea, Benjamin, Elgart, Michael, Kurniansyah, Nuzulul, Lyons, Genevieve, Morrison, Alanna, Carson, April, Haring, Bernhard, Mitchell, Braxton, Psaty, Bruce, Jaeger, Byron, Gu, C, Kooperberg, Charles, Levy, Daniel, Lloyd-Jones, Donald, Choi, Eunhee, Brody, Jennifer, Smith, Jennifer, Rotter, Jerome, Moll, Matthew, Fornage, Myriam, Simon, Noah, Castaldi, Peter, Casanova, Ramon, Chung, Ren-Hua, Kaplan, Robert, Loos, Ruth, Kardia, Sharon, Rich, Stephen, Redline, Susan, Kelly, Tanika, OConnor, Timothy, Zhao, Wei, Kim, Wonji, Guo, Xiuqing, Ida Chen, Yii-Der, and Sofer, Tamar

Subjects
Humans, Machine Learning, Blood Pressure, Multifactorial Inheritance, Phenotype, Genome-Wide Association Study, Risk Factors, Male, Female, Genetic Predisposition to Disease, Models, Genetic, Hypertension, Middle Aged, Genetic Risk Score
Abstract

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble models performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Published
2024

7. LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes

Author

Ding, Jingzhong, Nguyen, Anh Tram, Lohman, Kurt, Hensley, Michael T, Parker, Daniel, Hou, Li, Taylor, Jackson, Voora, Deepak, Sawyer, Janet K, Boudyguina, Elena, Bancks, Michael P, Bertoni, Alain, Pankow, James S, Rotter, Jerome I, Goodarzi, Mark O, Tracy, Russell P, Murdoch, David M, Duprez, Daniel, Rich, Stephen S, Psaty, Bruce M, Siscovick, David, Newgard, Christopher B, Herrington, David, Hoeschele, Ina, Shea, Steven, Stein, James H, Patel, Manesh, Post, Wendy, Jacobs, David, Parks, John S, and Liu, Yongmei

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Atherosclerosis, Clinical Research, Health Disparities, Obesity, Diabetes, Genetics, Nutrition, Prevention, Cardiovascular, 2.1 Biological and endogenous factors, Metabolic and endocrine, Humans, Prediabetic State, Male, Female, Diabetes Mellitus, Type 2, Middle Aged, Liver X Receptors, Cholesterol, Aged, Signal Transduction, ATP Binding Cassette Transporter, Subfamily G, Member 1, Monocytes, Risk Factors, ATP Binding Cassette Transporter 1, Aged, 80 and over, Expression profiling, Metabolism, Medical and Health Sciences, Immunology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

Published
2024

8. Determinants of mosaic chromosomal alteration fitness.

Author

Pershad, Yash, Mack, Taralynn, Poisner, Hannah, Jakubek, Yasminka, Stilp, Adrienne, Mitchell, Braxton, Lewis, Joshua, Boerwinkle, Eric, Loos, Ruth, Chami, Nathalie, Wang, Zhe, Barnes, Kathleen, Pankratz, Nathan, Fornage, Myriam, Redline, Susan, Psaty, Bruce, Bis, Joshua, Shojaie, Ali, Silverman, Edwin, Cho, Michael, Yun, Jeong, DeMeo, Dawn, Levy, Daniel, Johnson, Andrew, Mathias, Rasika, Taub, Margaret, Arnett, Donna, North, Kari, Raffield, Laura, Carson, April, Doyle, Margaret, Rich, Stephen, Guo, Xiuqing, Cox, Nancy, Roden, Dan, Franceschini, Nora, Desai, Pinkal, Reiner, Alex, Auer, Paul, Scheet, Paul, Jaiswal, Siddhartha, Weinstock, Joshua, Bick, Alexander, and Rotter, Jerome

Subjects
Humans, Mosaicism, Chromosome Aberrations, Clonal Hematopoiesis, Male, Female, Genome-Wide Association Study, Janus Kinase 2, Telomerase, Loss of Heterozygosity, Cross-Sectional Studies, Mutation, Middle Aged, Hematopoietic Stem Cells, Polymorphism, Single Nucleotide, Aged
Abstract

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Published
2024

9. MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Author

Sun, Quan, Yang, Yingxi, Rosen, Jonathan, Chen, Jiawen, Li, Xihao, Guan, Wyliena, Jiang, Min-Zhi, Wen, Jia, Pace, Rhonda, Blackman, Scott, Bamshad, Michael, Gibson, Ronald, Cutting, Garry, ONeal, Wanda, Knowles, Michael, Kooperberg, Charles, Reiner, Alexander, Raffield, Laura, Carson, April, Rich, Stephen, Rotter, Jerome, Loos, Ruth, Kenny, Eimear, Jaeger, Byron, Min, Yuan-I, Fuchsberger, Christian, and Li, Yun

Subjects
cross-cohort, genome-wide association studies, genotype imputation, imputation quality, machine learning, quality control, rare variants, variant filtering, whole-genome sequencing, Humans, Polymorphism, Single Nucleotide, Software, Genotype, Gene Frequency, Cohort Studies, Linkage Disequilibrium, Genome-Wide Association Study, Genome, Human, Quality Control, Machine Learning, Whole Genome Sequencing
Abstract

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Published
2024

10. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Author

Keaton, Jacob, Kamali, Zoha, Xie, Tian, Vaez, Ahmad, Williams, Ariel, Goleva, Slavina, Ani, Alireza, Evangelou, Evangelos, Hellwege, Jacklyn, Yengo, Loic, Young, William, Traylor, Matthew, Giri, Ayush, Zheng, Zhili, Zeng, Jian, Chasman, Daniel, Morris, Andrew, Caulfield, Mark, Hwang, Shih-Jen, Kooner, Jaspal, Conen, David, Attia, John, Morrison, Alanna, Loos, Ruth, Kristiansson, Kati, Schmidt, Reinhold, Hicks, Andrew, Pramstaller, Peter, Nelson, Christopher, Samani, Nilesh, Risch, Lorenz, Gyllensten, Ulf, Melander, Olle, Riese, Harriette, Wilson, James, Campbell, Harry, Rich, Stephen, Psaty, Bruce, Lu, Yingchang, Guo, Xiuqing, Rice, Kenneth, Vollenweider, Peter, Sundström, Johan, Langenberg, Claudia, Tobin, Martin, Giedraitis, Vilmantas, Luan, Jianan, Tuomilehto, Jaakko, Kutalik, Zoltan, Ripatti, Samuli, Salomaa, Veikko, Girotto, Giorgia, Trompet, Stella, Jukema, J, van der Harst, Pim, Ridker, Paul, Giulianini, Franco, Vitart, Veronique, Goel, Anuj, Watkins, Hugh, Harris, Sarah, Deary, Ian, van der Most, Peter, Oldehinkel, Albertine, Keavney, Bernard, Hayward, Caroline, Campbell, Archie, Boehnke, Michael, Scott, Laura, Boutin, Thibaud, Mamasoula, Chrysovalanto, Järvelin, Marjo-Riitta, Peters, Annette, Gieger, Christian, Lakatta, Edward, Cucca, Francesco, Hui, Jennie, Knekt, Paul, Enroth, Stefan, De Borst, Martin, Polašek, Ozren, Concas, Maria, Catamo, Eulalia, Cocca, Massimiliano, Li-Gao, Ruifang, Hofer, Edith, Schmidt, Helena, Spedicati, Beatrice, Waldenberger, Melanie, Strachan, David, Laan, Maris, Teumer, Alexander, Dörr, Marcus, Gudnason, Vilmundur, Cook, James, Ruggiero, Daniela, Kolcic, Ivana, Boerwinkle, Eric, Traglia, Michela, and Lehtimäki, Terho

