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1. Genetics of Latin American Diversity Project: Insights into population genetics and association studies in admixed groups in the Americas.

Author

Borda, Victor, Loesch, Douglas, Guo, Bing, Laboulaye, Roland, Veliz-Otani, Diego, French, Jennifer, Leal, Thiago, Gogarten, Stephanie, Ikpe, Sunday, Gouveia, Mateus, Mendes, Marla, Abecasis, Gonçalo, Alvim, Isabela, Arboleda-Bustos, Carlos, Arboleda, Gonzalo, Arboleda, Humberto, Barreto, Mauricio, Barwick, Lucas, Bezzera, Marcos, Blangero, John, Borges, Vanderci, Caceres, Omar, Cai, Jianwen, Chana-Cuevas, Pedro, Chen, Zhanghua, Custer, Brian, Dean, Michael, Dinardo, Carla, Domingos, Igor, Duggirala, Ravindranath, Dieguez, Elena, Fernandez, Willian, Ferraz, Henrique, Gilliland, Frank, Guio, Heinner, Horta, Bernardo, Curran, Joanne, Johnsen, Jill, Kaplan, Robert, Kelly, Shannon, Kenny, Eimear, Konkle, Barbara, Kooperberg, Charles, Lescano, Andres, Lima-Costa, M, Loos, Ruth, Manichaikul, Ani, Meyers, Deborah, Naslavsky, Michel, Nickerson, Deborah, North, Kari, Padilla, Carlos, Preuss, Michael, Raggio, Victor, Reiner, Alexander, Rich, Stephen, Rieder, Carlos, Rienstra, Michiel, Rotter, Jerome, Rundek, Tatjana, Sacco, Ralph, Sanchez, Cesar, Sankaran, Vijay, Santos-Lobato, Bruno, Schumacher-Schuh, Artur, Scliar, Marilia, Silverman, Edwin, Sofer, Tamar, Lasky-Su, Jessica, Tumas, Vitor, Weiss, Scott, Mata, Ignacio, Hernandez, Ryan, Tarazona-Santos, Eduardo, and OConnor, Timothy

Subjects
GLAD-match, GWAS, Latin America, identity-by-descent, imputation, local ancestry, migration, population structure, Humans, Latin America, Genetics, Population, Genome-Wide Association Study, Haplotypes, Algorithms, Genetic Variation, Software
Abstract

Latin Americans are underrepresented in genetic studies, increasing disparities in personalized genomic medicine. Despite available genetic data from thousands of Latin Americans, accessing and navigating the bureaucratic hurdles for consent or access remains challenging. To address this, we introduce the Genetics of Latin American Diversity (GLAD) Project, compiling genome-wide information from 53,738 Latin Americans across 39 studies representing 46 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene flow across the Americas. Additionally, we developed GLAD-match, a simulated annealing-based algorithm, to match the genetic background of external samples to our database, sharing summary statistics (i.e., allele and haplotype frequencies) without transferring individual-level genotypes. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic software in the presence of admixture. By providing this resource, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

Published
2024

3. Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis.

Author

Hasbani, Natalie, Westerman, Kenneth, Kwak, Soo, Chen, Han, Li, Xihao, Di Corpo, Daniel, Wessel, Jennifer, Bis, Joshua, Sarnowski, Chloè, Wu, Peitao, Bielak, Lawrence, Guo, Xiuqing, Heard-Costa, Nancy, Kinney, Gregory, Mahaney, Michael, Montasser, May, Palmer, Nicholette, Raffield, Laura, Terry, James, Yanek, Lisa, Bon, Jessica, Bowden, Donald, Brody, Jennifer, Duggirala, Ravindranath, Jacobs, David, Kalyani, Rita, Lange, Leslie, Mitchell, Braxton, Smith, Jennifer, Taylor, Kent, Carson, April, Curran, Joanne, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Gibbs, Richard, Gupta, Namrata, Kardia, Sharon, Kral, Brian, Momin, Zeineen, Newman, Anne, Post, Wendy, Viaud-Martinez, Karine, Young, Kendra, Becker, Lewis, Bertoni, Alain, Blangero, John, Carr, John, Pratte, Katherine, Psaty, Bruce, Rich, Stephen, Wu, Joseph, Malhotra, Rajeev, Peyser, Patricia, Morrison, Alanna, Vasan, Ramachandran, Lin, Xihong, Rotter, Jerome, Meigs, James, Manning, Alisa, and de Vries, Paul

Subjects
atherosclerosis, coronary artery disease, diabCs, type 2, genetics, risk factors, Humans, Coronary Artery Disease, Diabetes Mellitus, Type 2, Carotid Intima-Media Thickness, Risk Factors, Atherosclerosis, Plaque, Atherosclerotic, Genomics
Abstract

BACKGROUND: Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D. METHODS: We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test. RESULTS: Using a Bonferroni-corrected significance threshold of P

Published
2023

7. 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

8. 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

9. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Costanzo, Maria C, von Grotthuss, Marcin, Massung, Jeffrey, Jang, Dongkeun, Caulkins, Lizz, Koesterer, Ryan, Gilbert, Clint, Welch, Ryan P, Kudtarkar, Parul, Hoang, Quy, Boughton, Andrew P, Singh, Preeti, Sun, Ying, Duby, Marc, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Alexander, Benjamin R, Brandes, MacKenzie, Carmichael, Mary, Dornbos, Peter, Green, Todd, Huellas-Bruskiewicz, Kenneth C, Ji, Yue, Kluge, Alexandria, McMahon, Aoife C, Mercader, Josep M, Ruebenacker, Oliver, Sengupta, Sebanti, Spalding, Dylan, Taliun, Daniel, Consortium, AMP-T2D, Abecasis, Gonçalo, Akolkar, Beena, Allred, Nicholette D, Altshuler, David, Below, Jennifer E, Bergman, Richard, Beulens, Joline WJ, Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Bowden, Donald, Brosnan, M Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P, Cebola, Inês, Chambers, John, Chen, Yii-Der Ida, Cherkas, Andriy, Chu, Audrey Y, Clark, Christopher, Claussnitzer, Melina, Cox, Nancy J, Hoed, Marcel den, Dong, Duc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra JM, Engreitz, Jesse M, Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C, Fox, Caroline S, Frayling, Timothy M, Frazer, Kelly A, Gaulton, Kyle J, Gloyn, Anna L, Hanis, Craig L, Hanson, Robert, Hattersley, Andrew T, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne BR, Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M, Kim, Seung K, Lage, Kasper, Lange, Leslie A, and Lazar, Mitchell

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Good Health and Well Being, Humans, Diabetes Mellitus, Type 2, Access to Information, Prospective Studies, Genomics, Phenotype, AMP-T2D Consortium, CMDKP, GWAS, T2DKP, data sharing, diabetes, effector genes, genetic associations, genetic support, genomics, portal, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism
Abstract

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Published
2023

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

Author

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

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

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

Published
2023

11. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Author

Li, Xihao, Quick, Corbin, Zhou, Hufeng, Gaynor, Sheila M, Liu, Yaowu, Chen, Han, Selvaraj, Margaret Sunitha, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Haessler, Jeffrey, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Manichaikul, Ani, Martin, Lisa W, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Sitlani, Colleen M, Smith, Jennifer A, Taylor, Kent D, Vasan, Ramachandran S, Willer, Cristen J, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Natarajan, Pradeep, Peloso, Gina M, Li, Zilin, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Precision Medicine, Biotechnology, Human Genome, Good Health and Well Being, Genome-Wide Association Study, Whole Genome Sequencing, Exome Sequencing, Phenotype, Lipids, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Published
2023

12. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, Peralta Romero, Jose de Jesus, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Abstract

[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].

