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1. Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies

Author

Zhang, Daiwei, Gao, Boran, Feng, Qidi, Manichaikul, Ani, Peloso, Gina M, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D, Aguet, Francois, Ardlie, Kristin G, Blackwell, Thomas W, Gerszten, Robert E, Rich, Stephen S, Rotter, Jerome I, Scott, Laura J, Zhou, Xiang, and Lee, Seunggeun

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Atherosclerosis, Prevention, Heart Disease, Cardiovascular, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Multi-omics, blood lipids, proteome-wide association studies, transcriptome-wide association studies
Abstract

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWASs) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWASs) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWASs) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWASs and TWASs can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p values across all the genes, which suggests high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Published
2025

2. 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

3. 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
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4. 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 L. R., Kelly, Shannon, Konkle, Barbara A., Kumar, Rajesh, Loos, Ruth J. F., 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

Published
2024
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5. 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, Cardiovascular, Obesity, Atherosclerosis, Heart Disease, Heart Disease - Coronary Heart Disease, Prevention, Genetics, 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

6. 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, Brain Disorders, Human Genome, Biotechnology, Aging, 2.1 Biological and endogenous factors, 2.5 Research design and methodologies (aetiology), 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

7. 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, Human Genome, Biotechnology, Cancer, Precision Medicine, Cancer Genomics, 2.5 Research design and methodologies (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

8. 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

9. 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, Health Disparities, Prevention, Atherosclerosis, Human Genome, Obesity, Minority Health, Nutrition, Genetics, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and endocrine, Oral and gastrointestinal, 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

10. Multi-ancestry epigenome-wide analyses identify methylated sites associated with aortic augmentation index in TOPMed MESA

Author

Hu, Xiaowei, Logan, Jeongok G, Kwon, Younghoon, Lima, Joao AC, Jacobs, David R, Duprez, Daniel, Brumback, Lyndia, Taylor, Kent D, Durda, Peter, Johnson, W Craig, Cornell, Elaine, Guo, Xiuqing, Liu, Yongmei, Tracy, Russell P, Blackwell, Thomas W, Papanicolaou, George, Mitchell, Gary F, Rich, Stephen S, Rotter, Jerome I, Van Den Berg, David J, Chirinos, Julio A, Hughes, Timothy M, Garrett-Bakelman, Francine E, and Manichaikul, Ani

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Minority Health, Atherosclerosis, Health Disparities, Clinical Research, Cardiovascular, Human Genome, Precision Medicine, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Epigenesis, Genetic, Epigenome, Transforming Growth Factor beta3, Genome-Wide Association Study, DNA Methylation, CpG Islands
Abstract

Despite the prognostic value of arterial stiffness (AS) and pulsatile hemodynamics (PH) for cardiovascular morbidity and mortality, epigenetic modifications that contribute to AS/PH remain unknown. To gain a better understanding of the link between epigenetics (DNA methylation) and AS/PH, we examined the relationship of eight measures of AS/PH with CpG sites and co-methylated regions using multi-ancestry participants from Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA) with sample sizes ranging from 438 to 874. Epigenome-wide association analysis identified one genome-wide significant CpG (cg20711926-CYP1B1) associated with aortic augmentation index (AIx). Follow-up analyses, including gene set enrichment analysis, expression quantitative trait methylation analysis, and functional enrichment analysis on differentially methylated positions and regions, further prioritized three CpGs and their annotated genes (cg23800023-ETS1, cg08426368-TGFB3, and cg17350632-HLA-DPB1) for AIx. Among these, ETS1 and TGFB3 have been previously prioritized as candidate genes. Furthermore, both ETS1 and HLA-DPB1 have significant tissue correlations between Whole Blood and Aorta in GTEx, which suggests ETS1 and HLA-DPB1 could be potential biomarkers in understanding pathophysiology of AS/PH. Overall, our findings support the possible role of epigenetic regulation via DNA methylation of specific genes associated with AIx as well as identifying potential targets for regulation of AS/PH.

Published
2023

11. 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

12. TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data

Author

Huang, Le, Rosen, Jonathan D, Sun, Quan, Chen, Jiawen, Wheeler, Marsha M, Zhou, Ying, Min, Yuan-I, Kooperberg, Charles, Conomos, Matthew P, Stilp, Adrienne M, Rich, Stephen S, Rotter, Jerome I, Manichaikul, Ani, Loos, Ruth JF, Kenny, Eimear E, Blackwell, Thomas W, Smith, Albert V, Jun, Goo, Sedlazeck, Fritz J, Metcalf, Ginger, Boerwinkle, Eric, Consortium, NHLBI Trans-Omics for Precision Medicine, Raffield, Laura M, Reiner, Alex P, Auer, Paul L, and Li, Yun

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Biotechnology, Human Genome, Good Health and Well Being, Asian People, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Precision Medicine, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Current publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

Published
2022

13. Monogenic and Polygenic Contributions to QTc Prolongation in the Population.

Author

Nauffal, Victor, Morrill, Valerie N, Jurgens, Sean J, Choi, Seung Hoan, Hall, Amelia W, Weng, Lu-Chen, Halford, Jennifer L, Austin-Tse, Christina, Haggerty, Christopher M, Harris, Stephanie L, Wong, Eugene K, Alonso, Alvaro, Arking, Dan E, Benjamin, Emelia J, Boerwinkle, Eric, Min, Yuan-I, Correa, Adolfo, Fornwalt, Brandon K, Heckbert, Susan R, National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium, Kooperberg, Charles, Lin, Henry J, J F Loos, Ruth, Rice, Kenneth M, Gupta, Namrata, Blackwell, Thomas W, Mitchell, Braxton D, Morrison, Alanna C, Psaty, Bruce M, Post, Wendy S, Redline, Susan, Rehm, Heidi L, Rich, Stephen S, Rotter, Jerome I, Soliman, Elsayed Z, Sotoodehnia, Nona, Lunetta, Kathryn L, Ellinor, Patrick T, Lubitz, Steven A, and TOPMed Investigators

Subjects
National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Investigators, Humans, Long QT Syndrome, Electrocardiography, Heterozygote, Multifactorial Inheritance, Genome-Wide Association Study, Whole Genome Sequencing, QT interval, long QT syndrome, monogenic, polygenic, sudden cardiac death, Human Genome, Heart Disease, Prevention, Cardiovascular, Genetics, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundRare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population.MethodsWe performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed.ResultsFifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS).ConclusionsQTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Published
2022

14. Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential

Author

Nakao, Tetsushi, Bick, Alexander G, Taub, Margaret A, Zekavat, Seyedeh M, Uddin, Md M, Niroula, Abhishek, Carty, Cara L, Lane, John, Honigberg, Michael C, Weinstock, Joshua S, Pampana, Akhil, Gibson, Christopher J, Griffin, Gabriel K, Clarke, Shoa L, Bhattacharya, Romit, Assimes, Themistocles L, Emery, Leslie S, Stilp, Adrienne M, Wong, Quenna, Broome, Jai, Laurie, Cecelia A, Khan, Alyna T, Smith, Albert V, Blackwell, Thomas W, Codd, Veryan, Nelson, Christopher P, Yoneda, Zachary T, Peralta, Juan M, Bowden, Donald W, Irvin, Marguerite R, Boorgula, Meher, Zhao, Wei, Yanek, Lisa R, Wiggins, Kerri L, Hixson, James E, Gu, C Charles, Peloso, Gina M, Roden, Dan M, Reupena, Muagututi’a S, Hwu, Chii-Min, DeMeo, Dawn L, North, Kari E, Kelly, Shannon, Musani, Solomon K, Bis, Joshua C, Lloyd-Jones, Donald M, Johnsen, Jill M, Preuss, Michael, Tracy, Russell P, Peyser, Patricia A, Qiao, Dandi, Desai, Pinkal, Curran, Joanne E, Freedman, Barry I, Tiwari, Hemant K, Chavan, Sameer, Smith, Jennifer A, Smith, Nicholas L, Kelly, Tanika N, Hidalgo, Bertha, Cupples, L Adrienne, Weeks, Daniel E, Hawley, Nicola L, Minster, Ryan L, Deka, Ranjan, Naseri, Take T, de las Fuentes, Lisa, Raffield, Laura M, Morrison, Alanna C, Vries, Paul S, Ballantyne, Christie M, Kenny, Eimear E, Rich, Stephen S, Whitsel, Eric A, Cho, Michael H, Shoemaker, M Benjamin, Pace, Betty S, Blangero, John, Palmer, Nicholette D, Mitchell, Braxton D, Shuldiner, Alan R, Barnes, Kathleen C, Redline, Susan, Kardia, Sharon LR, Abecasis, Gonçalo R, Becker, Lewis C, Heckbert, Susan R, He, Jiang, Post, Wendy, Arnett, Donna K, Vasan, Ramachandran S, Darbar, Dawood, Weiss, Scott T, McGarvey, Stephen T, de Andrade, Mariza, Chen, Yii-Der Ida, Kaplan, Robert C, Meyers, Deborah A, Custer, Brian S, and Correa, Adolfo

