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Akbarzadeh M, Moghimbeigi A, Morris N, Daneshpour MS, Mahjub H, Soltanian AR. A Bayesian structural equation model in general pedigree data analysis. Stat Anal Data Min 2019. [DOI: 10.1002/sam.11434] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences Tehran Iran
| | - Abbas Moghimbeigi
- Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics, School of Public HealthHamadan University of Medical Sciences Hamadan Iran
| | - Nathan Morris
- Department of Population and Quantitative Health SciencesCase Western Reserve University Cleveland Ohio
| | - Maryam S. Daneshpour
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical Sciences Tehran Iran
| | - Hossein Mahjub
- Research Center for Health Sciences and Department of Biostatistics, School of Public HealthHamadan University of Medical Sciences Hamadan Iran
| | - Ali Reza Soltanian
- Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics, School of Public HealthHamadan University of Medical Sciences Hamadan Iran
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Lewis BA, Freebairn L, Tag J, Benchek P, Morris NJ, Iyengar SK, Taylor HG, Stein CM. Heritability and longitudinal outcomes of spelling skills in individuals with histories of early speech and language disorders. LEARNING AND INDIVIDUAL DIFFERENCES 2018; 65:1-11. [PMID: 30555216 DOI: 10.1016/j.lindif.2018.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This study examined the spelling skills in middle childhood and adolescence in individuals with histories of early childhood speech sound disorders (SSD) with and without language impairment (LI). Youth without such histories were also included (No SSD/LI group). The heritability of spelling skills at each age level was estimated. Children with SSD were classified as SSD-only, SSD with LI but without childhood apraxia of speech (SSD + LI/ No CAS), and CAS and LI (CAS + LI). The SSD-only group did not differ in spelling from the No SSD/LI group, suggesting that SSD-only did not increase risk for poor spelling. The SSD + LI/No CAS and CAS + LI groups had poorer spelling skills than the SSD-only and No SSD/LI groups. Spelling was associated with phonological awareness in the middle childhood and adolescent samples and with rapid automatized naming in the adolescent sample. Heritability of spelling skills was stronger in adolescence than in middle childhood. Differences in the correlates of spelling and in heritability at the two ages suggest developmental changes in the factors contributing to spelling.
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Affiliation(s)
- Barbara A Lewis
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Lisa Freebairn
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Jessica Tag
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Penelope Benchek
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Nathan J Morris
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - H Gerry Taylor
- Biobehavioral Health Center, Nationwide Children's Hospital Research Institute, and Department of Pediatrics, The Ohio State University, Columbus, OH, United States
| | - Catherine M Stein
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
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Abstract
Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.
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Affiliation(s)
- Catherine M Stein
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, OH, 44106-7281, USA.
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA.
| | - Nathan J Morris
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, OH, 44106-7281, USA
| | - Noémi B Hall
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, OH, 44106-7281, USA
| | - Nora L Nock
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, 2103 Cornell Road, Cleveland, OH, 44106-7281, USA
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Bruni M, Flax JF, Buyske S, Shindhelm AD, Witton C, Brzustowicz LM, Bartlett CW. Behavioral and Molecular Genetics of Reading-Related AM and FM Detection Thresholds. Behav Genet 2016; 47:193-201. [PMID: 27826669 DOI: 10.1007/s10519-016-9821-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 09/28/2016] [Indexed: 12/24/2022]
Abstract
Auditory detection thresholds for certain frequencies of both amplitude modulated (AM) and frequency modulated (FM) dynamic auditory stimuli are associated with reading in typically developing and dyslexic readers. We present the first behavioral and molecular genetic characterization of these two auditory traits. Two extant extended family datasets were given reading tasks and psychoacoustic tasks to determine FM 2 Hz and AM 20 Hz sensitivity thresholds. Univariate heritabilities were significant for both AM (h 2 = 0.20) and FM (h 2 = 0.29). Bayesian posterior probability of linkage (PPL) analysis found loci for AM (12q, PPL = 81 %) and FM (10p, PPL = 32 %; 20q, PPL = 65 %). Bivariate heritability analyses revealed that FM is genetically correlated with reading, while AM was not. Bivariate PPL analysis indicates that FM loci (10p, 20q) are not also associated with reading.
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Affiliation(s)
- Matthew Bruni
- The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Judy F Flax
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers The State University of New Jersey, Piscataway, NJ, USA
| | - Steven Buyske
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers The State University of New Jersey, Piscataway, NJ, USA.,Department of Statistics, Rutgers The State University of New Jersey, Piscataway, NJ, USA
| | - Amber D Shindhelm
- The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Caroline Witton
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK
| | - Linda M Brzustowicz
- Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers The State University of New Jersey, Piscataway, NJ, USA
| | - Christopher W Bartlett
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH, USA. .,Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital & The Ohio State University, 575 Children's Crossroad, Columbus, OH, 43205, USA.
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Song YE, Morris NJ, Stein CM. Structural equation modeling with latent variables for longitudinal blood pressure traits using general pedigrees. BMC Proc 2016; 10:303-307. [PMID: 27980653 PMCID: PMC5133482 DOI: 10.1186/s12919-016-0047-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates. The single nucleotide polymorphism (SNP) genotype information from genome-wide association studies (GWAS) data was used for the test of association. The adjustment for hypertension treatment effect was done by the censored regression approach. Two different longitudinal data models, autoregressive model and latent growth curve model, were used to fit the longitudinal BP traits. The test of association for SNP was done using a novel score test within the SEM framework of strum. We found the 10 SNPs within the GWAS suggestive P value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general SEM framework in strum is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data.
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Affiliation(s)
- Yeunjoo E Song
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Nathan J Morris
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA ; Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Catherine M Stein
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA ; Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106 USA
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Schillert A, Konigorski S. Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19. BMC Genet 2016; 17 Suppl 2:7. [PMID: 26866608 PMCID: PMC4895558 DOI: 10.1186/s12863-015-0317-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.
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Affiliation(s)
- Arne Schillert
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Stefan Konigorski
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Straße 10, 13125, Berlin-Buch, Germany.
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