Trait Based Association Mapping in Plants

Genome-wide association studies (GWAS) estimate the likelihood of association between genotype and phenotype in any large collection of material and hence has become an indispensable tool in genetic and molecular dissection of traits. GWAS is critically dependent on the pattern of linkage disequilibrium (LD) within the genome which in turn depends on the interplay of evolutionary forces. The rapid evolution of technologies to genotype single nucleotide polymorphisms through sequencing, PCR and array-based approaches have not only made fine mapping and identification of a lot of common but unknown variants possible for traits of economical, evolutionary and social interest, but also brought in a lot of big data statistics into plant biology. Further, the release of reference scale genomes in many agriculturally important plants have led to an explosion in the number of GWAS studies in plants. GWAS coupled with phenomics have given a fillip to molecular dissection of traits that are functionally relevant but difficult to phenotype. Breeding populations, a variety of germplasm resources and ephemeral lines, even with unknown pedigree structure, can be used in association mapping to find SNP markers related to traits of interest. Association mapping (AM) studies do have their disadvantages. The foremost one is the failure to detect rare variants associated with traits as crop plants have adapted to different sets of rare variants in different ecological niches. The rare variant problem is more aggravated in plant AM than animal species. This has led to meta-analysis of variants identified by different studies in a species and use of multi-parental populations for joint linkage and LD analysis. The availability of pan genome data in Arabidopsis has enabled the development of a meta-analysis-based catalogue of trait specific variants, available in the form of AraGWAS. In the post genomics era, pathway analysis and methylation based GWAS analysis have also come into the fore. This chapter briefly discusses the fundamental concept in AM studies, frequently used SNP genotyping methods, different statistical approaches available for GWAS and the way forward in trait-based AM in plants.