Effects of Genetic and Environmental Factors on Trait Network Predictions From Quantitative Trait Locus Data

Department of Biology, University of North Carolina, Greensboro, North Carolina 27402-6170, USA.
Genetics (Impact Factor: 5.96). 02/2009; 181(3):1087-99. DOI: 10.1534/genetics.108.092668
Source: PubMed


The use of high-throughput genomic techniques to map gene expression quantitative trait loci has spurred the development of path analysis approaches for predicting functional networks linking genes and natural trait variation. The goal of this study was to test whether potentially confounding factors, including effects of common environment and genes not included in path models, affect predictions of cause-effect relationships among traits generated by QTL path analyses. Structural equation modeling (SEM) was used to test simple QTL-trait networks under different regulatory scenarios involving direct and indirect effects. SEM identified the correct models under simple scenarios, but when common-environment effects were simulated in conjunction with direct QTL effects on traits, they were poorly distinguished from indirect effects, leading to false support for indirect models. Application of SEM to loblolly pine QTL data provided support for biologically plausible a priori hypotheses of QTL mechanisms affecting height and diameter growth. However, some biologically implausible models were also well supported. The results emphasize the need to include any available functional information, including predictions for genetic and environmental correlations, to develop plausible models if biologically useful trait network predictions are to be made.

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    • "Once the fit of all possible path models was evaluated, the best fitted model was required to fit the following four criteria as defined previously [46-48]: (1) Goodness of Fit Test P value ≥0.05 (indicating how likely the hypothesis is, or how well the observed data fit the expectation of the model); (2) 0.9 < Goodness of Fit Index (GoFI) ≤1; (3) Root Mean Squared Error Approximation (RMSEA) ≤0.05; (4) smallest negative Bayesian Information Criterion (BIC). Where multiple models fit to the data, the best fitted model was selected if its BIC was at least two units smaller than the next lowest BIC [48], otherwise none was selected. "
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