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Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models.

ABSTRACT: We present a method for examining the relative influence of familial, genetic, and environmental covariate information in flexible nonparametric risk models. Our goal is investigating the relative importance of these three sources of information as they are associated with a particular outcome. To that end, we developed a method for incorporating arbitrary pedigree information in a smoothing spline ANOVA (SS-ANOVA) model. By expressing pedigree data as a positive semidefinite kernel matrix, the SS-ANOVA model is able to estimate a log-odds ratio as a multicomponent function of several variables: one or more functional components representing information from environmental covariates and/or genetic marker data and another representing pedigree relationships. We report a case study on models for retinal pigmentary abnormalities in the Beaver Dam Eye Study. Our model verifies known facts about the epidemiology of this eye lesion--found in eyes with early age-related macular degeneration--and shows significantly increased predictive ability in models that include all three of the genetic, environmental, and familial data sources. The case study also shows that models that contain only two of these data sources, that is, pedigree-environmental covariates, or pedigree-genetic markers, or environmental covariates-genetic markers, have comparable predictive ability, but less than the model with all three. This result is consistent with the notions that genetic marker data encode--at least in part--pedigree data, and that familial correlations encode shared environment data as well.

SUBMITTER: Bravo HC

PROVIDER: S-EPMC2677979 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ADHD is the most common neurobehavioral disorder in school-aged children. In addition to genetic factors, environmental influences or gene x environmental interactions also play an important role in ADHD. One example of a well studied environmental risk factor for ADHD is exposure to polychlorinated biphenyls (PCBs). In this study, we investigated whether the well-established genetic model of ADHD based on the Spontaneously Hypertensive Rat (SHR) and a well established PCB-based model of ADHD exhibited similar molecular changes in brain circuits involved in ADHD. The brains from 28 male rats (8 SHR, 8 Sprague-Dawley (SD) controls, 8 Wistar-Kyoto (WKY) controls, and 4 PCB-exposed SD rats) were harvested at postnatal day 55-65 and RNA was isolated from six brain regions of interest. The RNA was analyzed for differences in expression of a set of 308 probe sets interrogating 218 unique genes considered highly relevant to ADHD or epigenetic gene regulation using the Rat RAE 230 2.0 GeneChip (Affymetrix). Selected observations were confirmed by real time quantitative RT-PCR. The results show that the expression levels of genes Gnal, COMT, Adrbk1, Ntrk2, Hk1, Syt11 and Csnk1a1 were altered in both the SHR rats and the PCB-exposed SD rats. Arrb2, Stx12, Aqp6, Syt1, Ddc and Pgk1 expression levels were changed only in the PCB-exposed SD rats. Genes with altered expression only in the SHRs included Oprm1, Calcyon, Calmodulin, Lhx1 and Hes6.The epigenetic genes Crebbp, Mecp2 and Hdac5 are significantly altered in both models. The data provide strong evidence that genes and environment can affect different set of genes in two different models of ADHD and yet result in the similar disease-like symptoms. The brains from 28 male rats (8 SHR, 8 Sprague-Dawley (SD) controls, 8 Wistar-Kyoto (WKY) controls, and 4 PCB-exposed SD rats) were harvested at postnatal day 55-65 and RNA was isolated from six brain regions of interest. The RNA was analyzed for differences in expression of a set of 308 probe sets interrogating 218 unique genes considered highly relevant to ADHD or epigenetic gene regulation using the Rat RAE 230 2.0 GeneChip (Affymetrix). Selected observations were confirmed by real time quantitative RT-PCR.

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Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models.

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