GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley - A review

Abstract

Understanding the genetic complexity of traits is an important objective of small grain temperate cereals yield and adaptation improvements. Bi-parental quantitative trait loci (QTL) linkage mapping is a powerful method to identify genetic regions that co-segregate in the trait of interest within the research population. However, recently, association or linkage disequilibrium (LD) mapping using a genome-wide association study (GWAS) became an approach for unraveling the molecular genetic basis underlying the natural phenotypic variation. Many causative allele(s)/loci have been identified using the power of this approach which had not been detected in QTL mapping populations. In barley (Hordeum vulgare L.), GWAS has been successfully applied to define the causative allele(s)/loci which can be used in the breeding crop for adaptation and yield improvement. This promising approach represents a tremendous step forward in genetic analysis and undoubtedly proved it is a valuable tool in the identification of candidate genes. In this review, we describe the recently used approach for genetic analyses (linkage mapping or association mapping), and then provide the basic genetic and statistical concepts of GWAS, and subsequently highlight the genetic discoveries using GWAS. The review explained how the candidate gene(s) can be detected using state-of-art bioinformatic tools.

Keywords: Association mapping; Barley breeding, GWAS; Gene identification; Hordeum vulgare L; QTL mapping.

Graphical abstract

Fig. 1

Visualization of population structure and number of subpopulations within the population. No clear population structure (a), whereas the population was well-structured (b). Log probability data as function of k (number of clusters/subpopulations) from the STRUCTURE run. No number of subpopulations (c), while two subpopulations are shown in (d). Each color in (a and b) represents a subgroup and each dot represents an accession/individual. PCA, principal component analysis.

Fig. 2

The most important three stages for performing a successful GWAS experiment. Stage I: Phenotyping, stage II: Genotyping and stage III: Genome-wide association study including statistical models, multiple-testing analyses, and software/packages for QTL and gene identification.

Fig. 3

The output results of GWAS. Manhattan plot (a). Horizontal-axis represents the position of markers over the barley chromosomes and vertical-axis represents -log₁₀(P-values) of the marker-trait association. Each dot denotes marker. Horizontal blue-line represents threshold of -log₁₀(0.001) and red-line represents the threshold of -log₁₀(p-value) passing false-discovery rate (FDR). Quantile-quantile (QQ) plot of different GWAS models (b). The plot shows the expected vs. observed -log₁₀(p-value) of each marker (dote). Red-line is the standered relationship among markers. General linear models (GLM), mixed linear models (MLM) and compressed MLM (CMLM). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Summary of the most important genes distributed over barley chromosomes, which are involved in developmental and agronomic traits.