Published
2023

13. Genetic diversity fuels gene discovery for tobacco and alcohol use

Author

Saunders, Gretchen RB, Wang, Xingyan, Chen, Fang, Jang, Seon-Kyeong, Liu, Mengzhen, Wang, Chen, Gao, Shuang, Jiang, Yu, Khunsriraksakul, Chachrit, Otto, Jacqueline M, Addison, Clifton, Akiyama, Masato, Albert, Christine M, Aliev, Fazil, Alonso, Alvaro, Arnett, Donna K, Ashley-Koch, Allison E, Ashrani, Aneel A, Barnes, Kathleen C, Barr, R Graham, Bartz, Traci M, Becker, Diane M, Bielak, Lawrence F, Benjamin, Emelia J, Bis, Joshua C, Bjornsdottir, Gyda, Blangero, John, Bleecker, Eugene R, Boardman, Jason D, Boerwinkle, Eric, Boomsma, Dorret I, Boorgula, Meher Preethi, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Iona, Cho, Michael H, Choquet, Hélène, Cole, John W, Cornelis, Marilyn C, Cucca, Francesco, Curran, Joanne E, de Andrade, Mariza, Dick, Danielle M, Docherty, Anna R, Duggirala, Ravindranath, Eaton, Charles B, Ehringer, Marissa A, Esko, Tõnu, Faul, Jessica D, Fernandes Silva, Lilian, Fiorillo, Edoardo, Fornage, Myriam, Freedman, Barry I, Gabrielsen, Maiken E, Garrett, Melanie E, Gharib, Sina A, Gieger, Christian, Gillespie, Nathan, Glahn, David C, Gordon, Scott D, Gu, Charles C, Gu, Dongfeng, Gudbjartsson, Daniel F, Guo, Xiuqing, Haessler, Jeffrey, Hall, Michael E, Haller, Toomas, Harris, Kathleen Mullan, He, Jiang, Herd, Pamela, Hewitt, John K, Hickie, Ian, Hidalgo, Bertha, Hokanson, John E, Hopfer, Christian, Hottenga, JoukeJan, Hou, Lifang, Huang, Hongyan, Hung, Yi-Jen, Hunter, David J, Hveem, Kristian, Hwang, Shih-Jen, Hwu, Chii-Min, Iacono, William, Irvin, Marguerite R, Jee, Yon Ho, Johnson, Eric O, Joo, Yoonjung Y, Jorgenson, Eric, Justice, Anne E, Kamatani, Yoichiro, Kaplan, Robert C, Kaprio, Jaakko, Kardia, Sharon LR, and Keller, Matthew C

Subjects
Genetics, Alcoholism, Alcohol Use and Health, Prevention, Human Genome, Substance Misuse, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Humans, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Multifactorial Inheritance, Risk Factors, Tobacco Use, Alcohol Drinking, Transcriptome, Sample Size, Genetic Loci, Internationality, Europe, 23andMe Research Team, Biobank Japan Project, General Science & Technology
Abstract

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

Published
2022

14. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Author

Li, Zilin, Li, Xihao, Zhou, Hufeng, Gaynor, Sheila M, Selvaraj, Margaret Sunitha, Arapoglou, Theodore, Quick, Corbin, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Blackwell, Thomas W, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Franceschini, Nora, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Lin, Bridget M, Manichaikul, Ani, Manning, Alisa K, Martin, Lisa W, Mathias, Rasika A, Meigs, James B, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Taylor, Kent D, Taub, Margaret A, Vasan, Ramachandran S, Weeks, Daniel E, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Willer, Cristen J, Natarajan, Pradeep, Peloso, Gina M, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Precision Medicine, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Genome, Phenotype, Genetic Variation, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Published
2022

15. Rare coding variants in RCN3 are associated with blood pressure

Author

He, Karen Y, Kelly, Tanika N, Wang, Heming, Liang, Jingjing, Zhu, Luke, Cade, Brian E, Assimes, Themistocles L, Becker, Lewis C, Beitelshees, Amber L, Bielak, Lawrence F, Bress, Adam P, Brody, Jennifer A, Chang, Yen-Pei Christy, Chang, Yi-Cheng, de Vries, Paul S, Duggirala, Ravindranath, Fox, Ervin R, Franceschini, Nora, Furniss, Anna L, Gao, Yan, Guo, Xiuqing, Haessler, Jeffrey, Hung, Yi-Jen, Hwang, Shih-Jen, Irvin, Marguerite Ryan, Kalyani, Rita R, Liu, Ching-Ti, Liu, Chunyu, Martin, Lisa Warsinger, Montasser, May E, Muntner, Paul M, Mwasongwe, Stanford, Naseri, Take, Palmas, Walter, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Sheu, Wayne H-H, Shimbo, Daichi, Smith, Jennifer A, Snively, Beverly M, Yanek, Lisa R, Zhao, Wei, Blangero, John, Boerwinkle, Eric, Chen, Yii-Der Ida, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Fornage, Myriam, He, Jiang, Hou, Lifang, Kaplan, Robert C, Kardia, Sharon LR, Kenny, Eimear E, Kooperberg, Charles, Lloyd-Jones, Donald, Loos, Ruth JF, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, North, Kari E, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Rao, DC, Redline, Susan, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Taylor, Kent D, Tracy, Russell, Vasan, Ramachandran S, Morrison, Alanna C, Levy, Daniel, Chakravarti, Aravinda, Arnett, Donna K, and Zhu, Xiaofeng

Subjects
Biological Sciences, Genetics, Hypertension, Human Genome, Precision Medicine, Biotechnology, Cardiovascular, Minority Health, 2.1 Biological and endogenous factors, Good Health and Well Being, Blood Pressure, Genetic Linkage, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Whole Genome Sequencing, Rare variant analysis, Blood pressure, Whole genome sequencing, Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA) Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundWhile large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.ResultsAssociations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).ConclusionsLow frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Published
2022

16. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, de Jesus Peralta Romero, Jose, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Subjects
Biological Sciences, Genetics, Nutrition, Minority Health, Human Genome, Obesity, 2.1 Biological and endogenous factors, Stroke, Hispanic/Latino, anthropometrics, diversity, fine-mapping, obesity, population stratification, trans-ancestral or trans-ethnic
Abstract

