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•Efficient phenotyping methods to evaluate wheat population under field conditions are needed to breed for high-yielding and heat-tolerant wheat varieties.•Here, we phenotyped the response of 184 genotypes of wheat to heat stress using aerial imagery and physiological dataset.•Wheat genotypes were classified using vegetative and stress indices for heat stress tolerance.•The multispectral-driven phenotypic traits can be used by breeders to select and develop heat-tolerant wheat varieties tolerant to heat stress.

The 1000 Bull Genomes Project is a collection of whole-genome sequences from 2,703 individuals capturing a significant proportion of the world's cattle diversity. So far, 84 million single-nucleotide polymorphisms (SNPs) and 2.5 million small insertion deletions have been identified in the collection, a very high level of genetic diversity. The project has greatly accelerated the identification of deleterious mutations for a range of genetic diseases, as well as for embryonic lethals. The rate of identification of causal mutations for complex traits has been slower, reflecting the typically small effect size of these mutations and the fact that many are likely in as-yet-unannotated regulatory regions. Both the deleterious mutations that have been identified and the mutations associated with complex trait variation have been included in low-cost SNP array designs, and these arrays are being genotyped in tens of thousands of dairy and beef cattle, enabling management of deleterious mutations in these populations as well as genomic selection.

Genomic selection (GS) has been recognized as offering numerous potential benefits for ryegrass (Lolium perenneL.) breeding. While the theoretical benefits of GS in ryegrass breeding are clear, the best way to apply GS in practical breeding programs remains to be determined. The present study aimed to investigate genomic breeding options that best balance genetic gain, breeding costs, and the level of inbreeding using stochastic simulation. Nine GS scenarios were tested, including different numbers of selection candidates (10,000, 5000, and 2000 F1seedlings) and three reference population sizes for GS composed of plots representing a sward-based trial (500, 200, and 100 plots). Low to moderate prediction accuracy was achieved for productivity traits across cycles (i.e., 0.1–0.45 for yield [h2= 0.3]). Scenarios with larger reference populations (i.e., 500 plots) achieved higher prediction accuracy but, when considering field trial costs, were more expensive per unit of genetic gain. All nine GS scenarios delivered significantly higher genetic gain (up to fourfold) than the conventional breeding scenario over a 20-yr period. Scenarios with moderate selection intensity on F1seedlings with fewer plots tested in field gave the most genetic gain per dollar invested (i.e., 2000 or 5000 F1seedlings and 100 plots). However, all GS scenarios reduced genetic diversity in the breeding population more than phenotypic selection, highlighting the need to mitigate inbreeding when applying GS in perennial ryegrass.

In livestock, genotype × environment interaction (G× E) has been widely investigated, with genotype defined at the level of subspecies, breeds, individual animals within a breed (for example performance of offspring of elite sires across environments), and genotypes at single‐nucleotide polymorphisms (SNPs). Environments can be described by category (e.g., tropical vs. temperate, high vs. low farm input levels, countries) and by continuous variables such as temperature. To predict breeding values of genotypes in environments described by categories, multitrait models with each category a different trait are used. The models are now being used to predict genomic estimated breeding values (GEBV) for different environments such as the value of a bull's genetics for his daughter's milk production in different countries. The multitrait genomic model has also been used to enable reference populations to be merged across environments and across countries, leading to more accurate GEBV. When the environment can be described by a continuous variable, random regression models have been used to predict response of genotypes to the environment. For example, these models have been used to determine if there are SNP genotypes associated with less sensitivity of milk production to increasing temperature. In both livestock and plant breeding, methods that use genomic information can better cope with a reduced degree of replication of individuals across environments, as it is actually the alleles that must be replicated across environments. More accurate estimates of G× Ewith the genomic approach may therefore be achievable than was possible in the past.