1
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Sun Q, Yang Y, Rosen JD, Jiang MZ, Chen J, Liu W, Wen J, Raffield LM, Pace RG, Zhou YH, Wright FA, Blackman SM, Bamshad MJ, Gibson RL, Cutting GR, Knowles MR, Schrider DR, Fuchsberger C, Li Y. MagicalRsq: Machine-learning-based genotype imputation quality calibration. Am J Hum Genet 2022; 109:1986-1997. [PMID: 36198314 PMCID: PMC9674945 DOI: 10.1016/j.ajhg.2022.09.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 09/16/2022] [Indexed: 01/26/2023] Open
Abstract
Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yingxi Yang
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Jonathan D Rosen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Min-Zhi Jiang
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rhonda G Pace
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yi-Hui Zhou
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Fred A Wright
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA; Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Scott M Blackman
- Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael J Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Ronald L Gibson
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Garry R Cutting
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael R Knowles
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christian Fuchsberger
- Institute for Biomedicine, Eurac Research (affiliated with the University of Lübeck), Bolzano, Italy.
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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2
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Wang S, Kim M, Jiang X, Harmanci AO. Evaluation of vicinity-based hidden Markov models for genotype imputation. BMC Bioinformatics 2022; 23:356. [PMID: 36038834 PMCID: PMC9422108 DOI: 10.1186/s12859-022-04896-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype-phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li-Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. RESULTS Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. CONCLUSIONS Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer .
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Affiliation(s)
- Su Wang
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Miran Kim
- Department of Mathematics, Hanyang University, Seoul, 04763, Republic of Korea
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Arif Ozgun Harmanci
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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3
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Liu W, Sun Q, Huang L, Bhattacharya A, Wang GW, Tan X, Kuban KCK, Joseph RM, O'Shea TM, Fry RC, Li Y, Santos HP. Innovative computational approaches shed light on genetic mechanisms underlying cognitive impairment among children born extremely preterm. J Neurodev Disord 2022; 14:16. [PMID: 35240980 PMCID: PMC8903548 DOI: 10.1186/s11689-022-09429-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Although survival rates for infants born extremely preterm (gestation < 28 weeks) have improved significantly in recent decades, neurodevelopmental impairment remains a major concern. Children born extremely preterm remain at high risk for cognitive impairment from early childhood to adulthood. However, there is limited evidence on genetic factors associated with cognitive impairment in this population. METHODS First, we used a latent profile analysis (LPA) approach to characterize neurocognitive function at age 10 for children born extremely preterm. Children were classified into two groups: (1) no or low cognitive impairment, and (2) moderate-to-severe cognitive impairment. Second, we performed TOPMed-based genotype imputation on samples with genotype array data (n = 528). Third, we then conducted a genome-wide association study (GWAS) for LPA-inferred cognitive impairment. Finally, computational analysis was conducted to explore potential mechanisms underlying the variant x LPA association. RESULTS We identified two loci reaching genome-wide significance (p value < 5e-8): TEA domain transcription factor 4 (TEAD4 at rs11829294, p value = 2.40e-8) and syntaxin 18 (STX18 at rs79453226, p value = 1.91e-8). Integrative analysis with brain expression quantitative trait loci (eQTL), chromatin conformation, and epigenomic annotations suggests tetraspanin 9 (TSPAN9) and protein arginine methyltransferase 8 (PRMT8) as potential functional genes underlying the GWAS signal at the TEAD4 locus. CONCLUSIONS We conducted a novel computational analysis by utilizing an LPA-inferred phenotype with genetics data for the first time. This study suggests that rs11829294 and its LD buddies have potential regulatory roles on genes that could impact neurocognitive impairment for extreme preterm born children.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xianming Tan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karl C K Kuban
- Department of Pediatrics, Boston University, Boston, MA, USA
| | - Robert M Joseph
- Department of Anatomy & Neurobiology, Boston University, Boston, MA, USA
| | - T Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hudson P Santos
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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4
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Kim M, Harmanci AO, Bossuat JP, Carpov S, Cheon JH, Chillotti I, Cho W, Froelicher D, Gama N, Georgieva M, Hong S, Hubaux JP, Kim D, Lauter K, Ma Y, Ohno-Machado L, Sofia H, Son Y, Song Y, Troncoso-Pastoriza J, Jiang X. Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation. Cell Syst 2021; 12:1108-1120.e4. [PMID: 34464590 PMCID: PMC9898842 DOI: 10.1016/j.cels.2021.07.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 04/21/2021] [Accepted: 07/29/2021] [Indexed: 02/06/2023]
Abstract
Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.
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Affiliation(s)
- Miran Kim
- Department of Computer Science and Engineering and Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Arif Ozgun Harmanci
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.,Corresponding authors: ,
| | | | - Sergiu Carpov
- Inpher, EPFL Innovation Park Bàtiment A, 3rd Fl, 1015 Lausanne, Switzerland.,CEA, LIST, 91191 Gif-sur-Yvette Cedex, France
| | - Jung Hee Cheon
- Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea.,Crypto Lab Inc., Seoul, 08826, Republic of Korea
| | | | - Wonhee Cho
- Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Nicolas Gama
- Inpher, EPFL Innovation Park Bàtiment A, 3rd Fl, 1015 Lausanne, Switzerland
| | - Mariya Georgieva
- Inpher, EPFL Innovation Park Bàtiment A, 3rd Fl, 1015 Lausanne, Switzerland
| | - Seungwan Hong
- Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Duhyeong Kim
- Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Yiping Ma
- University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lucila Ohno-Machado
- UCSD Health Department of Biomedical Informatics, University of California, San Diego, CA, 92093, USA
| | - Heidi Sofia
- National Institutes of Health (NIH) - National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | | | - Yongsoo Song
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Xiaoqian Jiang
- Center for Secure Artificial intelligence For hEalthcare (SAFE), School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.,Corresponding authors: ,
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5
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Hou G, Harley ITW, Lu X, Zhou T, Xu N, Yao C, Qin Y, Ouyang Y, Ma J, Zhu X, Yu X, Xu H, Dai D, Ding H, Yin Z, Ye Z, Deng J, Zhou M, Tang Y, Namjou B, Guo Y, Weirauch MT, Kottyan LC, Harley JB, Shen N. SLE non-coding genetic risk variant determines the epigenetic dysfunction of an immune cell specific enhancer that controls disease-critical microRNA expression. Nat Commun 2021; 12:135. [PMID: 33420081 PMCID: PMC7794586 DOI: 10.1038/s41467-020-20460-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 12/03/2020] [Indexed: 12/12/2022] Open
Abstract
Since most variants that impact polygenic disease phenotypes localize to non-coding genomic regions, understanding the consequences of regulatory element variants will advance understanding of human disease mechanisms. Here, we report that the systemic lupus erythematosus (SLE) risk variant rs2431697 as likely causal for SLE through disruption of a regulatory element, modulating miR-146a expression. Using epigenomic analysis, genome-editing and 3D chromatin structure analysis, we show that rs2431697 tags a cell-type dependent distal enhancer specific for miR-146a that physically interacts with the miR-146a promoter. NF-kB binds the disease protective allele in a sequence-specific manner, increasing expression of this immunoregulatory microRNA. Finally, CRISPR activation-based modulation of this enhancer in the PBMCs of SLE patients attenuates type I interferon pathway activation by increasing miR-146a expression. Our work provides a strategy to define non-coding RNA functional regulatory elements using disease-associated variants and provides mechanistic links between autoimmune disease risk genetic variation and disease etiology.
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Affiliation(s)
- Guojun Hou
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200032, China
- Shanghai Institute of Rheumatology, China-Australia Centre for Personalized Immunology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, 518040, China
| | - Isaac T W Harley
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Division of Rheumatology, School of Medicine, University of Colorado, Aurora, Colorado, 80045, USA
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, Colorado, 80045, USA
| | - Xiaoming Lu
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
| | - Tian Zhou
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Ning Xu
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Chao Yao
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences(SIBS), University of Chinese Academy of Sciences, Chinese Academy of Sciences (CAS), Shanghai, 200031, China
| | - Yuting Qin
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Ye Ouyang
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Jianyang Ma
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Xinyi Zhu
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Xiang Yu
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Hong Xu
- Department of Obstetrics and Gynecology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200127, China
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200127, China
| | - Dai Dai
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Huihua Ding
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Zhihua Yin
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, 518040, China
| | - Zhizhong Ye
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, 518040, China
| | - Jun Deng
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Mi Zhou
- Sheng Yushou Center of Cell Biology and Immunology, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University (SJTU), Shanghai, 200240, China
| | - Yuanjia Tang
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
| | - Ya Guo
- Sheng Yushou Center of Cell Biology and Immunology, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University (SJTU), Shanghai, 200240, China
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45229, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
| | - Leah C Kottyan
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45229, USA
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
| | - John B Harley
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45229, USA
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, 45229, USA
| | - Nan Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China.