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Published
2022

17. Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Author

Selvaraj, Margaret Sunitha, Li, Xihao, Li, Zilin, Pampana, Akhil, Zhang, David Y, Park, Joseph, Aslibekyan, Stella, Bis, Joshua C, Brody, Jennifer A, Cade, Brian E, Chuang, Lee-Ming, Chung, Ren-Hua, Curran, Joanne E, de las Fuentes, Lisa, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Graff, Mariaelisa, Guo, Xiuqing, Heard-Costa, Nancy, Hidalgo, Bertha, Hwu, Chii-Min, Irvin, Marguerite R, Kelly, Tanika N, Kral, Brian G, Lange, Leslie, Li, Xiaohui, Lisa, Martin, Lubitz, Steven A, Manichaikul, Ani W, Michael, Preuss, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Reupena, Muagututia S, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Tracy, Russell P, Tsai, Michael Y, Wang, Zhe, Wang, Yuxuan, Bao, Wei, Wilkins, John T, Yanek, Lisa R, Zhao, Wei, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Chen, Yii-Der Ida, Correa, Adolfo, Cupples, L Adrienne, Dutcher, Susan K, Ellinor, Patrick T, Fornage, Myriam, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, He, Jiang, Kaplan, Robert C, Kardia, Sharon LR, Kim, Ryan, Kooperberg, Charles, Loos, Ruth JF, Viaud-Martinez, Karine A, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Nickerson, Deborah, North, Kari E, Psaty, Bruce M, Redline, Susan, Reiner, Alexander P, Vasan, Ramachandran S, Rich, Stephen S, Willer, Cristen, Rotter, Jerome I, Rader, Daniel J, Lin, Xihong, Peloso, Gina M, and Natarajan, Pradeep

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Biotechnology, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Alleles, Cholesterol, LDL, Genome-Wide Association Study, Humans, Lipids, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Published
2022

18. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author

DiCorpo, Daniel, Gaynor, Sheila M, Russell, Emily M, Westerman, Kenneth E, Raffield, Laura M, Majarian, Timothy D, Wu, Peitao, Sarnowski, Chloé, Highland, Heather M, Jackson, Anne, Hasbani, Natalie R, de Vries, Paul S, Brody, Jennifer A, Hidalgo, Bertha, Guo, Xiuqing, Perry, James A, O’Connell, Jeffrey R, Lent, Samantha, Montasser, May E, Cade, Brian E, Jain, Deepti, Wang, Heming, D’Oliveira Albanus, Ricardo, Varshney, Arushi, Yanek, Lisa R, Lange, Leslie, Palmer, Nicholette D, Almeida, Marcio, Peralta, Juan M, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Bielak, Lawrence F, Chen, Chung-Shiuan, Chen, Yii-Der Ida, Choi, Won Jung, Goodarzi, Mark O, Floyd, James S, Irvin, Marguerite R, Kalyani, Rita R, Kelly, Tanika N, Lee, Seonwook, Liu, Ching-Ti, Loesch, Douglas, Manson, JoAnn E, Minster, Ryan L, Naseri, Take, Pankow, James S, Rasmussen-Torvik, Laura J, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Selvin, Elizabeth, Smith, Jennifer A, Weeks, Daniel E, Xu, Huichun, Yao, Jie, Zhao, Wei, Parker, Stephen, Alonso, Alvaro, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Duggirala, Ravindranath, He, Jiang, Heckbert, Susan R, Kardia, Sharon LR, Kim, Ryan W, Kooperberg, Charles, Liu, Simin, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Morrison, Alanna C, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Shuldiner, Alan R, Taylor, Kent D, Vasan, Ramachandran S, Viaud-Martinez, Karine A, Florez, Jose C, Wilson, James G, Sladek, Robert, Rich, Stephen S, Rotter, Jerome I, Lin, Xihong, Dupuis, Josée, Meigs, James B, Wessel, Jennifer, and Manning, Alisa K

Subjects
Biological Sciences, Genetics, Nutrition, Clinical Research, Human Genome, Precision Medicine, Biotechnology, Diabetes, Minority Health, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Diabetes Mellitus, Type 2, Fasting, Glucose, Humans, Insulin, National Heart, Lung, and Blood Institute (U.S.), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Receptors, Immunologic, United States, Biological sciences, Biomedical and clinical sciences
Abstract

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Published
2022

19. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes.

Author

Goodrich, Julia K, Singer-Berk, Moriel, Son, Rachel, Sveden, Abigail, Wood, Jordan, England, Eleina, Cole, Joanne B, Weisburd, Ben, Watts, Nick, Caulkins, Lizz, Dornbos, Peter, Koesterer, Ryan, Zappala, Zachary, Zhang, Haichen, Maloney, Kristin A, Dahl, Andy, Aguilar-Salinas, Carlos A, Atzmon, Gil, Barajas-Olmos, Francisco, Barzilai, Nir, Blangero, John, Boerwinkle, Eric, Bonnycastle, Lori L, Bottinger, Erwin, Bowden, Donald W, Centeno-Cruz, Federico, Chambers, John C, Chami, Nathalie, Chan, Edmund, Chan, Juliana, Cheng, Ching-Yu, Cho, Yoon Shin, Contreras-Cubas, Cecilia, Córdova, Emilio, Correa, Adolfo, DeFronzo, Ralph A, Duggirala, Ravindranath, Dupuis, Josée, Garay-Sevilla, Ma Eugenia, García-Ortiz, Humberto, Gieger, Christian, Glaser, Benjamin, González-Villalpando, Clicerio, Gonzalez, Ma Elena, Grarup, Niels, Groop, Leif, Gross, Myron, Haiman, Christopher, Han, Sohee, Hanis, Craig L, Hansen, Torben, Heard-Costa, Nancy L, Henderson, Brian E, Hernandez, Juan Manuel Malacara, Hwang, Mi Yeong, Islas-Andrade, Sergio, Jørgensen, Marit E, Kang, Hyun Min, Kim, Bong-Jo, Kim, Young Jin, Koistinen, Heikki A, Kooner, Jaspal Singh, Kuusisto, Johanna, Kwak, Soo-Heon, Laakso, Markku, Lange, Leslie, Lee, Jong-Young, Lee, Juyoung, Lehman, Donna M, Linneberg, Allan, Liu, Jianjun, Loos, Ruth JF, Lyssenko, Valeriya, Ma, Ronald CW, Martínez-Hernández, Angélica, Meigs, James B, Meitinger, Thomas, Mendoza-Caamal, Elvia, Mohlke, Karen L, Morris, Andrew D, Morrison, Alanna C, Ng, Maggie CY, Nilsson, Peter M, O'Donnell, Christopher J, Orozco, Lorena, Palmer, Colin NA, Park, Kyong Soo, Post, Wendy S, Pedersen, Oluf, Preuss, Michael, Psaty, Bruce M, Reiner, Alexander P, Revilla-Monsalve, Cristina, Rich, Stephen S, Rotter, Jerome I, Saleheen, Danish, Schurmann, Claudia, Sim, Xueling, Sladek, Rob, and Small, Kerrin S

Subjects
AMP-T2D-GENES Consortia, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Risk Assessment, Genotype, Multifactorial Inheritance, Penetrance, Adult, Dyslipidemias, Exome, Biomarkers, Biological Variation, Population
Abstract

