Genome-wide association studies for agronomical traits in a world wide spring barley collection

Abstract

Background: Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS.

Results: Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions.

Conclusions: Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.

Figure 1

SNP marker efficiency in the panel. Percentage of distribution of MAF of SNPs in the panel. SNPs with MAF < 0.05 were excluded from the analysis

Figure 2

STRUCTURE results using 918 SNPs. Log probability data (LnP(D)) as function of k (number of clusters) from the STRUCTURE run. The plateau of the graph at K = 6 indicates the minimum number of subgroups possible in the panel

Figure 3

Population sub-structuring in the panel. Bayesian clustering of the 212 barley accessions into six defined groups (G1, G2, G3, G4, G5, G6) based on 918 SNP markers. The number of accessions per group and their respective geographical origin and row type is presented

Figure 4

Intra-chromosomal LD (r²) decay of marker pairs over all chromosomes as a function of genetic distance (cM). The horizontal line indicates the 95th percentile distribution of unlinked r². The LOESS fitting curve (red line) illustrates the LD decay

Figure 5

Comparison of LD patterns and LD decay in the whole panel and subgroups. Mean r² values are plotted against the genetic distance for different groups

Figure 6

Evaluating the mapping resolution of the panel. Distribution of SNPs according to their re-mapped distances using the K-model of genome-wide association approach. The group 'identical' refers to the SNPs that mapped at exactly the same position and the group '0' refers to the SNPs that mapped within a distance of 0.01 to 0.99 cM

Figure 7

Comparison of different GWA models. Cumulative distribution of P-values computed from 918 SNPs and row-type phenotype for different association models are presented. The more uniform the distribution of P-values, the better is the model

Figure 8

GWA scan for the trait row type using 918 SNPs with K-model for statistical correction of population structure. Vertical axis represents -log10(P) values of the P-value of the marker trait association. SNPs in the vicinity of the genes vrs1. vrs2. vrs3, vrs4 and int-c are marked with arrows

Figure 9

GWA scans for traits HD (9a), PHT (9b), TGW (9c), SC (9 d) and CPC (9e) using 918 SNPs and the K-model. Vertical axis represents -log10(P) values of the P-value of the marker trait association. The peaks above minimum threshold of 1.5 (P-value = 0.03) can be considered as significantly associated

Figure 10

Association analysis for the trait HD for chromosome 2H with SNPs from IPK-OPA and the resequenced PpdH1 fragment [14]. Blue circles represent the IPK-OPA SNPs on chromosome 2H, Green circles represent the IPK-OPA SNPs that are significantly associated with HD and are in vicinity of the PpdH1 gene, green triangles represent SNPs from the re-sequenced PpdH1 fragment