Hematology and Hemostasis
Bidirectional Mendelian Randomization Revealed Bidirectional Causality Between Telomere Length and Clonal Hematopoiesis of Intermediate Potential
| Submitted by | Nakao, Tetsushi
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| Authors | Tetsushi Nakao1,2,3,4, Alexander G. Bick1,5, Margaret A. Taub6, Seyedeh M. Zekavat7, Md M. Uddin1,2, Abhishek Niroula1,3, Cara L. Carty8, John Lane9, Michael C. Honigberg1,2,10, Joshua S. Weinstock11, Akhil Pampana1,2, Christopher J. Gibson10, Gabriel K. Griffin1,12,13, Shoa L. Clarke14, Romit Bhattacharya2,15, Themistocles Assimes14,16, Leslie S. Emery17, Adrienne M. Stilp17, Quenna Wong17, Jai Broome17, Cecelia A. Laurie17, Alyna Khan17, Albert V. Smith11, Thomas W. Blackwell11, Zachary T. Yoneda18, Juan M. Peralta19, Donald W. Bowden20, Marguerite R. Irvin21, Meher Boorgula22, Wei Zhao23, Lisa R. Yanek24, James E. Hixson25, Charles Gu26, Gina M. Peloso27, Dan M. Roden28, Muagututi`a S. Reupena29, Chii-Min Hwu30,31, Dawn L. DeMeo32, Kari E. North33, Shannon Kelly34,35, Solomon K. Musani36, Joshua C. Bis37, Donald M. Lloyd-Jones38,39, Jill M. Johnsen40, Michael Preuss41, Russell P. Tracy42,60, Patricia A. Peyser23, Dandi Qiao32, Pinkal Desai43, Joanne E. Curran19, Barry I. Freedman44, Hemant Tiwari45, Sameer Chavan22, Jennifer A. Smith23,46, Nicholas L. Smith47,48,49, Tanika N. Kelly50,51, Bertha Hildalgo45, L. Adrienne Cupples27,52, Take Naseri53, The Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA) Group, Ranjan Deka54, Nicola L. Hawley55, Ryan L. Minster56, Daniel E. Weeks57, Lisa de las Fuentes26,58, Laura M. Raffield59, Paul S. Vries25, Alanna C. Morrison25, Christie Ballantyne61, Eimear E. Kenny62,63,64, Stephen S. Rich65, Whitsel A. Eric33,66, Michael H. Cho67, M. Benjamin Shoemaker18, Betty S. Pace68, John Blangero19, Nicholette D. Palmer20, Braxton D. Mitchell69,70, Alan R. Shuldiner71, Kathleen C. Barnes22, Susan Redline10,72,73, Sharon L.R. Kardia23, Abecasis Gonçalo11,74, Lewis C. Becker24, Susan Heckbert47,48, Jiang He50,51, Wendy Post75, Donna K. Arnett76, Ramachandran S. Vasan27,52,77, Dawood Darbar78, Scott T. Weiss10,32, Stephen T. McGarvey79, Mariza de Andrade80, Yii-Der Ida Chen81, Robert C. Kaplan82,83, Deborah A. Meyers84, Brian S. Custer34, Adolfo Correa85, Bruce M. Psaty37,47,86, Myriam Fornage25,87, JoAnn E. Manson10,88,89, Eric Boerwinkle11, Barbara A. Konkle90,91, Ruth J.F. Loos41, Jerome I. Rotter81, Edwin K. Silverman32, Charles Kooperberg92, Siddhartha Jaiswal93, Peter Libby4,10, Patrick T. Ellinor1,94, Nathan Pankratz9, Benjamin L. Ebert1,3,95, Alexander P. Reiner92, Rasika A. Mathias24, Ron Do41,64,96, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium & Pradeep Natarajan*1,2,10
1Broad Institute of MIT and Harvard, Cambridge, MA, USA. 2Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA. 3Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. 4Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 5Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA. 6Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 7Yale University School of Medicine, New Haven, CT, USA. 8Initiative for Research and Education to Advance Community Health, Washington State University, Seattle, WA, USA. 9Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, USA. 10Department of Medicine, Harvard Medical School, Boston, MA, USA. 11Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA. 12Department of Pathology, Harvard Medical School, Boston, MA, USA. 13Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. 14Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. 15Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA. 16VA Palo Alto Health Care System, Palo Alto, CA, USA. 17Department of Biostatistics, University of Washington, Seattle, WA, USA. 18Division of Cardiology, Vanderbilt University Medical Center, Nashville, TN, USA. 19Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA. 20Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA. 21Department of Biostatistics, School of Public Health, University of Alabama, Birmingham, AL, USA. 22Division of Biomedical Informatics & Personalized Medicine & the Colorado Center for Personalized Medicine, School of Medicine, University of Colorado, Aurora, CO, USA. 23Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA. 24GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 25Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA. 26Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA. 27Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 28Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. 29Ministry of Health, Government of Samoa, Apia, Samoa. 30Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. 31School of Medicine, National Yang-Ming University, Taipei, Taiwan. 32Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 33Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA. 34Vitalant Research Institute, San Francisco, CA, USA. 35UCSF, Benioff Children's Hospital Oakland, Oakland, CA, USA. 36Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA. 37Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA. 38Division of Cardiology Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 39Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 40Bloodworks Northwest Research Institute, Seattle, WA, USA. 41The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 42Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 43Division of Hematology and Oncology, Weill Cornell Medical College, New York, NY, USA. 44Internal Medicine–Nephrology, Wake Forest School of Medicine, NC, USA. 45Department of Epidemiology, School of Public Health, University of Alabama, Birmingham, AL, USA. 46Survey Research Center, Institute for Social Research, University of Michigan , Ann Arbor, MI, USA. 47Department of Epidemiology, University of Washington, Seattle, WA, USA. 48Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. 49Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA. 50Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA. 51Tulane University Translational Science Institute, New Orleans, LA, USA. 52National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, MA, USA. 53Department of Health, American Samoa Government, Pago Pago, American Samoa, USA. 54Department of Environmental Health, University of Cincinnati, OH, USA. 55Department of Chronic Disease Epidemiology, Yale University, CT, USA. 56Department of Human Genetics, University of Pittsburgh, PA, USA. 57Department of Human Genetics and Biostatistics, University of Pittsburgh, PA, USA. 58Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA. 59Department of Genetics, University of North Carolina, Chapel Hill, NC, USA. 60Department of Pathology and Biochemistry, University of Vermont College of Medicine, Burlington, VT, USA. 61Department of Medicine, Baylor College of Medicine, Houston, TX, USA. 62Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 63Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 64Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 65Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. 66Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA. 67Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 68Division of Hematology/Oncology, Department of Pediatrics, Augusta University, Augusta, GA, USA. 69Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA. 70Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA. 71Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA. 72Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 73Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. 74Regeneron Pharmaceuticals, Tarrytown, NY, USA. 75Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA. 76Dean's Office, College of Public Health, University of Kentucky, Lexington, KY, USA. 77Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA. 78Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA. 79Department of Epidemiology and International Health Institute, Brown University School of Public Health, Providence, RI, USA. 80Mayo Clinic, Department of Health Sciences Research, Rochester, MN, USA. 81The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA. 82Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA. 83Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA. 84Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA. 85Departments of Medicine and Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA. 86Department of Health Services, University of Washington, Seattle, WA, USA. 87Brown Foundation Institute of Molecular Medicine, McGovern medical School, University of Texas Health Science Center at Houston, Houston, TX, USA. 88Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 89Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 90Bloodworks Northwest, Seattle, WA, USA. 91Department of Medicine, University of Washington, Seattle, WA, USA. 92Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 93Department of Pathology, Stanford University, Stanford, CA, USA. 94Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA. 95Howard Hughes Medical Institute, Boston, MA, USA. 96Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. |
| Name and Date of Professional Meeting | CHARGE Consortium Meeting (May 6 and 7, 2021)
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| Associated paper proposal(s) | |
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| Abstract Text |
Background: Human genetic studies showed 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. Though TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies, the causal and mediative relationships between LTL, CHIP, and CAD were yet to be elucidated.
