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Vieland VJ, Seok SC. The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies. PLoS One 2021; 16:e0257164. [PMID: 34550985 PMCID: PMC8457474 DOI: 10.1371/journal.pone.0257164] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/24/2021] [Indexed: 11/18/2022] Open
Abstract
In earlier work, we have developed and evaluated an alternative approach to the analysis of GWAS data, based on a statistic called the PPLD. More recently, motivated by a GWAS for genetic modifiers of the X-linked Mendelian disorder Duchenne Muscular Dystrophy (DMD), we adapted the PPLD for application to time-to-event (TE) phenotypes. Because DMD itself is relatively rare, this is a setting in which the very large sample sizes generally assembled for GWAS are simply not attainable. For this reason, statistical methods specially adapted for use in small data sets are required. Here we explore the behavior of the TE-PPLD via simulations, comparing the TE-PPLD with Cox Proportional Hazards analysis in the context of small to moderate sample sizes. Our results will help to inform our approach to the DMD study going forward, and they illustrate several respects in which the TE-PPLD, and by extension the original PPLD, offer advantages over regression-based approaches to GWAS in this context.
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Affiliation(s)
- Veronica J. Vieland
- Battelle Center for Mathematical Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States of America
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States of America
- Department of Statistics, The Ohio State University, Columbus, OH, United States of America
- * E-mail:
| | - Sang-Cheol Seok
- Battelle Center for Mathematical Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, United States of America
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Wolock S, Yates A, Petrill SA, Bohland JW, Blair C, Li N, Machiraju R, Huang K, Bartlett CW. Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults. J Child Psychol Psychiatry 2013; 54:1109-19. [PMID: 23909413 PMCID: PMC3809890 DOI: 10.1111/jcpp.12119] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2013] [Indexed: 12/25/2022]
Abstract
BACKGROUND Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences. METHODS We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets. RESULTS Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes. CONCLUSIONS This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.
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Affiliation(s)
- Samuel Wolock
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Andrew Yates
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | | | - Jason W. Bohland
- Department of Health Sciences, Boston University, Boston, MA, USA
| | - Clancy Blair
- Department of Applied Psychology, New York University, New York, NY, USA
| | - Ning Li
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Raghu Machiraju
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
,Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
,Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
,The CCC Biomedical Informatics Shared Resource, The Ohio State University Columbus, OH, USA
| | - Christopher W. Bartlett
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
,Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
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Madsen AM, Ottman R, Hodge SE. Causal models for investigating complex genetic disease: II. what causal models can tell us about penetrance for additive, heterogeneity, and multiplicative two-locus models. Hum Hered 2011; 72:63-72. [PMID: 21912139 DOI: 10.1159/000330780] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2011] [Accepted: 07/11/2011] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND/AIMS Statistical geneticists commonly use certain two-locus penetrance models because these models are familiar and mathematically tractable. We investigate whether and under what circumstances these two-locus penetrance models correspond to models of causation. METHODS We describe a sufficient component cause model for a hypothetical disease with two genetic causes. We then use the potential outcomes framework to determine the expected two-locus penetrances from this causal model and contrast them with commonly used two-locus penetrance models (additive, heterogeneity, and multiplicative penetrance models, as formulated by Risch [Am J Hum Genet 1990;46:222-228]). RESULTS Conventional additive and multiplicative models can correspond to any two-locus causal model only when certain very specific algebraic relationships hold. The heterogeneity model corresponds to a two-locus causal model only if the model stipulates that no disease cases are caused by the combined presence of the causal genotypes at both loci (i.e. only when there is no causal gene-gene interaction). Hence the heterogeneity model provides a valid test of the null hypothesis of no gene-gene interaction, whereas the additive and multiplicative models do not. CONCLUSION We suggest that causal principles should provide the basis for statistical modeling in genetics.
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Affiliation(s)
- Ann M Madsen
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
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Madsen AM, Hodge SE, Ottman R. Causal models for investigating complex disease: I. A primer. Hum Hered 2011; 72:54-62. [PMID: 21912138 DOI: 10.1159/000330779] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2011] [Accepted: 07/11/2011] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND/AIMS To illustrate the utility of causal models for research in genetic epidemiology and statistical genetics. Causal models are increasingly applied in risk factor epidemiology, economics, and health policy, but seldom used in statistical genetics or genetic epidemiology. Unlike the statistical models usually used in genetic epidemiology, causal models are explicitly formulated in terms of cause and effect relationships occurring at the individual level. METHODS We describe two causal models, the sufficient component cause model and the potential outcomes model, and show how key concepts in genetic epidemiology, including penetrance, phenocopies, genetic heterogeneity, etiologic heterogeneity, gene-gene interaction, and gene-environment interaction, can be framed in terms of these causal models. We also illustrate how potential outcomes models can provide insight into the potential for confounding and bias in the measurement of causal effects in genetic studies. RESULTS Our analysis illustrates how causal models can elucidate the relationships among underlying causal mechanisms and measures obtained from statistical analysis of observed data. CONCLUSION Causal models can enhance research aimed at identifying causal genes.
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Affiliation(s)
- Ann M Madsen
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
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Simmons TR, Flax JF, Azaro MA, Hayter JE, Justice LM, Petrill SA, Bassett AS, Tallal P, Brzustowicz LM, Bartlett CW. Increasing genotype-phenotype model determinism: application to bivariate reading/language traits and epistatic interactions in language-impaired families. Hum Hered 2010; 70:232-44. [PMID: 20948219 PMCID: PMC3085518 DOI: 10.1159/000320367] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2010] [Accepted: 08/13/2010] [Indexed: 11/19/2022] Open
Abstract
While advances in network and pathway analysis have flourished in the era of genome-wide association analysis, understanding the genetic mechanism of individual loci on phenotypes is still readily accomplished using genetic modeling approaches. Here, we demonstrate two novel genotype-phenotype models implemented in a flexible genetic modeling platform. The examples come from analysis of families with specific language impairment (SLI), a failure to develop normal language without explanatory factors such as low IQ or inadequate environment. In previous genome-wide studies, we observed strong evidence for linkage to 13q21 with a reading phenotype in language-impaired families. First, we elucidate the genetic architecture of reading impairment and quantitative language variation in our samples using a bivariate analysis of reading impairment in affected individuals jointly with language quantitative phenotypes in unaffected individuals. This analysis largely recapitulates the baseline analysis using the categorical trait data (posterior probability of linkage (PPL) = 80%), indicating that our reading impairment phenotype captured poor readers who also have low language ability. Second, we performed epistasis analysis using a functional coding variant in the brain-derived neurotrophic factor (BDNF) gene previously associated with reduced performance on working memory tasks. Modeling epistasis doubled the evidence on 13q21 and raised the PPL to 99.9%, indicating that BDNF and 13q21 susceptibility alleles are jointly part of the genetic architecture of SLI. These analyses provide possible mechanistic insights for further cognitive neuroscience studies based on the models developed herein.
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Affiliation(s)
- Tabatha R Simmons
- Battelle Center for Mathematical Medicine, Research Institute at Nationwide Children's Hospital and Department of Pediatrics, Ohio State University, Columbus, OH 43205, USA
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