Subjects
Female, Humans, Male, Blood Pressure, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Hypertension, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors
Abstract

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P

Published
2024

11. Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

Author

Liu, Shuai, Zhu, Jingjing, Zhong, Hua, Wu, Chong, Xue, Haoran, Darst, Burcu F., Guo, Xiuqing, Durda, Peter, Tracy, Russell P., Liu, Yongmei, Johnson, W. Craig, Taylor, Kent D., Manichaikul, Ani W., Goodarzi, Mark O., Gerszten, Robert E., Clish, Clary B., Chen, Yii-Der Ida, Highland, Heather, Haiman, Christopher A., Gignoux, Christopher R., Lange, Leslie, Conti, David V., Raffield, Laura M., Wilkens, Lynne, Marchand, Loïc Le, North, Kari E., Young, Kristin L., Loos, Ruth J., Buyske, Steve, Matise, Tara, Peters, Ulrike, Kooperberg, Charles, Reiner, Alexander P., Yu, Bing, Boerwinkle, Eric, Sun, Quan, Rooney, Mary R., Echouffo-Tcheugui, Justin B., Daviglus, Martha L., Qi, Qibin, Mancuso, Nicholas, Li, Changwei, Deng, Youping, Manning, Alisa, Meigs, James B., Rich, Stephen S., Rotter, Jerome I., and Wu, Lang

Published
2024
Full Text
View/download PDF

12. Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese-O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop-Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman-Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette-Gabriele, and DiMeglio, Linda A.

Published
2024
Full Text
View/download PDF

13. Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Author

Mack, Taralynn M., Raddatz, Michael A., Pershad, Yash, Nachun, Daniel C., Taylor, Kent D., Guo, Xiuqing, Shuldiner, Alan R., O’Connell, Jeffrey R., Kenny, Eimear E., Loos, Ruth J. F., Redline, Susan, Cade, Brian E., Psaty, Bruce M., Bis, Joshua C., Brody, Jennifer A., Silverman, Edwin K., Yun, Jeong H., Cho, Michael H., DeMeo, Dawn L., Levy, Daniel, Johnson, Andrew D., Mathias, Rasika A., Yanek, Lisa R., Heckbert, Susan R., Smith, Nicholas L., Wiggins, Kerri L., Raffield, Laura M., Carson, April P., Rotter, Jerome I., Rich, Stephen S., Manichaikul, Ani W., Gu, C. Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Shoemaker, M. Benjamin, Roden, Dan M., Kooperberg, Charles, Auer, Paul L., Desai, Pinkal, Blackwell, Thomas W., Smith, Albert V., Reiner, Alexander P., Jaiswal, Siddhartha, Weinstock, Joshua S., and Bick, Alexander G.

Published
2024
Full Text
View/download PDF

14. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits.

Author

Wittich, Henry, Ardlie, Kristin, Taylor, Kent, Durda, Peter, Liu, Yongmei, Mikhaylova, Anna, Gignoux, Chris, Cho, Michael, Rich, Stephen, Rotter, Jerome, Manichaikul, Ani, Im, Hae, and Wheeler, Heather

Subjects
TWAS, autoimmune diseases, cis-eQTL, gene expression, genetic prediction, heritability, plasma proteome, trans-eQTL, transcription factors, Humans, Transcriptome, Proteome, Multifactorial Inheritance, Quantitative Trait Loci, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR

Published
2024

15. A methylation risk score for chronic kidney disease: a HyperGEN study

Author

Jones, Alana C, Patki, Amit, Srinivasasainagendra, Vinodh, Hidalgo, Bertha A, Tiwari, Hemant K, Limdi, Nita A, Armstrong, Nicole D, Chaudhary, Ninad S, Minniefield, Bré, Absher, Devin, Arnett, Donna K, Lange, Leslie A, Lange, Ethan M, Young, Bessie A, Diamantidis, Clarissa J, Rich, Stephen S, Mychaleckyj, Josyf C, Rotter, Jerome I, Taylor, Kent D, Kramer, Holly J, Tracy, Russell P, Durda, Peter, Kasela, Silva, Lappalinen, Tuuli, Liu, Yongmei, Johnson, W Craig, Van Den Berg, David J, Franceschini, Nora, Liu, Simin, Mouton, Charles P, Bhatti, Parveen, Horvath, Steve, Whitsel, Eric A, and Irvin, Marguerite R

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Prevention, Clinical Research, Human Genome, Health Disparities, Kidney Disease, Minority Health, Women's Health, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Renal and urogenital, Humans, Renal Insufficiency, Chronic, DNA Methylation, Male, Female, Middle Aged, Glomerular Filtration Rate, CpG Islands, Risk Factors, Black or African American, Aged, Genome-Wide Association Study, Epigenesis, Genetic, Adult, Genetic Predisposition to Disease, Chronic kidney disease, eGFR, Methylation risk score, Epigenetics
Abstract

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Published
2024

16. Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

Author

Keener, Rebecca, Chhetri, Surya B, Connelly, Carla J, Taub, Margaret A, Conomos, Matthew P, Weinstock, Joshua, Ni, Bohan, Strober, Benjamin, Aslibekyan, Stella, Auer, Paul L, Barwick, Lucas, Becker, Lewis C, Blangero, John, Bleecker, Eugene R, Brody, Jennifer A, Cade, Brian E, Celedon, Juan C, Chang, Yi-Cheng, Cupples, L Adrienne, Custer, Brian, Freedman, Barry I, Gladwin, Mark T, Heckbert, Susan R, Hou, Lifang, Irvin, Marguerite R, Isasi, Carmen R, Johnsen, Jill M, Kenny, Eimear E, Kooperberg, Charles, Minster, Ryan L, Naseri, Take, Viali, Satupa’itea, Nekhai, Sergei, Pankratz, Nathan, Peyser, Patricia A, Taylor, Kent D, Telen, Marilyn J, Wu, Baojun, Yanek, Lisa R, Yang, Ivana V, Albert, Christine, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Bis, Joshua C, Blackwell, Thomas W, Boerwinkle, Eric, Burchard, Esteban G, Carson, April P, Chen, Zhanghua, Chen, Yii-Der Ida, Darbar, Dawood, de Andrade, Mariza, Ellinor, Patrick T, Fornage, Myriam, Gelb, Bruce D, Gilliland, Frank D, He, Jiang, Islam, Talat, Kaab, Stefan, Kardia, Sharon LR, Kelly, Shannon, Konkle, Barbara A, Kumar, Rajesh, Loos, Ruth JF, Martinez, Fernando D, McGarvey, Stephen T, Meyers, Deborah A, Mitchell, Braxton D, Montgomery, Courtney G, North, Kari E, Palmer, Nicholette D, Peralta, Juan M, Raby, Benjamin A, Redline, Susan, Rich, Stephen S, Roden, Dan, Rotter, Jerome I, Ruczinski, Ingo, Schwartz, David, Sciurba, Frank, Shoemaker, M Benjamin, Silverman, Edwin K, Sinner, Moritz F, Smith, Nicholas L, Smith, Albert V, Tiwari, Hemant K, Vasan, Ramachandran S, Weiss, Scott T, Williams, L Keoki, Zhang, Yingze, Ziv, Elad, Raffield, Laura M, Reiner, Alexander P, Arvanitis, Marios, Greider, Carol W, Mathias, Rasika A, and Battle, Alexis

Subjects
Biological Sciences, Genetics, Human Genome, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Generic health relevance, Humans, Genome-Wide Association Study, Telomere, K562 Cells, Telomere Homeostasis, Polymorphism, Single Nucleotide, Gene Expression Regulation, CRISPR-Cas Systems, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology and Hemostasis Working Group, TOPMed Structural Variation Working Group
Abstract

