Genetic and genomic resources in Einkorn wheat enable trait discovery.

Like

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Wheat is one of the most important staple food crops for the majority of people worldwide. Bread wheat makes up over 95% of the global wheat production and is a primary source of calories and protein for much of the global population. Therefore, continuous efforts are undertaken to develop wheat varieties that are tolerant to biotic and abiotic stresses and at the same time maintaining its nutritional quality. One of the major limitations for genetic studies in bread wheat is its highly complex polyploid genome which consists of three whole copies of the genome (A, B and D genomes). Therefore, there is a need to develop a simplified system for gene discovery in bread wheat.


Einkorn wheat (known as Triticum monococcum; 2n = 2x = 14, AA genome) is a close relative of Triticum urartu which is a diploid A-genome donor of modern bread wheat. It is also one of the first domesticated and oldest cultivated crops, with a history dating back about 12,000 years [1,2]. Being an excellent source of some important genes for biotic and abiotic stress tolerance, einkorn wheat also holds impressive nutritional content and high genetic polymorphism.

All these features of einkorn wheat certainly make it an excellent model for gene discovery which can be easily translated to gene discovery in bread wheat. However, in order to proceed with trait discovery, there is a need to develop a large mapping population with high throughput genotyping data which can leverage the mapping of genomic regions for several agronomically important traits.

Recently, we assembled the reference genome of einkorn wheat [3], and it revealed information about genes stacked on the einkorn chromosomes and their organization. Followed by this, we developed and used a large recombinant inbred line (RIL) mapping population (>800 lines) of wild and domesticated einkorn wheat accessions to map agronomically important genes using a cost-effective low-coverage skim sequencing approach. The parents used for developing this mapping population show highly contrasting phenotypes for agronomically important traits and therefore make a perfect choice for developing a mapping population.

Integrating the phenotype and genotype data, we identified genomic regions which are involved in controlling the traits like spikelet number per spike, coleoptile color, brittle rachis, spike length and plant height. We then studied two traits in more detail and identified small physical regions for spikelet number per spike and coleoptile color which contained 3 to 4 genes in the physical interval. For future study, we are planning to validate the genes for spikelet number per spike so that these genes may be used for improving yield in bread wheat.

This study provides an exciting route for gene discovery in diploid wheat that has direct translation to improving hexaploid bread wheat and can serve as a milestone for simplifying gene discovery in complex bread wheat.

 

 

  1. Ahmed, H.I., Heuberger, M., Schoen, A. et al.Einkorn genomics sheds light on history of the oldest domesticated wheat. Nature (2023). https://doi.org/10.1038/s41586-023-06389-7
  2. Harlan, J. R. & Zohary, D. Distribution of wild wheat and barley. Science 153, 1074–1080 (1966). 9.
  3. Heun, M. et al. Site of einkorn wheat domestication identified by DNA fingerprinting. Science 278, 1312–1314 (1997)

 

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Biotechnology
Life Sciences > Biological Sciences > Biotechnology

Related Collections

With collections, you can get published faster and increase your visibility.

Tumour microenvironment

This Collection welcomes submissions on the interplay between tumours and their microenvironment, as well as how these interactions impact on cancer therapy.

Publishing Model: Open Access

Deadline: Sep 07, 2024