JANA OBŠTETER Janez JENKO John HICKEY Gregor GORJANC EAAP 2018

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Presentation transcript:

Testing different genomic selection scenarios in a small cattle population by simulation JANA OBŠTETER Janez JENKO John HICKEY Gregor GORJANC EAAP 2018 Dubrovnik, Croatia, 27th August 2018

Introduction Small cattle populations Genomic selection not fully integrated Sustainable strategy to catch up with other populations?

Aims 1) Develop the simulator 2) Sire selection scenarios 3) Sire use strategies

The simulator Cattle population under selection Overlapping generation User-defined parameters % selected, years in use, dosage, selection criterion ... Progeny and genomic testing (10K reference) Python wrapper around AlphaSim (Faux et al., 2012) and blupf90 (Misztal et al., 2002)

Sire selection scenarios SIRES OF SIRES SIRES OF DAMS PT GS-PS GS-C GS-BD GS Progeny testing - pre-selection on parent average Progeny testing - pre-selection on gEBV Genomic testing

Genetic gain 4.5 3.9 3.7 units 3.1 2.3

Selection efficiency 84 93 98 100 77 7

Effective population size Genomic (via locus heterozygosity) Pedigree Neutral Selection QTN PT 270 178 175 172 GS-PS 372 165 162 159 GS-C 330 134 132 129 GS-BD 267 122 121 119 GS 213 93 92 90

Sire use strategy 5 bulls / 5 years 1 bull / 5 years 5 bulls / 1 year PS GS-PS GS-C GS-BD GS PS GS-PS GS-C GS-BD GS PS GS-PS GS-C GS-BD GS

PT 100% 121% 110% GS-PS 136% 153% 159% GS-C 162% 193% 186% GS-BD 168% Genetic gain of the tested scenarios in the last generation. 5 bulls / 5 years 1 bull / 5 years 5 bulls / 1 year PT 100% 121% 110% GS-PS 136% 153% 159% GS-C 162% 193% 186% GS-BD 168% 181% 182% GS 194% 224% 242%

Efficiency of the tested scenarios in the last generation. Converted/Lost genic standard deviation 5 bulls / 5 years 1 bull / 5 years 5 bulls / 1 year 77 - 100 42 - 72 61 - 87 Genic standard deviation

Effective population size (genomic) of the tested scenarios in the last generation. 5 bulls / 5 years 1 bull / 5 years 5 bulls / 1 year PT 175 98 186 GS-PS 162 101 149 GS-C 131 65 126 GS-BD 120 96 113 GS 91 38 73

Conclusions Developed a realistic cattle breeding simulator Sire selection scenarios Genetic gain increases with genomics and reduced interval Hybrid scenarios the most efficient Use of genomics and increased intensity reduce Ne Sire use strategies Very high intensity obviously not recommended Rapid turn-over of bulls increases gain and only slightly decreases efficiency and Ne 13

Conclusions Developed a realistic cattle breeding simulator Sire selection scenarios Genetic gain increases with genomics and reduced interval Hybrid scenarios the most efficient Use of genomics and increased intensity reduce Ne Sire use strategies Very high intensity obviously not recommended Rapid turn-over of bulls increases gain and only slightly decreases efficiency and Ne 14