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Published
2024

17. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Author

Hughes, Odessica, Bentley, Amy R, Breeze, Charles E, Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M, Gilliland, Thomas, Meyer, Mariah C, Du, Jiawen, Raffield, Laura M, Kramer, Holly, Morton, Robert W, Gouveia, Mateus H, Atkinson, Elizabeth G, Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D, Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G, Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O, Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C, Kooperberg, Charles, Krieger, Jose E, Lange, Ethan M, Lanktree, Matthew B, Lash, James P, Lotufo, Paulo A, Loos, Ruth JF, My, Vy Thi Ha, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J, Rich, Stephen S, Rodriquez, Erik J, Tarazona-Santos, Eduardo, Taylor, Kent D, Umans, Jason G, Wen, Jia, Young, Bessie A, Yu, Zhi, Zhang, Ying, Chen, Yii-Der Ida, Rundek, Tanja, Rotter, Jerome I, Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C, Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A, Carson, April P, Lange, Leslie A, Li, Yun, Perez-Stable, Eliseo J, Do, Ron, Charchar, Fadi J, Tomaszewski, Maciej, Mychaleckyj, Josyf C, Rotimi, Charles, Morris, Andrew P, and Franceschini, Nora

Subjects
Biological Sciences, Genetics, Kidney Disease, Prevention, Human Genome, Renal and urogenital, Good Health and Well Being, Humans, Genome-Wide Association Study, Renal Insufficiency, Chronic, Glomerular Filtration Rate, Multifactorial Inheritance, Kidney, admixed populations, chronic kidney disease, eGFR, expression quantitative trait locus, fine-mapping, genome-wide association study, kidney function, multi-ancestry, polygenic scores
Abstract

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p

Published
2024

18. Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

Author

Felton, Jamie L., Redondo, Maria J., Oram, Richard A., Speake, Cate, Long, S. Alice, Onengut-Gumuscu, Suna, Rich, Stephen S., Monaco, Gabriela S. F., Harris-Kawano, Arianna, Perez, Dianna, Saeed, Zeb, Hoag, Benjamin, Jain, Rashmi, Evans-Molina, Carmella, DiMeglio, Linda A., Ismail, Heba M., Dabelea, Dana, Johnson, Randi K., Urazbayeva, Marzhan, Wentworth, John M., Griffin, Kurt J., and Sims, Emily K.

Published
2024
Full Text
View/download PDF

20. Correction to: Consensus guidance for monitoring individuals with islet autoantibody‑positive pre‑stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese‑O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop‑Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman‑Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette‑Gabriele, and DiMeglio, Linda A.

Published
2024
Full Text
View/download PDF

21. Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification.

Author

de Vries, Paul, Conomos, Matthew, Singh, Kuldeep, Nicholson, Christopher, Jain, Deepti, Hasbani, Natalie, Jiang, Wanlin, Lee, Sujin, Cardenas, Christian, Lutz, Sharon, Wong, Doris, Guo, Xiuqing, Yao, Jie, Young, Erica, Tcheandjieu, Catherine, Hilliard, Austin, Bis, Joshua, Bielak, Lawrence, Brown, Michael, Musharoff, Shaila, Clarke, Shoa, Terry, James, Palmer, Nicholette, Yanek, Lisa, Xu, Huichun, Heard-Costa, Nancy, Wessel, Jennifer, Selvaraj, Margaret, Li, Rebecca, Sun, Xiao, Turner, Adam, Stilp, Adrienne, Khan, Alyna, Newman, Anne, Rasheed, Asif, Freedman, Barry, Kral, Brian, McHugh, Caitlin, Hodonsky, Chani, Saleheen, Danish, Herrington, David, Jacobs, David, Nickerson, Deborah, Boerwinkle, Eric, Wang, Fei, Heiss, Gerardo, Jun, Goo, Kinney, Greg, Sigurslid, Haakon, Doddapaneni, HarshaVardhan, Hall, Ira, Bensenor, Isabela, Broome, Jai, Crapo, James, Wilson, James, Smith, Jennifer, Blangero, John, Vargas, Jose, Mosquera, Jose, Smith, Joshua, Viaud-Martinez, Karine, Ryan, Kathleen, Young, Kendra, Taylor, Kent, Lange, Leslie, Emery, Leslie, Bittencourt, Marcio, Montasser, May, Yu, Miao, Mahaney, Michael, Mahamdeh, Mohammed, Fornage, Myriam, Franceschini, Nora, Lotufo, Paulo, Natarajan, Pradeep, Wong, Quenna, Mathias, Rasika, Gibbs, Richard, Do, Ron, Mehran, Roxana, Tracy, Russell, Kim, Ryan, Nelson, Sarah, Damrauer, Scott, Kardia, Sharon, Rich, Stephen, Fuster, Valentin, Napolioni, Valerio, Zhao, Wei, Tian, Wenjie, Yin, Xianyong, Min, Yuan-I, Manning, Alisa, Peloso, Gina, Kelly, Tanika, ODonnell, Christopher, Morrison, Alanna, Curran, Joanne, Zapol, Warren, and Bowden, Donald

Abstract

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Published
2023

22. Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.

Author

Tobias, Deirdre K, Manning, Alisa K, Wessel, Jennifer, Raghavan, Sridharan, Westerman, Kenneth E, Bick, Alexander G, Dicorpo, Daniel, Whitsel, Eric A, Collins, Jason, Correa, Adolfo, Cupples, L Adrienne, Dupuis, Josée, Goodarzi, Mark O, Guo, Xiuqing, Howard, Barbara, Lange, Leslie A, Liu, Simin, Raffield, Laura M, Reiner, Alex P, Rich, Stephen S, Taylor, Kent D, Tinker, Lesley, Wilson, James G, Wu, Peitao, Carson, April P, Vasan, Ramachandran S, Fornage, Myriam, Psaty, Bruce M, Kooperberg, Charles, Rotter, Jerome I, Meigs, James, and Manson, JoAnn E

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Prevention, Cardiovascular, Precision Medicine, Stem Cell Research, Diabetes, Heart Disease, Hematology, Aging, Obesity, Genetics, 2.1 Biological and endogenous factors, Cancer, Metabolic and endocrine, Good Health and Well Being, Humans, Female, Middle Aged, Male, Clonal Hematopoiesis, Diabetes Mellitus, Type 2, Prospective Studies, Hematopoiesis, Clonal Evolution, Coronary Disease, Mutation, TOPMed Diabetes Working Group and National Heart, Lung, and Blood Institute TOPMed Consortium
Abstract

ObjectiveClonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D.Research design and methodsCHIP was derived from whole-genome sequencing of blood DNA in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) prospective cohorts. We performed analysis for 17,637 participants from six cohorts, without prior T2D, cardiovascular disease, or cancer. We evaluated baseline CHIP versus no CHIP prevalence with incident T2D, including associations with DNMT3A, TET2, ASXL1, JAK2, and TP53 variants. We estimated multivariable-adjusted hazard ratios (HRs) and 95% CIs with adjustment for age, sex, BMI, smoking, alcohol, education, self-reported race/ethnicity, and combined cohorts' estimates via fixed-effects meta-analysis.ResultsMean (SD) age was 63.4 (11.5) years, 76% were female, and CHIP prevalence was 6.0% (n = 1,055) at baseline. T2D was diagnosed in n = 2,467 over mean follow-up of 9.8 years. Participants with CHIP had 23% (CI 1.04, 1.45) higher risk of T2D than those with no CHIP. Specifically, higher risk was for TET2 (HR 1.48; CI 1.05, 2.08) and ASXL1 (HR 1.76; CI 1.03, 2.99) mutations; DNMT3A was nonsignificant (HR 1.15; CI 0.93, 1.43). Statistical power was limited for JAK2 and TP53 analyses.ConclusionsCHIP was associated with higher incidence of T2D. CHIP mutations located on genes implicated in CHD and mortality were also related to T2D, suggesting shared aging-related pathology.

