Molecular evaluation of orphan Afghan common wheat (Triticum aestivum L.) landraces collected by Dr. Kihara using single nucleotide polymorphic markers

Abstract

BackgroundLandraces are an important source of genetic diversity in common wheat, but archival collections of Afghan wheat landraces remain poorly characterised. The recent development of array based marker systems, particularly single nucleotide polymorphism (SNP) markers, provide an excellent tool for examining the genetic diversity of local populations. Here we used SNP analysis to demonstrate the importance of Afghan wheat landraces and found tremendous genetic diversity and province-specific characteristics unique to this geographic region.ResultsA total of 446 Afghan wheat landraces were analysed using genotype by sequencing (GBS) arrays containing ~10 K unique markers. Pair-wise genetic distance analyses revealed significant genetic distances between landraces, particularly among those collected from distanced provinces. From these analyses, we were able to divide the landraces into 14 major classes, with the greatest degree of diversity evident among landraces isolated from Badakhshan province. Population-based analyses revealed an additional 15 sub-populations within our germplasm, and significant correlations were evident in both the provincial and botanical varieties. Genetic distance analysis was used to identify differences among provinces, with the strongest correlations seen between landraces from Herat and Ghor province, followed closely by those between Badakhshan and Takhar provinces. This result closely resembles existing agro-climatic zones within Afghanistan, as well as the wheat varieties commonly cultivated within these regions. Molecular variance analysis showed a higher proportion of intra-province variation among landraces compared with variation among all landraces as a whole.ConclusionThe SNP analyses presented here highlight the importance and genetic diversity of Afghan wheat landraces. Furthermore, these data strongly refute a previous analysis that suggested low genetic diverse within this germplasm. Ongoing analyses include phenotypic characterisation of these landraces to identify functional traits associated with individual genotypes. Taken together, these analyses can be used to help improve wheat cultivation in Afghanistan, while providing insights into the evolution and selective pressures underlying these distinct landraces.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0320-5) contains supplementary material, which is available to authorized users.

Genetic Nature of Elemental Contents in Wheat Grains and Its Genomic Prediction: Toward the Effective Use of Wheat Landraces from Afghanistan

“…Ltd, Yarralumla, Australia. Details regarding genotyping and chromosomal mapping were as described by Manickavelu et al (2014) [9]. In the present study, DArT markers [30–31] were used in addition to single nucleotide polymorphisms (SNPs).…”

“…For the GWAS, we used the QK-model (i.e., linear mixed model with fixed effects explaining the effect of population structure and a random effect explaining polymorphic effects) [34]. The landraces used in this study were divided into 15 sub-populations, 6 of which covered 75% of all accessions [9]. In this study, six principal components of genome-wide marker scores were included in the QK-model to avoid false positives caused by population stratification in the materials.…”

“…Wheat landraces from Afghanistan collected by Dr. Hitoshi Kihara and his colleagues from 1950 to 1970 are an untapped genetic resource that may consist of wheat varieties with an ideal genotype for breeding new cultivars with increased elemental contents. Previous studies revealed considerable genetic diversity in this germplasm [9–11]. …”

Insights into population genetics and evolution of polyploids and their ancestors

“…Another simulation was conducted in order to compare the output from PolySim to a real wheat data set, which is a well‐described allohexaploid species (2 n = 6 x = 42), based on LD and He measurements of SNP markers (Manickavelu, Jighly, & Ban, ; Manickavelu et al., ). The simulation was performed for 1,000 individuals, for each of the allohexaploid genomes and its known progenitors.…”

“…Selfing rate was set to 90% (outcrossing 10%) as multiple studies reported variable outcrossing rates for wheat up to 10.6% (Hanson, Mallory‐Smith, Shafii, Thill, & Zemetra, ; Lawrie, Matus‐Cádiz, & Hucl, ). We assumed that our wheat population has a high outcrossing rate because of the massive diversity reported in this germplasm (Manickavelu et al., , ). The simulation was run for 30,000 generations, during which the three diploids emerged after 6,000; 6,200 and 10,000 generations; the allotetraploid evolved after 12,000 generations; and the allohexaploid genome appeared at generation 18,000.…”