Methods: We investigated the relationship between CHIP, LTL, and CAD in Trans-Omics for Precision Medicine (TOPMed) program (N=63,302) and UK Biobank (N=48,658) using observational studies, Mendelian randomization studies, and causal mediation analyses. Results: Observational studies showed inverse correlation between CHIP and LTL (β = -0.13; 95% CI -0.16:-0.096). The variant allele frequency (VAF) of the CHIP-related mutations (representing clone size of the cells which harbor CHIP-related mutations) had inverse correlation with LTL (β = -1.12 / 1% of VAF; 95% CI -1.40:-0.84). Bidirectional Mendelian randomization studies were consistent with LTL lengthening increasing propensity to develop CHIP (Estimate = 0.92, 95% CI 0.61:1.23; P = 6.7x10-9), but CHIP then in turn hastening LTL shortening (Estimate = -0.81; 95% CI -1.40:-0.23; P = 0.0063). LTL increased overall occurrence of mutations including passive mutations not only the CHIP-related driver mutations. Causal mediation analyses showed modest mediation between CHIP and CAD by LTL. Conclusions: We showed a bidirectional causal relationship between LTL and CHIP, shedding light on the mechanisms by which telomere length contributes to age-related disorders. The causal mediation effect of LTL on CHIP-related CAD incidence suggests the plausibility of developing harmonized therapies for both blood cancer and cardiovascular diseases. |
Multi-Omics Data Integration with Sparse Multiple Canonical Correlation Analysis in the Multi-Ethnic Study of Atherosclerosis (MESA) Study
| Submitted by | Jiang, Min-Zhi
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| Authors | Min-Zhi Jiang, Laura M. Raffield, Ai Ye, Kent D. Taylor, Xiuqing Guo, Jerome I. Rotter, Peter Durda, Elaine Cornell, Russell P. Tracy, W. Craig Johnson, Josh Smith, Yongmei Liu, Stephen S. Rich, David John Van Den Berg, Robert E. Gerszten, Silva Kasela, Tuuli Lappalainen, Stacey B. Gabriel, François Aguet, Kristin Ardlie, Michael I. Love, Yun Li, TOPMed MESA Multi-omics Working Group
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| Name and Date of Professional Meeting | ASHG Virtual Meeting 2020 (October 27-30)
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| Associated paper proposal(s) | |
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| Abstract Text |
Multi-omics data integration methods are being increasingly utilized for the identification of complex disease, its subtypes, and improved understanding of variation in quantitative traits. Multi-omics measures (e.g., genomics, DNA methylation, gene expression, metabolites, and proteins) are interdependent; thus, it is challenging to incorporate these different biological layers of information to predict phenotypic outcomes. Shared variation across assays, as opposed to variation within a specific assay type, is more likely to be driven by shared biology, as opposed to technical factors that are primarily assay specific. Sparse multiple canonical correlation analysis (SMCCA) is a correlation-based method enabling simultaneous analysis of multiple assays. SMCCA can be considered as an extension of principal component analysis (PCA), with PCA maximizing variation explained by the PCs within a single assay, while SMCCA finds the similar canonical variables (CVs) of each assay by maximizing the sum of pairwise correlations across all assays. We applied SMCCA implemented in the PMA R package to data from the Multi-Ethnic Study of Atherosclerosis (MESA). Incorporating proteomic (SOMAscan 1.3k array), methylomic (EPIC array) and transcriptomic (RNA-seq from peripheral blood mononuclear cells) in 476 overlapping samples from visit 5 (2010-2012), we first developed a pipeline to identify sample outliers driving the correlation between assays, identifying influential samples by examining relationships between PCs and interquartile range within each assay. We then evaluated and confirmed the utility of CVs to predict phenotypes (including age and hematological traits) using both unsupervised SMCCA and supervised sparse CCA (SSCCA). Supervised CVs revealed strong relationships between multi-omics assays and hematological traits and identified influential proteins driving these relationships (e.g., Calgranulin B and Lipocalin 2 with white blood cell count). Finally, to analyze the multi-omics and phenotype data integration in a supervised fashion, we extended the current SSCCA implementation in the PMA package, which accommodates only two multi-omics assays, to supervised sparse multiple CCA (SSMCCA) which allows >2 multi-omics assays modeled simultaneously. We anticipate SMCCA approaches will be a powerful tool for identifying biologically meaningful relationships across multi-omics assays, as well as between multi-omics assays and phenotypic traits, particularly when influential sample outliers are carefully examined and removed prior to analysis.