Published
2023

23. Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.

Author

Jakubek, Yasminka, Zhou, Ying, Stilp, Adrienne, Bacon, Jason, Wong, Justin, Ozcan, Zuhal, Arnett, Donna, Barnes, Kathleen, Bis, Joshua, Boerwinkle, Eric, Brody, Jennifer, Carson, April, Chasman, Daniel, Chen, Jiawen, Cho, Michael, Conomos, Matthew, Cox, Nancy, Doyle, Margaret, Fornage, Myriam, Guo, Xiuqing, Kardia, Sharon, Lewis, Joshua, Loos, Ruth, Ma, Xiaolong, Machiela, Mitchell, Mack, Taralynn, Mathias, Rasika, Mitchell, Braxton, Mychaleckyj, Josyf, North, Kari, Pankratz, Nathan, Peyser, Patricia, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Vasan, Ramachandran, Redline, Susan, Rich, Stephen, Silverman, Edwin, Smith, Jennifer, Smith, Aaron, Taub, Margaret, Taylor, Kent, Yun, Jeong, Li, Yun, Desai, Pinkal, Bick, Alexander, Reiner, Alexander, Scheet, Paul, Auer, Paul, and Rotter, Jerome

Subjects
Humans, Black People, Genome-Wide Association Study, Hispanic or Latino, Precision Medicine, Mosaicism, Genome, Human
Abstract

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

Published
2023

24. Association Between Whole Blood–Derived Mitochondrial DNA Copy Number, Low‐Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk

Author

Liu, Xue, Sun, Xianbang, Zhang, Yuankai, Jiang, Wenqing, Lai, Meng, Wiggins, Kerri L, Raffield, Laura M, Bielak, Lawrence F, Zhao, Wei, Pitsillides, Achilleas, Haessler, Jeffrey, Zheng, Yinan, Blackwell, Thomas W, Yao, Jie, Guo, Xiuqing, Qian, Yong, Thyagarajan, Bharat, Pankratz, Nathan, Rich, Stephen S, Taylor, Kent D, Peyser, Patricia A, Heckbert, Susan R, Seshadri, Sudha, Boerwinkle, Eric, Grove, Megan L, Larson, Nicholas B, Smith, Jennifer A, Vasan, Ramachandran S, Fitzpatrick, Annette L, Fornage, Myriam, Ding, Jun, Carson, April P, Abecasis, Goncalo, Dupuis, Josée, Reiner, Alexander, Kooperberg, Charles, Hou, Lifang, Psaty, Bruce M, Wilson, James G, Levy, Daniel, Rotter, Jerome I, Bis, Joshua C, Consortium, TOPMed mtDNA Working Group in NHLBI Trans‐Omics for Precision Medicine, Satizabal, Claudia L, Arking, Dan E, and Liu, Chunyu

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Obesity, Prevention, Heart Disease, Atherosclerosis, Heart Disease - Coronary Heart Disease, Aging, Cardiovascular, Genetics, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Humans, DNA, Mitochondrial, Risk Factors, Cardiovascular Diseases, Cholesterol, LDL, DNA Copy Number Variations, Cross-Sectional Studies, Coronary Disease, Cholesterol, HDL, Diabetes Mellitus, Hypertension, cardiometabolic risk factors, cardiovascular disease, low-density lipoprotein cholesterol, Mendelian randomization, mitochondrial DNA copy number, vascular atherosclerosis, TOPMed mtDNA Working Group in NHLBI Trans‐Omics for Precision Medicine (TOPMed) Consortium, low‐density lipoprotein cholesterol, Cardiorespiratory Medicine and Haematology, Cardiovascular medicine and haematology
Abstract

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

Published
2023

25. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

Author

Srinivasan, Shylaja, Wu, Peitao, Mercader, Josep M, Udler, Miriam S, Porneala, Bianca C, Bartz, Traci M, Floyd, James S, Sitlani, Colleen, Guo, Xiquing, Haessler, Jeffrey, Kooperberg, Charles, Liu, Jun, Ahmad, Shahzad, van Duijn, Cornelia, Liu, Ching-Ti, Goodarzi, Mark O, Florez, Jose C, Meigs, James B, Rotter, Jerome I, Rich, Stephen S, Dupuis, Josée, and Leong, Aaron

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Diabetes, Pediatric, Genetics, Autoimmune Disease, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, type 1 diabetes, type 2 diabetes, genetics, polygenic score, Cardiovascular medicine and haematology
Abstract

ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Published
2023

26. Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review.

Author

Felton, Jamie, Griffin, Kurt, Oram, Richard, Speake, Cate, Long, S, Onengut-Gumuscu, Suna, Rich, Stephen, Monaco, Gabriela, Evans-Molina, Carmella, DiMeglio, Linda, Ismail, Heba, Steck, Andrea, Dabelea, Dana, Johnson, Randi, Urazbayeva, Marzhan, Gitelman, Stephen, Wentworth, John, Redondo, Maria, and Sims, Emily

Abstract

BACKGROUND: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. METHODS: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. RESULTS: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. CONCLUSIONS: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.

Published
2023

27. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Author

Wang, Yuxuan, Selvaraj, Margaret, Li, Xihao, Li, Zilin, Holdcraft, Jacob, Arnett, Donna, Bis, Joshua, Blangero, John, Boerwinkle, Eric, Bowden, Donald, Cade, Brian, Carlson, Jenna, Carson, April, Chen, Yii-Der, Curran, Joanne, de Vries, Paul, Dutcher, Susan, Ellinor, Patrick, Floyd, James, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, Guo, Xiuqing, He, Jiang, Heard-Costa, Nancy, Hildalgo, Bertha, Hou, Lifang, Irvin, Marguerite, Joehanes, Roby, Kaplan, Robert, Kardia, Sharon, Kelly, Tanika, Kim, Ryan, Kooperberg, Charles, Kral, Brian, Levy, Daniel, Li, Changwei, Liu, Chunyu, Lloyd-Jone, Don, Loos, Ruth, Mahaney, Michael, Martin, Lisa, Mathias, Rasika, Minster, Ryan, Mitchell, Braxton, Montasser, May, Morrison, Alanna, Murabito, Joanne, Naseri, Take, OConnell, Jeffrey, Palmer, Nicholette, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Rao, Dabeeru, Redline, Susan, Reiner, Alexander, Rich, Stephen, Ruepena, Muagututia, Sheu, Wayne, Smith, Jennifer, Smith, Albert, Tiwari, Hemant, Tsai, Michael, Viaud-Martinez, Karine, Wang, Zhe, Yanek, Lisa, Zhao, Wei, Lin, Xihong, Natarajan, Pradeep, Peloso, Gina, and Rotter, Jerome

Subjects
association, blood lipid, cholesterol, lncRNA, rare variants, whole-genome sequencing, Humans, RNA, Long Noncoding, Genome-Wide Association Study, Precision Medicine, Whole Genome Sequencing, Lipids, Polymorphism, Single Nucleotide
Abstract

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Published
2023

28. Associations between Ambient Air Pollutants and Clonal Hematopoiesis of Indeterminate Potential.

Author

Leiser, Claire L, Whitsel, Eric A, Reiner, Alexander, Rich, Stephen S, Rotter, Jerome I, Taylor, Kent D, Tracy, Russel P, Kooperberg, Charles, Smith, Albert Vernon, Manson, JoAnn E, Mychaleckyj, Josyf C, Bick, Alexander G, Szpiro, Adam A, and Kaufman, Joel D

