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Chen Z, Liang H, Wei P. Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers. Genet Epidemiol 2023; 47:617-636. [PMID: 37822029 DOI: 10.1002/gepi.22537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 07/22/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023]
Abstract
Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels.p $p$ -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.
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Affiliation(s)
- Zhongyuan Chen
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA
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2
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Boutry S, Helaers R, Lenaerts T, Vikkula M. Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations. Brief Bioinform 2023; 24:bbad412. [PMID: 37974506 DOI: 10.1093/bib/bbad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 10/14/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.
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Affiliation(s)
- Simon Boutry
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Raphaël Helaers
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Artificial Intelligence laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- WELBIO department, WEL Research Institute, avenue Pasteur, 6, 1300 Wavre, Belgium
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3
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Hodonsky CJ, Baldassari AR, Bien SA, Raffield LM, Highland HM, Sitlani CM, Wojcik GL, Tao R, Graff M, Tang W, Thyagarajan B, Buyske S, Fornage M, Hindorff LA, Li Y, Lin D, Reiner AP, North KE, Loos RJF, Kooperberg C, Avery CL. Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics. BMC Genomics 2020; 21:228. [PMID: 32171239 PMCID: PMC7071748 DOI: 10.1186/s12864-020-6626-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/26/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. RESULTS We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. CONCLUSION This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.
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Affiliation(s)
- Chani J. Hodonsky
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
- University of Virginia Center for Public Health Genomics, 1355 Lee St, Charlottesville, VA 22908 USA
| | - Antoine R. Baldassari
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Stephanie A. Bien
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109 USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599 USA
| | - Heather M. Highland
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Colleen M. Sitlani
- University of Washington, 1730 Minor Ave, Ste 1360, Seattle, WA 98101 USA
| | - Genevieve L. Wojcik
- Stanford University School of Medicine, 291 Campus Dr, Stanford, CA 94305 USA
| | - Ran Tao
- Vanderbilt University, 2525 West End Ave #1100, Nashville, TN 37203 USA
| | - Marielisa Graff
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Weihong Tang
- University of Minnesota, 420 Delaware St SE, Minneapolis, MN 55455 USA
| | | | - Steve Buyske
- Rutgers University, 683 Hoes Ln W, Piscataway, NJ 08854 USA
| | - Myriam Fornage
- University of Texas Houston, 7000 Fannin Street, Houston, TX 77030 USA
| | - Lucia A. Hindorff
- National Human Genome Research Institute, 31 Center Dr, Bethesda, MD 20894 USA
| | - Yun Li
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Danyu Lin
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109 USA
- University of Washington, 1705 NE Pacific St, Seattle, WA 98195 USA
| | - Kari E. North
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599 USA
| | - Ruth J. F. Loos
- Icahn School of Medicine at Mount Sinai, 1468 Madison Ave, New York, NY 10029 USA
| | | | - Christy L. Avery
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
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Yang T, Kim J, Wu C, Ma Y, Wei P, Pan W. An adaptive test for meta-analysis of rare variant association studies. Genet Epidemiol 2020; 44:104-116. [PMID: 31830326 PMCID: PMC6980317 DOI: 10.1002/gepi.22273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/12/2019] [Accepted: 11/25/2019] [Indexed: 01/02/2023]
Abstract
Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data. One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github.com/ytzhong/metaRV and will be on CRAN soon.
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Affiliation(s)
- Tianzhong Yang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Junghi Kim
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Yiding Ma
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Dapas M, Sisk R, Legro RS, Urbanek M, Dunaif A, Hayes MG. Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in polycystic ovary syndrome. J Clin Endocrinol Metab 2019; 104:3835-3850. [PMID: 31038695 PMCID: PMC6660913 DOI: 10.1210/jc.2018-02496] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 04/17/2019] [Indexed: 02/07/2023]
Abstract
CONTEXT Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5-15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date. OBJECTIVE The objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.Design, Patients, and Methods: We performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis. RESULTS We found rare variants in DENND1A (P=5.31×10-5, Padj=0.039) that were significantly associated with reproductive and metabolic traits in PCOS families. CONCLUSIONS Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.
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Affiliation(s)
- Matthew Dapas
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ryan Sisk
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Richard S Legro
- Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Margrit Urbanek
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Reproductive Science, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes, and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York
- Correspondence and Reprint Requests: M. Geoffrey Hayes, PhD, Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, 303 East Chicago Avenue, Chicago, Illinois 60611. E-mail: ; or Andrea Dunaif, MD, Division of Endocrinology, Diabetes, and Bone Disease, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, 3rd Floor, New York, New York 10029. E-mail:
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Anthropology, Northwestern University, Evanston, Illinois
- Correspondence and Reprint Requests: M. Geoffrey Hayes, PhD, Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, 303 East Chicago Avenue, Chicago, Illinois 60611. E-mail: ; or Andrea Dunaif, MD, Division of Endocrinology, Diabetes, and Bone Disease, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, 3rd Floor, New York, New York 10029. E-mail:
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Ma Y, Wei P. FunSPU: A versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data. PLoS Genet 2019; 15:e1008081. [PMID: 31034468 PMCID: PMC6508749 DOI: 10.1371/journal.pgen.1008081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 05/09/2019] [Accepted: 03/11/2019] [Indexed: 11/19/2022] Open
Abstract
Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n = 1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n = 1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package "FunSPU".
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Affiliation(s)
- Yiding Ma
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, Texas, United States of America
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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7
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Yang T, Chen H, Tang H, Li D, Wei P. A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis. Stat Med 2019; 38:1230-1244. [PMID: 30460711 PMCID: PMC6399020 DOI: 10.1002/sim.8037] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/20/2022]
Abstract
As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called "aGE" tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package "aGE" is available at http://github.com/ytzhong/projects/.
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Affiliation(s)
- Tianzhong Yang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, TX 77030, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health,The University of Texas Health Science Center at Houston, TX77030, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX77030, USA
| | - Hongwei Tang
- Departments of Gastrointestinal Medical Oncology and Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX77030, USA
| | - Donghui Li
- Departments of Gastrointestinal Medical Oncology and Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX77030, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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8
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9
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Jo Hodonsky C, Schurmann C, Schick UM, Kocarnik J, Tao R, van Rooij FJ, Wassel C, Buyske S, Fornage M, Hindorff LA, Floyd JS, Ganesh SK, Lin DY, North KE, Reiner AP, Loos RJ, Kooperberg C, Avery CL. Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE Study. Am J Hematol 2018; 93:10.1002/ajh.25161. [PMID: 29905378 PMCID: PMC6300146 DOI: 10.1002/ajh.25161] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/29/2018] [Accepted: 05/29/2018] [Indexed: 12/17/2022]
Abstract
Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p<1.7x10-4 ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p=1.9x10-7 ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chani Jo Hodonsky
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ursula M Schick
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Jonathan Kocarnik
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Frank Ja van Rooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3000, the Netherlands
| | - Christina Wassel
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Burlington, VT
| | - Steve Buyske
- Department of Statistics and Biostatistics, Hill Center, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd. Piscataway, NY
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX
| | - Lucia A Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National institutes of Health, Bethesda, MD
| | - James S Floyd
- Departments of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Santhi K Ganesh
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Kari E North
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
- Carolina Population Center, University of North Carolina, Chapel Hill, NC
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