Understanding Sequence-based diversity in CIMMYT germplasm and its implication to crossing and selection strategy

Description of the topic

CIMMYT Global Wheat Program (GWP) has created a library of over 80 thousand advanced breeding lines available sequence data via genotype-by-sequencing (GBS) and other high-throughput genotyping methods. The International Wheat Genome Consortium has recently released the Chinese Spring reference sequence vs 2.0. In addition, the 10+ Wheat Genome project has completed the sequencing of 15 international key wheat varieties at a similar assembly quality. Beside these high-quality genome sequences, CIMMYT has started re-sequencing 34 important historical lines that demonstrate important lineages to germplasm distributed world-wide and 70 actual high yield potential, disease resistance breeding lines. The GBS data on advanced lines have been used in genomics-assisted breeding. To complete the wealth of available sequencing data, sequence-based marker data have been in pre-breeding populations, land races, and other gene bank accessions e.g. a diverse set of wheat wide relative species. Although there is variation in depth and quality of the data, they can potentially be combined to explore the sequence-based diversity in CIMMYT relevant breeding germplasm and to design new crossing and selection strategies. Novel methodologies could allow a better understanding of the distribution of linkage disequilibrium blocks across the genome and variability at a haplotype level for key regions that play a role in breeding.


The objective of this study will be to estimate the sequence-based diversity in CIMMYT relevant breeding germplasm and verify the variation of haplotypes at the genome level and in regions that are known to play its role to determine traits such as grain yield, rust resistance, maturity, plant height. 

Activities and required skills

The student or research fellow will work with large datasets of available sequence dataset and need considerable bioinformatics and programming skills. Some basic training can be provided; however, the student should also be able learn and work independently. Ideally, the students should have scientific programming skills in languages such as R, Python and Unix shell such as Bash and working with High-Performance computing servers.