GWAS for plant growth stages and yield components in spring wheat (Triticum aestivum L.) harvested in three regions of Kazakhstan
© The Author(s). 2017
Published: 14 November 2017
Spring wheat is the largest agricultural crop grown in Kazakhstan with an annual sowing area of 12 million hectares in 2016. Annually, the country harvests around 15 million tons of high quality grain. Despite environmental stress factors it is predicted that the use of new technologies may lead to increases in productivity from current levels of 1.5 to up to 3 tons per hectare. One way of improving wheat productivity is by the application of new genomic oriented approaches in plant breeding projects. Genome wide association studies (GWAS) are emerging as powerful tools for the understanding of the inheritance of complex traits via utilization of high throughput genotyping technologies and phenotypic assessments of plant collections. In this study, phenotyping and genotyping data on 194 spring wheat accessions from Kazakhstan, Russia, Europe, and CIMMYT were assessed for the identification of marker-trait associations (MTA) of agronomic traits by using GWAS.
Field trials in Northern, Central and Southern regions of Kazakhstan using 194 spring wheat accessions revealed strong correlations of yield with booting date, plant height, biomass, number of spikes per plant, and number of kernels per spike. The accessions from Europe and CIMMYT showed high breeding potential for Southern and Central regions of the country in comparison with the performance of the local varieties. The GGE biplot method, using average yield per plant, suggested a clear separation of accessions into their three breeding origins in relationship to the three environments in which they were evaluated. The genetic variation in the three groups of accessions was further studied using 3245 polymorphic SNP (single nucleotide polymorphism) markers. The application of Principal Coordinate analysis clearly grouped the 194 accessions into three clades according to their breeding origins. GWAS on data from nine field trials allowed the identification of 114 MTAs for 12 different agronomic traits.
Field evaluation of foreign germplasm revealed its poor yield performance in Northern Kazakhstan, which is the main wheat growing region in the country. However, it was found that EU and CIMMYT germplasm has high breeding potential to improve yield performance in Central and Southern regions. The use of Principal Coordinate analysis clearly separated the panel into three distinct groups according to their breeding origin. GWAS based on use of the TASSEL 5.0 package allowed the identification of 114 MTAs for twelve agronomic traits. The study identifies a network of key genes for improvement of yield productivity in wheat growing regions of Kazakhstan.
Hexaploid bread wheat (Triticum aestivum L.) is a major commodity for export in Kazakhstan and is grown annually on more than 12 million hectares. The history of wheat cultivation in Kazakhstan shows that most of wheat cultivars have been developed in collaboration with Russian breeders and using Russian wheat genetic resources . Even after the breakup of the USSR, this trend is still in the place as the two countries share their expertise and genetic resources based on bilateral projects and international activities under the CIMMYT umbrella [2, 3]. It is also found that Kazakh-Russian wheat germplasm is genetically close to wheats from the USA . It is hypothesized that the heavy importation of this crop from Russia at the end of the nineteenth century after the successful introduction of Turkish Red Wheat types to the US by Russian Mennonites  might be a main reason behind the close genetic relationship of Kazakh and US accessions .
Over 80% of the wheat harvesting area in Kazakhstan is grown with spring type and it is cultivated at higher latitudes in parts of the country, including Northern and North-Eastern Kazakhstan. The other important growing regions stretch along the Tian-Shan mountain chain in Southern and South-Eastern parts, where both winter and spring types are grown successfully. The climatic conditions in these regions are very variable, as are the soil types, the temperature during the growing season, the precipitation levels, and the photoperiod length . Therefore, studies of yield performance in different ecological niches are important for strategies in current and future breeding activities across wheat growing regions of the country. It is projected that the improvement of agronomy and use of new breeding methods in this country may lead to the development of new varieties, and, consequently, improve the yield productivity up to 3 tons per hectares . In the past, plant breeders successfully relied on using conventional tools and methodologies. Nowadays, the availability of new genomic tools and resources is leading to new opportunities to dissect the genetic mechanisms of complex traits associated with yield improvement .
As costs for high throughput genotyping are decreasing, genome-wide association studies (GWAS) are becoming a powerful approach for the detection of QTL (quantitative trait loci) associated with wheat agronomic traits, with the final goal of accelerating local breeding activities based on the application of marker-assistant selection [9, 10]. The success of GWAS in wheat is largely based on the development of high-density SNP genotyping platforms by Affymetrix [11, 12] and Illumina [9, 13], which are now providing rich resources for high-throughput genotyping data for wheat diversity panels. In GWAS, detection of significant associations relies primarily on genetic marker coverage, the number of individuals studied, and linkage disequilibrium (LD) between causative and linked polymorphisms [14, 15]. Currently, GWAS has been used successful in hexaploid wheat for identification of QTL for yield components [16–18], abiotic stress resistance [19–21] disease resistance [22, 23], and grain quality [24, 25]. A survey of the literature shows that GWAS is actively applied in wheat studies in many different parts of the World, including North America , Central America , Europe [18, 24], Africa , Australia , and Asia .
