Skip to main content

Genomic Selection for Potato Improvement

Periodic Reporting for period 1 - GenSPI (Genomic Selection for Potato Improvement)

Reporting period: 2015-09-21 to 2017-09-20

Potatoes destined for crisping are normally stored at 8 degrees, below this reducing sugars accumulate leading to very dark fry colours and potential acrylamide build up. Unfortunately, sprouting occurs above 8 degrees and impacts product quality. This necessitates the use of sprout suppressant chemicals such as chlorpropham. The EU is moving to phase out the use of such chemicals due to health concerns, and it is therefore necessary to develop potatoes that can be stored below 8 degrees without suffering from low temperature sweetening. Low temperature sweetening is under polygenic control and therefore challenging for traditional breeding programmes, particularly when it needs to be combined with other traits such as yield and disease resistance. This is where new breeding methodologies such as genomic selection can assist traditional breeding programmes. Genomic selection is a form of marker assisted selection that simultaneously estimates all loci, haplotype, or marker effects across the entire genome to calculate breeding values. These breeding values can then be used to select individuals for advancement in the breeding cycle without direct phenotyping. Indirect selection with genomic data for characteristics such as resistance to low temperature sweetening would radically enhance potato breeding, lead to the development of more suitable processing varieties for industry, and remove dependence on sprout suppressant chemicals. Our overall objective in this project is to align a pilot genomic selection programme to a traditional breeding programme to evaluate the potential for genomic selection in potato breeding. We envisage that genomic selection will enable the screening of an extremely large number of individuals at a seedling stage, ensuring that only the most valuable material is advanced for extensive and expensive phenotyping. This will enable a dramatic increase in the genetic progress for the development of improved potato varieties.
A training population to evaluate the accuracy of genomic selection for fry colour and resistance to low temperature sweetening was established over the two years of this action. This was made up of potato entries at the year five stage of evaluation in the breeding programme, where the number of candidate varieties has gone from just under 100,000 lines to approximately 300 lines. The final training population consisted of 450 entries that were evaluated for fry colour ‘off-the-field’, and at various time points during storage at 4.50C and 80C (with sprout suppressant) . In total 9,440 tubers were sliced into crisps, deep fried, and analysed for fry colour using a HunterLab LabScan XE spectrophotometer. Results of this enhanced phenotyping effort have already been exploited for parental selection and advancement of lines within the breeding programme. The training population was also genotyped using a genotyping-by-sequencing approach and a database of 129,008 Single Nucleotide Polymorphisms (SNPs) was established, which characterised genetic variation across the genome within the training population. These data-sets were brought together and used to evaluate the accuracy of genomic selection for fry colour and resistance to low temperature sweetening. The aim was to build predictive models with genome-wide SNP data and determine how well these data can predict our traits. We evaluated various statistical algorithms and determined factors affecting predictive ability (e.g. training population size, marker density, and relationship between training and testing sets). Predictive ability was high (ranging from 0.61 to 0.72) when predicting fry colour at various time points during storage and reducing SNP number had limited impact on predictive ability. Much of the predictive ability was due to SNPs capturing familial relationships, and predictive ability dropped when material unrelated to the training set was included in the testing set. However, we also performed a genome-wide association study to identify individual SNPs associated with fry colour. This differs from genomic selection in that we are testing each marker in turn for association with the trait after correcting for structure in the training population. This enabled the identification of a subset of molecular markers that together were capable of predicting fry colour and resistance to low temperature sweetening with high accuracy. Identified markers had the same predictive ability as the entire marker set and greater predictive ability than a similar number of randomly selected markers, indicating they are in linkage disequilibrium with quantitative trait loci. Work initiated in this action will continue at the hosting organisation for the foreseeable future. All findings and data from this action will be made available via open access publications and according to FAIR principles.
This project provides a working example of genomic selection in a potato breeding programme and knowledge on factors affecting predictive ability. Prior to this action, the state of the art in the hosting organisation’s potato breeding programme was phenotypic and single marker-assisted selection. Through this action the breeding programme can now move beyond this and begin to implement genomics-assisted breeding strategies. The identification of molecular markers predictive of fry colour and resistance to low temperature sweetening will be used to develop an inexpensive genotyping platform. This genotyping platform will be deployed for indirect selection in early stages of the breeding programme when direct phenotyping of these traits is impractical. We expect that the work carried out in this action will lead to the release of one or more varieties suitable for the processing industry. In particular we expect to develop a variety that can maintain an excellent fry colour through low-temperature storage, thus avoiding the need for chemical sprout suppressants.
DNA based selection for the perfect crisp.