Subjects
Epidemiology, Health Sciences, Aging, Cancer, Hematology, Climate-Related Exposures and Conditions, Prevention, Atherosclerosis, Genetics, 2.2 Factors relating to the physical environment, Humans, Female, Air Pollutants, Environmental Pollutants, Nitrogen Dioxide, Clonal Hematopoiesis, Cross-Sectional Studies, Environmental Exposure, Air Pollution, Particulate Matter, Medical and Health Sciences, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundClonal hematopoiesis of indeterminate potential (CHIP) is an age-related somatic mutation associated with incident hematologic cancer. Environmental stressors which, like air pollution, generate oxidative stress at the cellular level, may induce somatic mutations and some mutations may provide a selection advantage for persistence and expansion of specific clones.MethodsWe used data from the Multi-Ethnic Study of Atherosclerosis (MESA) N = 4,379 and the Women's Health Initiative (WHI) N = 7,701 to estimate cross-sectional associations between annual average air pollution concentrations at participant address the year before blood draw using validated spatiotemporal models. We used covariate-adjusted logistic regression to estimate risk of CHIP per interquartile range increases in particulate matter (PM2.5; 4 μg/m3) and nitrogen dioxide (NO2; 10 ppb) as ORs (95% confidence intervals).ResultsPrevalence of CHIP at blood draw (variant allele fraction > 2%) was 4.4% and 8.7% in MESA and WHI, respectively. The most common CHIP driver mutation was in DNMT3A. Neither pollutant was associated with CHIP: ORMESA PM2.5 = 1.00 (0.68-1.45), ORMESA NO2 = 1.05 (0.69-1.61), ORWHI PM2.5 = 0.97 (0.86-1.09), ORWHI NO2 = 0.98 (0.88-1.10); or with DNMT3A-driven CHIP.ConclusionsWe did not find evidence that air pollution contributes to CHIP prevalence in two large observational cohorts.ImpactThis is the first study to estimate associations between air pollution and CHIP.

Published
2023

29. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Author

Araujo, Daniel S, Nguyen, Chris, Hu, Xiaowei, Mikhaylova, Anna V, Gignoux, Chris, Ardlie, Kristin, Taylor, Kent D, Durda, Peter, Liu, Yongmei, Papanicolaou, George, Cho, Michael H, Rich, Stephen S, Rotter, Jerome I, Consortium, NHLBI TOPMed, Im, Hae Kyung, Manichaikul, Ani, and Wheeler, Heather E

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, Aetiology, 2.5 Research design and methodologies (aetiology), Generic health relevance, Humans, Transcriptome, Genome-Wide Association Study, Quantitative Trait Loci, Gene Frequency, Linkage Disequilibrium, NHLBI TOPMed Consortium, PrediXcan, genetics, genomics, human genetics, multi-ancestry GWAS, multivarite adaptive shrinkage, transcriptome prediction, transcriptome-wide association studies
Abstract

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Published
2023

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

Author

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

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

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

Published
2023

31. Intake of B vitamins and the risk of developing islet autoimmunity and type 1 diabetes in the TEDDY study

Author

Hakola, Leena, Mramba, Lazarus K., Uusitalo, Ulla, Andrén Aronsson, Carin, Hummel, Sandra, Niinistö, Sari, Erlund, Iris, Yang, Jimin, Rewers, Marian J., Akolkar, Beena, McIndoe, Richard A., Rich, Stephen S., Hagopian, William A., Ziegler, Anette, Lernmark, Åke, Toppari, Jorma, Krischer, Jeffrey P., Norris, Jill M., and Virtanen, Suvi M.

Published
2024
Full Text
View/download PDF

33. Lac-Phe mediates the effects of metformin on food intake and body weight

Author

Xiao, Shuke, Li, Veronica L., Lyu, Xuchao, Chen, Xudong, Wei, Wei, Abbasi, Fahim, Knowles, Joshua W., Tung, Alan Sheng-Hwa, Deng, Shuliang, Tiwari, Gaurav, Shi, Xu, Zheng, Shuning, Farrell, Laurie, Chen, Zsu-Zsu, Taylor, Kent D., Guo, Xiuqing, Goodarzi, Mark O., Wood, Alexis C., Chen, Yii-Der Ida, Lange, Leslie A., Rich, Stephen S., Rotter, Jerome I., Clish, Clary B., Tahir, Usman A., Gerszten, Robert E., Benson, Mark D., and Long, Jonathan Z.

Published
2024
Full Text
View/download PDF

34. Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans

Author

Tahir, Usman A., Barber, Jacob L., Cruz, Daniel E., Kars, Meltem Ece, Deng, Shuliang, Tuftin, Bjoernar, Gillman, Madeline G., Benson, Mark D., Robbins, Jeremy M., Chen, Zsu-Zsu, Rao, Prashant, Katz, Daniel H., Farrell, Laurie, Sofer, Tamar, Hall, Michael E., Ekunwe, Lynette, Tracy, Russell P., Durda, Peter, Taylor, Kent D., Liu, Yongmei, Johnson, W. Craig, Guo, Xiuqing, Chen, Yii-Der Ida, Manichaikul, Ani W., Jain, Deepti, Wang, Thomas J., Reiner, Alex P., Natarajan, Pradeep, Itan, Yuval, Rich, Stephen S., Rotter, Jerome I., Wilson, James G., Raffield, Laura M., and Gerszten, Robert E.

Subjects
Medical research, Medicine, Experimental, Proteomics -- Methods, Medical records -- Usage, Genetic variation -- Research, Electronic records -- Usage, Disease susceptibility -- Research, Genome-wide association studies -- Methods, African Americans -- Health aspects -- Genetic aspects -- Statistics
Abstract

BACKGROUND. Most GWAS of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry-enriched protein quantitative loci (pQTL). METHODS. We conducted a discovery GWAS of approximately 3,000 plasma proteins measured by the antibody-based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS) and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs was further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome-wide association study (PheWAS) across 2 large multiethnic electronic health record (EHR) systems in All of Us and BioMe. RESULTS. We identified 1,002 pQTLs for 925 protein assays. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including c/s-pQTLs for cathepsin L (CTSL) and Siglec-9, which were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, WBC count, and multiple sclerosis. CONCLUSIONS. Our findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal. FUNDING. NIH K08 HL161445-01A1; 5T32HL160522-03; HHSN268201600034I; HL133870., Introduction Large-scale GWAS have uncovered thousands of loci implicated in human disease. However, genotype is often far removed from phenotype, limiting insight into the pathological processes that result in disease. [...]

Published
2024
Full Text
View/download PDF

35. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk.

Author

Yu, Zhi, Fidler, Trevor, Ruan, Yunfeng, Vlasschaert, Caitlyn, Nakao, Tetsushi, Uddin, Md, Mack, Taralynn, Niroula, Abhishek, Heimlich, J, Zekavat, Seyedeh, Gibson, Christopher, Griffin, Gabriel, Wang, Yuxuan, Peloso, Gina, Heard-Costa, Nancy, Levy, Daniel, Vasan, Ramachandran, Aguet, François, Ardlie, Kristin, Taylor, Kent, Rich, Stephen, Libby, Peter, Jaiswal, Siddhartha, Ebert, Benjamin, Bick, Alexander, Tall, Alan, Natarajan, Pradeep, and Rotter, Jerome

Subjects
Cardiology, Cardiovascular disease, Genetics, Mouse models, Population genetics, Humans, Animals, Mice, Cardiovascular Diseases, Clonal Hematopoiesis, Risk Factors, Inflammasomes, Hematopoiesis, Inflammation, Heart Disease Risk Factors, Mutation
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of cardiovascular diseases (CVDs), putatively via inflammasome activation. We pursued an inflammatory gene modifier scan for CHIP-associated CVD risk among 424,651 UK Biobank participants. We identified CHIP using whole-exome sequencing data of blood DNA and modeled as a composite, considering all driver genes together, as well as separately for common drivers (DNMT3A, TET2, ASXL1, and JAK2). We developed predicted gene expression scores for 26 inflammasome-related genes and assessed how they modify CHIP-associated CVD risk. We identified IL1RAP as a potential key molecule for CHIP-associated CVD risk across genes and increased AIM2 gene expression leading to heightened JAK2- and ASXL1-associated CVD risk. We show that CRISPR-induced Asxl1-mutated murine macrophages had a particularly heightened inflammatory response to AIM2 agonism, associated with an increased DNA damage response, as well as increased IL-10 secretion, mirroring a CVD-protective effect of IL10 expression in ASXL1 CHIP. Our study supports the role of inflammasomes in CHIP-associated CVD and provides evidence to support gene-specific strategies to address CHIP-associated CVD risk.