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

Published
2021

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

Author

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

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

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

Published
2021

21. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Abecasis, Gonçalo, Akolkar, Beena, Alexander, Benjamin R., Allred, Nicholette D., Altshuler, David, Below, Jennifer E., Bergman, Richard, Beulens, Joline W.J., Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Boughton, Andrew P., Bowden, Donald, Brosnan, M. Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P., Carmichael, Mary, Caulkins, Lizz, Cebola, Inês, Chambers, John, Ida Chen, Yii-Der, Cherkas, Andriy, Chu, Audrey Y., Clark, Christopher, Claussnitzer, Melina, Costanzo, Maria C., Cox, Nancy J., Hoed, Marcel den, Dong, Duc, Duby, Marc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra J.M., Engreitz, Jesse M., Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C., Fox, Caroline S., Frayling, Timothy M., Frazer, Kelly A., Gaulton, Kyle J., Gilbert, Clint, Gloyn, Anna L., Green, Todd, Hanis, Craig L., Hanson, Robert, Hattersley, Andrew T., Hoang, Quy, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne B.R., Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M., Kim, Seung K., Kluge, Alexandria, Koesterer, Ryan, Kudtarkar, Parul, Lage, Kasper, Lange, Leslie A., Lazar, Mitchell, Lehman, Donna, Liu, Ching-Ti, Loos, Ruth J.F., Ma, Ronald Ching-wan, MacDonald, Patrick, Massung, Jeffrey, Maurano, Matthew T., McCarthy, Mark I., McVean, Gil, Meigs, James B., Mercader, Josep M., Miller, Melissa R., Mitchell, Braxton, Mohlke, Karen L., Morabito, Samuel, Morgan, Claire, Mullican, Shannon, Narendra, Sharvari, Ng, Maggie C.Y., Nguyen, Lynette, Palmer, Colin N.A., Parker, Stephen C.J., Parrado, Antonio, Parsa, Afshin, Pawlyk, Aaron C., Pearson, Ewan R., Plump, Andrew, Province, Michael, Quertermous, Thomas, Redline, Susan, Reilly, Dermot F., Ren, Bing, Rich, Stephen S., Richards, J. Brent, Rotter, Jerome I., Ruebenacker, Oliver, Ruetten, Hartmut, Salem, Rany M., Sander, Maike, Sanders, Michael, Sanghera, Dharambir, Scott, Laura J., Sengupta, Sebanti, Siedzik, David, Sim, Xueling, Singh, Preeti, Sladek, Robert, Small, Kerrin, Smith, Philip, Stein, Peter, Spalding, Dylan, Stringham, Heather M., Sun, Ying, Susztak, Katalin, ’t Hart, Leen M., Taliun, Daniel, Taylor, Kent, Thomas, Melissa K., Todd, Jennifer A., Udler, Miriam S., Voight, Benjamin, von Grotthuss, Marcin, Wan, Andre, Welch, Ryan P., Wholley, David, Yuksel, Kaan, Zaghloul, Norann A., Jang, Dongkeun, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Brandes, MacKenzie, Dornbos, Peter, Huellas-Bruskiewicz, Kenneth C., Ji, Yue, McMahon, Aoife C., Fauman, Eric B., Kamphaus, Tania Nayak, and Abecasis, Gonçalo R.

Published
2023
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22. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Author

Flannick, Jason, Mercader, Josep M, Fuchsberger, Christian, Udler, Miriam S, Mahajan, Anubha, Wessel, Jennifer, Teslovich, Tanya M, Caulkins, Lizz, Koesterer, Ryan, Barajas-Olmos, Francisco, Blackwell, Thomas W, Boerwinkle, Eric, Brody, Jennifer A, Centeno-Cruz, Federico, Chen, Ling, Chen, Siying, Contreras-Cubas, Cecilia, Córdova, Emilio, Correa, Adolfo, Cortes, Maria, DeFronzo, Ralph A, Dolan, Lawrence, Drews, Kimberly L, Elliott, Amanda, Floyd, James S, Gabriel, Stacey, Garay-Sevilla, Maria Eugenia, García-Ortiz, Humberto, Gross, Myron, Han, Sohee, Heard-Costa, Nancy L, Jackson, Anne U, Jørgensen, Marit E, Kang, Hyun Min, Kelsey, Megan, Kim, Bong-Jo, Koistinen, Heikki A, Kuusisto, Johanna, Leader, Joseph B, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Lyssenko, Valeriya, Manning, Alisa K, Marcketta, Anthony, Malacara-Hernandez, Juan Manuel, Martínez-Hernández, Angélica, Matsuo, Karen, Mayer-Davis, Elizabeth, Mendoza-Caamal, Elvia, Mohlke, Karen L, Morrison, Alanna C, Ndungu, Anne, Ng, Maggie CY, O’Dushlaine, Colm, Payne, Anthony J, Pihoker, Catherine, Post, Wendy S, Preuss, Michael, Psaty, Bruce M, Vasan, Ramachandran S, Rayner, N William, Reiner, Alexander P, Revilla-Monsalve, Cristina, Robertson, Neil R, Santoro, Nicola, Schurmann, Claudia, So, Wing Yee, Soberón, Xavier, Stringham, Heather M, Strom, Tim M, Tam, Claudia HT, Thameem, Farook, Tomlinson, Brian, Torres, Jason M, Tracy, Russell P, van Dam, Rob M, Vujkovic, Marijana, Wang, Shuai, Welch, Ryan P, Witte, Daniel R, Wong, Tien-Yin, Atzmon, Gil, Barzilai, Nir, Blangero, John, Bonnycastle, Lori L, Bowden, Donald W, Chambers, John C, Chan, Edmund, Cheng, Ching-Yu, Cho, Yoon Shin, Collins, Francis S, de Vries, Paul S, Duggirala, Ravindranath, Glaser, Benjamin, Gonzalez, Clicerio, Gonzalez, Ma Elena, Groop, Leif, Kooner, Jaspal Singh, and Kwak, Soo Heon

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Prevention, Biotechnology, Human Genome, Diabetes, 2.1 Biological and endogenous factors, Metabolic and endocrine, Animals, Case-Control Studies, Decision Support Techniques, Diabetes Mellitus, Type 2, Exome, Female, Gene Frequency, Genome-Wide Association Study, Humans, Male, Mice, Mice, Knockout, Exome Sequencing, Broad Genomics Platform, DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, AMP-T2D-GENES, General Science & Technology
Abstract

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Published
2019

23. Genetic determinants of metabolic biomarkers and their associations with cardiometabolic traits in Hispanic/Latino adolescents

Author

Kim, Daeeun, Justice, Anne E., Chittoor, Geetha, Blanco, Estela, Burrows, Raquel, Graff, Mariaelisa, Howard, Annie Green, Wang, Yujie, Rohde, Rebecca, Buchanan, Victoria L., Voruganti, V. Saroja, Almeida, Marcio, Peralta, Juan, Lehman, Donna M., Curran, Joanne E., Comuzzie, Anthony G., Duggirala, Ravindranath, Blangero, John, Albala, Cecilia, Santos, José L., Angel, Bárbara, Lozoff, Betsy, Gahagan, Sheila, and North, Kari E.