Subjects
Cardiovascular, Genetics, Aging, Heart Disease, Heart Disease - Coronary Heart Disease, Human Genome, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA) Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

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

Published
2022

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

Author

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

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

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

Published
2022

16. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Author

Wheeler, Marsha M, Stilp, Adrienne M, Rao, Shuquan, Halldórsson, Bjarni V, Beyter, Doruk, Wen, Jia, Mihkaylova, Anna V, McHugh, Caitlin P, Lane, John, Jiang, Min-Zhi, Raffield, Laura M, Jun, Goo, Sedlazeck, Fritz J, Metcalf, Ginger, Yao, Yao, Bis, Joshua B, Chami, Nathalie, de Vries, Paul S, Desai, Pinkal, Floyd, James S, Gao, Yan, Kammers, Kai, Kim, Wonji, Moon, Jee-Young, Ratan, Aakrosh, Yanek, Lisa R, Almasy, Laura, Becker, Lewis C, Blangero, John, Cho, Michael H, Curran, Joanne E, Fornage, Myriam, Kaplan, Robert C, Lewis, Joshua P, Loos, Ruth JF, Mitchell, Braxton D, Morrison, Alanna C, Preuss, Michael, Psaty, Bruce M, Rich, Stephen S, Rotter, Jerome I, Tang, Hua, Tracy, Russell P, Boerwinkle, Eric, Abecasis, Goncalo R, Blackwell, Thomas W, Smith, Albert V, Johnson, Andrew D, Mathias, Rasika A, Nickerson, Deborah A, Conomos, Matthew P, Li, Yun, Þorsteinsdóttir, Unnur, Magnússon, Magnús K, Stefansson, Kari, Pankratz, Nathan D, Bauer, Daniel E, Auer, Paul L, and Reiner, Alex P

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Hematology, Human Genome, Precision Medicine, Clinical Research, Health Disparities, Minority Health, Biotechnology, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Blood Cells
Abstract

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Published
2022

17. Pangenomics enables genotyping of known structural variants in 5202 diverse genomes

Author

Sirén, Jouni, Monlong, Jean, Chang, Xian, Novak, Adam M, Eizenga, Jordan M, Markello, Charles, Sibbesen, Jonas A, Hickey, Glenn, Chang, Pi-Chuan, Carroll, Andrew, Gupta, Namrata, Gabriel, Stacey, Blackwell, Thomas W, Ratan, Aakrosh, Taylor, Kent D, Rich, Stephen S, Rotter, Jerome I, Haussler, David, Garrison, Erik, and Paten, Benedict

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Generic health relevance, Algorithms, Alleles, Computational Biology, Genetic Variation, Genome, Fungal, Genome, Human, Genomics, Genotype, Genotyping Techniques, Haplotypes, High-Throughput Nucleotide Sequencing, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Saccharomyces, Saccharomyces cerevisiae, Sequence Analysis, DNA, General Science & Technology
Abstract

We introduce Giraffe, a pangenome short-read mapper that can efficiently map to a collection of haplotypes threaded through a sequence graph. Giraffe maps sequencing reads to thousands of human genomes at a speed comparable to that of standard methods mapping to a single reference genome. The increased mapping accuracy enables downstream improvements in genome-wide genotyping pipelines for both small variants and larger structural variants. We used Giraffe to genotype 167,000 structural variants, discovered in long-read studies, in 5202 diverse human genomes that were sequenced using short reads. We conclude that pangenomics facilitates a more comprehensive characterization of variation and, as a result, has the potential to improve many genomic analyses.

Published
2021

18. Association of mitochondrial DNA copy number with cardiometabolic diseases

Author

Liu, Xue, Longchamps, Ryan J, Wiggins, Kerri L, Raffield, Laura M, Bielak, Lawrence F, Zhao, Wei, Pitsillides, Achilleas, Blackwell, Thomas W, Yao, Jie, Guo, Xiuqing, Kurniansyah, Nuzulul, Thyagarajan, Bharat, Pankratz, Nathan, Rich, Stephen S, Taylor, Kent D, Peyser, Patricia A, Heckbert, Susan R, Seshadri, Sudha, Cupples, L Adrienne, Boerwinkle, Eric, Grove, Megan L, Larson, Nicholas B, Smith, Jennifer A, Vasan, Ramachandran S, Sofer, Tamar, Fitzpatrick, Annette L, Fornage, Myriam, Ding, Jun, Correa, Adolfo, Abecasis, Goncalo, 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
Biological Sciences, Genetics, Aging, Metabolic and endocrine, Generic health relevance, Good Health and Well Being, TOPMed mtDNA Working Group in NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

Mitochondrial DNA (mtDNA) is present in multiple copies in human cells. We evaluated cross-sectional associations of whole blood mtDNA copy number (CN) with several cardiometabolic disease traits in 408,361 participants of multiple ancestries in TOPMed and UK Biobank. Age showed a threshold association with mtDNA CN: among younger participants (

Published
2021

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

Author

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

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

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

Published
2021

20. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Author

Kasela, Silva, Aguet, François, Kim-Hellmuth, Sarah, Brown, Brielin C., Nachun, Daniel C., Tracy, Russell P., Durda, Peter, Liu, Yongmei, Taylor, Kent D., Johnson, W. Craig, Van Den Berg, David, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D., Blackwell, Thomas W., Rotter, Jerome I., Ardlie, Kristin G., Manichaikul, Ani, Rich, Stephen S., Barr, R. Graham, and Lappalainen, Tuuli

Published
2024
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21. Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Author

Kwong, Alan M, Blackwell, Thomas W, LeFaive, Jonathon, de Andrade, Mariza, Barnard, John, Barnes, Kathleen C, Blangero, John, Boerwinkle, Eric, Burchard, Esteban G, Cade, Brian E, Chasman, Daniel I, Chen, Han, Conomos, Matthew P, Cupples, L Adrienne, Ellinor, Patrick T, Eng, Celeste, Gao, Yan, Guo, Xiuqing, Irvin, Marguerite Ryan, Kelly, Tanika N, Kim, Wonji, Kooperberg, Charles, Lubitz, Steven A, Mak, Angel CY, Manichaikul, Ani W, Mathias, Rasika A, Montasser, May E, Montgomery, Courtney G, Musani, Solomon, Palmer, Nicholette D, Peloso, Gina M, Qiao, Dandi, Reiner, Alexander P, Roden, Dan M, Shoemaker, M Benjamin, Smith, Jennifer A, Smith, Nicholas L, Su, Jessica Lasky, Tiwari, Hemant K, Weeks, Daniel E, Weiss, Scott T, Consortium, TOPMed Analysis Working Group NHLBI Trans-Omics for Precision Medicine, Scott, Laura J, Smith, Albert V, Abecasis, Gonçalo R, Boehnke, Michael, and Kang, Hyun Min

