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Estimation and Testing of Gene Expression Heterosis.

ABSTRACT: Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid off-spring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression heterosis. Standard statistical approaches for separately analyzing expression data for each gene can produce biased and highly variable estimates and unreliable tests of heterosis. To address these shortcomings, we develop a hierarchical model to borrow information across genes. Using our modeling framework, we derive empirical Bayes estimators and an inference strategy to identify gene expression heterosis. Simulation results show that our proposed method outperforms the more traditional strategy used to detect gene expression heterosis. This article has supplementary material online.

SUBMITTER: Ji T

PROVIDER: S-EPMC4244911 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Estimation and Testing of Gene Expression Heterosis.

Ji Tieming T Liu Peng P Nettleton Dan D

Journal of agricultural, biological, and environmental statistics 20140901 3

Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid off-spring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression hete ...[more]

PMID: 25435758

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Project description:BackgroundSustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable.ResultsTwo methodologies for assessing predictive error are described: a cross-validation method and a posterior predictive method. As a nonparametric method of estimating prediction error from observed expression levels, cross validation provides an empirical approach to assessing algorithms for detecting differential gene expression that is fully justified for large numbers of biological replicates. Because it leverages the knowledge that only a small portion of genes are differentially expressed, the posterior predictive method is expected to provide more reliable estimates of algorithm performance, allaying concerns about limited biological replication. In practice, the posterior predictive method can assess when its approximations are valid and when they are inaccurate. Under conditions in which its approximations are valid, it corroborates the results of cross validation. Both comparison methodologies are applicable to both single-channel and dual-channel microarrays. For the data sets considered, estimating prediction error by cross validation demonstrates that empirical Bayes methods based on hierarchical models tend to outperform algorithms based on selecting genes by their fold changes or by non-hierarchical model-selection criteria. (The latter two approaches have comparable performance.) The posterior predictive assessment corroborates these findings.ConclusionsAlgorithms for detecting differential gene expression may be compared by estimating each algorithm's error in predicting expression ratios, whether such ratios are defined across microarray channels or between two independent groups.According to two distinct estimators of prediction error, algorithms using hierarchical models outperform the other algorithms of the study. The fact that fold-change shrinkage performed as well as conventional model selection criteria calls for investigating algorithms that combine the strengths of significance testing and fold-change estimation.

Project description:IntroductionNumerous crops have transitioned to hybrid seed production to increase yields and yield stability through heterosis. However, the molecular mechanisms underlying heterosis and its stability across environments are not yet fully understood.ObjectivesThis study aimed to (1) elucidate the genetic and molecular mechanisms underlying heterosis in sunflower, and (2) determine how heterosis is maintained under different environments.MethodsGenome-wide association (GWA) analyses were employed to assess the effects of presence/absence variants (PAVs) and stop codons on 16 traits phenotyped in the sunflower association mapping population at three locations. To link the GWA results to transcriptomic variation, we sequenced the transcriptomes of two sunflower cultivars and their F1 hybrid (INEDI) under both control and drought conditions and analyzed patterns of gene expression and alternative splicing.ResultsThousands of PAVs were found to affect phenotypic variation using a relaxed significance threshold, and at most such loci the "absence" allele reduced values of heterotic traits, but not those of non-heterotic traits. This pattern was strengthened for PAVs that showed expression complementation in INEDI. Stop codons were much rarer than PAVs and less likely to reduce heterotic trait values. Hybrid expression patterns were enriched for the GO category, sensitivity to stimulus, but all genotypes responded to drought similarily - by up-regulating water stress response pathways and down-regulating metabolic pathways. Changes in alternative splicing were strongly negatively correlated with expression variation, implying that alternative splicing in this system largely acts to reinforce expression responses.ConclusionOur results imply that complementation of expression of PAVs in hybrids is a major contributor to heterosis in sunflower, consistent with the dominance model of heterosis. This mechanism can account for yield stability across different environments. Moreover, given the much larger numbers of PAVs in plant vs. animal genomes, it also offers an explanation for the stronger heterotic responses seen in the former.

Dataset Information

Estimation and Testing of Gene Expression Heterosis.

Publications

Estimation and Testing of Gene Expression Heterosis.

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