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200032, China.
- Shanghai Institute of Rheumatology, China-Australia Centre for Personalized Immunology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, 200001, China.
- Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, 518040, China.
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45229, USA.
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6
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Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL, Shan Y, Jain D, Argos M, Arnett DK, Avery C, Barnes KC, Becker LC, Bien SA, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Buyske S, Cai J, Cho MH, Choi SH, Choquet H, Cupples LA, Cushman M, Daya M, de Vries PS, Ellinor PT, Faraday N, Fornage M, Gabriel S, Ganesh SK, Graff M, Gupta N, He J, Heckbert SR, Hidalgo B, Hodonsky CJ, Irvin MR, Johnson AD, Jorgenson E, Kaplan R, Kardia SLR, Kelly TN, Kooperberg C, Lasky-Su JA, Loos RJF, Lubitz SA, Mathias RA, McHugh CP, Montgomery C, Moon JY, Morrison AC, Palmer ND, Pankratz N, Papanicolaou GJ, Peralta JM, Peyser PA, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith NL, Taylor KD, Thornton TA, Tiwari HK, Tracy RP, Wang T, Weiss ST, Weng LC, Wiggins KL, Wilson JG, Yanek LR, Zöllner S, North KE, Auer PL, Raffield LM, Reiner AP, Li Y. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet 2019; 15:e1008500. [PMID: 31869403 PMCID: PMC6953885 DOI: 10.1371/journal.pgen.1008500] [Citation(s) in RCA: 158] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 01/10/2020] [Accepted: 10/30/2019] [Indexed: 01/10/2023] Open
Abstract
Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.
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Affiliation(s)
- Madeline H. Kowalski
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Huijun Qian
- Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Ziyi Hou
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jonathan D. Rosen
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Yue Shan
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Maria Argos
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America
| | - Christy Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Kathleen C. Barnes
- Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Lewis C. Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Stephanie A. Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America
| | - Eric Boerwinkle
- Human Genome Sequencing Center, University of Texas Health Science Center at Houston; Baylor College of Medicine, Houston, Texas, United States of America
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Jianwen Cai
- Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Mary Cushman
- Departments of Medicine & Pathology, Larner College of Medicine, University of Vermont, Colchester, Vermont, United States of America
| | - Michelle Daya
- Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Patrick T. Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Nauder Faraday
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Myriam Fornage
- School of Public Health, The University of Texas Health Science Center, Houston, Texas, United States of America
| | - Stacey Gabriel
- Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Santhi K. Ganesh
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Misa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Namrata Gupta
- Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Los Angeles, United States of America
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
| | - Bertha Hidalgo
- Department of Epidemiology, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Chani J. Hodonsky
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Andrew D. Johnson
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, Massachusetts, United States of America
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Tanika N. Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Los Angeles, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jessica A. Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Steven A. Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rasika A. Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Caitlin P. McHugh
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Jee-Young Moon
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - George J. Papanicolaou
- National Heart, Lung, and Blood Institute, Division of Cardiovascular Sciences, PPSP/EB, NIH, Bethesda, Maryland, United States of America
| | - Juan M. Peralta
- Department of Human Genetics and South Texas Diabetes Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nicholas L. Smith
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, Washington, United States of America
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hemant K. Tiwari
- Department of Biostatistics, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Russell P. Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larrner College of Medicine, University of Vermont, Colchester, Vermont, United States of America
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lu-Chen Weng
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Lisa R. Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Sebastian Zöllner
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center of Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Paul L. Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America
| | | | | | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America
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7
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Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, Min JL, Chen L, Astle W, Cocca M, Deelen P, Elding H, Farmaki AE, Franklin CS, Franberg M, Gaunt TR, Hofman A, Jiang T, Kleber ME, Lachance G, Luan J, Malerba G, Matchan A, Mead D, Memari Y, Ntalla I, Panoutsopoulou K, Pazoki R, Perry JR, Rivadeneira F, Sabater-Lleal M, Sennblad B, Shin SY, Southam L, Traglia M, van Dijk F, van Leeuwen EM, Zaza G, Zhang W, Amin N, Butterworth A, Chambers JC, Dedoussis G, Dehghan A, Franco OH, Franke L, Frontini M, Gambaro G, Gasparini P, Hamsten A, Issacs A, Kooner JS, Kooperberg C, Langenberg C, Marz W, Scott RA, Swertz MA, Toniolo D, Uitterlinden AG, van Duijn CM, Watkins H, Zeggini E, Maurano MT, Timpson NJ, Reiner AP, Auer PL, Soranzo N. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat Genet 2016; 48:1303-1312. [PMID: 27668658 PMCID: PMC5279872 DOI: 10.1038/ng.3668] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 08/15/2016] [Indexed: 12/21/2022]
Abstract
Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.