Although GWAS has proven to be a very efficient approach for capturing important marker-trait associations (MTA), results reported from studies in different regions of the World are revealing the tendency for a strong influence of the growth environment in which yield QTL are identified with significant genotype x environment interaction revealed (GEI). For instance, results obtained from three different GWAS studies related to identification of QTL for yield performance in Europe , India , and Mexico  showed different responses and QTL for yield components in different parts of the genome. This trend is also confirmed in studies when the same germplasm was tested in different regions of Asia . This outcome is congruent with result reported by Quarrie et al. (2005) from studies using bi-parental mapping populations , and can be explained by the sensitivity to environmental factors at crucial growth phases which determines the potential number of grains per ear . Therefore, the success of regional projects may largely depend on separate, local, GWAS experiments using genotyped adapted germplasm. The main goal of this work was GWAS using spring wheat accessions from Kazakhstan, Russia, Europe, and CIMMYT (Mexico) for identification of MTA in field trials in three diverse environments of Kazakhstan. The study is the first attempt to employ GWAS for identification of important QTL and enhancing of spring wheat breeding projects in this county.
The spring wheat panel consisted of 96 commercial and prospective cultivars from Kazakhstan and the Russian Federation, 38 cultivars from Europe, and 60 CIMCOG (CIMMYT Mexico Core Germplasm) lines (CIMMYT, Mexico) (Additional file 1). Currently, 61 cultivars from Kazakhstan and Russia in this genetic panel have been registered through the State Seed Trials Commission of the Republic of Kazakhstan (2015), and are grown officially in Kazakhstan. The panel also included 29 prospective cultivars developed in Kazakhstan and Russia (Additional file 1). The European cultivar collection predominantly comprised accessions originating in the United Kingdom. The CIMCOG lines are a special population developed for studying opportunities for improvements in photosynthesis and biomass . The field trials were conducted in three different latitude regions of Kazakhstan (Additional file 2), specifically at the Karabalyk breeding station (Northern Kazakhstan), Karaganda breeding Research Institute (Central Kazakhstan), and at the Kazakh Rice Research Institute (Southern Kazakhstan). The collection was planted at each site in randomized experiments each of three replicates in the seasons of 2013–2015. The distance between rows was 15 cm and the distance between plants within a row was 5 cm. The experiments in the Northern and Southern regions were conducted in 1 metre blocks, while in the Southern region the accessions were planted in 3 rows per repetition. In total, the data for mean values of 12 agronomic traits of the 194 hexaploid wheat accessions harvested in nine environments were subjected to further statistical analysis. The 12 traits included the following: days to booting (BD), days to heading time (HT), days to maturity (MT), thermal time at heading (TT-H), thermal time at maturity (TT-M), plant height (PH), peduncle length (PL), number of fertile spikes (NFS), number of kernels per spike (NKS), thousand kernel weight (TKW), dry biomass per plant (BPP) and yield per plant (YPP).
DNA samples were extracted and purified from single seeds of individual cultivars using commercial kits (Qiagene, CA, USA). The DNA concentration for each sample was adjusted to 50 ng/μl. Accessions were genotyped using the wheat 90 K Illumina iSelect SNP array as described in .
Statistical analyses of data, including multiple factor ANOVA, Pearson’s correlation and t-test were calculated using the software package GraphPad Prism 5.0 . GGE Biplot methods were employed by using the GenStat package (17th release, VSN International, Hertfordshire, UK). The symmetric scaling option of both methods and available field data for all three sites were used in estimations.
GWAS analysis of QTL governing plant growth stages and yield parameters in the set of 194 accessions was performed with the TASSEL 5.0 package . For this, the SNP dataset was filtered using a 10% cutoff for missing data and only markers with a minor allele frequency ≥ 0.10 were considered for GWAS. The STRUCTURE and STRUCTURE HARVESTER  programs were used for the development of delta K values (ΔK) and Q-matrix for identified clusters.
Field performance of the spring wheat collection in three regions of Kazakhstan
Three-way ANOVA performed on the traits studied in nine environments
Year x Origin
Year x Region
Origin x Region
Year x Origin x Region
The magnitude of the main effects for yield (Year, Region, and Origin), and their interactions were ranked region > origin > year > Y x R > Y x O x R > O x R, and the least effect was Y x O, as indicated by the F-values (Table 1). The only case where the origin effect was greater over the regional effect was the result received from the PH, where the F-value for the origin effect was 1.7 times higher than for the region effect.
Genetic variation of spring wheat with different breeding origins based on SNP data
Genetic diversity indices in the three groups of spring wheat accessions using 3245 SNPs analyzed using GeneAlex
1.563 + 0.005
0.507 + 0.003
0.339 + 0.002
1.428 + 0,006
0.409 + 0.004
0.270 + 0.003
1.393 + 0,006
0.379 + 0.004
0.247 + 0.003
Identification of SNP markers for growth stages and yield components based on GWAS
In this study, the genotyping HapMap file consisted of 194 spring wheat accessions and 3245 polymorphic SNP markers. The set of polymorphic SNP data was prepared after filtering with a 10% of cutoff for missing data and markers with minor allele frequencies ≥ 0.10. After running Structure Harvester, a Q-matrix for the three identified clusters was selected for further analysis based on analysis of delta K value (ΔK). The genotyping set was analyzed separately for each of nine studied environments in Northern, Central, and Southern Kazakhstan.
