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WP4 - Improving predictive models for genomic selection

WP Leader: Dr. Marco Bink – HG 
Deputy WP leader: Dr. Mario Calus – WU
Involved partners: HG, INRA, UEDIN, WU, UU, IRTA
Background
Genomic selection has been implemented in many breeding programs for the main livestock species in the last 5-10 years. Increasing the accuracy of genomic selection so far has mostly been achieved by increasing the size of the reference population. This approach, however, is subject to strongly diminishing returns. The alternative approach of increasing marker density up to whole-genome sequence level has yielded, at best, limited improvements in accuracy when markers were pre-selected based on their association with the predicted phenotype. All these approaches are purely data-driven, and it has been proposed to use underlying biology as an additional source of information to guide predictions. The increasing amount of biological information in the form of functional annotations generated in Pillar 1 of GENE-SWitCH provide an excellent opportunity to empirically investigate the potential benefit of using functional annotation in genomic prediction. The aim of WP4 is to develop and validate genomic prediction models that use functional information on top of the commonly used phenotypic and genotypic data, and to investigate the extent to which this may increase prediction accuracy.
Objective
To develop and validate new predictive models using functional genome annotations and:          - - new expression QTL (eQTL) data in pigs
- relying on existing high-resolution QTL data in chicken
Tasks
  • T4.1 - Develop Bayesian models using known functionality as prior information
  • T4.2 - Develop machine learning models for genomic prediction using known functionality as prior information
  • T4.3 - Identifying eQTL in key porcine tissues
  • T4.4 - Fine mapping and functional annotation of QTL in an experimentally selected chicken population
  • T4.5 - Multi-level empirical validation of developed models with pig data
  • T4.6 - Empirical validation of developed models with chicken data
WP1 - Sample collection and assays-by-sequence

WP2 - New annotation maps of pig and chicken genomes

WP3 - FAANG Data Coordination, Standardisation and Integration

​WP4 - Improving predictive models for genomic selection

​WP5 - Influence of maternal diet on epigenetic programming of offspring

​WP6 - Outreach, dissemination and training

WP7 - Project management and consortium coordination
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​WP8 - Ethics