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Persian or English walnut (Juglans regia L.), the walnut species cultivated for nut production, is one of the oldest food sources known. Persian walnuts, native to the mountain valleys of Central Asia, are grown worldwide in temperate areas. World production exceeds three million tons since 2012, mostly provided by China, the USA, and Iran. Despite very ancient culture of walnut species (Juglans spp.), breeding actually started in the twentieth century. Using a range of methodologies, from morphological markers to the most recent advances in genome analysis, many genetic studies of walnut have been conducted during the past 30 years, including examination of diversity, determination of relationships within or among germplasm collections and populations, phylogenetic and origin elucidation, genetic map construction, and biotic or abiotic stress investigations. The genetic improvement of walnut has undergone great evolution. The producing countries of the Middle East have widely studied morphological characteristics of walnut. The USA and France, for example, are behind important cultivar releases such as “Chandler” and “Franquette.” Finally, genomics represents a major breakthrough in walnut improvement, in particular by recent sequencing of both chloroplast and nuclear genomes. This review summarizes worldwide molecular and “omics” studies and gives an overview of the main walnut breeding programs.

Background In recent decades, Holstein–Friesian (HF) selection schemes have undergone profound changes, including the introduction of optimal contribution selection (OCS; around 2000), a major shift in breeding goal composition (around 2000) and the implementation of genomic selection (GS; around 2010). These changes are expected to have influenced genetic diversity trends. Our aim was to evaluate genome-wide and region-specific diversity in HF artificial insemination (AI) bulls in the Dutch-Flemish breeding program from 1986 to 2015. Methods Pedigree and genotype data (~ 75.5 k) of 6280 AI-bulls were used to estimate rates of genome-wide inbreeding and kinship and corresponding effective population sizes. Region-specific inbreeding trends were evaluated using regions of homozygosity (ROH). Changes in observed allele frequencies were compared to those expected under pure drift to identify putative regions under selection. We also investigated the direction of changes in allele frequency over time. Results Effective population size estimates for the 1986–2015 period ranged from 69 to 102. Two major breakpoints were observed in genome-wide inbreeding and kinship trends. Around 2000, inbreeding and kinship levels temporarily dropped. From 2010 onwards, they steeply increased, with pedigree-based, ROH-based and marker-based inbreeding rates as high as 1.8, 2.1 and 2.8% per generation, respectively. Accumulation of inbreeding varied substantially across the genome. A considerable fraction of markers showed changes in allele frequency that were greater than expected under pure drift. Putative selected regions harboured many quantitative trait loci (QTL) associated to a wide range of traits. In consecutive 5-year periods, allele frequencies changed more often in the same direction than in opposite directions, except when comparing the 1996–2000 and 2001–2005 periods. Conclusions Genome-wide and region-specific diversity trends reflect major changes in the Dutch-Flemish HF breeding program. Introduction of OCS and the shift in breeding goal were followed by a drop in inbreeding and kinship and a shift in the direction of changes in allele frequency. After introduction of GS, rates of inbreeding and kinship increased substantially while allele frequencies continued to change in the same direction as before GS. These results provide insight in the effect of breeding practices on genomic diversity and emphasize the need for efficient management of genetic diversity in GS schemes.

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of \"linking genotype and phenotype,\" considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders.