Subjects
Genetics, Alleles, Gene Frequency, Genetics, Population, Genotype, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Phenotype, Software, population structure, principal components analysis, next-generation sequencing, genotype likelihoods, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group, Developmental Biology
Abstract

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Published
2021

22. 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

23. Mapping the 17q12-21.1 Locus for Variants Associated with Early-Onset Asthma in African Americans.

Author

Gui, Hongsheng, Levin, Albert M, Hu, Donglei, Sleiman, Patrick, Xiao, Shujie, Mak, Angel CY, Yang, Mao, Barczak, Andrea J, Huntsman, Scott, Eng, Celeste, Hochstadt, Samantha, Zhang, Ellen, Whitehouse, Kyle, Simons, Samantha, Cabral, Whitney, Takriti, Sami, Abecasis, Gonçalo, Blackwell, Thomas W, Kang, Hyun Min, Nickerson, Deborah A, Germer, Soren, Lanfear, David E, Gilliland, Frank, Gauderman, W James, Kumar, Rajesh, Erle, David J, Martinez, Fernando D, Hakonarson, Hakon, Burchard, Esteban G, and Williams, L Keoki

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Lung, Genetics, Asthma, Clinical Research, Minority Health, Human Genome, 2.1 Biological and endogenous factors, Respiratory, Adolescent, Adult, Black or African American, Age of Onset, Child, Child, Preschool, Chromosome Mapping, Chromosomes, Human, Pair 17, Female, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Humans, Infant, Infant, Newborn, Linkage Disequilibrium, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, United States, White People, Young Adult, asthma, African Americans, chromosome 17, GSDMB, ORMDL3, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Rationale: The 17q12-21.1 locus is one of the most highly replicated genetic associations with asthma. Individuals of African descent have lower linkage disequilibrium in this region, which could facilitate identifying causal variants.Objectives: To identify functional variants at 17q12-21.1 associated with early-onset asthma among African American individuals.Methods: We evaluated African American participants from SAPPHIRE (Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity) (n = 1,940), SAGE II (Study of African Americans, Asthma, Genes and Environment) (n = 885), and GCPD-A (Study of the Genetic Causes of Complex Pediatric Disorders-Asthma) (n = 2,805). Associations with asthma onset at ages under 5 years were meta-analyzed across cohorts. The lead signal was reevaluated considering haplotypes informed by genetic ancestry (i.e., African vs. European). Both an expression-quantitative trait locus analysis and a phenome-wide association study were performed on the lead variant.Measurements and Main Results: The meta-analyzed results from SAPPHIRE, SAGE II, and the GCPD-A identified rs11078928 as the top association for early-onset asthma. A haplotype analysis suggested that the asthma association partitioned most closely with the rs11078928 genotype. Genetic ancestry did not appear to influence the effect of this variant. In the expression-quantitative trait locus analysis, rs11078928 was related to alternative splicing of GSDMB (gasdermin-B) transcripts. The phenome-wide association study of rs11078928 suggested that this variant was predominantly associated with asthma and asthma-associated symptoms.Conclusions: A splice-acceptor polymorphism appears to be a causal variant for asthma at the 17q12-21.1 locus. This variant appears to have the same magnitude of effect in individuals of African and European descent.

Published
2021

24. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Taliun, Daniel, Harris, Daniel N, Kessler, Michael D, Carlson, Jedidiah, Szpiech, Zachary A, Torres, Raul, Taliun, Sarah A Gagliano, Corvelo, André, Gogarten, Stephanie M, Kang, Hyun Min, Pitsillides, Achilleas N, LeFaive, Jonathon, Lee, Seung-been, Tian, Xiaowen, Browning, Brian L, Das, Sayantan, Emde, Anne-Katrin, Clarke, Wayne E, Loesch, Douglas P, Shetty, Amol C, Blackwell, Thomas W, Smith, Albert V, Wong, Quenna, Liu, Xiaoming, Conomos, Matthew P, Bobo, Dean M, Aguet, François, Albert, Christine, Alonso, Alvaro, Ardlie, Kristin G, Arking, Dan E, Aslibekyan, Stella, Auer, Paul L, Barnard, John, Barr, R Graham, Barwick, Lucas, Becker, Lewis C, Beer, Rebecca L, Benjamin, Emelia J, Bielak, Lawrence F, Blangero, John, Boehnke, Michael, Bowden, Donald W, Brody, Jennifer A, Burchard, Esteban G, Cade, Brian E, Casella, James F, Chalazan, Brandon, Chasman, Daniel I, Chen, Yii-Der Ida, Cho, Michael H, Choi, Seung Hoan, Chung, Mina K, Clish, Clary B, Correa, Adolfo, Curran, Joanne E, Custer, Brian, Darbar, Dawood, Daya, Michelle, de Andrade, Mariza, DeMeo, Dawn L, Dutcher, Susan K, Ellinor, Patrick T, Emery, Leslie S, Eng, Celeste, Fatkin, Diane, Fingerlin, Tasha, Forer, Lukas, Fornage, Myriam, Franceschini, Nora, Fuchsberger, Christian, Fullerton, Stephanie M, Germer, Soren, Gladwin, Mark T, Gottlieb, Daniel J, Guo, Xiuqing, Hall, Michael E, He, Jiang, Heard-Costa, Nancy L, Heckbert, Susan R, Irvin, Marguerite R, Johnsen, Jill M, Johnson, Andrew D, Kaplan, Robert, Kardia, Sharon LR, Kelly, Tanika, Kelly, Shannon, Kenny, Eimear E, Kiel, Douglas P, Klemmer, Robert, Konkle, Barbara A, Kooperberg, Charles, Köttgen, Anna, Lange, Leslie A, Lasky-Su, Jessica, Levy, Daniel, Lin, Xihong, Lin, Keng-Han, Liu, Chunyu, and Loos, Ruth JF

Subjects
Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Cytochrome P-450 CYP2D6, Genetic Variation, Genome, Human, Genomics, Haplotypes, Heterozygote, Humans, INDEL Mutation, Loss of Function Mutation, Mutagenesis, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Population Density, Precision Medicine, Quality Control, Sample Size, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, General Science & Technology
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Published
2021

