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Rosenthal EA, Makaryan V, Burt AA, Crosslin DR, Kim DS, Smith JD, Nickerson DA, Reiner AP, Rich SS, Jackson RD, Ganesh SK, Polfus LM, Qi L, Dale DC, Jarvik GP. Association Between Absolute Neutrophil Count and Variation at TCIRG1: The NHLBI Exome Sequencing Project. Genet Epidemiol 2016; 40:470-4. [PMID: 27229898 DOI: 10.1002/gepi.21976] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 02/22/2016] [Accepted: 04/05/2016] [Indexed: 11/11/2022]
Abstract
Neutrophils are a key component of innate immunity. Individuals with low neutrophil count are susceptible to frequent infections. Linkage and association between congenital neutropenia and a single rare missense variant in TCIRG1 have been reported in a single family. Here, we report on nine rare missense variants at evolutionarily conserved sites in TCIRG1 that are associated with lower absolute neutrophil count (ANC; p = 0.005) in 1,058 participants from three cohorts: Atherosclerosis Risk in Communities (ARIC), Coronary Artery Risk Development in Young Adults (CARDIA), and Jackson Heart Study (JHS) of the NHLBI Grand Opportunity Exome Sequencing Project (GO ESP). These results validate the effects of TCIRG1 coding variation on ANC and suggest that this gene may be associated with a spectrum of mild to severe effects on ANC.
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Affiliation(s)
- Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Vahagn Makaryan
- Division of General Internal Medicine, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Amber A Burt
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | - David R Crosslin
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Daniel Seung Kim
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Joshua D Smith
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Alex P Reiner
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, United States of America
| | - Stephen S Rich
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Rebecca D Jackson
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America
| | - Santhi K Ganesh
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.,Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Linda M Polfus
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas, United States of America
| | - Lihong Qi
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California, United States of America
| | - David C Dale
- Division of General Internal Medicine, School of Medicine, University of Washington, Seattle, Washington, United States of America
| | | | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington, United States of America.,Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
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Blue EM, Sun L, Tintle NL, Wijsman EM. Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond. Genet Epidemiol 2014; 38 Suppl 1:S21-8. [PMID: 25112184 DOI: 10.1002/gepi.21821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide polymorphisms, NGS data, and imputed data are generally concordant but that errors are particularly likely at rare variants, for homozygous genotypes, within regions with repeated sequences or structural variants, and within sequence data imputed from unrelated individuals. Admixture complicated identification of cryptic relatedness, but information from Mendelian transmission improved error detection and provided an estimate of the de novo mutation rate. Computationally, fast rule-based imputation was accurate but could not cover as many loci or subjects as more computationally demanding probability-based methods. Incorporating population-level data into pedigree-based imputation methods improved results. Observed data outperformed imputed data in association testing, but imputed data were also useful. We discuss the strengths and weaknesses of existing methods and suggest possible future directions, such as improving communication between data collectors and data analysts, establishing thresholds for and improving imputation quality, and incorporating error into imputation and analytical models.
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Affiliation(s)
- Elizabeth M Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
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