In farmed fish, selective breeding for feed conversion ratio (FCR) may be possible via indirectly selecting for easily-measured indicator traits correlated with FCR. We tested the hypothesis that rainbow trout with low lipid% have genetically better FCR, and that lipid% may be genetically related to retention efficiency of macronutrients, making lipid% a useful indicator trait. A quantitative genetic analysis was used to quantify the benefit of replacing feed intake in a selection index with one of three lipid traits: body lipid%, muscle lipid% or viscera% weight of total body weight (reflecting visceral lipid). The index theory calculations showed that simultaneous selection for weight gain and against feed intake (direct selection to improve FCR) increased the expected genetic response in FCR by 1·50-fold compared with the sole selection for growth. Replacing feed intake in the selection index with body lipid%, muscle lipid% or viscera% increased genetic response in FCR by 1·29-, 1·49- and 1·02-fold, respectively, compared with the sole selection for growth. Consequently, indirect selection for weight gain and against muscle lipid% was almost as effective as direct selection for FCR. Fish with genetically low body and muscle lipid% were more efficient in turning ingested protein into protein weight gain. Both physiological and genetic mechanisms promote the hypothesis that low-lipid% fish are more efficient. These results highlight that in breeding programmes of rainbow trout, control of lipid deposition improves not only FCR but also protein-retention efficiency. This improves resource efficiency of aquaculture and reduces nutrient load to the environment.

Genetic assessment of species that have experienced dramatic population declines provides critical information that is instrumental for the design of conservation recovery programs. Here, we use different sources of molecular data (mtDNA and ddRAD‐seq) to evaluate the genetic status of wild and captive populations of marbled teal (Marmaronetta angustirostris), a duck species classified as critically endangered in Spain and near threatened at a global scale. First, we determined the evolutionary and demographic trajectories of the wild population from Spain and the currently much larger population from Iraq, which is also the documented source of European zoo stocks. Second, we evaluated the suitability of the different captive populations for ongoing restocking programs in Spain and assessed their potential impact on the genetic composition of wild populations. Populations from Spain and Iraq were assigned to distinct genetic clusters, albeit with an overall low level of genetic differentiation, in line with their recent divergence (<8000 years ago) and lack of phylogeographic structure in the species. Demogenomic inferences revealed that the two populations have experienced parallel demographic trajectories, with a marked bottleneck during the last glacial period followed by a sudden demographic expansion and stability since the onset of the Holocene. The wild population from Spain presented high levels of inbreeding, but we found no evidence of recent genetic bottlenecks compatible with the human‐driven decline of the species during the past century. The captive populations from the two Spanish centers involved in restocking programs showed genetic introgression from European zoos; however, we found limited evidence of introgression from the zoo genetic stock into the wild population from Spain, suggesting captive‐bred birds have limited breeding success in the wild. Our study illustrates how ex situ conservation programs should consider the genetic distinctiveness of populations when establishing breeding stocks and highlights the importance of genetically assessing captive populations prior to reinforcement actions.

Genetic selection has led to spectacular advances in animal production in many domestic species. However, it is still little applied to honey bees (Apis mellifera), whose complex genetic and reproductive characteristics are a challenge to model statistically. Advances in informatics now enable creation of a statistical model consistent with honey bee genetics, and, consequently, genetic selection for this species. The aim of this project was to determine the genetic parameters of several traits important for Canadian beekeepers with a view to establishing a breeding program in a northern context. Our results show that the five traits measured (Varroa destructor infestation, spring development, honey production, winter consumption, and hygienic behavior) are heritable. Thus, the rate of V. destructor infestation has a high heritability (h2 = 0.44 ± 0.56), spring development and honey production have a medium heritability (respectively, h2 = 0.30 ± 0.14 and h2 = 0.20 ± 0.13), and winter consumption and hygienic behavior have a low heritability (respectively, h2 = 0.11 ± 0.09 and h2 = 0.18 ± 0.13). Furthermore, the genetic correlations between these traits are all positive or null, except between hygienic behavior and V. destructor infestation level. These genetic parameters will be instrumental to the development of a selection index that will be used to improve the capacity of honey bees to thrive in northern conditions.

