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Pang H, Xia Y, Luo S, Huang G, Li X, Xie Z, Zhou Z. Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus. J Med Genet 2021; 58:289-296. [PMID: 33753534 PMCID: PMC8086251 DOI: 10.1136/jmedgenet-2020-107350] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/06/2021] [Accepted: 01/10/2021] [Indexed: 12/12/2022]
Abstract
Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.
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Affiliation(s)
- Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ying Xia
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuoming Luo
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Gan Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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2
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Zhao L, Zhang Z, Rodriguez SMB, Vardarajan BN, Renton AE, Goate AM, Mayeux R, Wang GT, Leal SM. A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease. Eur J Hum Genet 2020; 28:1734-1742. [PMID: 32740652 PMCID: PMC7785016 DOI: 10.1038/s41431-020-0703-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 07/09/2020] [Accepted: 07/22/2020] [Indexed: 12/18/2022] Open
Abstract
To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.
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Affiliation(s)
- Linhai Zhao
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Zhihui Zhang
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA ,grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Sandra M. Barral Rodriguez
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Badri N. Vardarajan
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Alan E. Renton
- grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Alison M. Goate
- grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA ,grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029 USA
| | - Richard Mayeux
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Gao T. Wang
- grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA ,grid.170205.10000 0004 1936 7822Department of Human Genetics, The University of Chicago, Chicago, IL 60637 USA
| | - Suzanne M. Leal
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA ,grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
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3
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Sewda A, Agopian AJ, Goldmuntz E, Hakonarson H, Morrow BE, Musfee F, Taylor D, Mitchell LE. Gene-based analyses of the maternal genome implicate maternal effect genes as risk factors for conotruncal heart defects. PLoS One 2020; 15:e0234357. [PMID: 32516339 PMCID: PMC7282656 DOI: 10.1371/journal.pone.0234357] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/22/2020] [Indexed: 12/12/2022] Open
Abstract
Congenital heart defects (CHDs) affect approximately 1% of newborns. Epidemiological studies have identified several genetically-mediated maternal phenotypes (e.g., pregestational diabetes, chronic hypertension) that are associated with the risk of CHDs in offspring. However, the role of the maternal genome in determining CHD risk has not been defined. We present findings from gene-level, genome-wide studies that link CHDs to maternal effect genes as well as to maternal genes related to hypertension and proteostasis. Maternal effect genes, which provide the mRNAs and proteins in the oocyte that guide early embryonic development before zygotic gene activation, have not previously been implicated in CHD risk. Our findings support a role for and suggest new pathways by which the maternal genome may contribute to the development of CHDs in offspring.
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Affiliation(s)
- Anshuman Sewda
- Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, Texas, United States of America
| | - A. J. Agopian
- Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, Texas, United States of America
| | - Elizabeth Goldmuntz
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Division of Cardiology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Hakon Hakonarson
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Bernice E. Morrow
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Fadi Musfee
- Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, Texas, United States of America
| | - Deanne Taylor
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Laura E. Mitchell
- Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, Texas, United States of America
- * E-mail:
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4
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Gene-based genome-wide association studies and meta-analyses of conotruncal heart defects. PLoS One 2019; 14:e0219926. [PMID: 31314787 PMCID: PMC6636758 DOI: 10.1371/journal.pone.0219926] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 07/02/2019] [Indexed: 12/28/2022] Open
Abstract
Conotruncal heart defects (CTDs) are among the most common and severe groups of congenital heart defects. Despite evidence of an inherited genetic contribution to CTDs, little is known about the specific genes that contribute to the development of CTDs. We performed gene-based genome-wide analyses using microarray-genotyped and imputed common and rare variants data from two large studies of CTDs in the United States. We performed two case-parent trio analyses (N = 640 and 317 trios), using an extension of the family-based multi-marker association test, and two case-control analyses (N = 482 and 406 patients and comparable numbers of controls), using a sequence kernel association test. We also undertook two meta-analyses to combine the results from the analyses that used the same approach (i.e. family-based or case-control). To our knowledge, these analyses are the first reported gene-based, genome-wide association studies of CTDs. Based on our findings, we propose eight CTD candidate genes (ARF5, EIF4E, KPNA1, MAP4K3, MBNL1, NCAPG, NDFUS1 and PSMG3). Four of these genes (ARF5, KPNA1, NDUFS1 and PSMG3) have not been previously associated with normal or abnormal heart development. In addition, our analyses provide additional evidence that genes involved in chromatin-modification and in ribonucleic acid splicing are associated with congenital heart defects.