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

Author

Weinstock, Joshua S., Gopakumar, Jayakrishnan, Burugula, Bala Bharathi, Uddin, Md Mesbah, Jahn, Nikolaus, Belk, Julia A., Bouzid, Hind, Daniel, Bence, Miao, Zhuang, Ly, Nghi, Mack, Taralynn M., Luna, Sofia E., Prothro, Katherine P., Mitchell, Shaneice R., Laurie, Cecelia A., Broome, Jai G., Taylor, Kent D., Guo, Xiuqing, Sinner, Moritz F., von Falkenhausen, Aenne S., Kääb, Stefan, Shuldiner, Alan R., O’Connell, Jeffrey R., Lewis, Joshua P., Boerwinkle, Eric, Barnes, Kathleen C., Chami, Nathalie, Kenny, Eimear E., Loos, Ruth J. F., Fornage, Myriam, Hou, Lifang, Lloyd-Jones, Donald M., Redline, Susan, Cade, Brian E., Psaty, Bruce M., Bis, Joshua C., Brody, Jennifer A., Silverman, Edwin K., Yun, Jeong H., Qiao, Dandi, Palmer, Nicholette D., Freedman, Barry I., Bowden, Donald W., Cho, Michael H., DeMeo, Dawn L., Vasan, Ramachandran S., Yanek, Lisa R., Becker, Lewis C., Kardia, Sharon L. R., Peyser, Patricia A., He, Jiang, Rienstra, Michiel, Van der Harst, Pim, Kaplan, Robert, Heckbert, Susan R., Smith, Nicholas L., Wiggins, Kerri L., Arnett, Donna K., Irvin, Marguerite R., Tiwari, Hemant, Cutler, Michael J., Knight, Stacey, Muhlestein, J. Brent, Correa, Adolfo, Raffield, Laura M., Gao, Yan, de Andrade, Mariza, Rotter, Jerome I., Rich, Stephen S., Tracy, Russell P., Konkle, Barbara A., Johnsen, Jill M., Wheeler, Marsha M., Smith, J. Gustav, Melander, Olle, Nilsson, Peter M., Custer, Brian S., Duggirala, Ravindranath, Curran, Joanne E., Blangero, John, McGarvey, Stephen, Williams, L. Keoki, Xiao, Shujie, Yang, Mao, Gu, C. Charles, Chen, Yii-Der Ida, Lee, Wen-Jane, Marcus, Gregory M., Kane, John P., Pullinger, Clive R., Shoemaker, M. Benjamin, Darbar, Dawood, Roden, Dan M., Albert, Christine, Kooperberg, Charles, Zhou, Ying, Manson, JoAnn E., Desai, Pinkal, Johnson, Andrew D., Mathias, Rasika A., Blackwell, Thomas W., Abecasis, Goncalo R., Smith, Albert V., Kang, Hyun M., Satpathy, Ansuman T., Natarajan, Pradeep, Kitzman, Jacob O., Whitsel, Eric A., Reiner, Alexander P., Bick, Alexander G., and Jaiswal, Siddhartha

Published
2023
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26. Identification of CFTR variants in Latino patients with cystic fibrosis from the Dominican Republic and Puerto Rico

Author

Zeiger, Andrew M, McGarry, Meghan E, Mak, Angel CY, Medina, Vivian, Salazar, Sandra, Eng, Celeste, Liu, Amy K, Oh, Sam S, Nuckton, Thomas J, Jain, Deepti, Blackwell, Thomas W, Kang, Hyun Min, Abecasis, Goncalo, Oñate, Leandra Cordero, Seibold, Max A, Burchard, Esteban G, and Rodriguez‐Santana, Jose

Subjects
Paediatrics, Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Cystic Fibrosis, Rare Diseases, Clinical Research, Genetics, Lung, Congenital, Good Health and Well Being, Adolescent, Base Sequence, Black People, Cystic Fibrosis Transmembrane Conductance Regulator, Dominican Republic, Female, Hispanic or Latino, Humans, Male, Puerto Rico, White People, CFTR variants, health disparities, Latino, whole genome sequencing, Paediatrics and Reproductive Medicine, Respiratory System, Cardiovascular medicine and haematology
Abstract

BackgroundIn cystic fibrosis (CF), the spectrum and frequency of CFTR variants differ by geography and race/ethnicity. CFTR variants in White patients are well-described compared with Latino patients. No studies of CFTR variants have been done in patients with CF in the Dominican Republic or Puerto Rico.MethodsCFTR was sequenced in 61 Dominican Republican patients and 21 Puerto Rican patients with CF and greater than ​​​​60 mmol/L sweat chloride. The spectrum of CFTR variants was identified and the proportion of patients with 0, 1, or 2 CFTR variants identified was determined. The functional effects of identified CFTR variants were investigated using clinical annotation databases and computational prediction tools.ResultsOur study found 10% of Dominican patients had two CFTR variants identified compared with 81% of Puerto Rican patients. No CFTR variants were identified in 69% of Dominican patients and 10% of Puerto Rican patients. In Dominican patients, there were 19 identified CFTR variants, accounting for 25 out of 122 disease alleles (20%). In Puerto Rican patients, there were 16 identified CFTR variants, accounting for 36 out of 42 disease alleles (86%) in Puerto Rican patients. Thirty CFTR variants were identified overall. The most frequent variants for Dominican patients were p.Phe508del and p.Ala559Thr and for Puerto Rican patients were p.Phe508del, p.Arg1066Cys, p.Arg334Trp, and p.I507del.ConclusionsIn this first description of the CFTR variants in patients with CF from the Dominican Republic and Puerto Rico, there was a low detection rate of two CFTR variants after full sequencing with the majority of patients from the Dominican Republic without identified variants.

Published
2020

27. Genotyping common, large structural variations in 5,202 genomes using pangenomes, the Giraffe mapper, and the vg toolkit

Author

Sirén, Jouni, Monlong, Jean, Chang, Xian, Novak, Adam M, Eizenga, Jordan M, Markello, Charles, Sibbesen, Jonas A, Hickey, Glenn, Chang, Pi-Chuan, Carroll, Andrew, Gupta, Namrata, Gabriel, Stacey, Blackwell, Thomas W, Ratan, Aakrosh, Taylor, Kent D, Rich, Stephen S, Rotter, Jerome I, Haussler, David, Garrison, Erik, and Paten, Benedict

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome
Abstract

ABSTRACT We introduce Giraffe, a pangenome short read mapper that can efficiently map to a collection of haplotypes threaded through a sequence graph. Giraffe, part of the variation graph toolkit (vg) 1 , maps reads to thousands of human genomes at around the same speed BWA-MEM 2 maps reads to a single reference genome, while maintaining comparable accuracy to VG-MAP, vg’s original mapper. We have developed efficient genotyping pipelines using Giraffe. We demonstrate improvements in genotyping for single-nucleotide variants (SNVs), small insertions and deletions (indels) and structural variations (SVs) genome-wide. We use Giraffe to genotype about 167 thousand structural variants ascertained from long read studies in 5,202 human genomes sequenced with short reads, including the complete 1000 Genomes Project dataset, at an average cost of $1.50 per sample. We determine the frequency of these variations in diverse human populations, characterize their complex allelic variations and identify thousands of expression quantitative trait loci (eQTLs) driven by these variations.

Published
2020

28. Asthma and its relationship to mitochondrial copy number: Results from the Asthma Translational Genomics Collaborative (ATGC) of the Trans-Omics for Precision Medicine (TOPMed) program.

Author

Cocco, Maxwell P, White, Evan, Xiao, Shujie, Hu, Donglei, Mak, Angel, Sleiman, Patrick, Yang, Mao, Bobbitt, Kevin R, Gui, Hongsheng, Levin, Albert M, Hochstadt, Samantha, Whitehouse, Kyle, Rynkowski, Dean, Barczak, Andrea J, Abecasis, Gonçalo, Blackwell, Thomas W, Kang, Hyun Min, Nickerson, Deborah A, Germer, Soren, Ding, Jun, Lanfear, David E, Gilliland, Frank, Gauderman, W James, Kumar, Rajesh, Erle, David J, Martinez, Fernando, Hakonarson, Hakon, Burchard, Esteban G, and Williams, L Keoki

Subjects
Leukocytes, Humans, Asthma, Electron Transport Chain Complex Proteins, DNA, Mitochondrial, RNA, Flow Cytometry, Logistic Models, Proportional Hazards Models, Sensitivity and Specificity, Cohort Studies, Base Sequence, Adult, Middle Aged, African Americans, Female, Male, Young Adult, DNA Copy Number Variations, Translational Medical Research, Whole Genome Sequencing, Human Genome, Biotechnology, Lung, Clinical Research, Genetics, Respiratory, General Science & Technology
Abstract

BackgroundMitochondria support critical cellular functions, such as energy production through oxidative phosphorylation, regulation of reactive oxygen species, apoptosis, and calcium homeostasis.ObjectiveGiven the heightened level of cellular activity in patients with asthma, we sought to determine whether mitochondrial DNA (mtDNA) copy number measured in peripheral blood differed between individuals with and without asthma.MethodsWhole genome sequence data was generated as part of the Trans-Omics for Precision Medicine (TOPMed) Program on participants from the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) and the Study of African Americans, Asthma, Genes, & Environment II (SAGE II). We restricted our analysis to individuals who self-identified as African American (3,651 asthma cases and 1,344 controls). Mitochondrial copy number was estimated using the sequencing read depth ratio for the mitochondrial and nuclear genomes. Respiratory complex expression was assessed using RNA-sequencing.ResultsAverage mitochondrial copy number was significantly higher among individuals with asthma when compared with controls (SAPPHIRE: 218.60 vs. 200.47, P