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A compendium of recurrent somatic variation in 46,080 TOPMed whole genomes
| Submitted by | Weinstock, Joshua
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| Authors | J. S. Weinstock, R. Mathias, A. Reiner, P. Natarajan, T. Blackwell, G. R. Abecasis, A. G. Bick
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| Name and Date of Professional Meeting | ASHG 2020
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| Associated paper proposal(s) | |
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| Abstract Text |
INTRODUCTION: Clonal hematopoiesis (CH) is a clonal expansion in blood cells driven by somatic mutations and has been associated with hematologic malignancy, coronary artery disease and death. Drivers of CH include rare somatic point mutations in leukemogenic genes, somatic structural variation, and somatic chromosomal aneuploidy. Whether recurrent somatic variation exists outside specific leukemogenic loci and its relationship to CH is presently unknown.
OBJECTIVES: Somatic variant calls on diverse samples from the TOPMed consortium enables an unbiased survey of somatic variation in 46,080 deep whole genomes. Here we identify a novel class of recurrently observed somatic variants. We use the burden of recurrent somatic variants to define a new clonal phenomenon and describe its genetic causes and clinical consequences. METHODS: We identified somatic point mutations using Mutect2. We performed stringent filtering to exclude germline variants including any germline variant called in TOPMed Freeze 5 and any variant with allele fraction > 35%. We identified variants associated with age using elastic-net regression. We performed a GWAS of the recurrent variant burden using SAIGE to identify the germline basis of this phenomenon, including age, study, and the first 10 genetic ancestry principal components as covariates. We derived a biological aging clock that estimates chronological age from the variant allele fraction of specific recurrent somatic variants. RESULTS: We identified 87,608 recurrent somatic variants (present in >= 50 individuals and in >= 5 studies) in 46,080 samples, of which 1,192 are associated with age. An increase in age by a decade is associated with 28.4 additional mutations (95% CI: 27.3-29.5). We found that CH with rare driver mutations is not associated with recurrent variant burden after adjusting for age, suggesting the clonal phenomena are distinct. We performed mutation signature analysis and found an enrichment of COSMIC signatures SBS40 and SBS3, implicating DNA damage repair. A GWAS of recurrent variant burden identifies 13 genome-wide significant loci, suggesting germline variation influences acquisition of recurrent somatic variants. We defined age acceleration as the residual between the aging clock and chronological age. A standard deviation increase in age acceleration was associated with 35% increased risk of incident CAD (pvalue = 2.9e-15) and increased osteoprotegerin (pvalue = 6e-3). CONCLUSION: Our compendium of recurrent somatic variation identifies a novel clonal phenomenon distinct from CH with leukemogenic mutations. We identified predisposing molecular pathways and clinical consequences. |
Genome-wide association study of telomere length in individuals of Samoan ancestry
| Submitted by | Krishnan, Mohanraj
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| Authors | M. A. Taub, J. C. Carlson, H. Cheng, T. Naseri, M. Reupena, R. Deka, N. L. Hawley, S. T. McGarvey, D. E. Weeks, R. A. Mathias, R. L. Minster, TOPMed Hematology & Hemostasis and Structural Variation Working Groups
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| Name and Date of Professional Meeting | ASHG Conference (October 27, 2020)
