Potato Multi-Allele Scanning Haplotags – a low cost, genome-scanning marker system for use in potato breeding

Informacje na temat projektu

PotatoMASH

Identyfikator umowy o grant: 797162

DOI

10.3030/797162

Projekt został zamknięty

Data podpisania przez KE 20 Marca 2018

Data rozpoczęcia 24 Września 2018

Data zakończenia 23 Września 2020

Finansowanie w ramach

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Koszt całkowity

€ 175 866,00

Wkład UE

€ 175 866,00

Koordynowany przez

TEAGASC - AGRICULTURE AND FOOD DEVELOPMENT AUTHORITY
Ireland

Periodic Reporting for period 1 - PotatoMASH (Potato Multi-Allele Scanning Haplotags – a low cost, genome-scanning marker system for use in potato breeding)

Okres sprawozdawczy: 2018-09-24 do 2020-09-23

Podsumowanie kontekstu i ogólnych celów projektu

The goal of potato breeders is to produce varieties that have improved sustainability, utilisation and consumer characteristics. Parent varieties with characteristics that the breeder wishes to combine are crossed to produce a population, from which individual lines that outperform the parents and exhibit desirable combinations of characteristics, are selected, and advanced as new varieties. This process takes over 10 years, and is challenging due to the fact that up to 40 characteristics have to be combined (including yield, resistance to pests, diseases and stress conditions, and quality characteristics). Marker assisted selection (MAS) and genomic selection (GS) use genetic fingerprinting (genotyping) to help make the process of selection more efficient by allowing the genes controlling the characteristics under selection to be tracked during crossing and selection, allowing better selection of parents and the identification of progeny with good trait combinations. Unfortunately, these approaches can be too expensive for breeders to implement, especially for characteristics controlled by multiple genes, which require genotyping approaches that scan the whole genome to track all of these genes simultaneously. The goal of PotatoMASH (Potato Multi-Allele Scanning Haplotags) is to develop a novel, affordable, genotyping test for use in GS and MAS for multiple important traits in potato breeding.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

PotatoMASH exploits advances in DNA sequencing technology and knowledge of the genetic architecture of potato to efficiently scan genetic variation across the genome. Using a bioinformatics analysis of the published genome sequence of the crop plant, we targeted 339 regions placed at equal spacing throughout the gene-rich portion of the 12 chromosomes of potato (Figure 1). The spacing was carefully chosen to minimise the cost of the genotyping assay whilst maximising the likelihood that genetic variation in each region would describe which forms (alleles) of genes for desirable traits were present in the surrounding regions. An approach called “Genotyping in Thousands by Sequencing” (GT-Seq, Figure 2), combined with next-generation DNA sequencing technology, allows thousands of potato samples to be tested simultaneously at a cost of approximately €5 per sample (excluding labour costs). We adopted the GT-Seq approach to enable genetic variation to be scanned across our target regions. We applied the PotatoMASH system to a population of over 700 plants from the Teagasc/IPM Potato Group breeding programme and found that it was very efficient in surveying genetic variation in the population, finding an average of six different variants (haplotags) at each of the regions. Previous work at Teagasc suggests that this level of variant discrimination should enable prediction of traits such as fry colour and disease resistance in breeding lines, and we are currently validating this in the population.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

Prior to this action, the state of the art in the hosting organisation’s potato breeding programme was to apply marker assisted selection (MAS) annually for approximately 20-30 disease resistance traits, enabling early selection for these traits in thousands of breeding lines in the programme. This is an extremely useful tool that has enabled the efficient selection of varieties exhibiting resistance to multiple pests and diseases. Because this relatively small set of traits is controlled by individual genes/loci, it is cost effective to use individual genotyping tests for each trait. However, traits such as fry colour, which are controlled by the action of many genes simultaneously, remain out of reach in terms of genotyping, because of the difficulty in identifying all beneficial gene variants and the cost of developing genetic markers to track them (Genomic Selection, or GS). Conversely, because PotatoMASH efficiently surveys genetic variation throughout the potato genome, it can simultaneously diagnose the presence of all of the target disease and pest resistance genes, and track variation associated with genes beneficial for more complex traits including fry colour and many other important traits. Therefore, the breeding programme can now move beyond MAS and begin to implement GS breeding strategies, and PotatoMASH can be implemented as a single low-cost, genotyping platform that will allow routine and simultaneous application of both MAS and GS in the commercial potato breeding programme at Teagasc.
The platform has already shown such potential that it forms a central pillar in several subsequent funded projects, including a successful multinational ERA-NET application about hybrid potato breeding (EU SusCrop Project DIFFUGAT, led by D. Milbourne). PotatoMASH is also the proposed genotyping platform to search for powdery scab resistance as an important work package of another action granted by EU and also Teagasc (Research Leaders 2025 project, ScabEomics) led by the former PotatoMASH MSCA fellow, which is starting in 2021 in coordination with Wageningen University in The Netherlands. All findings and data from this action will be made available via open access publications and according to FAIR principles.

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