How to achieve a higher selection plateau in forest tree breeding? Fostering heterozygote × homozygote relationships in optimal contribution selection in the case study of Populus nigra

Abstract

In breeding, optimal contribution selection (OCS) is one of the most effective strategies to balance short‐ and long‐term genetic responses, by maximizing genetic gain and minimizing global coancestry. Considering genetic diversity in the selection dynamic—through coancestry—is undoubtedly the reason for the success of OCS, as it avoids preliminary loss of favorable alleles. Originally formulated with the pedigree relationship matrix, global coancestry can nowadays be assessed with one of the possible formulations of the realized genomic relationship matrix. Most formulations were optimized for genomic evaluation, but few for the management of coancestry. We introduce here an alternative formulation specifically developed for genomic OCS (GOCS), intended to better control heterozygous loci, and thus better account for Mendelian sampling. We simulated a multigeneration breeding program with mate allocation and under GOCS for twenty generations, solved with quadratic programming. With the case study of Populus nigra, we have shown that, although the dynamic was mainly determined by the trade‐off between genetic gain and genetic diversity, better formulations of the genomic relationship matrix, especially those fostering individuals carrying multiple heterozygous loci, can lead to better short‐term genetic gain and a higher selection plateau.

Mate selection: A useful approach to maximize genetic gain and control inbreeding in genomic and conventional oil palm (Elaeis guineensis Jacq.) hybrid breeding

“…In the current study, we preferred considering an approach optimizing both selection and matings. A recent study [ 40 ] proposes a weighting method to modify the additive relationship matrix used on OCS and promote in turn individuals carrying multiple heterozygous loci, resulting in benefits in short-term genetic gains and higher selection plateau. Although it is a promising alternative to OCS without the computational burden of mate selection, it is not intended to control explicitly inbreeding at a per generation basis.…”

Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain

“…To take advantage of genetic marker data, OCS can be carried out using GEBVs as the measure of genetic value and the genomic relationship matrix (GRM) as the measure of relatedness. One variation on OCS is Tiret et al (2021)’s genomic OCS (GOCS), which adds a term to heterozygote x heterozygote crosses in the GRM and in doing so, sees improvements over OCS in genetic gain at the same coancestry measure when using non-inbred populations. Because OCS for selecting mating pairs is often used to produce full sibling families out of which the best are then selected, Akdemir & Sánchez (2016) developed their Genomic Mating variant of OCS, which replaces the genetic value term in the constraints with a “risk” term composed of a pair’s GEBVs and expected progeny variance.…”

“…A similar strategy is Allier et al (2019)’s usefulness criterion parental contribution (UCPC) method, which uses OCS-like parental contribution constraints but defines the genetic value objective using expected progeny mean and variation. Both GOCS, over 20 generations of recurrent selection (Tiret et al 2021), Genomic Mating, over 20 cycles of recurrent selection (Akdemir & Sánchez 2016), and UCPC, over 60 generations of recurrent selection (Allier et al 2019, Sanchez et al 2023), slightly outperformed OCS for genetic gain. Recurrent OCS itself can provide rates of gain in line with or outperforming truncation selection within 15-20 generations of simulation, after a slight lag for the first few generations (Akdemir & Sánchez 2016, Sanchez et al 2023).…”

“…Genomic OCS (GOCS, Tiret et al 2021) adds a term to the GRM to improve genetic gain when using non-inbred populations. Recurrent application of OCS itself can provide rates of gain in line with or outperforming recurrent truncation selection within 15-20 generations of simulation, after a slight lag for the first few generations (Akdemir & Sánchez 2016, Sanchez et al 2023.…”

“…Genomic Mating (Akdemir & Sánchez 2016) and Usefulness Criterion Parental Contribution (UCPC, Allier et al 2019) incorporate the expected variance in progeny value into the computation, to slightly improve results. Genomic OCS (GOCS, Tiret et al 2021) adds a term to the GRM to improve genetic gain when using non-inbred populations. Recurrent application of OCS itself can provide rates of gain in line with or outperforming recurrent truncation selection within 15-20 generations of simulation, after a slight lag for the first few generations (Akdemir & Sánchez 2016, Sanchez et al 2023).…”