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| Associated paper proposal(s) | |
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| Abstract Text |
Telomere length (TL) is a proposed biomarker of biological age and is a risk factor for age-related diseases. Prior GWAS have identified numerous loci harboring genetic determinants of TL; several of these being implicated in human disease. However, studies have been limited to samples of European and Asian ancestry. We performed a GWAS of TL with 1,261 participants of Samoan ancestry. Whole-genome sequencing was conducted by the TOPMed Program, and TelSeq was used to compute TL from the sequences. We tested for association via linear mixed models as implemented in GENESIS, with age and sex as fixed effect covariates. We adjusted for relatedness and population stratification with values from PC-AiR and PC-Relate, respectively. There was one signal associated with TL at p<5×10⁻⁸ and two additional signals associated with TL at p<1×10⁻⁶. The peak variant is located on 14q12. This top signal (p=2.7×10⁻8) was rs4982872, an intronic variant in NEDD8, with the minor allele (MAF=0.26) associated with greater mean TL (0.12bp). This variant is in high LD (r²=1) with rs28372734, which is located in the 5′-UTR of TINF2, a gene that encodes for a member protein of the telosome/shelterin complex. Given that TINF2 has been reported to be associated with TL in people of Asian ancestry, we conditioned on rs28372734, to determine if there is more than one locus mapping to this region in our study. The association with rs4982872 is lost, suggesting that the peak variant (rs4982872) here, also maps to the previously reported TINF2 locus. Importantly, the MAF of the variants mapping to the TINF2 locus is quite different between Samoans and populations of continental ancestries. The MAF of rs28372734 is 23% in Samoans and is 9% in individuals of Asian ancestries but is ≤ 1% in individuals of African and European ancestries; in fact, TINF2 is completely missed in prior GWAS focused on European ancestries. Additionally, we observed novel association at p<1×10⁻6 of two variants in intergenic regions near PTPRG and AC005562.1 located on chromosomes 3 and 17, respectively. The minor alleles for both variants are rare in the Samoans (MAF= 0.006 and 0.008, respectively) and are not observed in populations of continental ancestries. These variants have not been reported to be associated with TL in any studies to date. Among participants of Samoan ancestry, we see the prior association with a high MAF variant in the TINF2 locus and identify two novel associations associated with TL. Additional work is necessary to replicate these novel associations and understand the biological mechanisms that may explain differences in TL associated with these variants.
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Trans-ethnic meta-analysis reveals novel loci, genes, and pathways regulating adult telomere length.
| Submitted by | Keener, Rebecca
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| Authors | Rebecca Keener, Margaret A. Taub, Matthew Conomos, Joshua Weinstock, John Lane, Kruthika Iyer, Lisa Yanek, Nathan Pankratz, Alexander Reiner, Rasika Mathias, Alexis Battle
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| Name and Date of Professional Meeting | American Society of Human Genetics (October, 2020)
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| Associated paper proposal(s) |
Assessing genetic determinants of telomere length leveraging whole genome sequence data in the NHLBI Trans-Omics for Precision Medicine Program – breakout proposal.
Assessing genetic determinants of telomere length leveraging whole genome sequence data in the NHLBI Trans-Omics for Precision Medicine Program.