Published
2022
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24. Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes

Author

Hindy, George, Dornbos, Peter, Chaffin, Mark D., Liu, Dajiang J., Wang, Minxian, Selvaraj, Margaret Sunitha, Zhang, David, Park, Joseph, Aguilar-Salinas, Carlos A., Antonacci-Fulton, Lucinda, Ardissino, Diego, Arnett, Donna K., Aslibekyan, Stella, Atzmon, Gil, Ballantyne, Christie M., Barajas-Olmos, Francisco, Barzilai, Nir, Becker, Lewis C., Bielak, Lawrence F., Bis, Joshua C., Blangero, John, Boerwinkle, Eric, Bonnycastle, Lori L., Bottinger, Erwin, Bowden, Donald W., Bown, Matthew J., Brody, Jennifer A., Broome, Jai G., Burtt, Noël P., Cade, Brian E., Centeno-Cruz, Federico, Chan, Edmund, Chang, Yi-Cheng, Chen, Yii-Der I., Cheng, Ching-Yu, Choi, Won Jung, Chowdhury, Rajiv, Contreras-Cubas, Cecilia, Córdova, Emilio J., Correa, Adolfo, Cupples, L. Adrienne, Curran, Joanne E., Danesh, John, de Vries, Paul S., DeFronzo, Ralph A., Doddapaneni, Harsha, Duggirala, Ravindranath, Dutcher, Susan K., Ellinor, Patrick T., Emery, Leslie S., Florez, Jose C., Fornage, Myriam, Freedman, Barry I., Fuster, Valentin, Garay-Sevilla, Ma. Eugenia, García-Ortiz, Humberto, Germer, Soren, Gibbs, Richard A., Gieger, Christian, Glaser, Benjamin, Gonzalez, Clicerio, Gonzalez-Villalpando, Maria Elena, Graff, Mariaelisa, Graham, Sarah E., Grarup, Niels, Groop, Leif C., Guo, Xiuqing, Gupta, Namrata, Han, Sohee, Hanis, Craig L., Hansen, Torben, He, Jiang, Heard-Costa, Nancy L., Hung, Yi-Jen, Hwang, Mi Yeong, Irvin, Marguerite R., Islas-Andrade, Sergio, Jarvik, Gail P., Kang, Hyun Min, Kardia, Sharon L.R., Kelly, Tanika, Kenny, Eimear E., Khan, Alyna T., Kim, Bong-Jo, Kim, Ryan W., Kim, Young Jin, Koistinen, Heikki A., Kooperberg, Charles, Kuusisto, Johanna, Kwak, Soo Heon, Laakso, Markku, Lange, Leslie A., Lee, Jiwon, Lee, Juyoung, Lee, Seonwook, Lehman, Donna M., Lemaitre, Rozenn N., Linneberg, Allan, Liu, Jianjun, Loos, Ruth J.F., Lubitz, Steven A., Lyssenko, Valeriya, Ma, Ronald C.W., Martin, Lisa Warsinger, Martínez-Hernández, Angélica, Mathias, Rasika A., McGarvey, Stephen T., McPherson, Ruth, Meigs, James B., Meitinger, Thomas, Melander, Olle, Mendoza-Caamal, Elvia, Metcalf, Ginger A., Mi, Xuenan, Mohlke, Karen L., Montasser, May E., Moon, Jee-Young, Moreno-Macías, Hortensia, Morrison, Alanna C., Muzny, Donna M., Nelson, Sarah C., Nilsson, Peter M., O’Connell, Jeffrey R., Orho-Melander, Marju, Orozco, Lorena, Palmer, Colin N.A., Palmer, Nicholette D., Park, Cheol Joo, Park, Kyong Soo, Pedersen, Oluf, Peralta, Juan M., Peyser, Patricia A., Post, Wendy S., Preuss, Michael, Psaty, Bruce M., Qi, Qibin, Rao, D.C., Redline, Susan, Reiner, Alexander P., Revilla-Monsalve, Cristina, Rich, Stephen S., Samani, Nilesh, Schunkert, Heribert, Schurmann, Claudia, Seo, Daekwan, Seo, Jeong-Sun, Sim, Xueling, Sladek, Rob, Small, Kerrin S., So, Wing Yee, Stilp, Adrienne M., Tai, E. Shyong, Tam, Claudia H.T., Taylor, Kent D., Teo, Yik Ying, Thameem, Farook, Tomlinson, Brian, Tsai, Michael Y., Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tusié-Luna, Teresa, Udler, Miriam S., van Dam, Rob M., Vasan, Ramachandran S., Viaud Martinez, Karine A., Wang, Fei Fei, Wang, Xuzhi, Watkins, Hugh, Weeks, Daniel E., Wilson, James G., Witte, Daniel R., Wong, Tien-Yin, Yanek, Lisa R., Kathiresan, Sekar, Rader, Daniel J., Rotter, Jerome I., Boehnke, Michael, McCarthy, Mark I., Willer, Cristen J., Natarajan, Pradeep, Flannick, Jason A., Khera, Amit V., and Peloso, Gina M.

Published
2022
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25. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Author

Jun, Goo, Manning, Alisa, Almeida, Marcio, Zawistowski, Matthew, Wood, Andrew R, Teslovich, Tanya M, Fuchsberger, Christian, Feng, Shuang, Cingolani, Pablo, Gaulton, Kyle J, Dyer, Thomas, Blackwell, Thomas W, Chen, Han, Chines, Peter S, Choi, Sungkyoung, Churchhouse, Claire, Fontanillas, Pierre, King, Ryan, Lee, SungYoung, Lincoln, Stephen E, Trubetskoy, Vasily, DePristo, Mark, Fingerlin, Tasha, Grossman, Robert, Grundstad, Jason, Heath, Alison, Kim, Jayoun, Kim, Young Jin, Laramie, Jason, Lee, Jaehoon, Li, Heng, Liu, Xuanyao, Livne, Oren, Locke, Adam E, Maller, Julian, Mazur, Alexander, Morris, Andrew P, Pollin, Toni I, Ragona, Derek, Reich, David, Rivas, Manuel A, Scott, Laura J, Sim, Xueling, Tearle, Rick G, Teo, Yik Ying, Williams, Amy L, Zöllner, Sebastian, Curran, Joanne E, Peralta, Juan, Akolkar, Beena, Bell, Graeme I, Burtt, Noël P, Cox, Nancy J, Florez, Jose C, Hanis, Craig L, McKeon, Catherine, Mohlke, Karen L, Seielstad, Mark, Wilson, James G, Atzmon, Gil, Below, Jennifer E, Dupuis, Josée, Nicolae, Dan L, Lehman, Donna, Park, Taesung, Won, Sungho, Sladek, Robert, Altshuler, David, McCarthy, Mark I, Duggirala, Ravindranath, Boehnke, Michael, Frayling, Timothy M, Abecasis, Gonçalo R, and Blangero, John

Subjects
Diabetes, Genetics, Human Genome, Clinical Research, Obesity, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Diabetes Mellitus, Type 2, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Mexican Americans, Pedigree, Phenotype, Quantitative Trait Loci, Whole Genome Sequencing, genetics, sequencing, type 2 diabetes, eQTL, rare variants
Abstract

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Published
2018

28. Prosaposin is a regulator of progranulin levels and oligomerization.

Author

Nicholson, Alexandra M, Finch, NiCole A, Almeida, Marcio, Perkerson, Ralph B, van Blitterswijk, Marka, Wojtas, Aleksandra, Cenik, Basar, Rotondo, Sergio, Inskeep, Venette, Almasy, Laura, Dyer, Thomas, Peralta, Juan, Jun, Goo, Wood, Andrew R, Frayling, Timothy M, Fuchsberger, Christian, Fowler, Sharon, Teslovich, Tanya M, Manning, Alisa K, Kumar, Satish, Curran, Joanne, Lehman, Donna, Abecasis, Goncalo, Duggirala, Ravindranath, Pottier, Cyril, Zahir, Haaris A, Crook, Julia E, Karydas, Anna, Mitic, Laura, Sun, Ying, Dickson, Dennis W, Bu, Guojun, Herz, Joachim, Yu, Gang, Miller, Bruce L, Ferguson, Shawn, Petersen, Ronald C, Graff-Radford, Neill, Blangero, John, and Rademakers, Rosa

