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BIONÆR-Bionæringsprogram

Large scale single step genomic selection in practice

Alternative title: Storskala ettstegs genomisk seleksjon i praksis

Awarded: NOK 8.5 mill.

Project Manager:

Project Number:

309611

Project Period:

2020 - 2024

Funding received from:

Organisation:

Partner countries:

Automated collection of genotype and phetnoype information enabled creation of big data sets. However, these data come often with imperfections and many missing records which is causing problems in breeding values evaluation. The goal of this project is to reduce these problems and maximise the accuracy of predicted breeding values. Furthermore, we would like to prevent for potential biases that may result from the large-scale use of data on ungenotyped animals. In order to achieve this we will improve computational methods so they will be able to handle millions of animals with millions of genetic markers. We will look for chromosomal regions of importance to multiple traits and use this information to enhance prediction accuracies. We will estimate model parameters such that prediction accuracies are maximized, and biases minimized. Finally all the projects advances will be implemented into a software package that can handle large scale practical data. So far we have already decreased the bias of predictions for genotyped animals with missing pedigree information in Norwegian Red. Furthermore, we have developed new models for the estimation of breeding values for the exterior traits for Norwegian Red. The new models increased the accuracy of predicted breeding values which will enable faster genetic improvement. Finally, using machine learning methods we have developed new technique that will improve the prediction accuracy for polledness for ungenotyped animals.

Summary: Norwegian breeding schemes have traditionally been based on large-scale collection of practical data. The advance of novel automated electronic recording systems further increases the data volumes coming from practical herds. However, these practical data come often with imperfections and many missing records. In the context of modern genomic selection breeding schemes, especially ungenotyped animals and thus missing genotypes cause biases and inaccuracies of breeding value estimates. A first major objective of the current project is to develop genetic evaluations for cattle and pigs that make seamless use of genotyped and ungenotyped animals, and other patterns of missing data. A second major objective is to develop algorithms that can handle massive amounts of practical records, including the ever-increasing numbers of animals genotyped with ever-increasing marker density. Also, a software package implementing the novel algorithms will be developed. A third major aim is to combine records on practical and elite breeding animals to pinpoint important genomic regions for the traits of interest, in order to maximize the accuracy of the genomic breeding value estimates. Statistical, animal breeding and machine learning approaches will be combined to tackle these objectives. The above challenges are faced by both the cattle and pig breeding industries and they thus join forces here to address them. By an increasing use of practical information, GENO and Norsvin aim to genetically improve the characteristics of the animals that are important under practical circumstances. Hence, making the animals better adapted to perform under practical conditions and thereby improve the sustainability of the cattle and pig production sectors.

Funding scheme:

BIONÆR-Bionæringsprogram