Two decades of association mapping: Insights on disease resistance in major crops

Abstract

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

Genome-Wide Association Study (GWAS) for Identifying SNPs and Genes Related to Phosphate-Induced Phenotypic Traits in Tomato (Solanum lycopersicum L.)

“…The key advantages of using the BLINK model in the GWAS include its ability to mitigate false positive associations caused by population stratification and relatedness, making it particularly suitable for diverse study populations. The BLINK model also offers improved statistical power to detect true genetic associations, thereby increasing the likelihood of identifying relevant genetic variants responsible for complex traits or diseases [21]. Candidate genes were mapped on the tomato genome browser [https://plants.ensembl.org/Solanum_lycopersicum, accessed on 3 October 2023] within 70-120 kilobases from the identified SNP.…”

Major viral diseases in grain legumes: designing disease resistant legumes from plant breeding and OMICS integration

“…Advances in next-generation sequencing-based approaches and bioinformatic analyses have facilitated the identification of target genomic regions conferring disease resistance in various crops, including grain legumes ( Thudi et al., 2020 ; Gangurde et al., 2022 ). Further, these technologies have enabled WGRS of large-scale global germplasm and pangenome construction for various legumes, including soybean ( Li et al., 2014 ), chickpea ( Varshney et al., 2021 ), pigeon pea ( Zhao et al., 2020 ), cowpea ( Liang et al., 2022 ), and mungbean ( Liu et al., 2022b ).…”

Multi-locus genome-wide association study of chickpea reference set identifies genetic determinants of Pratylenchus thornei resistance

“…GWAS takes advantage of evolutionary recombination events in crops to determine the molecular variations associated with the traits including disease responses (Nordborg and Weigel, 2008;Braulio and Sylvie, 2012). It has been widely used in different crop species to map genomic loci associated with economically important traits (Gangurde et al, 2022). In chickpea, genomic regions associated with Ascochyta blight (Li et al, 2017;Farahani et al, 2022;Raman et al, 2022), Pythium ultimum (Agarwal et al, 2022), nutrient traits (Diapari et al, 2014;Jadhav et al, 2015;Upadhyaya et al, 2016a;Upadhyaya et al, 2016b) and abiotic stress (Thudi et al, 2014) have been mapped using GWAS.…”