Subjects
Hela Cells, Animals, Mice, Knockout, Humans, Mice, Parkinson Disease, Alzheimer Disease, Saposins, Intercellular Signaling Peptides and Proteins, Polymorphism, Single Nucleotide, Gene Knockdown Techniques, Frontotemporal Dementia, Haploinsufficiency, Protein Interaction Maps, Progranulins, HeLa Cells, Biotechnology, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurosciences, Neurodegenerative, Brain Disorders, Acquired Cognitive Impairment, Dementia, 2.1 Biological and endogenous factors, Neurological, Knockout, Polymorphism, Single Nucleotide
Abstract

Progranulin (GRN) loss-of-function mutations leading to progranulin protein (PGRN) haploinsufficiency are prevalent genetic causes of frontotemporal dementia. Reports also indicated PGRN-mediated neuroprotection in models of Alzheimer's and Parkinson's disease; thus, increasing PGRN levels is a promising therapeutic for multiple disorders. To uncover novel PGRN regulators, we linked whole-genome sequence data from 920 individuals with plasma PGRN levels and identified the prosaposin (PSAP) locus as a new locus significantly associated with plasma PGRN levels. Here we show that both PSAP reduction and overexpression lead to significantly elevated extracellular PGRN levels. Intriguingly, PSAP knockdown increases PGRN monomers, whereas PSAP overexpression increases PGRN oligomers, partly through a protein-protein interaction. PSAP-induced changes in PGRN levels and oligomerization replicate in human-derived fibroblasts obtained from a GRN mutation carrier, further supporting PSAP as a potential PGRN-related therapeutic target. Future studies should focus on addressing the relevance and cellular mechanism by which PGRN oligomeric species provide neuroprotection.

Published
2016

29. Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes

Author

Wood, Andrew R, Tuke, Marcus A, Nalls, Mike, Hernandez, Dena, Gibbs, J Raphael, Lin, Haoxiang, Xu, Christopher S, Li, Qibin, Shen, Juan, Jun, Goo, Almeida, Marcio, Tanaka, Toshiko, Perry, John RB, Gaulton, Kyle, Rivas, Manny, Pearson, Richard, Curran, Joanne E, Johnson, Matthew P, Göring, Harald HH, Duggirala, Ravindranath, Blangero, John, Mccarthy, Mark I, Bandinelli, Stefania, Murray, Anna, Weedon, Michael N, Singleton, Andrew, Melzer, David, Ferrucci, Luigi, and Frayling, Timothy M

Subjects
Clinical Research, Human Genome, Genetics, Detection, screening and diagnosis, Aetiology, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Adult, Aged, Aged, 80 and over, Female, Gene Frequency, Genetic Association Studies, Genetic Markers, Genetic Variation, Genome, Human, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Young Adult, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Initial results from sequencing studies suggest that there are relatively few low-frequency (

Published
2015

30. APOC3 genetic variation, serum triglycerides, and risk of coronary artery disease in Asian Indians, Europeans, and other ethnic groups

Author

Goyal, Shiwali, Tanigawa, Yosuke, Zhang, Weihua, Chai, Jin-Fang, Almeida, Marcio, Sim, Xueling, Lerner, Megan, Chainakul, Juliane, Ramiu, Jonathan Garcia, Seraphin, Chanel, Apple, Blair, Vaughan, April, Muniu, James, Peralta, Juan, Lehman, Donna M., Ralhan, Sarju, Wander, Gurpreet S., Singh, Jai Rup, Mehra, Narinder K., Sidorov, Evgeny, Peyton, Marvin D., Blackett, Piers R., Curran, Joanne E., Tai, E. Shyong, van Dam, Rob, Cheng, Ching-Yu, Duggirala, Ravindranath, Blangero, John, Chambers, John C., Sabanayagam, Charumathi, Kooner, Jaspal S., Rivas, Manuel A., Aston, Christopher E., and Sanghera, Dharambir K.

Published
2021
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31. Further evidence supporting a potential role for ADH1B in obesity

Author

Morales, Liza D., Cromack, Douglas T., Tripathy, Devjit, Fourcaudot, Marcel, Kumar, Satish, Curran, Joanne E., Carless, Melanie, Göring, Harald H. H., Hu, Shirley L., Lopez-Alvarenga, Juan Carlos, Garske, Kristina M., Pajukanta, Päivi, Small, Kerrin S., Glastonbury, Craig A., Das, Swapan K., Langefeld, Carl, Hanson, Robert L., Hsueh, Wen-Chi, Norton, Luke, Arya, Rector, Mummidi, Srinivas, Blangero, John, DeFronzo, Ralph A., Duggirala, Ravindranath, and Jenkinson, Christopher P.

Published
2021
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32. Serum carotenoids and Pediatric Metabolic Index predict insulin sensitivity in Mexican American children

Author

Mummidi, Srinivas, Farook, Vidya S., Reddivari, Lavanya, Hernandez-Ruiz, Joselin, Diaz-Badillo, Alvaro, Fowler, Sharon P., Resendez, Roy G., Akhtar, Feroz, Lehman, Donna M., Jenkinson, Christopher P., Arya, Rector, Lynch, Jane L., Canas, Jose A., DeFronzo, Ralph A., Hale, Daniel E., Blangero, John, Lopez-Alvarenga, Juan Carlos, Duggirala, Ravindranath, and Vanamala, Jairam K. P.

Published
2021
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33. Rare DEGS1 variant significantly alters de novo ceramide synthesis pathway

Author

Blackburn, Nicholas B., Michael, Laura F., Meikle, Peter J., Peralta, Juan M., Mosior, Marian, McAhren, Scott, Bui, Hai H., Bellinger, Melissa A., Giles, Corey, Kumar, Satish, Leandro, Ana C., Almeida, Marcio, Weir, Jacquelyn M., Mahaney, Michael C., Dyer, Thomas D., Almasy, Laura, VandeBerg, John L., Williams-Blangero, Sarah, Glahn, David C., Duggirala, Ravindranath, Kowala, Mark, Blangero, John, and Curran, Joanne E.

Published
2019
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34. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus.