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5
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Katsevich E, Sabatti C. MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS. Ann Appl Stat 2019; 13:1-33. [PMID: 31687060 PMCID: PMC6827557 DOI: 10.1214/18-aoas1185] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We tackle the problem of selecting from among a large number of variables those that are "important" for an outcome. We consider situations where groups of variables are also of interest. For example, each variable might be a genetic polymorphism, and we might want to study how a trait depends on variability in genes, segments of DNA that typically contain multiple such polymorphisms. In this context, to discover that a variable is relevant for the outcome implies discovering that the larger entity it represents is also important. To guarantee meaningful results with high chance of replicability, we suggest controlling the rate of false discoveries for findings at the level of individual variables and at the level of groups. Building on the knockoff construction of Barber and Candès [Ann. Statist. 43 (2015) 2055-2085] and the multilayer testing framework of Barber and Ramdas [J. Roy. Statist. Soc. Ser. B 79 (2017) 1247-1268], we introduce the multilayer knockoff filter (MKF). We prove that MKF simultaneously controls the FDR at each resolution and use simulations to show that it incurs little power loss compared to methods that provide guarantees only for the discoveries of individual variables. We apply MKF to analyze a genetic dataset and find that it successfully reduces the number of false gene discoveries without a significant reduction in power.
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Affiliation(s)
- Eugene Katsevich
- DEPARTMENT OF STATISTICS, STANFORD UNIVERSITY, 390 SERRA MALL, STANFORD, CALIFORNIA 94305, ,
| | - Chiara Sabatti
- DEPARTMENT OF STATISTICS, STANFORD UNIVERSITY, 390 SERRA MALL, STANFORD, CALIFORNIA 94305, ,
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6
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Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease. CURRENT GENETIC MEDICINE REPORTS 2019. [DOI: 10.1007/s40142-019-0161-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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7
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Wang MH, Weng H, Sun R, Lee J, Wu WKK, Chong KC, Zee BCY. A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics 2018; 33:2330-2336. [PMID: 28334355 DOI: 10.1093/bioinformatics/btx130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 03/09/2017] [Indexed: 01/24/2023] Open
Abstract
Motivation Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power. Results We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. Availability and Implementation The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html. Contact maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maggie Haitian Wang
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Haoyi Weng
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Rui Sun
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Jack Lee
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - William Ka Kei Wu
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR
| | - Ka Chun Chong
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
| | - Benny Chung-Ying Zee
- Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.,CUHK Shenzhen Research Institute, Shenzhen, China
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8
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Sebastiani P, Gurinovich A, Bae H, Andersen S, Malovini A, Atzmon G, Villa F, Kraja AT, Ben-Avraham D, Barzilai N, Puca A, Perls TT. Four Genome-Wide Association Studies Identify New Extreme Longevity Variants. J Gerontol A Biol Sci Med Sci 2017; 72:1453-1464. [PMID: 28329165 DOI: 10.1093/gerona/glx027] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 02/14/2017] [Indexed: 01/10/2023] Open
Abstract
The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators. To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort. We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years). The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease. The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.
