Dataset Information

Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations.

ABSTRACT: To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models.

SUBMITTER: Zhu J

PROVIDER: S-EPMC1851982 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundFatty acid composition of oil extracted from peanut (Arachis hypogaea L.) seed is an important quality trait because it may affect the flavor and shelf life of resulting food products. In particular, a high ratio of oleic (C18:1) relative to linoleic (C18:2) fatty acid (O/L ≥ 10) results in a longer shelf life. Previous reports suggest that the high oleic (~80%) trait was controlled by recessive alleles of ahFAD2A and ahFAD2B, the former of which is thought to have a high frequency in US runner- and virginia-type cultivars. Functional mutations, G448A in ahFAD2A and 442insA in ahFAD2B eliminate or knock down desaturase activity and have been demonstrated to produce peanut oil with high O/L ratios. In order to employ marker assisted selection (MAS) to select a high oleic disease resistant peanut and to evaluate genotypic and phenotypic variation, crosses were made between high oleic (~80%) and normal oleic (~50%) peanuts to produce segregating populations.ResultsA total of 539 F2 progenies were randomly selected to empirically determine each ahFAD2 genotype and the resulting fatty acid composition. Five of the six crosses segregated for the high oleic trait in a digenic fashion. The remaining cross was consistent with monogenic segregation because both parental genotypes were fixed for the ahFAD2A mutation. Segregation distortion was significant in ahFAD2A in one cross; however, the remaining crosses showed no distortion. Quantitative analyses revealed that dominance was incomplete for the wild type allele of ahFAD2, and both loci showed significant additive effects. Oleic and linoleic acid displayed five unique phenotypes, based on the number of ahFAD2 mutant alleles. Further, the ahFAD2 loci did exhibit pleiotropic interactions with palmitic (C16:0), oleic (C18:1), linoleic (C18:2) acids and the O/L ratio. Fatty acid levels in these progeny were affected by the parental genotype suggesting that other genes also influence fatty acid composition in peanut. As far as the authors are aware, this is the first study in which all of the nine possible ahFAD2 genotypes were quantitatively measured.ConclusionsThe inheritance of the high oleic trait initially was suggested to be controlled by dominant gene action from two homoeologous genes (ahFAD2A and ahFAD2B) exhibiting complete recessivity. Analyzing the ahFAD2 genotypes and fatty acid compositions of these segregating peanut populations clearly demonstrated that the fatty acid contents are quantitative in nature although much of the variability in the predominant fatty acids (oleic, linoleic, and palmitic) is controlled by only two loci.

Dataset Information

Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets