Genome-wide association studies (GWAS) based on Linkage Disequilibrium (LD) provides a promising tool for detecting and fine mapping of Quantitative trait loci (QTL) underlying complex traits. The LD between the genotyped marker and the causal gene allows the detection of QTL depending on the extent of LD in the association mapping panel.
One of the major objectives of this thesis was to identify QTL for agronomic traits in a diverse spring barley panel using GWAS approach. The association panel comprising of 224 worldwide spring barleys was used for GWAS, as well as for LD and diversity studies. The Phenotypic traits row type (RT), heading date (HD), plant height (PHT), thousand grain weight (TGW), starch content (SC) and crude protein content (CPC) were investigated. The panel was initially genotyped using a customized DNA marker assay (IPK OPA assay) which yielded 918 successful SNPs with approximate genome coverage of one SNP per 1.18 cM. Average LD was observed to decay below a critical level (r2-value= 0.2) within a map distance of 5-10 cM. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. The mixed linear model (MLM) with kinship to control spurious LD effects, performed best in this panel. Using MLM with kinship (K-model), a total of 171 significant marker trait associations were detected, which delineated into 107 QTL regions. Across all traits these were grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions.
These 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 accounted only for a small portion of phenotypic variation. 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.
123
Consequently the current spring barley panel was genotyped using a newly established iSelect assay, which yielded 7000 successful SNPs. Finally 6467 SNPs were used for GWAS, after the SNPs with minor allele frequency less than 5% were excluded. Multifold increase in the number of SNPs associating with the trait was observed. The significance of the associations also increased in many cases with new markers in the region. The effects of use of different kinship matrices on the GWAS results were compared. The kinship matrix generated using evenly distributed 362 SNPs excelled in performance when used with K-model. GWA scans with iSelect SNPs showed 297, 269, 240, 266, 304 and 245 SNPs associating with the traits RT, HD, PHT, TGW, SC and CPC respectively. In GWAS with iSelect SNPs, for most of the traits we detected associations closely linked to major candidate genes affecting the trait. It is possible to predict candidate genes underlying a QTL in few cases by using the genome models exploiting the syntenic conservation between rice, Brachypodium and barley.
The variance explained by individual associated marker also increased for each trait when compared to GWAS results with SNPs from IPK OPA assay. However, the variances are still much less when compared to those observed in bi-parental QTL mapping. This study demonstrates the advantages of an increased marker density on the number of QTL detected and on QTL significances. The resolution of the panel and the marker density are sufficient for detecting QTL, but for further fine mapping or gene identification the present resolution is still limiting in many cases. Therefore, a large population of spring landraces was developed for increased genetic resolution that can be used in fine mapping of traits
Landraces offer important genetic resources for cultivated barley, which has narrow genetic background due to intensive breeding. Besides, LD decays faster in landraces than in the cultivar collection. This different LD patterns can be exploited for high resolution mapping of the QTL using GWAS in cultivar and landrace collections. Therefore, it is pragmatic to study the genetics of the traits in landrace collections for precise mapping, and also before they are utilized for crop improvement. Hence we investigated a large collection of barley landraces (1491 accessions) for genetic diversity and population structure to establish spring barley landrace association mapping
124
population. The collection comprises two-rowed, six-rowed, naked and hulled barleys from 41 countries. The landrace collection was evaluated with 45 SSR markers to assess the genetic diversity, population structure and genetic differentiation among the collection. A total of 372 alleles among which 152 are rare alleles (allele frequency < 1%) were detected. The collection was diverse with an average gene diversity of 0.603 and average allelic richness over all loci was 5.74.
The landraces were differentiated into subgroups majorly based on row type and their geographical origins. The genetic distance between the accessions was significantly correlated with the geographical, latitudinal, longitudinal distances and also with other eco-geographical parameters.
Beyond providing insights into the diversity, our data allow to construct core groups based on maximizing allelic diversity approaches. Different core groups were generated for heat adaptation studies from the whole collection and compared the genetic diversity among these groups. Core groups above the size of 800 accessions showed similar allelic richness equal to the whole collection. Further small core groups showed declining allelic richness with the sample size.
Simulating populations of different sizes from this landrace collection and comparing their genetic diversity revealed that the population size n=745 captures the diversity present in the whole collection. This collection can also be used for allele mining strategies to discover new alleles, as already our results indicate high allelic diversity in the collection. For further research, the landrace collection can be used for fine mapping of these detected QTL. The markers corresponding to the QTL detected in this study can be verified in other populations and then efficiently used for barley crop improvement.
125