Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

Key message New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions. Abstract In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (G$$\times$$ × E). With genomic prediction, G$$\times$$ × E can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of G$$\times$$ × E. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.

Genetic inbreeding depression load for fertility traits in Pura Raza Española mares

Fertility is a key factor in the economic success of horse farms. However, it has received little attention due to the difficulty of measuring fertility objectively. Since its studbook creation (1912), the Pura Raza Española (PRE) breed has been a closed population and become high in-bred resulting in inbreeding depression (poor phenotypic values). Nevertheless, heterogeneous effects of inbreeding depression have been detected among founders and non-founders. The aims of this study were (1) to analyse the genetic parameters for reproductive traits in mares of the PRE horse breed, and (2) to estimate, for the first time, the inbreeding depression load associated with common ancestors of the breed. A total of 22,799 mares were analysed. Heritability estimates ranged from 0.05 (interval between first and second foaling) to 0.16 (age at first foaling), while inbreeding depression load ratios ranged from 0.06 (parturition efficiency at 6 th foaling) to 0.17 (age at first foaling), for a partial inbreeding coefficient of 10%. While heritability is related to the variability expressed in the population, inbreeding depression load ratios measure the potential variability, whether expressed in the population or not. Most correlations between additive and inbreeding depression load genetic values were significant (P-values <0.001), and of low to moderate magnitude. Our results confirm that individual inbreeding depression loads allow us to select horses that have a genetic value resistant to the deleterious effects of inbreeding.

Peculiarities in Scots pine (Pinus sylvestris L.) clones seeding on CSP (Cloning seed plantation) in western Polissya of Rivne region

The main purpose of seed orchards is to obtain regularly seeds of the highest genetic value for forest plantations. Long-term research in Ukraine and foreign experience show that there is a significant reduction in the cone yield on orchards that are over 26–28 years old even with free spacing of ramets. The aim of the study was to identify peculiarities in reproduction of Scots pine on Clonal Seed Orchards (CSO), based on comparison for performances of flowering intensity and seed production from different clones, their groups and CSO in Rivne region. The object of the study is the clonal seed orchards of Scots pine (Pinus sylvestris L.). The subject of research is the reproduction of clones on clonal seed orchards of Scots pine. The intensity of «flowering» and seeding of clones on CSO was determined by continuous list of each ramet. To assess the intensity of female «flowering», we determined the growth and the reproductive layer of the crown, the rank position of pine clones on plantations, which we divided into three groups: high, medium and weak. We analyzed the intensity of growth, «flowering» and seed production of Scots pine clones (Pinus silvestris L.) for many years on the plantations of 1977 and 1984. At CSP of 1977, the «flowering» within clones is characterized by a mixed-sexual type; on the plantation of 1984, the most clones (54.2%) were dominated by female «flowering». The rate of microstrobiles formation in clones on the younger plantation (CSP-1984) is lower than the intensity of megastrobiles formation by 4.0%, while on the older one (1977), on the contrary, the intensity of microstrobiles formation was dominant by 118.2%. The average yield of strobiles from the number of megastrobiles on the CSP of 1977 and 1984 was 70.1 and 74.3%, respectively. The close correlations were found between the number of megastrobiles and the number of strobiles (r = 0.94 and 0.84). The variation in the number of strobiles per tree over the years of observation is quite high and varied in clones of 1977 from 40.2% in 2004 to 70.7 in 2003 and averaged 52.6%; clones of 1984 ranged from 43.6% in 2004 to 78.8% in 2005 and averaged 59.3%. The number of female strobiles for the six-year period at CSP of 1977 averaged 45.8% from the number of male ones; for three years — 4.0% in clones of 1984. Their number ranged from 25.5 to 61.3% in clones of 1977; in clones of 1984 – from 16.5 to 77.5%. Depending on the clone and the weather conditions of the growing season, the ratio between the number of female and male strobiles within each clone is from 29.8 to 166.7% on CSP of 1977 and from 49.4 to 1005.4% of 1984. The correlation between the number of mega- and microstrobiles on CSP of 1977 is weak (r = 0.17), and the ones of 1984 – high (r = 0.77). No dependences were found between the preservation of megastrobiles by their number (abundance of «flowering») in clones of 1984 and the average correlation (r = 0.33) was found in clones of 1977. According to long-term data on CSP of 1977, the most productive is clone No 22. The highest yield of strobiles from the number of megastrobiles on the plantation of 1984 – in clone No 130 (88.1%), the lowest — No 121 (46.7%). Only two clones (No 22 and 97) and eight clones of 1984 (No 116, 118, 127, 129, 130, 137, 138, 139) are included into the group of clones of 1977 with abundant «flowering», harvest and high yield of strobiles). The clones that have the high and medium growth intensity, with high and medium flowering intensity, with high and medium yield of strobiles are perspective for seed plantations among the selected groups.

The Conservation of Native Domestic Animal Breeds in Nordic Countries: From Genetic Resources to Cultural Heritage and Good Governance

Native breeds are domestic animal populations that have adapted to their habitats. The genetic value of breeds has been known for a long time, and recently more attention has been paid to their cultural value. Due to both ecological and cultural significance, it is important that native breeds continue to be bred in their native environments. This is supported by various financial support schemes. Support schemes rarely cover the financial gap in output compared to commercial breeds. A solution to this has been sought in special products, such as cheese or wool, and other businesses, such as animal-assisted care and tourism. Less attention has been paid to the role of administration and good governance in the maintenance of native breeds. In this study, a questionnaire was sent to all registered keepers of native breeds in Finland. This survey clarified their reasons for keeping native breeds and their ideas for improving governance structures and practices. The results were discussed in stakeholder workshops, and in a Nordic context. The results show that genetic and cultural values are recognised in several documents and programmes, but farmers need to be engaged more in the design of support schemes and practices.

Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

Key message The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Abstract Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.

Automatic Phenotyping of Tomatoes in Production Greenhouses Using Robotics and Computer Vision: From Theory to Practice

High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment.

The utility of genomic prediction models in evolutionary genetics

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

Genetic relationships among Cucurbita pepo ornamental gourds based on EST-SSR markers

The ornamental gourd Cucurbita pepo L. is a ubiquitous crop native to North America, exhibiting highly diverse fruit characteristics. Studying the genetic diversity of ornamental gourds can help identify and evaluate the curated germplasm resources, understand the phylogenetic relationships among them, and highlight ways in which the germplasm resources can be used to address gaps in the understanding. In this study, a set of 85 of 323 previously identified polymorphic expressed sequence tag-simple sequence repeat (EST-SSR) genetic markers were selected to evaluate the genetic relationships among 47 C. pepo accessions and one C. foetidissima accession. This collection consisted of accessions from the subspecies pepo, texana, and the hybrid texana × pepo. Our analyses yielded a total of 271 alleles, with an average of 3.2 alleles per genetic locus. The dendrogram construction, principal coordinate analyses, and genetic value calculation revealed several robust subclusters in the texana subspecies accessions. From these results, we propose five new distinct morphotypes based on our construction of a concise SSR fingerprint. Moreover, our study confirms that the fruit shape similarity among accessions is a fair reflection of genetic relatedness.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.