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Affiliation(s)
- Valentina Iotchkova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Jie Huang
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- Boston VA Research Institute, Boston, Massachusetts, USA
| | - John A. Morris
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Caterina Barbieri
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Klaudia Walter
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Josine L. Min
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Lu Chen
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- Department of Hematology, University of Cambridge, Cambridge, UK
| | - William Astle
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Massimilian Cocca
- Medical Genetics, Institute for Maternal and Child Health IRCCS “Burlo Garofolo”, Trieste, Italy
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Patrick Deelen
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands
| | - Heather Elding
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | | | - Mattias Franberg
- Cardiovascular Medicine Unit, Dep. Medicine, Karolinska Institute, Stockholm, Sweden
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Tao Jiang
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Genevieve Lachance
- Department of Twin Research & Genetic Epidemiology, King's College London, Londo, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Giovanni Malerba
- Biology and Genetics, Department Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Angela Matchan
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Daniel Mead
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Yasin Memari
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
| | - Ioanna Ntalla
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Raha Pazoki
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - John R.B. Perry
- Department of Twin Research & Genetic Epidemiology, King's College London, Londo, UK
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Maria Sabater-Lleal
- Cardiovascular Medicine Unit, Dep. Medicine, Karolinska Institute, Stockholm, Sweden
| | - Bengt Sennblad
- Cardiovascular Medicine Unit, Dep. Medicine, Karolinska Institute, Stockholm, Sweden
| | - So-Youn Shin
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Lorraine Southam
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, UK
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Freerk van Dijk
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands
| | | | - Gianluigi Zaza
- Renal Unit, Department of Medicine, University of Verona, Verona, Italy
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s campus, London, UK
| | | | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Adam Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Cambridge, UK
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s campus, London, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lude Franke
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands
| | | | - Giovanni Gambaro
- Division of Nephrology and Dialysis, Institute of Internal Medicine, Renal Program, Columbus-Gemelli University Hospital, Catholic University, Rome, Italy
| | - Paolo Gasparini
- Medical Genetics, Institute for Maternal and Child Health IRCCS “Burlo Garofolo”, Trieste, Italy
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
- Experimental Genetics Division, Sidra, Doha, Qatar
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Dep. Medicine, Karolinska Institute, Stockholm, Sweden
| | - Aaron Issacs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jaspal S. Kooner
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Winfried Marz
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Medical Clinic V (Nephrology, Hypertensiology, Rheumatology, Endocrinolgy, Diabetology), Mannheim Medical Faculty, Heidelberg University, Mannheim, Germany
| | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Morris A. Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, Netherlands
- LifeLines Cohort Study, University Medical Center Groningen, Groningen, Netherlands
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Andre G. Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Hugh Watkins
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | | | - Mathew T. Maurano
- Institute for Systems Genetics, New York University Langone Medical Center, New York, USA
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Paul L. Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Nicole Soranzo
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- Department of Hematology, University of Cambridge, Cambridge, UK
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Cambridge, UK
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8
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Kanai M, Tanaka T, Okada Y. Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. J Hum Genet 2016; 61:861-866. [PMID: 27305981 PMCID: PMC5090169 DOI: 10.1038/jhg.2016.72] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Revised: 04/18/2016] [Accepted: 05/16/2016] [Indexed: 01/22/2023]
Abstract
To assess the statistical significance of associations between variants and traits, genome-wide association studies (GWAS) should employ an appropriate threshold that accounts for the massive burden of multiple testing in the study. Although most studies in the current literature commonly set a genome-wide significance threshold at the level of P=5.0 × 10-8, the adequacy of this value for respective populations has not been fully investigated. To empirically estimate thresholds for different ancestral populations, we conducted GWAS simulations using the 1000 Genomes Phase 3 data set for Africans (AFR), Europeans (EUR), Admixed Americans (AMR), East Asians (EAS) and South Asians (SAS). The estimated empirical genome-wide significance thresholds were Psig=3.24 × 10-8 (AFR), 9.26 × 10-8 (EUR), 1.83 × 10-7 (AMR), 1.61 × 10-7 (EAS) and 9.46 × 10-8 (SAS). We additionally conducted trans-ethnic meta-analyses across all populations (ALL) and all populations except for AFR (ΔAFR), which yielded Psig=3.25 × 10-8 (ALL) and 4.20 × 10-8 (ΔAFR). Our results indicate that the current threshold (P=5.0 × 10-8) is overly stringent for all ancestral populations except for Africans; however, we should employ a more stringent threshold when conducting a meta-analysis, regardless of the presence of African samples.
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Affiliation(s)
- Masahiro Kanai
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Toshihiro Tanaka
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.,Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yukinori Okada
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
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9
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When Does Choice of Accuracy Measure Alter Imputation Accuracy Assessments? PLoS One 2015; 10:e0137601. [PMID: 26458263 PMCID: PMC4601794 DOI: 10.1371/journal.pone.0137601] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 08/19/2015] [Indexed: 11/20/2022] Open
Abstract
Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen’s kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.
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10
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Horikoshi M, Mӓgi R, van de Bunt M, Surakka I, Sarin AP, Mahajan A, Marullo L, Thorleifsson G, Hӓgg S, Hottenga JJ, Ladenvall C, Ried JS, Winkler TW, Willems SM, Pervjakova N, Esko T, Beekman M, Nelson CP, Willenborg C, Wiltshire S, Ferreira T, Fernandez J, Gaulton KJ, Steinthorsdottir V, Hamsten A, Magnusson PKE, Willemsen G, Milaneschi Y, Robertson NR, Groves CJ, Bennett AJ, Lehtimӓki T, Viikari JS, Rung J, Lyssenko V, Perola M, Heid IM, Herder C, Grallert H, Müller-Nurasyid M, Roden M, Hypponen E, Isaacs A, van Leeuwen EM, Karssen LC, Mihailov E, Houwing-Duistermaat JJ, de Craen AJM, Deelen J, Havulinna AS, Blades M, Hengstenberg C, Erdmann J, Schunkert H, Kaprio J, Tobin MD, Samani NJ, Lind L, Salomaa V, Lindgren CM, Slagboom PE, Metspalu A, van Duijn CM, Eriksson JG, Peters A, Gieger C, Jula A, Groop L, Raitakari OT, Power C, Penninx BWJH, de Geus E, Smit JH, Boomsma DI, Pedersen NL, Ingelsson E, Thorsteinsdottir U, Stefansson K, Ripatti S, Prokopenko I, McCarthy MI, Morris AP. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation. PLoS Genet 2015; 11:e1005230. [PMID: 26132169 PMCID: PMC4488845 DOI: 10.1371/journal.pgen.1005230] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 04/18/2015] [Indexed: 11/19/2022] Open
Abstract
Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated. Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
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Affiliation(s)
- Momoko Horikoshi
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Reedik Mӓgi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Ida Surakka
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | | | - Sara Hӓgg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas W. Winkler
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Sara M. Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Children’s Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Marian Beekman
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Disease Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Christina Willenborg
- Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Steven Wiltshire
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Teresa Ferreira
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Juan Fernandez
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Kyle J. Gaulton
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Anders Hamsten
- Cardiovascular Genetics and Genomics Group, Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Neil R. Robertson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Amanda J. Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Terho Lehtimӓki
- Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland
| | - Jorma S. Viikari
- Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland
| | - Johan Rung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Steno Diabetes Center A/S, Gentofte, Denmark
| | - Markus Perola
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Iris M. Heid
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Düsseldorf, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Düsseldorf, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Elina Hypponen
- School of Population Health, University of South Australia, Adelaide, Australia
- Centre for Paediatric Epidemiology and Biostatistics, University College London Institute of Child Health, London, United Kingdom
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Center for Medical Systems Biology, Leiden, The Netherlands
| | - Elisabeth M. van Leeuwen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lennart C. Karssen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | - Anton J. M. de Craen
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Joris Deelen
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Aki S. Havulinna
- Unit of Chronic Disease Epidemiology and Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Matthew Blades
- Bioinformatics and Biostatistics Support Hub (B/BASH), University of Leicester, Leicester, United Kingdom
| | - Christian Hengstenberg
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Munich, Munich, Germany
| | - Jeanette Erdmann
- Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Munich, Munich, Germany
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
- The Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Martin D. Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Disease Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden
| | - Veikko Salomaa