Published
2020

29. 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

30. Presence and transmission of mitochondrial heteroplasmic mutations in human populations of European and African ancestry

Author

Liu, Chunyu, Fetterman, Jessica L., Qian, Yong, Sun, Xianbang, Blackwell, Thomas W., Pitsillides, Achilleas, Cade, Brian E., Wang, Heming, Raffield, Laura M., Lange, Leslie A., Anugu, Pramod, Abecasis, Goncalo, Adrienne Cupples, L, Redline, Susan, Correa, Adolfo, Vasan, Ramachandran S., Wilson, James G., Ding, Jun, and Levy, Daniel

Published
2021
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31. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

32. 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

33. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Genetic Variation, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

34. A Low-Frequency Inactivating Akt2 Variant Enriched in the Finnish Population is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk.

Author

Manning, Alisa, Highland, Heather M, Gasser, Jessica, Sim, Xueling, Tukiainen, Taru, Fontanillas, Pierre, Grarup, Niels, Rivas, Manuel A, Mahajan, Anubha, Locke, Adam E, Cingolani, Pablo, Pers, Tune H, Viñuela, Ana, Brown, Andrew A, Wu, Ying, Flannick, Jason, Fuchsberger, Christian, Gamazon, Eric R, Gaulton, Kyle J, Im, Hae Kyung, Teslovich, Tanya M, Blackwell, Thomas W, Bork-Jensen, Jette, Burtt, Noël P, Chen, Yuhui, Green, Todd, Hartl, Christopher, Kang, Hyun Min, Kumar, Ashish, Ladenvall, Claes, Ma, Clement, Moutsianas, Loukas, Pearson, Richard D, Perry, John RB, Rayner, N William, Robertson, Neil R, Scott, Laura J, van de Bunt, Martijn, Eriksson, Johan G, Jula, Antti, Koskinen, Seppo, Lehtimäki, Terho, Palotie, Aarno, Raitakari, Olli T, Jacobs, Suzanne BR, Wessel, Jennifer, Chu, Audrey Y, Scott, Robert A, Goodarzi, Mark O, Blancher, Christine, Buck, Gemma, Buck, David, Chines, Peter S, 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, Trakalo, Joseph, Banks, Eric, Carey, Jason, Carneiro, Mauricio O, DePristo, Mark, Farjoun, Yossi, Fennell, Timothy, Goldstein, Jacqueline I, Grant, George, Hrabé de Angelis, Martin, Maguire, Jared, Neale, Benjamin M, Poplin, Ryan, Purcell, Shaun, Schwarzmayr, Thomas, Shakir, Khalid, Smith, Joshua D, Strom, Tim M, Wieland, Thomas, Lindstrom, Jaana, Brandslund, Ivan, Christensen, Cramer, Surdulescu, Gabriela L, Lakka, Timo A, Doney, Alex SF, Nilsson, Peter, Wareham, Nicholas J, Langenberg, Claudia, Varga, Tibor V, Franks, Paul W, Rolandsson, Olov, Rosengren, Anders H, and Farook, Vidya S

Subjects
Genetics, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Black or African American, Alleles, Asian People, Case-Control Studies, Diabetes Mellitus, Type 2, Fasting, Finland, Gene Frequency, Genetic Predisposition to Disease, Genotype, Hispanic or Latino, Humans, Insulin, Insulin Resistance, Odds Ratio, Proto-Oncogene Proteins c-akt, White People, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Published
2017

35. Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Author

Zhang, Daiwei, primary, Gao, Boran, additional, Feng, Qidi, additional, Manichaikul, Ani W., additional, Peloso, Gina M., additional, Tracy, Russell, additional, Durda, Peter, additional, Taylor, Kent, additional, Liu, Yongmei, additional, Johnson, W. Craig, additional, Gabriel, Stacey, additional, Gupta, Namrata, additional, Smith, Joshua D., additional, Aguet, Francois, additional, Ardlie, Kristin G., additional, Blackwell, Thomas W., additional, Gerszten, Robert E., additional, Rich, Stephen S., additional, Rotter, Jerome I., additional, Scott, Laura, additional, Zhou, Xiang, additional, and Lee, Seunggeun, additional

Published
2023
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36. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Author

Kasela, Silva, primary, Aguet, François, additional, Kim-Hellmuth, Sarah, additional, Brown, Brielin C., additional, Nachun, Daniel C., additional, Tracy, Russell P., additional, Durda, Peter, additional, Liu, Yongmei, additional, Taylor, Kent D., additional, Johnson, W. Craig, additional, Van Den Berg, David, additional, Gabriel, Stacey, additional, Gupta, Namrata, additional, Smith, Joshua D., additional, Blackwell, Thomas W., additional, Rotter, Jerome I., additional, Ardlie, Kristin G., additional, Manichaikul, Ani, additional, Rich, Stephen S., additional, Barr, R. Graham, additional, and Lappalainen, Tuuli, additional

Published
2023
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37. 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., Lappalainen, Tuuli, 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

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., QC 20231003

Published
2023
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38. Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis

Author

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

Published
2022
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39. Association Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk.

Author

Xue Liu, Xianbang Sun, Yuankai Zhang, Wenqing Jiang, Meng Lai, Wiggins, Kerri L., Raffield, Laura M., Bielak, Lawrence F., Wei Zhao, Pitsillides, Achilleas, Haessler, Jeffrey, Yinan Zheng, Blackwell, Thomas W., Jie Yao, Xiuqing Guo, Yong Qian, Thyagarajan, Bharat, Pankratz, Nathan, Rich, Stephen S., and Taylor, Kent D.

Published
2023
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40. The genetic architecture of type 2 diabetes

