Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data.

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用于作物基因表达研究的定量性状基因座基因集分析的统计方法

全基因组表达研究是一种强大的基因组技术，用于量化基因组中基因的表达动态。在基因表达研究中，基因集分析已成为深入了解植物疾病或逆境生物学基础的首选。它还降低了统计分析的复杂性，并增强了初级下游差异表达分析所获得结果的解释力。基因组分析方法在微阵列和 RNA-seq 基因表达数据分析中得到了很好的发展。这些方法主要集中于使用基因本体或通路注释数据来分析基因集。然而，在植物生物学中，此类方法可能无法在基因型和表型之间建立任何正式关系，因为大多数性状都是定量的并由多基因控制。现有的基于数量性状位点（QTL）的基因集分析方法仅关注所选基因的过度代表性分析，而忽略了其相关基因评分。因此，我们开发了一种创新的统计方法 GSQSeq，用性状丰富的 QTL 数据来分析基因集。该方法在分析基因集时考虑基因的相关差异表达分数。在从真实作物基因表达研究中获得的五种不同作物基因表达数据集上测试了所开发方法的性能。我们的分析结果表明，通过所提出的方法对基因集的性状特异性分析比现有技术更加稳健和成功。此外，所开发的方法为整合基因表达数据与QTL数据提供了一个有价值的平台。