- Unit of Chronic Disease Epidemiology and Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Cecilia M. Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - P. Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Center for Medical Systems Biology, Leiden, The Netherlands
| | - Johan G. Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Vasa Central Hospital, Vasa, Finland
- Department of Health Promotion and Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Antti Jula
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland
| | - Leif Groop
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Olli T. Raitakari
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Chris Power
- Centre for Paediatric Epidemiology and Biostatistics, University College London Institute of Child Health, London, United Kingdom
| | | | - Eco de Geus
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
- EMGO Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam, The Netherlands
| | - Johannes H. Smit
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Unnur Thorsteinsdottir
- deCode Genetic - Amgen Inc, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCode Genetic - Amgen Inc, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
- The Department of Public Health, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
| | - Inga Prokopenko
- Deparment of Genomics of Common Disease, School of Public Health, Imperial College London, London, United Kingdom
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom
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11
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Liu Q, Cirulli ET, Han Y, Yao S, Liu S, Zhu Q. Systematic assessment of imputation performance using the 1000 Genomes reference panels. Brief Bioinform 2015; 16:549-62. [PMID: 25246238 PMCID: PMC4580532 DOI: 10.1093/bib/bbu035] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Accepted: 08/21/2014] [Indexed: 12/30/2022] Open
Abstract
Genotype imputation has been widely adopted in the postgenome-wide association studies (GWAS) era. Owing to its ability to accurately predict the genotypes of untyped variants, imputation greatly boosts variant density, allowing fine-mapping studies of GWAS loci and large-scale meta-analysis across different genotyping arrays. By leveraging genotype data from 90 whole-genome deeply sequenced individuals as the evaluation benchmark and the 1000 Genomes Project data as reference panels, we systematically examined four important issues related to genotype imputation practice. First, in a study of imputation accuracy, we found that IMPUTE2 and minimac have the best imputation performance among the three popular imputing software evaluated and that using a multi-population reference panel is beneficial. Second, the optimal imputation quality cutoff for removing poorly imputed variants varies according to the software used. Third, the major contributing factors to consistently poor imputation are low variant heterozygosity, high sequence similarity to other genomic regions, high GC content, segmental duplication and being far from genotyping markers. Lastly, in an evaluation of the imputability of all known GWAS regions, we found that GWAS loci associated with hematological measurements and immune system diseases are harder to impute, as compared with other human traits. Recommendations made based on the above findings may provide practical guidance for imputation exercise in future genetic studies.
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12
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Huang KC, Sun W, Wu Y, Chen M, Mohlke KL, Lange LA, Li Y. Association studies with imputed variants using expectation-maximization likelihood-ratio tests. PLoS One 2014; 9:e110679. [PMID: 25383782 PMCID: PMC4226494 DOI: 10.1371/journal.pone.0110679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Accepted: 09/17/2014] [Indexed: 12/18/2022] Open
Abstract
Genotype imputation has become standard practice in modern genetic studies. As sequencing-based reference panels continue to grow, increasingly more markers are being well or better imputed but at the same time, even more markers with relatively low minor allele frequency are being imputed with low imputation quality. Here, we propose new methods that incorporate imputation uncertainty for downstream association analysis, with improved power and/or computational efficiency. We consider two scenarios: I) when posterior probabilities of all potential genotypes are estimated; and II) when only the one-dimensional summary statistic, imputed dosage, is available. For scenario I, we have developed an expectation-maximization likelihood-ratio test for association based on posterior probabilities. When only imputed dosages are available (scenario II), we first sample the genotype probabilities from its posterior distribution given the dosages, and then apply the EM-LRT on the sampled probabilities. Our simulations show that type I error of the proposed EM-LRT methods under both scenarios are protected. Compared with existing methods, EM-LRT-Prob (for scenario I) offers optimal statistical power across a wide spectrum of MAF and imputation quality. EM-LRT-Dose (for scenario II) achieves a similar level of statistical power as EM-LRT-Prob and, outperforms the standard Dosage method, especially for markers with relatively low MAF or imputation quality. Applications to two real data sets, the Cebu Longitudinal Health and Nutrition Survey study and the Women’s Health Initiative Study, provide further support to the validity and efficiency of our proposed methods.