Author

Fuchsberger, Christian, Flannick, Jason, Teslovich, Tanya M., Mahajan, Anubha, Agarwala, Vineeta, Gaulton, Kyle J., Ma, Clement, Fontanillas, Pierre, Moutsianas, Loukas, McCarthy, Davis J., Rivas, Manuel A., Perry, John R. B., Sim, Xueling, Blackwell, Thomas W., Robertson, Neil R., Rayner, N. William, Cingolani, Pablo, Locke, Adam E., Tajes, Juan Fernandez, Highland, Heather M., Dupuis, Josee, Chines, Peter S., Lindgren, Cecilia M., Hartl, Christopher, Jackson, Anne U., Chen, Han, Huyghe, Jeroen R., van de Bunt, Martijn, Pearson, Richard D., Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M., Gamazon, Eric R., Lee, Jaehoon, Chen, Yuhui, Scott, Robert A., Below, Jennifer E., Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L., Pasko, Dorota, Parker, Stephen C. J., Varga, Tibor V., Green, Todd, Beer, Nicola L., Day-Williams, Aaron G., Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J., Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P., Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F., Han, Bok-Ghee, Jenkinson, Christopher P., Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie C. Y., Palmer, Nicholette D., Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E., Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D., Neale, Benjamin M., Purcell, Shaun, Butterworth, Adam S., Howson, Joanna M. M., Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, Lam, Vincent K. L., Park, Kyong Soo, Saleheen, Danish, So, Wing Yee, Tam, Claudia H. T., Afzal, Uzma, Aguilar, David, Arya, Rector, Aung, Tin, Chan, Edmund, Navarro, Carmen, Cheng, Ching-Yu, Palli, Domenico, Correa, Adolfo, Curran, Joanne E., Rybin, Denis, Farook, Vidya S., Fowler, Sharon P., Freedman, Barry I., Griswold, Michael, Hale, Daniel Esten, Hicks, Pamela J., Khor, Chiea-Chuen, Kumar, Satish, Lehne, Benjamin, Thuillier, Dorothée, Lim, Wei Yen, Liu, Jianjun, van der Schouw, Yvonne T., Loh, Marie, Musani, Solomon K., Puppala, Sobha, Scott, William R., Yengo, Loïc, Tan, Sian-Tsung, Taylor Jr., Herman A., Thameem, Farook, Wilson, Gregory, Wong, Tien Yin, Njølstad, Pål Rasmus, Levy, Jonathan C., Mangino, Massimo, Bonnycastle, Lori L., Schwarzmayr, Thomas, Fadista, João, Surdulescu, Gabriela L., Herder, Christian, Groves, Christopher J., Wieland, Thomas, Bork-Jensen, Jette, Brandslund, Ivan, Christensen, Cramer, Koistinen, Heikki A., Doney, Alex S. F., Kinnunen, Leena, Esko, Tõnu, Farmer, Andrew J., Hakaste, Liisa, Hodgkiss, Dylan, Kravic, Jasmina, Lyssenko, Valeriya, Hollensted, Mette, Jørgensen, Marit E., Jørgensen, Torben, Ladenvall, Claes, Justesen, Johanne Marie, Käräjämäki, Annemari, Kriebel, Jennifer, Rathmann, Wolfgang, Lannfelt, Lars, Lauritzen, Torsten, Narisu, Narisu, Linneberg, Allan, Melander, Olle, Milani, Lili, Neville, Matt, Orho-Melander, Marju, Qi, Lu, Qi, Qibin, Roden, Michael, Rolandsson, Olov, Swift, Amy, Rosengren, Anders H., Stirrups, Kathleen, Wood, Andrew R., Mihailov, Evelin, Blancher, Christine, Carneiro, Mauricio O., Maguire, Jared, Poplin, Ryan, Shakir, Khalid, Fennell, Timothy, DePristo, Mark, Hrabé de Angelis, Martin, Deloukas, Panos, Gjesing, Anette P., Jun, Goo, Nilsson, Peter, Murphy, Jacquelyn, Onofrio, Robert, Thorand, Barbara, Hansen, Torben, Meisinger, Christa, Hu, Frank B., Isomaa, Bo, Karpe, Fredrik, Liang, Liming, Peters, Annette, Huth, Cornelia, O’Rahilly, Stephen P., Palmer, Colin N. A., Pedersen, Oluf, Rauramaa, Rainer, Tuomilehto, Jaakko, Salomaa, Veikko, Watanabe, Richard M., Syvänen, Ann-Christine, Bergman, Richard N., Bharadwaj, Dwaipayan, Bottinger, Erwin P., Cho, Yoon Shin, Chandak, Giriraj R., Chan, Juliana C. N., Chia, Kee Seng, Daly, Mark J., Ebrahim, Shah B., Langenberg, Claudia, Elliott, Paul, Jablonski, Kathleen A., Lehman, Donna M., Jia, Weiping, Ma, Ronald C. W., Pollin, Toni I., Sandhu, Manjinder, Tandon, Nikhil, Froguel, Philippe, Barroso, Inês, Teo, Yik Ying, Zeggini, Eleftheria, Loos, Ruth J. F., Small, Kerrin S., Ried, Janina S., DeFronzo, Ralph A., Grallert, Harald, Glaser, Benjamin, Metspalu, Andres, Wareham, Nicholas J., Walker, Mark, Banks, Eric, Gieger, Christian, Ingelsson, Erik, Im, Hae Kyung, Illig, Thomas, Franks, Paul W., Buck, Gemma, Trakalo, Joseph, Buck, David, Prokopenko, Inga, Mägi, Reedik, Lind, Lars, Farjoun, Yossi, Owen, Katharine R., Gloyn, Anna L., Strauch, Konstantin, Tuomi, Tiinamaija, Kooner, Jaspal Singh, Lee, Jong-Young, Park, Taesung, Donnelly, Peter, Morris, Andrew D., Hattersley, Andrew T., Bowden, Donald W., Collins, Francis S., Atzmon, Gil, Chambers, John C., Spector, Timothy D., Laakso, Markku, Strom, Tim M., Bell, Graeme I., Blangero, John, Duggirala, Ravindranath, Tai, E. Shyong, McVean, Gilean, Hanis, Craig L., Wilson, James G., Seielstad, Mark, Frayling, Timothy M., Meigs, James B., Cox, Nancy J., Sladek, Rob, Lander, Eric S., Gabriel, Stacey, Burtt, Noël P., Mohlke, Karen L., Meitinger, Thomas, Groop, Leif, Abecasis, Goncalo, Florez, Jose C., Scott, Laura J., Morris, Andrew P., Kang, Hyun Min, Boehnke, Michael, Altshuler, David, and McCarthy, Mark I.

Published
2016
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41. The functional impact of rare variation across the regulatory cascade

Author

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

Published
2022
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42. Multiset correlation and factor analysis enables exploration of multi-omic data

Author

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

Published
2022
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43. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Author

Wheeler, Marsha M., Stilp, Adrienne M., Rao, Shuquan, Halldórsson, Bjarni V., Beyter, Doruk, Wen, Jia, Mihkaylova, Anna V., McHugh, Caitlin P., Lane, John, Jiang, Min Zhi, Raffield, Laura M., Jun, Goo, Sedlazeck, Fritz J., Metcalf, Ginger, Yao, Yao, Bis, Joshua B., Chami, Nathalie, de Vries, Paul S., Desai, Pinkal, Floyd, James S., Gao, Yan, Kammers, Kai, Kim, Wonji, Moon, Jee Young, Ratan, Aakrosh, Yanek, Lisa R., Almasy, Laura, Becker, Lewis C., Blangero, John, Cho, Michael H., Curran, Joanne E., Fornage, Myriam, Kaplan, Robert C., Lewis, Joshua P., Loos, Ruth J.F., Mitchell, Braxton D., Morrison, Alanna C., Preuss, Michael, Psaty, Bruce M., Rich, Stephen S., Rotter, Jerome I., Tang, Hua, Tracy, Russell P., Boerwinkle, Eric, Abecasis, Goncalo R., Blackwell, Thomas W., Smith, Albert V., Johnson, Andrew D., Mathias, Rasika A., Nickerson, Deborah A., Conomos, Matthew P., Li, Yun, Þorsteinsdóttir, Unnur, Magnússon, Magnús K., Stefansson, Kari, Pankratz, Nathan D., Bauer, Daniel E., Auer, Paul L., Reiner, Alex P., Wheeler, Marsha M., Stilp, Adrienne M., Rao, Shuquan, Halldórsson, Bjarni V., Beyter, Doruk, Wen, Jia, Mihkaylova, Anna V., McHugh, Caitlin P., Lane, John, Jiang, Min Zhi, Raffield, Laura M., Jun, Goo, Sedlazeck, Fritz J., Metcalf, Ginger, Yao, Yao, Bis, Joshua B., Chami, Nathalie, de Vries, Paul S., Desai, Pinkal, Floyd, James S., Gao, Yan, Kammers, Kai, Kim, Wonji, Moon, Jee Young, Ratan, Aakrosh, Yanek, Lisa R., Almasy, Laura, Becker, Lewis C., Blangero, John, Cho, Michael H., Curran, Joanne E., Fornage, Myriam, Kaplan, Robert C., Lewis, Joshua P., Loos, Ruth J.F., Mitchell, Braxton D., Morrison, Alanna C., Preuss, Michael, Psaty, Bruce M., Rich, Stephen S., Rotter, Jerome I., Tang, Hua, Tracy, Russell P., Boerwinkle, Eric, Abecasis, Goncalo R., Blackwell, Thomas W., Smith, Albert V., Johnson, Andrew D., Mathias, Rasika A., Nickerson, Deborah A., Conomos, Matthew P., Li, Yun, Þorsteinsdóttir, Unnur, Magnússon, Magnús K., Stefansson, Kari, Pankratz, Nathan D., Bauer, Daniel E., Auer, Paul L., and Reiner, Alex P.