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Affiliation(s)
- Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Massachusetts
| | | | - Harold Bae
- College of Public Health and Human Sciences, Oregon State University, Corvallis
| | - Stacy Andersen
- Geriatrics Section, Department of Medicine, Boston University School of Medicine & Boston Medical Center, Massachusetts
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy
| | - Gil Atzmon
- Department of Natural Science, University of Haifa, Israel.,Department of Medicine.,Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Francesco Villa
- IRCCS MultiMedica, Milan, Italy.,Department of Medicine and Surgery, University of Salerno, Baronissi, Italy
| | - Aldi T Kraja
- Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, Missouri
| | - Danny Ben-Avraham
- Department of Medicine.,Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Nir Barzilai
- Department of Medicine.,Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Annibale Puca
- IRCCS MultiMedica, Milan, Italy.,Department of Medicine and Surgery, University of Salerno, Baronissi, Italy
| | - Thomas T Perls
- Geriatrics Section, Department of Medicine, Boston University School of Medicine & Boston Medical Center, Massachusetts
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9
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To aggregate or not, that is the question. A commentary on single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data. J Hum Genet 2017; 62:523. [DOI: 10.1038/jhg.2017.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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10
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Ji T, Chen J. Statistical models for DNA copy number variation detection using read-depth data from next generation sequencing experiments. AUST NZ J STAT 2016. [DOI: 10.1111/anzs.12175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Tieming Ji
- Department of Statistics; University of Missouri at Columbia; Columbia MI 65211 USA
| | - Jie Chen
- Department of Biostatistics and Epidemiology; Medical College of Georgia, Augusta University; Augusta GA 30912 USA
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11
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Blangero J, Teslovich TM, Sim X, Almeida MA, Jun G, Dyer TD, Johnson M, Peralta JM, Manning A, Wood AR, Fuchsberger C, Kent JW, Aguilar DA, Below JE, Farook VS, Arya R, Fowler S, Blackwell TW, Puppala S, Kumar S, Glahn DC, Moses EK, Curran JE, Thameem F, Jenkinson CP, DeFronzo RA, Lehman DM, Hanis C, Abecasis G, Boehnke M, Göring H, Duggirala R, Almasy L. Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19. BMC Proc 2016; 10:71-77. [PMID: 27980614 PMCID: PMC5133484 DOI: 10.1186/s12919-016-0008-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data. Methods GAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.
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Affiliation(s)
- John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Tanya M Teslovich
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Xueling Sim
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Marcio A Almeida
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Goo Jun
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA ; Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Thomas D Dyer
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Matthew Johnson
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Juan M Peralta
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Alisa Manning
- Department of Genetics, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Andrew R Wood
- Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK
| | - Christian Fuchsberger
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Jack W Kent
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX 78227 USA
| | - David A Aguilar
- Cardiovascular Division, Baylor College of Medicine, Houston, TX 77030 USA
| | - Jennifer E Below
- Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Vidya S Farook
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Rector Arya
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Sharon Fowler
- Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA
| | - Tom W Blackwell
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX 78227 USA
| | - Satish Kumar
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - David C Glahn
- Department of Psychiatry, Yale University, New Haven, CT 06106 USA
| | - Eric K Moses
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Farook Thameem
- Department of Biochemistry, Faculty of Medicine, Kuwait University, Safat, Kuwait City, 13110 Kuwait
| | - Christopher P Jenkinson
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Ralph A DeFronzo
- Texas Diabetes Institute, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA
| | - Donna M Lehman
- Division of Clinical Epidemiology, Department of Medicine, University of San Antonio Health Science Center at San Antonio, San Antonio, TX 78229 USA
| | - Craig Hanis
- Department of Epidemiology, Human Genetics and Environmenal Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Goncalo Abecasis
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Michael Boehnke
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Harald Göring
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA
| | - Laura Almasy
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Harlingen, TX 78550 USA ; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104 USA
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Solomon T, Smith EN, Matsui H, Braekkan SK, Wilsgaard T, Njølstad I, Mathiesen EB, Hansen JB, Frazer KA. Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins: The Tromsø Study. ACTA ACUST UNITED AC 2016; 9:375-83. [PMID: 27329291 PMCID: PMC4982757 DOI: 10.1161/circgenetics.115.001327] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 06/16/2016] [Indexed: 01/09/2023]
Abstract
Supplemental Digital Content is available in the text. Background— Genetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes. Methods and Results— As part of a larger case–control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein’s respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes. Conclusions— We have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.
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Affiliation(s)
- Terry Solomon
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Erin N Smith
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Hiroko Matsui
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Sigrid K Braekkan
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | | | - Tom Wilsgaard
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Inger Njølstad
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Ellisiv B Mathiesen
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - John-Bjarne Hansen
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.)
| | - Kelly A Frazer
- From the Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.), Department of Pediatrics, Rady's Children's Hospital, San Diego, La Jolla, CA (E.N.S., H.M., K.A.F.); Institute for Genomic Medicine, University of California, San Diego, La Jolla (K.A.F.); Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Centre (TREC) (E.N.S., S.K.B., I.N., E.B.M., J.-B.H., K.A.F.), Department of Community Medicine (T.W., I.N.), and Brain and Circulation Research Group, Department of Clinical Medicine (E.B.M.), UiT The Arctic University of Norway; and Division of Internal Medicine, University Hospital of North Norway, Tromsø (S.K.B., J.-B.H.).
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