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Affiliation(s)
- Kuan-Chieh Huang
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Wei Sun
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Mengjie Chen
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Leslie A. Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America
- * E-mail:
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13
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Gwinn M, Ioannidis JP, Little J, Khoury MJ. Editorial: Updated guidance on human genome epidemiology (HuGE) reviews and meta-analyses of genetic associations. Am J Epidemiol 2014; 180:559-61. [PMID: 25164421 DOI: 10.1093/aje/kwu196] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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14
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Zhang L, Pei YF, Fu X, Lin Y, Wang YP, Deng HW. FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. ACTA ACUST UNITED AC 2014; 30:1876-83. [PMID: 24618466 DOI: 10.1093/bioinformatics/btu143] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
MOTIVATION Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand. RESULTS In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm 'merge-and-recover (MAR)' to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects. AVAILABILITY The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.
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Affiliation(s)
- Lei Zhang
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
| | - Yu-Fang Pei
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaoying Fu
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
| | - Yong Lin
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
| | - Yu-Ping Wang
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
| | - Hong-Wen Deng
- School of Public Health, Xi'an Jiaotong University, Shaanxi, China, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA and Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
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15
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Zhang L, Choi HJ, Estrada K, Leo PJ, Li J, Pei YF, Zhang Y, Lin Y, Shen H, Liu YZ, Liu Y, Zhao Y, Zhang JG, Tian Q, Wang YP, Han Y, Ran S, Hai R, Zhu XZ, Wu S, Yan H, Liu X, Yang TL, Guo Y, Zhang F, Guo YF, Chen Y, Chen X, Tan L, Zhang L, Deng FY, Deng H, Rivadeneira F, Duncan EL, Lee JY, Han BG, Cho NH, Nicholson GC, McCloskey E, Eastell R, Prince RL, Eisman JA, Jones G, Reid IR, Sambrook PN, Dennison EM, Danoy P, Yerges-Armstrong LM, Streeten EA, Hu T, Xiang S, Papasian CJ, Brown MA, Shin CS, Uitterlinden AG, Deng HW. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet 2013; 23:1923-33. [PMID: 24249740 DOI: 10.1093/hmg/ddt575] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Aiming to identify novel genetic variants and to confirm previously identified genetic variants associated with bone mineral density (BMD), we conducted a three-stage genome-wide association (GWA) meta-analysis in 27 061 study subjects. Stage 1 meta-analyzed seven GWA samples and 11 140 subjects for BMDs at the lumbar spine, hip and femoral neck, followed by a Stage 2 in silico replication of 33 SNPs in 9258 subjects, and by a Stage 3 de novo validation of three SNPs in 6663 subjects. Combining evidence from all the stages, we have identified two novel loci that have not been reported previously at the genome-wide significance (GWS; 5.0 × 10(-8)) level: 14q24.2 (rs227425, P-value 3.98 × 10(-13), SMOC1) in the combined sample of males and females and 21q22.13 (rs170183, P-value 4.15 × 10(-9), CLDN14) in the female-specific sample. The two newly identified SNPs were also significant in the GEnetic Factors for OSteoporosis consortium (GEFOS, n = 32 960) summary results. We have also independently confirmed 13 previously reported loci at the GWS level: 1p36.12 (ZBTB40), 1p31.3 (GPR177), 4p16.3 (FGFRL1), 4q22.1 (MEPE), 5q14.3 (MEF2C), 6q25.1 (C6orf97, ESR1), 7q21.3 (FLJ42280, SHFM1), 7q31.31 (FAM3C, WNT16), 8q24.12 (TNFRSF11B), 11p15.3 (SOX6), 11q13.4 (LRP5), 13q14.11 (AKAP11) and 16q24 (FOXL1). Gene expression analysis in osteogenic cells implied potential functional association of the two candidate genes (SMOC1 and CLDN14) in bone metabolism. Our findings independently confirm previously identified biological pathways underlying bone metabolism and contribute to the discovery of novel pathways, thus providing valuable insights into the intervention and treatment of osteoporosis.
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Affiliation(s)
- Lei Zhang
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, China
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