Author

Mahajan, Anubha, Sim, Xueling, Ng, Hui Jin, Manning, Alisa, Rivas, Manuel A, Highland, Heather M, Locke, Adam E, Grarup, Niels, Im, Hae Kyung, Cingolani, Pablo, Flannick, Jason, Fontanillas, Pierre, Fuchsberger, Christian, Gaulton, Kyle J, Teslovich, Tanya M, Rayner, N William, Robertson, Neil R, Beer, Nicola L, Rundle, Jana K, Bork-Jensen, Jette, Ladenvall, Claes, Blancher, Christine, Buck, David, Buck, Gemma, Burtt, Noël P, Gabriel, Stacey, Gjesing, Anette P, Groves, Christopher J, Hollensted, Mette, Huyghe, Jeroen R, Jackson, Anne U, Jun, Goo, Justesen, Johanne Marie, Mangino, Massimo, Murphy, Jacquelyn, Neville, Matt, Onofrio, Robert, Small, Kerrin S, Stringham, Heather M, Syvänen, Ann-Christine, Trakalo, Joseph, Abecasis, Goncalo, Bell, Graeme I, Blangero, John, Cox, Nancy J, Duggirala, Ravindranath, Hanis, Craig L, Seielstad, Mark, Wilson, James G, Christensen, Cramer, Brandslund, Ivan, Rauramaa, Rainer, Surdulescu, Gabriela L, Doney, Alex SF, Lannfelt, Lars, Linneberg, Allan, Isomaa, Bo, Tuomi, Tiinamaija, Jørgensen, Marit E, Jørgensen, Torben, Kuusisto, Johanna, Uusitupa, Matti, Salomaa, Veikko, Spector, Timothy D, Morris, Andrew D, Palmer, Colin NA, Collins, Francis S, Mohlke, Karen L, Bergman, Richard N, Ingelsson, Erik, Lind, Lars, Tuomilehto, Jaakko, Hansen, Torben, Watanabe, Richard M, Prokopenko, Inga, Dupuis, Josee, Karpe, Fredrik, Groop, Leif, Laakso, Markku, Pedersen, Oluf, Florez, Jose C, Morris, Andrew P, Altshuler, David, Meigs, James B, Boehnke, Michael, McCarthy, Mark I, Lindgren, Cecilia M, Gloyn, Anna L, and T2D-GENES consortium and GoT2D consortium

Subjects
T2D-GENES consortium and GoT2D consortium, Humans, Diabetes Mellitus, Type 2, Insulin, Glucose-6-Phosphatase, Blood Glucose, Receptors, Glucagon, Glycemic Index, Gene Frequency, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Exome, Glucagon-Like Peptide-1 Receptor, Diabetes Mellitus, Type 2, Receptors, Glucagon, Polymorphism, Single Nucleotide, Genetics, Developmental Biology
Abstract

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P

Published
2015

35. Functional characterization of the disease-associated CCL2 rs1024611G-rs13900T haplotype: The role of the RNA-binding protein HuR

Author

Akhtar, Feroz, primary, Ruiz, Joselin Hernandez, additional, Liu, Ya-Guang, additional, Resendez, Roy G., additional, Feliers, Denis, additional, Morales, Liza D., additional, Diaz-Badillo, Alvaro, additional, Lehman, Donna M., additional, Arya, Rector, additional, Lopez-Alvarenga, Juan Carlos, additional, Blangero, John, additional, Duggirala, Ravindranath, additional, and Mummidi, Srinivas, additional

Published
2024
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36. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

Author

Thompson, Paul M, Stein, Jason L, Medland, Sarah E, Hibar, Derrek P, Vasquez, Alejandro Arias, Renteria, Miguel E, Toro, Roberto, Jahanshad, Neda, Schumann, Gunter, Franke, Barbara, Wright, Margaret J, Martin, Nicholas G, Agartz, Ingrid, Alda, Martin, Alhusaini, Saud, Almasy, Laura, Almeida, Jorge, Alpert, Kathryn, Andreasen, Nancy C, Andreassen, Ole A, Apostolova, Liana G, Appel, Katja, Armstrong, Nicola J, Aribisala, Benjamin, Bastin, Mark E, Bauer, Michael, Bearden, Carrie E, Bergmann, Ørjan, Binder, Elisabeth B, Blangero, John, Bockholt, Henry J, Bøen, Erlend, Bois, Catherine, Boomsma, Dorret I, Booth, Tom, Bowman, Ian J, Bralten, Janita, Brouwer, Rachel M, Brunner, Han G, Brohawn, David G, Buckner, Randy L, Buitelaar, Jan, Bulayeva, Kazima, Bustillo, Juan R, Calhoun, Vince D, Cannon, Dara M, Cantor, Rita M, Carless, Melanie A, Caseras, Xavier, Cavalleri, Gianpiero L, Chakravarty, M Mallar, Chang, Kiki D, Ching, Christopher RK, Christoforou, Andrea, Cichon, Sven, Clark, Vincent P, Conrod, Patricia, Coppola, Giovanni, Crespo-Facorro, Benedicto, Curran, Joanne E, Czisch, Michael, Deary, Ian J, de Geus, Eco JC, den Braber, Anouk, Delvecchio, Giuseppe, Depondt, Chantal, de Haan, Lieuwe, de Zubicaray, Greig I, Dima, Danai, Dimitrova, Rali, Djurovic, Srdjan, Dong, Hongwei, Donohoe, Gary, Duggirala, Ravindranath, Dyer, Thomas D, Ehrlich, Stefan, Ekman, Carl Johan, Elvsåshagen, Torbjørn, Emsell, Louise, Erk, Susanne, Espeseth, Thomas, Fagerness, Jesen, Fears, Scott, Fedko, Iryna, Fernández, Guillén, Fisher, Simon E, Foroud, Tatiana, Fox, Peter T, Francks, Clyde, Frangou, Sophia, Frey, Eva Maria, Frodl, Thomas, Frouin, Vincent, Garavan, Hugh, Giddaluru, Sudheer, Glahn, David C, Godlewska, Beata, Goldstein, Rita Z, Gollub, Randy L, and Grabe, Hans J

Subjects
Biological Psychology, Health Sciences, Psychology, Human Genome, Genetics, Schizophrenia, Neurosciences, Biomedical Imaging, Mental Health, Mental Illness, Brain Disorders, 2.1 Biological and endogenous factors, Mental health, Neurological, Good Health and Well Being, Brain Mapping, Cooperative Behavior, Genome-Wide Association Study, Humans, Meta-Analysis as Topic, Neuroimaging, MRI, GWAS, Consortium, Meta-analysis, Multi-site, Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, IMAGEN Consortium, Saguenay Youth Study (SYS) Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Experimental Psychology, Biomedical and clinical sciences, Health sciences
Abstract

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Published
2014

38. A genetic association study of carotid intima-media thickness (CIMT) and plaque in Mexican Americans and European Americans with rheumatoid arthritis

Author

Arya, Rector, Escalante, Agustin, Farook, Vidya S., Restrepo, Jose F., Battafarano, Daniel F., Almeida, Marcio, Kos, Mark Z., Fourcaudot, Marcel J., Mummidi, Srinivas, Kumar, Satish, Curran, Joanne E., Jenkinson, Christopher P., Blangero, John, Duggirala, Ravindranath, and del Rincon, Inmaculada

Published
2018
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39. A Genome-Wide Search for Linkage of Estimated Glomerular Filtration Rate (eGFR) in the Family Investigation of Nephropathy and Diabetes (FIND)

Author

Thameem, Farook, Igo, Robert P, Freedman, Barry I, Langefeld, Carl, Hanson, Robert L, Schelling, Jeffrey R, Elston, Robert C, Duggirala, Ravindranath, Nicholas, Susanne B, Goddard, Katrina A. B, Divers, Jasmin, Guo, Xiuqing, Ipp, Eli, Kimmel, Paul L, Meoni, Lucy A, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Knowler, William C, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Kao, W. H. Linda, Rasooly, Rebekah S, Adler, Sharon G, Abboud, Hanna E, Iyengar, Sudha K, Sedor, John R, and Cai, Tao

Subjects
Chronic Kidney-Disease, Renal-Function, Mexican-Americans, Genetic-Variants, Serum Creatinine, Identifies 6, Association, Loci, Albuminuria, Traits
Published
2013