To facilitate the discovery of MTAs, three groups of wheat accessions with different breeding origins were studied. In genetic terms, the accessions were clearly separated into the three subgroups, with the first subgroup (Russia/Kazakhstan) being the most diverse, and the third subgroup (CIMCOG) being the least diverse group in the analysis. The separation of accessions into the three subgroups was also congruent with population structure analysis using on STRUCTURE software.
Yield performance during the 3 years was lowest in Northern Kazakhstan. Yield performance in the Southern region was always higher. However, the size of arable land in this region is limited and insignificant in comparison with the wheat growing area in Northern Kazakhstan. Results suggest that accessions in the EU collection can successfully be used in the Central region for obtaining better yield, and selected CIMCOG lines can be efficiently used for improvement of TKW in all three regions. The GGE biplot based on yield data helps to confirm that groups within the three breeding origins have different genetic backgrounds, and local accessions are well adapted to Northern and Southern regions.
Incorporation of the CIMCOG lines in the analysis resulted in a reduction of polymorphic SNP markers available for GWAS, as genotyping data for those lines was restricted to those shared between the Axiom and Illumina SNP arrays. Therefore, only 3245 aligned polymorphic SNPs were used in the GWAS with 1340 SNPs positioned in the A genome, 1448 in the B genome, and 457 in the D genome. The Diversity index was relatively high in the A and B genomes and lower in the D genome, which is well in agreement with previous observations [10, 18]. GWAS was performed separately for nine field trials over 3 years and identified 114 MTAs (Additional file 6). Only 12 of the identified MTA were significant in two environments both within and between regions (Additional file 6), suggesting that stability of the associations was undermined by a strong influence of environmental factors.
List of SNPs for selected yield components identified in this study and comparison of their locations to QTL mapped elsewhere
Sukumuran et al. (2015)
Zanke et al. (2015)
Jaiswal et al. (2016)
Guo et al. (2017)
Quarrie et al. (2005), ranges in cM
Despite similarities in the genetic positions of MTAs identified with other studies, a number of MTAs from this study were missing in GWAS conducted in different geographic regions. This study revealed four MTAs related to NKS, which is one of the major yield components in wheat . Surveys of the literature suggests that the loci identified on chromosomes 3B (56.9 cM) and 5B (144.1 cM) were not identified in previous GWAS studies in other regions of the World [17, 25, 28] nor in Quarrie et al. . Therefore, it was hypothesized that these MTAs, along with other associations shown in Table 3, are new MTAs.
Recently, there have been a number of discussions related to the importance of size and level of genetic variation in diversity panels for the success of GWAS projects [14, 15, 37]. It was pointed out that experiments with less than 384 accessions  and large LD blocks  may lead to the identification of false positive associations. The study by Turner et al. (2016) indicated that smaller panels may allow the detection of false negative associations that would not have been detected in the larger panels . Results in this study using a relatively small panel (n = 194) are largely in agreement with the study by Turner et al. .
The study confirms the efficiency of GWAS for the identification of molecular markers which tag important agronomic traits. In total 114 MTAs for 12 physiological and agronomic traits determined using spring wheat samples from Kazakhstan, Russia, EU and CIMMYT studied in field conditions of three regions of Kazakhstan. Locations of identified MTAs for plant height were similar with genetic positions corresponding to Rht-B1, Rht-D1, and Rht8 genes of wheat. In addition, from field trials it was found that EU and CIMMYT germplasm has high breeding potential to improve yield performance in Central and Southern regions of the country. The use of Principal Coordinate analysis clearly separated the panel into three distinct groups according to their breeding origin. The study identifies a network of key genes that will be further validated for improvement of yield productivity in wheat growing regions of Kazakhstan.
The work was funded by the ADAPTAWHEAT project supported by the 7th framework program of the European Union, and the 1784/GF4 project supported by the Ministry of Education and Sciences of the Republic of Kazakhstan.
Publication of this article has been funded by the project 1784/GF4 from the Ministry of Education and Sciences of the Republic of Kazakhstan.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article.
About this supplement
This article has been published as part of BMC Plant Biology Volume 17 Supplement 1, 2017: Selected articles from PlantGen 2017. The full contents of the supplement are available online at https://0-bmcplantbiol-biomedcentral-com.brum.beds.ac.uk/articles/supplements/volume-17-supplement-1.
YT and SA carried out the experimental design. LT, VC, and GS performed field trials in three different regions of Kazakhstan, MG and SG provided SNP genotyping data, YT, AB and KY performed the GWAS analysis. YT, SA and SG participated in preparation of the manuscript. All authors reviewed the draft of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study does not contain any research requiring ethical consent or approval.
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