Published
2023

36. Myocardial Fibrosis and Cardiomyopathy Risk: A Genetic Link in the MESA

Author

Shabani, Mahsima, Wang, Min, Jenkins, Gregory D, Rotter, Jerome I, Rich, Stephen S, Batzler, Anthony, Taylor, Kent D, Mychaleckyj, Josyf C, Liu, Duan, Lima, Joao AC, and Pereira, Naveen L

Subjects
Biomedical and Clinical Sciences, Medical Physiology, Cardiovascular Medicine and Haematology, Genetics, Cardiovascular, Clinical Research, Heart Disease, Minority Health, Rare Diseases, Health Disparities, 2.1 Biological and endogenous factors, Humans, Cardiomyopathy, Dilated, Magnetic Resonance Imaging, Cine, Heart Failure, Cardiomyopathies, Myocardium, Cardiomyopathy, Hypertrophic, Fibrosis, Contrast Media, Ventricular Function, Left, cardiomyopathy, fibroblasts, fibrosis, genetics, heart failure, magnetic resonance imaging, risk, Biochemistry and Cell Biology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Medical physiology
Abstract

BackgroundCommon genetic variants are associated with risk for hypertrophic cardiomyopathy and dilated cardiomyopathy and with left ventricular (LV) traits. Whether these variants are associated with myocardial fibrosis, an important pathophysiological mediator of cardiomyopathy, is unknown.MethodsMulti-Ethnic Study of Atherosclerosis participants with T1-mapping cardiac magnetic resonance imaging in-whom extracellular volume was assessed, and genotyping information was available were included (N=1255). Log extracellular volume (%) was regressed on 50 candidate single nucleotide polymorphisms (previously identified to be associated with hypertrophic cardiomyopathy, dilated cardiomyopathy, and LV traits) adjusting for age, sex, diabetes, blood pressure, and principal components of ancestry. Ancestry-specific results were pooled by fixed-effect meta-analyses. Gene knockdown experiments were performed in human cardiac fibroblasts.ResultsThe SMARCB1 rs2186370 intronic variant (minor allele frequency: 0.18 in White and 0.50 in Black participants), previously identified as a risk variant for dilated cardiomyopathy and hypertrophic cardiomyopathy, was significantly associated with increased extracellular volume (P=0.0002) after adjusting for confounding clinical variables. The SMARCB1 rs2070458 locus previously associated with increased LV wall thickness and mass was similarly significantly associated with increased extracellular volume (P=0.0002). The direction of effect was similar in all 4 ancestry groups, but the effect was strongest in Black participants. The variants are strong expression quantitative loci in human LV tissue and associated with genotype-dependent decreased expression of SMARCB1 (P=7.3×10-22). SMARCB1 knockdown in human cardiac fibroblasts resulted in increased TGF (transforming growth factor)-β1-mediated α-smooth muscle actin and collagen expression.ConclusionsCommon genetic variation in SMARCB1 previously associated with risk for cardiomyopathies and increased LV wall thickness is associated with increased cardiac magnetic resonance imaging-based myocardial fibrosis and increased TGF-β1 mediated myocardial fibrosis in vitro. Whether these findings suggest a pathophysiologic link between myocardial fibrosis and cardiomyopathy risk remains to be proven.

Published
2023

37. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Author

Benson, Mark D, Eisman, Aaron S, Tahir, Usman A, Katz, Daniel H, Deng, Shuliang, Ngo, Debby, Robbins, Jeremy M, Hofmann, Alissa, Shi, Xu, Zheng, Shuning, Keyes, Michelle, Yu, Zhi, Gao, Yan, Farrell, Laurie, Shen, Dongxiao, Chen, Zsu-Zsu, Cruz, Daniel E, Sims, Mario, Correa, Adolfo, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Guo, Xiuqing, Yao, Jie, Chen, Yii-Der Ida, Manichaikul, Ani W, Jain, Deepti, Yang, Qiong, Consortium, NHLBI Trans-Omics for Precision Medicine, Bouchard, Claude, Sarzynski, Mark A, Rich, Stephen S, Rotter, Jerome I, Wang, Thomas J, Wilson, James G, Clish, Clary B, Sarkar, Indra Neil, Natarajan, Pradeep, and Gerszten, Robert E

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Genetics, Medical Biochemistry and Metabolomics, Biotechnology, 2.1 Biological and endogenous factors, Humans, Animals, Mice, Proteomics, Metabolomics, Signal Transduction, Genome-Wide Association Study, Polymorphism, Single Nucleotide, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, GWAS, functional genomics, genome-wide association study, genomics, metabolomics, multi-omics, pathway discovery, proteomics, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Published
2023

38. Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts.

Author

Ngo, Debby, Pratte, Katherine, Flexeder, Claudia, Petersen, Hans, Dang, Hong, Ma, Yanlin, Keyes, Michelle, Gao, Yan, Deng, Shuliang, Peterson, Bennet, Farrell, Laurie, Bhambhani, Victoria, Palacios, Cesar, Quadir, Juweria, Gillenwater, Lucas, Xu, Hanfei, Emson, Claire, Gieger, Christian, Suhre, Karsten, Graumann, Johannes, Jain, Deepti, Conomos, Matthew, Tracy, Russell, Guo, Xiuqing, Liu, Yongmei, Johnson, W, Cornell, Elaine, Durda, Peter, Taylor, Kent, Papanicolaou, George, Rich, Stephen, Rotter, Jerome, Rennard, Steven, Curtis, Jeffrey, Woodruff, Prescott, Comellas, Alejandro, Silverman, Edwin, Crapo, James, Larson, Martin, Vasan, Ramachandran, Wang, Thomas, Correa, Adolfo, Sims, Mario, Wilson, James, Gerszten, Robert, OConnor, George, Barr, R, Couper, David, Dupuis, Josée, Manichaikul, Ani, ONeal, Wanda, Tesfaigzi, Yohannes, Schulz, Holger, and Bowler, Russell

Subjects
airflow obstruction, biomarkers, proteomics, Humans, Forced Expiratory Volume, Lung, Proteomics, Pulmonary Disease, Chronic Obstructive, Vital Capacity, Spirometry, Biomarkers
Abstract

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β  = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q

Published
2023

39. Multiset correlation and factor analysis enables exploration of multi-omics data

Author

Brown, Brielin C, Wang, Collin, Kasela, Silva, Aguet, François, Nachun, Daniel C, Taylor, Kent D, Tracy, Russell P, Durda, Peter, Liu, Yongmei, Johnson, W Craig, Van Den Berg, David, Gupta, Namrata, Gabriel, Stacy, Smith, Joshua D, Gerzsten, Robert, Clish, Clary, Wong, Quenna, Papanicolau, George, Blackwell, Thomas W, Rotter, Jerome I, Rich, Stephen S, Barr, R Graham, Ardlie, Kristin G, Knowles, David A, and Lappalainen, Tuuli

Subjects
Biological Sciences, Genetics, Aging, Biotechnology, Cardiovascular, Human Genome, 2.5 Research design and methodologies (aetiology), Aetiology, Good Health and Well Being
Abstract

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Published
2023

40. Leveraging type 1 diabetes human genetic and genomic data in the T1D knowledge portal.

Author

Kudtarkar, Parul, Costanzo, Maria, Sun, Ying, Jang, Dongkeun, Koesterer, Ryan, Mychaleckyj, Josyf, Nayak, Uma, Onengut-Gumuscu, Suna, Rich, Stephen, Flannick, Jason, Burtt, Noël, and Gaulton, Kyle

Subjects
Humans, Diabetes Mellitus, Type 1, Genomics, Human Genetics
Abstract

To address the challenge of translating genetic discoveries for type 1 diabetes (T1D) into mechanistic insight, we have developed the T1D Knowledge Portal (T1DKP), an open-access resource for hypothesis development and target discovery in T1D.

