Genomics selection represents a major breakthrough in quantitative genetics, as it generally allows a greater accuracy in predicting expected breeding values (EBVs), especially for traits that are problematic to measure directly on selection candidates (e.g. carcass characteristics, disease resistance). The rapidly decreasing costs of large scale genotyping boosted the application of genomics selection in all the main terrestrial livestock species and it is rapidly expanding across aquaculture breeding programs. Genomic selection accuracy, however, is significantly lower the more genetically divergent are the training and the test populations due to linkage disequilibrium decay. Using causative variants over linked non causative loci is expected to provide high accuracy even in genetically  unrelated populations. It has been shown in model species that prioritizing the choice of variants included in predicting equations on the basis of their functional relevance holds the promise to enrich marker panels in such causative variants. Using resistance to nervous necrosis virus and the European sea bass as study species, several layers of genomic functional annotations will be tested to assess the efficacy of variant prioritization in Bayesian and machine-learning based prediction models.

Five publications related to the Research Topic for the candidate interview:

1. Xiang R, MacLeod IM, Daetwyler HD, de Jong G, O'Connor E, Schrooten C, Chamberlain AJ, Goddard ME. Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations. Nat Commun. 2021 Feb 8;12(1):860. DOI: https://doi.org/10.1038/s41467-021-21001-0
2. Allal F, Nguyen NH. Genomic Selection in Aquaculture Species. Methods Mol Biol. 2022;2467:469-491. DOI: doi: 10.1007/978-1-0716-2205-6_17
3. Cheruiyot EK, Haile-Mariam M, Cocks BG, MacLeod IM, Mrode R, Pryce JE. Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance. Genet Sel Evol. 2022 Feb 19;54(1):17. DOI: https://doi.org/10.1186/s12711-022-00708-8
4. Zheng Z, Liu S, Sidorenko J, Wang Y, Lin T, Yengo L, Turley P, Ani A, Wang R, Nolte IM, Snieder H; LifeLines Cohort Study; Yang J, Wray NR, Goddard ME, Visscher PM, Zeng J. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet. 2024 May;56(5):767-777. DOI: https://doi.org/10.1038/s41588-024-01704-y
5. Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, Matsuda K, Murakami Y, Price AL, Kawakami E, Terao C, Raychaudhuri S. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet. 2020 Dec;52(12):1346-1354. DOI: https://doi.org/10.1038/s41588-020-00740-8

Tutor: Prof. Luca Bargelloni
mail: luca.bargelloni@unipd.it