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.

Keywords: genome wide association studies; haplotypes; k-mers; multi-parent populations; pangenomes; plant diseases.

Figure 1

Statistical tools and model developed during last two decades. The new models developed improved the statistical power, computational speed and accuracy of detecting candidate genes or genetic loci associated with trait of interest.

Figure 2

Summary of advances in association analysis. Different types of GWAS approaches are arranged in the chronological order, starting with GWAS based on halpotypes, extreme phenotypes, pangenomes, multi-parent populations, k-mers, meta data and transcriptomes. Key feature or major advantage of the approach is also mentioned.

Figure 3

Illustration of genome-wide associations studies to identify genes associated with disease resistance. The partially structured (NAM and MAGIC) and unstructured populations (germplasm lines, association panels) can be used for high throughput phenotyping and genotyping to perform high resolution association mapping with advance tools for genome wide association analysis (GWAS). The peaks identified in GWAS analysis can be used for identification of LD blocks. Each LD block includes one or few candidate genes associated with the trait can be used for validation or development of diagnostic markers for genomics associated breeding. The validated genes can be further used for identification of haplotypes for disease resistance or disease susceptibility.