Abstract

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Published
2022

44. TOP-LD:A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data

Author

Huang, Le, Rosen, Jonathan D., Sun, Quan, Chen, Jiawen, Wheeler, Marsha M., Zhou, Ying, Min, Yuan I., Kooperberg, Charles, Conomos, Matthew P., Stilp, Adrienne M., Rich, Stephen S., Rotter, Jerome I., Manichaikul, Ani, Loos, Ruth J.F., Kenny, Eimear E., Blackwell, Thomas W., Smith, Albert V., Jun, Goo, Sedlazeck, Fritz J., Metcalf, Ginger, Boerwinkle, Eric, Raffield, Laura M., Reiner, Alex P., Auer, Paul L., Li, Yun, Huang, Le, Rosen, Jonathan D., Sun, Quan, Chen, Jiawen, Wheeler, Marsha M., Zhou, Ying, Min, Yuan I., Kooperberg, Charles, Conomos, Matthew P., Stilp, Adrienne M., Rich, Stephen S., Rotter, Jerome I., Manichaikul, Ani, Loos, Ruth J.F., Kenny, Eimear E., Blackwell, Thomas W., Smith, Albert V., Jun, Goo, Sedlazeck, Fritz J., Metcalf, Ginger, Boerwinkle, Eric, Raffield, Laura M., Reiner, Alex P., Auer, Paul L., and Li, Yun

Abstract

Current publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

Published
2022

45. Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Author

Taub, Margaret A., Conomos, Matthew P., Keener, Rebecca, Iyer, Kruthika R., Weinstock, Joshua S., Yanek, Lisa R., Lane, John, Miller-Fleming, Tyne W., Brody, Jennifer A., Raffield, Laura M., McHugh, Caitlin P., Jain, Deepti, Gogarten, Stephanie M., Laurie, Cecelia A., Keramati, Ali, Arvanitis, Marios, Smith, Albert V., Heavner, Benjamin, Barwick, Lucas, Becker, Lewis C., Bis, Joshua C., Blangero, John, Bleecker, Eugene R., Burchard, Esteban G., Celedón, Juan C., Chang, Yen Pei C., Custer, Brian, Darbar, Dawood, de las Fuentes, Lisa, DeMeo, Dawn L., Freedman, Barry I., Garrett, Melanie E., Gladwin, Mark T., Heckbert, Susan R., Hidalgo, Bertha A., Irvin, Marguerite R., Islam, Talat, Johnson, W. Craig, Kaab, Stefan, Launer, Lenore, Lee, Jiwon, Liu, Simin, Moscati, Arden, North, Kari E., Peyser, Patricia A., Rafaels, Nicholas, Seidman, Christine, Weeks, Daniel E., Wen, Fayun, Wheeler, Marsha M., Williams, L. Keoki, Yang, Ivana V., Zhao, Wei, Aslibekyan, Stella, Auer, Paul L., Bowden, Donald W., Cade, Brian E., Chen, Zhanghua, Cho, Michael H., Cupples, L. Adrienne, Curran, Joanne E., Daya, Michelle, Deka, Ranjan, Eng, Celeste, Fingerlin, Tasha E., Guo, Xiuqing, Hou, Lifang, Hwang, Shih-Jen, Johnsen, Jill M., Kenny, Eimear E., Levin, Albert M., Liu, Chunyu, Minster, Ryan L., Naseri, Take, Nouraie, Mehdi, Reupena, Muagututi‘a Sefuiva, Sabino, Ester C., Smith, Jennifer A., Smith, Nicholas L., Lasky-Su, Jessica, Taylor, James G., VI, Telen, Marilyn J., Tiwari, Hemant K., Tracy, Russell P., White, Marquitta J., Zhang, Yingze, Wiggins, Kerri L., Weiss, Scott T., Vasan, Ramachandran S., Taylor, Kent D., Sinner, Moritz F., Silverman, Edwin K., Shoemaker, M. Benjamin, Sheu, Wayne H.-H., Sciurba, Frank, Schwartz, David A., Rotter, Jerome I., Roden, Daniel, Redline, Susan, Raby, Benjamin A., Psaty, Bruce M., Peralta, Juan M., Palmer, Nicholette D., Nekhai, Sergei, Montgomery, Courtney G., Mitchell, Braxton D., Meyers, Deborah A., McGarvey, Stephen T., Mak, Angel C.Y., Loos, Ruth J.F., Kumar, Rajesh, Kooperberg, Charles, Konkle, Barbara A., Kelly, Shannon, Kardia, Sharon L.R., Kaplan, Robert, He, Jiang, Gui, Hongsheng, Gilliland, Frank D., Gelb, Bruce D., Fornage, Myriam, Ellinor, Patrick T., de Andrade, Mariza, Correa, Adolfo, Chen, Yii-Der Ida, Boerwinkle, Eric, Barnes, Kathleen C., Ashley-Koch, Allison E., Arnett, Donna K., Albert, Christine, Laurie, Cathy C., Abecasis, Goncalo, Nickerson, Deborah A., Wilson, James G., Rich, Stephen S., Levy, Daniel, Ruczinski, Ingo, Aviv, Abraham, Blackwell, Thomas W., Thornton, Timothy, O’Connell, Jeff, Cox, Nancy J., Perry, James A., Armanios, Mary, Battle, Alexis, Pankratz, Nathan, Reiner, Alexander P., and Mathias, Rasika A.

Published
2022
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46. Clonal hematopoiesis is driven by aberrant activation of TCL1A

Author

Weinstock, Joshua S., primary, Gopakumar, Jayakrishnan, additional, Burugula, Bala Bharathi, additional, Uddin, Md Mesbah, additional, Jahn, Nikolaus, additional, Belk, Julia A., additional, Daniel, Bence, additional, Ly, Nghi, additional, Mack, Taralyn M., additional, Laurie, Cecelia A., additional, Broome, Jai G., additional, Taylor, Kent D., additional, Guo, Xiuqing, additional, Sinner, Moritz F., additional, von Falkenhausen, Aenne S., additional, Kääb, Stefan, additional, Shuldiner, Alan R., additional, O’Connell, Jeffrey R., additional, Lewis, Joshua P., additional, Boerwinkle, Eric, additional, Barnes, Kathleen C., additional, Chami, Nathalie, additional, Kenny, Eimear E., additional, Loos, Ruth J., additional, Fornage, Myriam, additional, Hou, Lifang, additional, Lloyd-Jones, Donald M., additional, Redline, Susan, additional, Cade, Brian E., additional, Psaty, Bruce M., additional, Bis, Joshua C., additional, Brody, Jennifer A., additional, Silverman, Edwin K., additional, Yun, Jeong H., additional, Qiao, Dandi, additional, Palmer, Nicholette D., additional, Freedman, Barry I., additional, Bowden, Donald W., additional, Cho, Michael H., additional, DeMeo, Dawn L., additional, Vasan, Ramachandran S., additional, Yanek, Lisa R., additional, Becker, Lewis C., additional, Kardia, Sharon, additional, Peyser, Patricia A., additional, He, Jiang, additional, Rienstra, Michiel, additional, Harst, Pim Van der, additional, Kaplan, Robert, additional, Heckbert, Susan R., additional, Smith, Nicholas L., additional, Wiggins, Kerri L., additional, Arnett, Donna K., additional, Irvin, Marguerite R., additional, Tiwari, Hemant, additional, Cutler, Michael J., additional, Knight, Stacey, additional, Muhlestein, J Brent., additional, Correa, Adolfo, additional, Raffield, Laura M., additional, Gao, Yan, additional, Andrade, Mariza de, additional, Rotter, Jerome I., additional, Rich, Stephen S., additional, Tracy, Russell P., additional, Konkle, Barbara A., additional, Johnsen, Jill M., additional, Wheeler, Marsha M., additional, Smith, J. Gustav, additional, Melander, Olle, additional, Nilsson, Peter M., additional, Custer, Brian S., additional, Duggirala, Ravindranath, additional, Curran, Joanne E., additional, Blangero, John, additional, McGarvey, Stephen, additional, Williams, L. Keoki, additional, Xiao, Shujie, additional, Yang, Mao, additional, Gu, C. Charles., additional, Chen, Yii-Der Ida., additional, Lee, Wen-Jane, additional, Marcus, Gregory M., additional, Kane, John P., additional, Pullinger, Clive R., additional, Shoemaker, M. Benjamin, additional, Darbar, Dawood, additional, Roden, Dan, additional, Albert, Christine, additional, Kooperberg, Charles, additional, Zhou, Ying, additional, Manson, JoAnn E., additional, Desai, Pinkal, additional, Johnson, Andrew, additional, Mathias, Rasika, additional, Blackwell, Thomas W., additional, Abecasis, Goncalo R., additional, Smith, Albert V., additional, Kang, Hyun M., additional, Satpathy, Ansuman T., additional, Natarajan, Pradeep, additional, Kitzman, Jacob, additional, Whitsel, Eric, additional, Reiner, Alexander P., additional, Bick, Alexander G., additional, and Jaiswal, Sidd, additional