Published
2023

41. Incidence of interstitial lung abnormalities: the MESA Lung Study

Author

McGroder, Claire F, Hansen, Spencer, Stukovsky, Karen Hinckley, Zhang, David, Nath, P Hrudaya, Salvatore, Mary M, Sonavane, Sushilkumar K, Terry, Nina, Stowell, Justin T, D'Souza, Belinda M, Leb, Jay S, Dumeer, Shifali, Aziz, Muhammad U, Batra, Kiran, Hoffman, Eric A, Bernstein, Elana J, Kim, John S, Podolanczuk, Anna J, Rotter, Jerome I, Manichaikul, Ani W, Rich, Stephen S, Lederer, David J, Barr, R Graham, McClelland, Robyn L, and Garcia, Christine Kim

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Cardiovascular, Lung, Atherosclerosis, Genetics, Biomedical Imaging, Prevention, Respiratory, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology
Abstract

The incidence of newly developed interstitial lung abnormalities (ILA) and fibrotic ILA have not been previously reported.Trained thoracic radiologists evaluated 13 944 cardiac CT scans for the presence of ILA in 6197 Multi-Ethnic Study of Atherosclerosis longitudinal cohort study participants >45 years of age from 2000 to 2012. 5% of the scans were re-read by the same or a different observer in a blinded fashion. After exclusion of participants with ILA at baseline, incidence rates and incidence rate ratios for ILA and fibrotic ILA were calculated.The intra-reader agreement of ILA was 92.0% (Gwet AC1=0.912, ICC=0.982) and the inter-reader agreement of ILA was 83.5% (Gwet AC1=0.814; ICC=0.969). Incidence of ILA and fibrotic ILA was estimated to be 13.1 cases/1000 person-years and 3.5/1000 person-years, respectively. In multivariable analyses, age (HR 1.06 (1.05, 1.08), p

Published
2023

42. MUC5B, telomere length and longitudinal quantitative interstitial lung changes: the MESA Lung Study

Author

Kim, John S, Manichaikul, Ani W, Hoffman, Eric A, Balte, Pallavi, Anderson, Michaela R, Bernstein, Elana J, Madahar, Purnema, Oelsner, Elizabeth C, Kawut, Steven M, Wysoczanski, Artur, Laine, Andrew F, Adegunsoye, Ayodeji, Z, Jennie, Taub, Margaret A, Mathias, Rasika A, Rich, Stephen S, Rotter, Jerome I, Noth, Imre, Garcia, Christine Kim, Barr, R Graham, and Podolanczuk, Anna J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Atherosclerosis, Genetics, Lung, Good Health and Well Being, Adult, Humans, Lung Diseases, Interstitial, Genotype, Telomere, Mucin-5B, Imaging/CT MRI etc, Interstitial Fibrosis, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundThe MUC5B promoter variant (rs35705950) and telomere length are linked to pulmonary fibrosis and CT-based qualitative assessments of interstitial abnormalities, but their associations with longitudinal quantitative changes of the lung interstitium among community-dwelling adults are unknown.MethodsWe used data from participants in the Multi-Ethnic Study of Atherosclerosis with high-attenuation areas (HAAs, Examinations 1-6 (2000-2018)) and MUC5B genotype (n=4552) and telomere length (n=4488) assessments. HAA was defined as the per cent of imaged lung with attenuation of -600 to -250 Hounsfield units. We used linear mixed-effects models to examine associations of MUC5B risk allele (T) and telomere length with longitudinal changes in HAAs. Joint models were used to examine associations of longitudinal changes in HAAs with death and interstitial lung disease (ILD).ResultsThe MUC5B risk allele (T) was associated with an absolute change in HAAs of 2.60% (95% CI 0.36% to 4.86%) per 10 years overall. This association was stronger among those with a telomere length below an age-adjusted percentile of 5% (p value for interaction=0.008). A 1% increase in HAAs per year was associated with 7% increase in mortality risk (rate ratio (RR)=1.07, 95% CI 1.02 to 1.12) for overall death and 34% increase in ILD (RR=1.34, 95% CI 1.20 to 1.50). Longer baseline telomere length was cross-sectionally associated with less HAAs from baseline scans, but not with longitudinal changes in HAAs.ConclusionsLongitudinal increases in HAAs were associated with the MUC5B risk allele and a higher risk of death and ILD.

Published
2023

43. Association of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function

Author

Zhang, Yuankai, Liu, Xue, Wiggins, Kerri L, Kurniansyah, Nuzulul, Guo, Xiuqing, Rodrigue, Amanda L, Zhao, Wei, Yanek, Lisa R, Ratliff, Scott M, Pitsillides, Achilleas, Aguirre Patiño, Juan Sebastian, Sofer, Tamar, Arking, Dan E, Austin, Thomas R, Beiser, Alexa S, Blangero, John, Boerwinkle, Eric, Bressler, Jan, Curran, Joanne E, Hou, Lifang, Hughes, Timothy M, Kardia, Sharon LR, Launer, Lenore J, Levy, Daniel, Mosley, Thomas H, Nasrallah, Ilya M, Rich, Stephen S, Rotter, Jerome I, Seshadri, Sudha, Tarraf, Wassim, González, Kevin A, Ramachandran, Vasan, Yaffe, Kristine, Nyquist, Paul A, Psaty, Bruce M, DeCarli, Charles S, Smith, Jennifer A, Glahn, David C, González, Hector M, Bis, Joshua C, Fornage, Myriam, Heckbert, Susan R, Fitzpatrick, Annette L, Liu, Chunyu, Satizabal, Claudia L, Aguilera, Norma, Ament, Seth, Ammous, Farah, Arnett, Donna K, Becker, Diane, Bis, Joshua, Blue, Elizabeth, Breaux, Camille, Chaar, Dima, MHI, Clarkson-Townsend, Danielle, Cooper, Brigidann, Coresh, Josef, Correa, Adolfo, DeStefano, Anita, Ding, Jingzhong, Fardo, David, Fitzpatrick, Annette, French, Jennifer, Glahn, David, Gonzalez, Hector, Granot-Hershkovitz, Einat, Hanly, Patrick, Hayden, Kathleen, Heckbert, Susan, Heemann, Scott, Horvath, Steve, Hoth, Karin, Hughes, Timothy, Jaiswal, Sidd, Jian, Xueqiu, Katsumata, Yuriko, Kho, Minjung, Kooperberg, Charles, Launer, Lenore, Lin, Honghuang, Litkowski, Elizabeth, Longstreth, Will, Martin, Alexandra, Mayeux, Richard, Mikulla, Julie, Miller, Amy, Misra, Biswapriya, Mosley, Thomas, Nyquist, Paul, O'Connell, Jeff, Olivier, Michael, Peloso, Gina, Perry, James, Psaty, Bruce, Purcell, Shaun, and Raffield, Laura

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Clinical Research, Precision Medicine, Acquired Cognitive Impairment, Behavioral and Social Science, Genetics, Dementia, Neurodegenerative, Aging, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Brain Disorders, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Mental health, Neurological, Good Health and Well Being, Middle Aged, Humans, Female, Aged, Male, DNA, Mitochondrial, DNA Copy Number Variations, Prospective Studies, Cross-Sectional Studies, Alzheimer Disease, Magnetic Resonance Imaging, Cognition, Brain, NHLBI Trans-Omics for Precision Medicine (TOPMed) program, Mitochondrial and Neurocognitive Working Groups, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract

