Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Abstract

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Keywords: Genome-wide association studies; Genomic selection; High-throughput phenotyping; Machine and deep learning; Wheat.

Fig. 1

Timeline of advancements in genotyping of whole-genome variants employed for GWAS in wheat

Fig. 2

The number of publications related to GWAS in wheat published from 01/01/2009 to 31/12/2020. Source: PubMed (keywords “wheat AND GWAS” were used to search the number of publications in PubMed

Fig. 3

Histogram showing the number of GWAS over the last decade under different categories

Fig. 4

Histogram showing the number of GWAS over the last decade for various a biotic stresses and b abiotic stresses. Trait name is given as reported in published reports

Fig. 5

Histogram showing the number of GWAS over the last decade for various a agronomic and b quality traits. Trait name is given as reported in published reports

Fig. 6

Steps are depicted for GWAS assisted genomic selection. The results from GWA studies (association population) are used as fixed effects in the genomic selection pipeline. Genomic selection models are trained on previous data sets and significant QTLs are included as fixed effects during the prediction of breeding values. Concept for development of this figure is taken from (Crossa et al. ; Sehgal et al. 2020b; Sandhu et al. 2021c)