Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values

Abstract

BackgroundFemale reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro embryo production and embryo transfer (JIVET) can boost rates of genetic gain but they can also increase rates of inbreeding. Inbreeding can be managed using the principles of optimal contribution selection (OCS), which maximizes genetic gain while placing a penalty on the rate of inbreeding. We evaluated the potential benefits and synergies that exist between genomic selection (GS) and reproductive technologies under OCS for sheep and cattle breeding programs.MethodsVarious breeding program scenarios were simulated stochastically including: (1) a sheep breeding program for the selection of a single trait that could be measured either early or late in life; (2) a beef breeding program with an early or late trait; and (3) a dairy breeding program with a sex limited trait. OCS was applied using a range of penalties (severe to no penalty) on co-ancestry of selection candidates, with the possibility of using multiple ovulation and embryo transfer (MOET) and/or juvenile in vitro embryo production and embryo transfer (JIVET) for females. Each breeding program was simulated with and without genomic selection.ResultsAll breeding programs could be penalized to result in an inbreeding rate of 1 % increase per generation. The addition of MOET to artificial insemination or natural breeding (AI/N), without the use of GS yielded an extra 25 to 60 % genetic gain. The further addition of JIVET did not yield an extra genetic gain. When GS was used, MOET and MOET + JIVET programs increased rates of genetic gain by 38 to 76 % and 51 to 81 % compared to AI/N, respectively.ConclusionsLarge increases in genetic gain were found across species when female reproductive technologies combined with genomic selection were applied and inbreeding was managed, especially for breeding programs that focus on the selection of traits measured late in life or that are sex-limited. Optimal contribution selection was an effective tool to optimally allocate different combinations of reproductive technologies. Applying a range of penalties to co-ancestry of selection candidates allows a comprehensive exploration of the inbreeding vs. genetic gain space.

Increasing reproductive rates of both sexes in dairy cattle breeding optimizes response to selection

“…This is because fewer males are needed for breeding compared with females. The high intensity of selecting males was reported in other studies (Kosgey et al, 2005;Pedersen et al, 2012;Granleese et al, 2015). MOET technology enables each cow to produce more offspring per year, thus increasing the number of selection candidates.…”

Genomic-Based Optimum Contribution in Conservation and Genetic Improvement Programs with Antagonistic Fitness and Productivity Traits

“…This error term was computed assuming a targeted accuracy r of the GEBVs (TBV-GEBV correlation) and a normal distribution N [0, (1- r 2 )V α ] for the error term. The use of this approach to simulate GEBVs has been developed and used in previous studies ( Dekkers, 2007 ; Granleese et al, 2015 ).…”

Influence of the Ovine Genital Tract Microbiota on the Species Artificial Insemination Outcome. A Pilot Study in Commercial Sheep Farms

“…Artificial insemination (AI) is a reproductive technique playing an important role in genetic breeding programs of milk ruminants as it: (i) enables progeny tests to predict rams genetic breeding values (EBVs) according to their daughters’ performances; (ii) contributes to connect herds, which is necessary to establish a common genetic basis to compare EBVs among animals of the whole population and (iii) enables dissemination of the genetic improvement achieved by the genetic program to the whole population, using genetically elite rams. However, AI success is variable among livestock species, with sheep being one of the species with the lowest AI pregnancy rates among ruminants, ranging from 30% to 70%, depending on breeds, season and production systems [ 1 , 2 , 3 ]. Low AI efficiency has a negative economic impact on dairy sheep breeding programs for two main reasons; firstly, it extends the generational interval, thus delaying genetic enhancement and, secondly, it increases the number of rams to be tested to ensure sufficient progeny.…”