Background and objectivesPrevious studies suggest that lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer disease (AD) and AD-related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.MethodsWe included dementia-free participants from 9 diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within ±5 years (i.e., cross-sectional analyses) or 5-20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (

Published
2023

44. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

45. The genetic determinants of recurrent somatic mutations in 43,693 blood genomes

Author

Weinstock, Joshua S, Laurie, Cecelia A, Broome, Jai G, Taylor, Kent D, Guo, Xiuqing, Shuldiner, Alan R, O’Connell, Jeffrey R, Lewis, Joshua P, Boerwinkle, Eric, Barnes, Kathleen C, Chami, Nathalie, Kenny, Eimear E, Loos, Ruth JF, Fornage, Myriam, Redline, Susan, Cade, Brian E, Gilliland, Frank D, Chen, Zhanghua, Gauderman, W James, Kumar, Rajesh, Grammer, Leslie, Schleimer, Robert P, Psaty, Bruce M, Bis, Joshua C, Brody, Jennifer A, Silverman, Edwin K, Yun, Jeong H, Qiao, Dandi, Weiss, Scott T, Lasky-Su, Jessica, DeMeo, Dawn L, Palmer, Nicholette D, Freedman, Barry I, Bowden, Donald W, Cho, Michael H, Vasan, Ramachandran S, Johnson, Andrew D, Yanek, Lisa R, Becker, Lewis C, Kardia, Sharon, He, Jiang, Kaplan, Robert, Heckbert, Susan R, Smith, Nicholas L, Wiggins, Kerri L, Arnett, Donna K, Irvin, Marguerite R, Tiwari, Hemant, Correa, Adolfo, Raffield, Laura M, Gao, Yan, de Andrade, Mariza, Rotter, Jerome I, Rich, Stephen S, Manichaikul, Ani W, Konkle, Barbara A, Johnsen, Jill M, Wheeler, Marsha M, Custer, Brian S, Duggirala, Ravindranath, Curran, Joanne E, Blangero, John, Gui, Hongsheng, Xiao, Shujie, Williams, L Keoki, Meyers, Deborah A, Li, Xingnan, Ortega, Victor, McGarvey, Stephen, Gu, C Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Shoemaker, M Benjamin, Darbar, Dawood, Roden, Dan, Albert, Christine, Kooperberg, Charles, Desai, Pinkal, Blackwell, Thomas W, Abecasis, Goncalo R, Smith, Albert V, Kang, Hyun M, Mathias, Rasika, Natarajan, Pradeep, Jaiswal, Siddhartha, Reiner, Alexander P, Bick, Alexander G, and Consortium, NHLBI Trans-Omics for Precision Medicine

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Lung, Genetic Testing, Clinical Research, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Humans, Middle Aged, Hematopoiesis, Mutation, Germ-Line Mutation, Mutation, Missense, Phenotype, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

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.

Published
2023

46. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals.

Author

Mester, Rachel, Belbin, Gillian, Buyske, Steve, Conti, David, Darst, Burcu, Fornage, Myriam, Gignoux, Chris, Guo, Xiuqing, Haiman, Christopher, Kenny, Eimear, Kim, Michelle, Kooperberg, Charles, Lange, Leslie, Manichaikul, Ani, North, Kari, Peters, Ulrike, Rasmussen-Torvik, Laura, Rich, Stephen, Hou, Kangcheng, Ding, Yi, Xu, Ziqi, Wu, Yue, Wheeler, Heather, Wojcik, Genevieve, Zhou, Ying, Sankararaman, Sriram, Pasaniuc, Bogdan, Rotter, Jerome, and Bhattacharya, Arjun

Subjects
Humans, Genetics, Population, Multifactorial Inheritance, Genome-Wide Association Study, Racial Groups, Black or African American, Polymorphism, Single Nucleotide
Abstract

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.

Published
2023

47. Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis.

Author

Weinstock, Joshua, Gopakumar, Jayakrishnan, Burugula, Bala, Uddin, Md, Jahn, Nikolaus, Belk, Julia, Bouzid, Hind, Daniel, Bence, Miao, Zhuang, Ly, Nghi, Mack, Taralynn, Luna, Sofia, Prothro, Katherine, Mitchell, Shaneice, Laurie, Cecelia, Broome, Jai, Taylor, Kent, Guo, Xiuqing, Sinner, Moritz, von Falkenhausen, Aenne, Kääb, Stefan, Shuldiner, Alan, OConnell, Jeffrey, Lewis, Joshua, Boerwinkle, Eric, Barnes, Kathleen, Chami, Nathalie, Kenny, Eimear, Loos, Ruth, Fornage, Myriam, Hou, Lifang, Lloyd-Jones, Donald, Redline, Susan, Cade, Brian, Psaty, Bruce, Bis, Joshua, Brody, Jennifer, Silverman, Edwin, Yun, Jeong, Qiao, Dandi, Palmer, Nicholette, Freedman, Barry, Bowden, Donald, Cho, Michael, DeMeo, Dawn, Vasan, Ramachandran, Yanek, Lisa, Becker, Lewis, Kardia, Sharon, Peyser, Patricia, He, Jiang, Rienstra, Michiel, Van der Harst, Pim, Kaplan, Robert, Heckbert, Susan, Smith, Nicholas, Wiggins, Kerri, Arnett, Donna, Irvin, Marguerite, Tiwari, Hemant, Cutler, Michael, Knight, Stacey, Muhlestein, J, Correa, Adolfo, Raffield, Laura, Gao, Yan, de Andrade, Mariza, Rotter, Jerome, Rich, Stephen, Tracy, Russell, Konkle, Barbara, Johnsen, Jill, Wheeler, Marsha, Smith, J, Melander, Olle, Nilsson, Peter, Custer, Brian, Duggirala, Ravindranath, Curran, Joanne, Blangero, John, McGarvey, Stephen, Williams, L, Xiao, Shujie, Yang, Mao, Gu, C, Chen, Yii-Der, Lee, Wen-Jane, Kane, John, Pullinger, Clive, Shoemaker, M, Darbar, Dawood, Roden, Dan, Albert, Christine, Kooperberg, Charles, Zhou, Ying, Manson, JoAnn, Desai, Pinkal, Johnson, Andrew, Mathias, Rasika, and Blackwell, Thomas

Subjects
Animals, Humans, Mice, Alleles, Clonal Hematopoiesis, Genome-Wide Association Study, Hematopoiesis, Hematopoietic Stem Cells, Mutation, Promoter Regions, Genetic
Abstract

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

Published
2023

48. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria

Published
2024
Full Text
View/download PDF

49. 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

50. Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis

Author

Vargas, Luciana B, Lange, Leslie A, Ferrier, Kendra, Aguet, François, Ardlie, Kristin, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D, Blackwell, Thomas W, Ding, Jingzhong, Durda, Peter, Tracy, Russell P, Liu, Yongmei, Taylor, Kent D, Craig Johnson, W, Rich, Stephen S, Rotter, Jerome I, Lange, Ethan M, and Konigsberg, Iain R

Subjects
Epidemiology, Health Sciences, Obesity, Minority Health, Atherosclerosis, Genetics, Nutrition, Health Disparities, Prevention, Human Genome, 2.1 Biological and endogenous factors, Metabolic and endocrine, Oral and gastrointestinal, Stroke, Cardiovascular, Adult, Child, Humans, Body Mass Index, Gene Expression, Medical and Health Sciences, Education, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

Background/objectivesObesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity.Subjects/methodsRNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups.ResultsWithin individual SIRE groups, there were zero to three genes whose expression is significantly (p

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results