A statistical perspective of gene set analysis with trait-specific QTL in molecular crop breeding

Over the last decade, gene set analysis (GSA) has become the first choice for gaining insights into underlying complex biology of stresses in plants through gene expression (GE) studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait-specific phenotype. In plant biology and breeding, analysis of gene sets with trait-specific quantitative trait loci (QTLs) data are considered as great source for biological knowledge discovery. Therefore we discuss various aspects of the GSA with QTLs, such as null hypothesis, sampling model, and nature of the test statistic, for interpreting high-throughput GE data in context of gene sets with the traits. Here, we also presented the key biological and statistical challenges in current GSA, which will be addressed by statisticians and biologists collectively in order to develop the next generation of GSA approaches.