Genetic improvement in production efficiency traits can also drive reduction in greenhouse gas emissions. This study used international ‘best-practice’ methodology to quantify the improvements in system-wide CO2 equivalent emissions per unit of genetic progress in the Irish Maternal Replacement (MR) and Terminal (T) beef cattle indexes. Effects of each index trait on system gross emissions (GE) and system emissions intensity (EI) were modelled by estimating effects of trait changes on per-animal feed consumption and associated methane production, per-animal meat production and numbers of animals in the system. Trait responses to index selection were predicted from linear regression of individual bull estimated breeding values for each index trait on their MR or T index value, and the resulting regression coefficients were used to calculate trait-wise responses in GE and EI from index selection. Summed over all trait responses, the MR index was predicted to reduce system GE by 0.810 kg CO2e/breeding cow per year per € index and system EI by 0.009 kg CO2e/kg meat per breeding cow per year per € index. These reductions were mainly driven by improvements in cow survival, reduced mature cow maintenance feed requirements, shorter calving interval and reduced offspring mortality. The T index was predicted to reduce system EI by 0.021 kg CO2e/kg meat per breeding cow per year per € index, driven by increased meat production from improvements in carcass weight, conformation and fat. Implications for incorporating an EI reduction index to the current production indexes and long-term projections for national breeding programs are discussed.

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals. MoBPS utilizes a highly efficient implementation with bit-wise data storage and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations. Individual haplotypes are not stored but instead automatically derived based on points of recombination and mutations. The modular structure of MoBPS allows to combine rather coarse simulations, as needed to generate founder populations, with a very detailed modeling of todays’ complex breeding programs, making use of all available biotechnologies. MoBPS provides pre-implemented functions for common breeding practices such as optimum genetic contributions and single-step GBLUP but also allows the user to replace certain steps with personalized and/or self-written solutions.

Rye ( Secale cereale L.) chromosome arm 1RS has been used world-wide by wheat ( Triticum aestivum L.) breeding programs as a source of pest- and pathogen-resistance genes, and to improve grain yield and stress tolerance. The most common vehicles used to access 1RS are various 1AL.1RS and 1BL.1RS wheat-rye chromosomal translocations. Over the past 25 years, advanced North American wheat breeding lines were evaluated, first by assay of secalin storage proteins, and later by use of DNA marker TSM0120, for the presence of these two translocations. Both methods provide accurate and efficient means of identifying and differentiating 1BL.1RS and 1A.1RS. Both 1Al.1RS and 1BL.1RS wheats were found in all tested years. 1AL.1RS lines were more common in southern Great Plains breeding programs. 1AL.1RS lines were released as cultivars at a frequency identical to that of wild-type breeding lines. In contrast, 1BL.1RS breeding lines were developed by breeding programs throughout the Great Plains, but fewer were released as cultivars. Both 1RS translocation types persist in Great Plains breeding programs. The lower rate of release of 1BL.1RS cultivars no doubt is a consequence of the more drastic effects on breadmaking quality relative to those observed with 1AL.1RS.

Height is a key trait in the indices applied when selecting genotypes for use in both tree breeding populations and production populations in seed orchards. Thus, measurement of tree height is an important activity in the Swedish Norway spruce breeding program. However, traditional measurement techniques are time-consuming, expensive, and often involve work in bad weather, so automatization of the data acquisition would be beneficial. Possibilities for such automatization have been opened by advances in unmanned aerial vehicle (UAV) technology. Therefore, to test its applicability in breeding programs, images acquired by a consumer-level UAV (DJI Phantom 4 Pro V2.0) system were used to predict the height and breast height diameter of Norway spruce trees in a 12-year-old genetic field trial established with 2.0 × 2.0 m initial spacing. The tree heights were also measured in the field using an ultrasonic system. Three additive regression models with different numbers of predictor variables were used to estimate heights of individual trees. On stand level, the average height estimate derived from UAV data was 2% higher than the field-measured average. The estimation of family means was very accurate, but the genotype-level accuracy, which is crucial for selection in the Norway spruce breeding program, was not high enough. There was just ca. 60% matching of genotypes in groups selected using actual and estimated heights. In addition, heritability values calculated from the predicted values were underestimated and overestimated for height and diameter, respectively, with deviations from measurement-based estimates ranging between −19% and +12%. However, the use of more sophisticated UAV and camera equipment could significantly improve the results and enable automatic individual tree detection.