40. Genomewide linkage scan for diabetic renal failure and albuminuria: the FIND study.

Author

Igo, Robert P, Jr, Iyengar, Sudha K, Nicholas, Susanne B, Goddard, Katrina A B, Langefeld, Carl D, Hanson, Robert L, Duggirala, Ravindranath, Divers, Jasmin, Abboud, Hanna, Adler, Sharon G, Arar, Nedal H, Horvath, Amanda, Elston, Robert C, Bowden, Donald W, Guo, Xiuqing, Ipp, Eli, Kao, W H Linda, Kimmel, Paul L, Knowler, William C, Meoni, Lucy A, Molineros, Julio, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Rasooly, Rebekah S, Schelling, Jeffrey R, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Sedor, John R, and Freedman, Barry I

Subjects
Aged, Albuminuria: genetics, metabolism, Chromosome Mapping, Diabetic Nephropathies: genetics, metabolism, Ethnic Groups, Female, Genetic Linkage, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Pedigree, Polymorphism, Single Nucleotide, Renal Insufficiency: genetics, metabolism, Risk, Time Factors
Abstract

Diabetic nephropathy (DN) is a leading cause of mortality and morbidity in patients with type 1 and type 2 diabetes. The multicenter FIND consortium aims to identify genes for DN and its associated quantitative traits, e.g. the urine albumin:creatinine ratio (ACR). Herein, the results of whole-genome linkage analysis and a sparse association scan for ACR and a dichotomous DN phenotype are reported in diabetic individuals.A genomewide scan comprising more than 5,500 autosomal single nucleotide polymorphism markers (average spacing of 0.6 cM) was performed on 1,235 nuclear and extended pedigrees (3,972 diabetic participants) ascertained for DN from African-American (AA), American-Indian (AI), European-American (EA) and Mexican-American (MA) populations.Strong evidence for linkage to DN was detected on chromosome 6p (p = 8.0 × 10(-5), LOD = 3.09) in EA families as well as suggestive evidence for linkage to chromosome 7p in AI families. Regions on chromosomes 3p in AA, 7q in EA, 16q in AA and 22q in MA displayed suggestive evidence of linkage for urine ACR. The linkage peak on chromosome 22q overlaps the MYH9/APOL1 gene region, previously implicated in AA diabetic and nondiabetic nephropathies.These results strengthen the evidence for previously identified genomic regions and implicate several novel loci potentially involved in the pathogenesis of DN.

Published
2011

42. Functional characterization of the disease-associatedCCL2rs1024611G-rs13900T haplotype: The role of the RNA-binding protein HuR

Author

Akhtar, Feroz, primary, Ruiz, Joselin Hernandez, additional, Liu, Ya-Guang, additional, Resendez, Roy G., additional, Feliers, Denis, additional, Morales, Liza D., additional, Diaz-Badillo, Alvaro, additional, Lehman, Donna M., additional, Arya, Rector, additional, Lopez-Alvarenga, Juan Carlos, additional, Blangero, John, additional, Duggirala, Ravindranath, additional, and Mummidi, Srinivas, additional

Published
2023
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43. Genetics of serum carotenoid concentrations and their correlation with obesity-related traits in Mexican American children

Author

Farook, Vidya S, Reddivari, Lavanya, Mummidi, Srinivas, Puppala, Sobha, Arya, Rector, Lopez-Alvarenga, Juan Carlos, Fowler, Sharon P, Chittoor, Geetha, Resendez, Roy G, Kumar, Birunda Mohan, Comuzzie, Anthony G, Curran, Joanne E, Lehman, Donna M, Jenkinson, Christopher P, Lynch, Jane L, DeFronzo, Ralph A, Blangero, John, Hale, Daniel E, Duggirala, Ravindranath, and Vanamala, Jairam KP

Published
2017
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45. LRG1 is an adipokine that mediates obesity-induced hepatosteatosis and insulin resistance

Author

He, Sijia, Ryu, Jiyoon, Liu, Juanhong, Luo, Hairong, Lv, Ying, Langlais, Paul R., Wen, Jie, Dong, Feng, Sun, Zhe, Xia, Wenjuan, Lynch, Jane L., Duggirala, Ravindranath, Nicholson, Bruce J., Zang, Mengwei, Shi, Yuguang, Zhang, Fang, Liu, Feng, Bai, Juli, and Dong, Lily Q.

Subjects
Care and treatment, Complications and side effects, Development and progression, Genetic aspects, Health aspects, Obesity -- Genetic aspects -- Complications and side effects -- Care and treatment, Glycoproteins -- Health aspects, Insulin resistance -- Genetic aspects -- Development and progression -- Care and treatment
Abstract

Introduction Adipose tissue dysfunction plays a critical role in the development of insulin resistance (IR), a major risk factor for type 2 diabetes, fatty liver diseases, and cardiovascular complication (1, [...], Dysregulation in adipokine biosynthesis and function contributes to obesity- induced metabolic diseases. However, the identities and functions of many of the obesity-induced secretory molecules remain unknown. Here, we report the identification of leucine-rich alpha-2-glycoprotein 1 (LRG1) as an obesity- associated adipokine that exacerbates high fat diet-induced hepatosteatosis and insulin resistance. Serum levels of LRG1 were markedly elevated in obese humans and mice compared with their respective controls. LRG1 deficiency in mice greatly alleviated diet-induced hepatosteatosis, obesity, and insulin resistance. Mechanistically, LRG1 bound with high selectivity to the liver and promoted hepatosteatosis by increasing de novo lipogenesis and suppressing fatty acid p-oxidation. LRG1 also inhibited hepatic insulin signaling by downregulating insulin receptor substrates 1 and 2. Our study identified LRG1 as a key molecule that mediates the crosstalk between adipocytes and hepatocytes in diet-induced hepatosteatosis and insulin resistance. Suppressing LRG1 expression and function may be a promising strategy for the treatment of obesity-related metabolic diseases.

Published
2021
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49. List of Contributors

Author

Arya, Rector, primary, Botton, Mariana R., additional, Campos, Karla Claudio, additional, Caudle, Kelly E., additional, Cavallari, Larisa H., additional, Drozda, Katarzyna, additional, Duconge, Jorge, additional, Duggirala, Ravindranath, additional, Dunnenberger, Henry M., additional, Gammal, Roseann S., additional, Jenkinson, Christopher P., additional, Lam, Y. W. Francis, additional, Lee, Edmund Jon Deoon, additional, Limenta, Michael, additional, Mallayasamy, Surulivelrajan, additional, Mohamed, Elsa Haniffah Mejia, additional, Momary, Kathryn M., additional, Owusu Obeng, Aniwaa, additional, Penzak, Scott R., additional, Samwald, Matthias, additional, Scott, Erick R., additional, Scott, Stuart A., additional, Someya, Toshiyuki, additional, Swen, Jesse J., additional, Tan-Koi, Wei Chuen, additional, Vandell, Alexander, additional, Wallsten, Richard L., additional, and Yin, Ophelia, additional

Published
2019
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50. Erratum: Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium.

Author

Fernández-Rhodes, Lindsay, Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, Peralta Romero, Jose de Jesus, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, Hanis, Craig, Fernández-Rhodes, Lindsay, Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, Peralta Romero, Jose de Jesus, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Abstract

[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].

Published
2023

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