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| Working Group(s) | |
| Abstract Text |
Telomere length (TL) regulation is critical for human health. Individuals with very short TL exhibit Short Telomere Syndromes (STS) leading to organ failure, while individuals with very long TL may be predisposed to cancer. Genome-wide association studies (GWAS) have yielded several TL-associated loci, but the mechanisms underlying most of these signals remains poorly characterized. TL is reported to differ between ancestry groups, yet the majority of previous TL GWAS were limited to European individuals. We recently estimated TL from whole genome sequencing (WGS) bioinformatically using TelSeq in a pooled trans-ethnic study (N=109,122) from the Trans-Omics for Precision Medicine (TOPMed) Program. GWAS using TOPMed data yielded 59 independent, genome-wide significant signals at 36 loci, 21 of which were novel signals. To extend these discoveries beyond TOPMed, we performed a meta-analysis including three additional GWAS from European (1), Singaporean Chinese (2), and South Asian (3) ancestries and the TOPMed data stratified by descent (European, African, Hispanic/Latino, Asian, Brazilian and Samoan) and identified an additional 18 novel loci for a total of 54 GWAS loci. The genes closest to the GWAS and meta-analysis signals were enriched for gene ontology terms involving telomere biology, DNA repair, and DNA metabolism. To understand the mechanisms underlying these signals we performed credible set analysis, colocalization analysis, and functional characterization of all signals. These fine-mapping approaches identified several zinc-finger nucleases and transcription factors (TF) as likely causal genes. ZCCHC8 is a zinc-finger protein and rare variants in ZCCHC8 are reported to cause STS; BANP is a TF which contributes to T cell development and STS patients are reported to have T-cell immunodeficiency. The diversity in our dataset made it possible to identify population-specific signals driven by differences in allele frequency, which included signals near OBFC1, a telomere binding protein. Colocalization analysis with expression quantitative trait loci (eQTLs) from GTEx tissues demonstrated that all signals near OBFC1 colocalize with OBFC1 eQTLs. Notably, the primary OBFC1 signal was driven by European, African, and Hispanic/Latino individuals while an independent, secondary signal was driven exclusively by Asian and Hispanic/Latino individuals. Our results demonstrate the value of including diverse ancestries in GWAS and of WGS in the identification of ancestry-specific and low frequency signals to better understand genetic variation underlying TL regulation.
1. PMC7058826 2. PMC6554354 3. PMC5749304 |
Whole genome sequencing association analyses of red blood cell traits in a large, multi-ethnic sample from the Trans-Omics for Precision Medicine (TOPMed) Program
| Submitted by | Stilp, Adrienne M.
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| Authors | A. Stilp, Y. Hu, C. McHugh, D. Jain, J. G. Broome, M. P. Conomos, S. M. Gogarten, J. Lane, C. C. Laurie, C. A. Laurie, N. Pankratz, X. Zheng, the TOPMed Hematology and Hemostasis Working Group, A. P. Reiner
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| Name and Date of Professional Meeting | American Society of Human Genetics (October 2020)
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| Associated paper proposal(s) | |
| Working Group(s) | |
| Abstract Text |
Genome-wide association studies of red blood cell measurements have identified thousands of associated genetic variants in a variety of populations. However, investigations in multi-ethnic populations are limited, and causal variants have not been identified. Whole genome sequencing (WGS) data from the NHLBI TOPMed Program provides a large, multi-ethnic sample set in which causal variants in novel and known loci can be identified. We performed single variant and gene-based aggregate association analyses in the TOPMed dataset for hemoglobin (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC), and red cell distribution width (RDW) measured on 62,653 TOPMed participants from self-identified European, African, Hispanic, and Asian populations across 13 studies. The inclusion of populations other than European increased the sample size by approximately 50%. The analyses were performed as a mixed model for each phenotype that adjusted for age, sex, study, sequencing phase, disease status, relatedness, population structure, and residual heteroscedasticity. After accounting for 3,262 known variants for these phenotypes, we identified novel signals that have not been previously significantly associated with red blood cell traits, including non-coding variants or extended haplotype regions within or near genes for ELL2 (MCH), CDC42BPB (MCH), MIDN (MCH, MCV), TMPRSS6 (MCH, MCV), ARL15 (RBC), and DCLK1 (RBC), as well as conditionally independent variants in the alpha-globin (HBA1/HBA2) and beta-globin (HBB) gene clusters on chromosomes 16 and 11, respectively. Significance was determined for single variant tests using p < 5e-9, and for gene-based aggregate tests using p < 0.05 with a Bonferroni correction for the number of tested aggregation units. These results demonstrate that association testing using WGS data from a large, multi-ethnic sample is necessary to capture variants that are not commonly tested in SNP-chip arrays or rare variants that are poorly imputed.
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