Browsing by Subject "Genetic gain"

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Optimization strategies to adapt sheep breeding programs to pasture-based production environments: A simulation study
(2023) Martin, Rebecca; Pook, Torsten; Bennewitz, Jörn; Schmid, Markus
Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.
Optimum strategies to implement genomic selection in hybrid breeding
(2022) Marulanda Martinez, Jose Joaquin; Melchinger, Albrecht E.
To satisfy the rising demand for more agricultural production, a boost in the annual expected selection gain (ΔGa) of traits such as grain yield and especially yield stability has to be rapidly achieved. Hybrid breeding has contributed to a notable increment in performance for numerous allogamous species and has been proposed as a way to match the increased demand for autogamous cereals such as rice, wheat, and barley. An additional tool to increase the rate of annual selection gain is genomic selection (GS), a method to assess the merit of an individual by simultaneously accounting for the effects associated with hundreds to thousands of DNA markers. Successful integration of GS and hybrid breeding should go beyond the study of GS prediction accuracy and focus on the design of breeding strategies, for which GS maximizes ΔGa and optimizes the allocation of resources. The main goal of this thesis was to examine strategies for optimum implementation of GS in hybrid breeding with emphasis on estimation set design to perform GS within biparental populations and on the optimization of hybrid breeding strategies through model calculations. One strategy, GSrapid, with moderate nursery selection, one stage of GS, and one stage of phenotypic selection, reached the greatest ΔGa for single trait selection regardless of the budget, costs, variance components, and accuracy of genomic prediction. GSrapid was also the most efficient strategy for the simultaneous improvement of two traits regardless of the correlation between traits, selection index chosen, and economic weights assigned to each trait. The success of this strategy relies principally on the reduction of breeding cycle length and marginally on the increase in selection intensity. Moving from traditional breeding strategies based on phenotypic selection to strategies using GS for single trait improvement in hybrid breeding could lead not only to increments in ΔGa but also to large savings in the budget. The implementation of nursery selection in breeding strategies boosted the importance of efficient systems for inbred generation accompanied by improvements in the methods of hybrid seed production for experimental tests. When it comes to multiple trait improvement, the choice between optimum and base selection indices had minor impact on the net merit. However, considerable differences for ΔGa of single traits were observed when applying optimum or base indices if the variance components of the traits to be improved differed. The role of the economic weights assigned to each trait was determinant and small variations in the weights led to a remarkable genetic loss in one of the traits. The optimum design of estimation sets to perform GS within biparental populations should be based on phenotypic data, rather than molecular marker data. This finding poses major challenges for GS-based strategies aiming to select the best new inbreds within second cycle breeding populations, as breeding cycle length might not be reduced. Then, the ES design to optimize GS within biparental populations would have a defined application on the exploitation of within-family variation by increasing selection intensity in biparental populations with the largest potential of producing high-performing inbreds. Based on the results of this thesis, future challenges for the optimum implementation of GS in hybrid breeding strategies include (i) reductions in breeding cycle length and increments in selection intensity by refinements of DH technology or implementation of speed breeding, (ii) improvements in the methods for hybrid seed production, facilitating the reallocation of resources to the production of more candidates tested during the breeding cycle, and (iii) precise estimation of economic weights, reflecting the importance of the traits for breeding programs and farmers, and maximizing long term ΔGa for the most relevant traits.