Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci

Abstract

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 (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ (https://cran.r-project.org/web/packages/GSAQ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

“…Statistical tests of GSA with a competitive null hypothesis are known as competitive GSA approaches, and the underlying null hypothesis can be expressed as, H 0 : genes/SNPs in predefined gene sets are associated with the underlying trait (phenotype) as much as are genes/SNPs outside the predefined gene set, against H 1 : genes/SNPs in predefined gene sets are more associated with the trait (phenotype) than genes outside predefined gene set. Here, the competitive GSA approaches consider genes (SNPs) from both the predefined gene set and the outside gene set [ 6 , 10 ]. The self-contained null hypothesis is invariably more restrictive than the competitive null hypothesis.…”

“…Classical statistical tests are based on an experimental design having microarray/RNA-seq samples as subjects, where each subject has the same set of (GE) measurements [ 6 , 10 , 24 ]. In the usual supervised setting, the sampling model consists of M independent realizations (for M subjects) of ( X 1 , y 1 ), ( X 2 , y 2 ), …, ( X s , y s ), …, ( X M , y M ), where, X s represents the N -dimensional vector ( N : total number of genes) of the GE levels for s-th subject and y s is the corresponding class label (e.g., case: +1 vs. control: −1), s = 1, 2, …, M .…”

“…In the last decade, GSA has completely shifted the focus in GE and association data analysis from individual gene to gene set level [ 7 , 8 , 9 , 10 , 11 ]. Further, GSA has been extensively used in complex disease biology due to the polygenic nature of these disorders.…”

“…Recently, GSA was able to provide biological insights into mechanisms and possible treatment targets for complex diseases, including schizophrenia [ 13 ], bipolar disorder [ 14 ], Crohn’s disease [ 15 ], rheumatoid arthritis [ 16 ], breast cancer [ 17 ], and obesity [ 18 ]. Moreover, GSA has also been applied in plant biology to understand the abiotic stress response mechanisms in Arabidopsis thaliana , Oryza sativa , Zea mays , and Gossypium raimondii [ 9 , 10 ]. The GSA applications have led to novel biological hypotheses about the diseases/stress responses, and have suggested new avenues for molecular drug designing/crop breeding intervention [ 6 , 7 , 10 , 19 , 20 , 21 , 22 ].…”

“…Moreover, GSA has also been applied in plant biology to understand the abiotic stress response mechanisms in Arabidopsis thaliana , Oryza sativa , Zea mays , and Gossypium raimondii [ 9 , 10 ]. The GSA applications have led to novel biological hypotheses about the diseases/stress responses, and have suggested new avenues for molecular drug designing/crop breeding intervention [ 6 , 7 , 10 , 19 , 20 , 21 , 22 ].…”