Published
2021
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47. Genotyping common, large structural variations in 5,202 genomes using pangenomes, the Giraffe mapper, and the vg toolkit

Author

Sirén, Jouni, primary, Monlong, Jean, additional, Chang, Xian, additional, Novak, Adam M., additional, Eizenga, Jordan M., additional, Markello, Charles, additional, Sibbesen, Jonas A., additional, Hickey, Glenn, additional, Chang, Pi-Chuan, additional, Carroll, Andrew, additional, Gupta, Namrata, additional, Gabriel, Stacey, additional, Blackwell, Thomas W., additional, Ratan, Aakrosh, additional, Taylor, Kent D., additional, Rich, Stephen S., additional, Rotter, Jerome I., additional, Haussler, David, additional, Garrison, Erik, additional, and Paten, Benedict, additional

Published
2020
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48. Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database

Author

Omenn, Gilbert S., primary, States, David J., additional, Adamski, Marcin, additional, Blackwell, Thomas W., additional, Menon, Rajasree, additional, Hermjakob, Henning, additional, Apweiler, Rolf, additional, Haab, Brian B., additional, Simpson, Richard J., additional, Eddes, James S., additional, Kapp, Eugene A., additional, Moritz, Robert L., additional, Chan, Daniel W., additional, Rai, Alex J., additional, Admon, Arie, additional, Aebersold, Ruedi, additional, Eng, Jimmy, additional, Hancock, William S., additional, Hefta, Stanley A., additional, Meyer, Helmut, additional, Paik, Young-Ki, additional, Yoo, Jong-Shin, additional, Ping, Peipei, additional, Pounds, Joel, additional, Adkins, Joshua, additional, Qian, Xiaohong, additional, Wang, Rong, additional, Wasinger, Valerie, additional, Wu, Chi Yue, additional, Zhao, Xiaohang, additional, Zeng, Rong, additional, Archakov, Alexander, additional, Tsugita, Akira, additional, Beer, Ilan, additional, Pandey, Akhilesh, additional, Pisano, Michael, additional, Andrews, Philip, additional, Tammen, Harald, additional, Speicher, David W., additional, and Hanash, Samir M., additional

Published
2006
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49. Novel genetic determinants of telomere length from a multi-ethnic analysis of 75,000 whole genome sequences in TOPMed

Author

Taub, Margaret A, Weinstock, Joshua S, Iyer, Kruthika R, Yanek, Lisa R, Conomos, Matthew P, Brody, Jennifer A, Keramati, Ali, Laurie, Cecelia A, Arvanitis, Marios, Smith, Albert V, Lane, John, Becker, Lewis C, Bis, Joshua C, Blangero, John, Bleecker, Eugene R, Burchard, Esteban G, Celedon, Juan C, Chang, Yen Pei C, Custer, Brian, Darbar, Dawood, de las Fuentes, Lisa, DeMeo, Dawn L, Freedman, Barry I, Garrett, Melanie E, Gladwin, Mark T, Heckbert, Susan R, Hidalgo, Bertha A, Ingram, Christie, Irvin, Marguerite R, Craig Johnson, W, Kaab, Stefan, Launer, Lenore, Lee, Jiwon, Liu, Simin, Moscati, Arden, North, Kari E, Peyser, Patricia A, Rafaels, Nicholas, Raffield, Laura M, Weeks, Daniel E, Wheeler, Marsha M, Keoki Williams, L., Zhao, Wei, Armanios, Mary, Aslibekyan, Stella, Auer, Paul L, Bowden, Donald W, Cade, Brian E, Yii-Der Chen, Ida, Cho, Michael H, Cupples, L Adrienne, Curran, Joanne E, Daya, Michelle, Deka, Ranjan, Guo, Xiuqing, Hou, Lifang, Hwang, Shih-Jen, Johnsen, Jill M, Kenny, Eimear E, Levin, Albert M, Liu, Chunyu, Minster, Ryan L, Nouraie, Mehdi, Sabino, Ester C, Smith, Jennifer A, Smith, Nicholas L, Lasky Su, Jessica, Telen, Marilyn J, Tiwari, Hemant K, Tracy, Russell P, White, Marquitta J, Zhang, Yingze, Wiggins, Kerri L, Weiss, Scott T, Vasan, Ramachandran S, Taylor, Kent D, Sinner, Moritz F, Silverman, Edwin K, Benjamin Shoemaker, M., Sheu, Wayne H-H, Rotter, Jerome I, Redline, Susan, Psaty, Bruce M, Peralta, Juan M, Palmer, Nicholette D, Loos, Ruth JF, Montgomery, Courtney G, Mitchell, Braxton D, Meyers, Deborah A, McGarvey, Stephen T, Mak, Angel CY, Kumar, Rajesh, Kooperberg, Charles, Konkle, Barbara A, Kelly, Shannon, Kardia, Sharon LR, Kaplan, Robert, He, Jiang, Gui, Hongsheng, Fornage, Myriam, Ellinor, Patrick T, de Andrade, Mariza, Correa, Adolfo, Boerwinkle, Eric, Barnes, Kathleen C, Ashley-Koch, Allison E, Arnett, Donna K, Albert, Christine, Laurie, Cathy C, Abecasis, Goncalo, Aviv, Abraham, Nickerson, Deborah A, Wilson, James G, Rich, Stephen S, Levy, Daniel, Battle, Alexis, Blackwell, Thomas W, Ruczinski, Ingo, Thornton, Timothy, O’Connell, Jeff, Perry, James A, Pankratz, Nathan, Reiner, Alexander P, and Mathias, Rasika A

Abstract

Telomeres shorten in replicating somatic cells and with age; in human leukocytes, telomere length (TL) is associated with a host of aging-related diseases 1,2 . To date, 16 genome-wide association studies (GWAS) have identified twenty-three loci associated with leukocyte TL 3–18 , but prior studies were primarily in individuals of European and Asian ancestry and relied on laboratory assays including Southern Blot and qPCR to quantify TL. Here, we estimated TL bioinformatically, leveraging whole genome sequencing (WGS) of whole blood from n=75,176 subjects in the Trans-Omics for Precision Medicine (TOPMed) Program. We performed the largest multi-ethnic and only WGS-based genome-wide association analysis of TL to date. We identified 22 associated loci (p-value

Published
2019
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