Conventional Phenotypic Selection Research Articles

A Bayesian optimization R package for multitrait parental selection.

Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package-an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions-EvalMPS, FastMPS, and ApproxMPS-catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.

Performance, heritability, and candidate genes for growth in dusky kob (Argyrosomus japonicus): Implications for genetic improvement during early phase domestication

Dusky kob, is a Sciaenid finfish that has a global distribution and is indigenous to South Africa. Historically, the species was a prime target for fisheries, but is currently an emerging candidate for aquaculture due to declining wild populations. Aquaculture breeding pose challenges to conventional genetic selection methods, especially when species are new (essentially wild), and industries are small: Founder populations are limited, and sexual maturity is often late; mass spawning leads to large variances in broodstock contributions and pedigree records are difficult to keep; and production environments are often highly variable. To overcome such challenges, this study consolidated inferred pedigree and phenotypic data across three aquaculture facilities and was able to estimate robust heritability values for the correlated growth traits, wet weight (0.46 ± 0.29) and standard length (0.41 ± 0.27). We further suggest that conventional phenotypic selection be combined with marker assisted selection to improve potential genetic gains. To this aim a case-control association analysis was done using a candidate gene approach, prioritising, and validating genetic variants from a previously developed exome resource for dusky kob. Three genes with large effect sizes on phenotypes were identified: tankyrase [Haplotypic Odd Ratio (OR) = 22.98]; myoblast determination protein 1 (highest individual SNP OR = 5.28); and bone morphogenetic protein 2 (Haplotypic OR = 13.33). If a cooperative model for genetic improvement was to be implemented across farms, using a combinational approach, it could facilitate making genetic gains during the early phases of domestication.

The Pivotal Role of Major Chromosomes of Sub-Genomes A and D in Fiber Quality Traits of Cotton.

Lack of precise information about the candidate genes involved in a complex quantitative trait is a major obstacle in the cotton fiber quality improvement, and thus, overall genetic gain in conventional phenotypic selection is low. Recent molecular interventions and advancements in genome sequencing have led to the development of high-throughput molecular markers, quantitative trait locus (QTL) fine mapping, and single nucleotide polymorphisms (SNPs). These advanced tools have resolved the existing bottlenecks in trait-specific breeding. This review demonstrates the significance of chromosomes 3, 7, 9, 11, and 12 of sub-genomes A and D carrying candidate genes for fiber quality. However, chromosome 7 carrying SNPs for stable and potent QTLs related to fiber quality provides great insights for fiber quality-targeted research. This information can be validated by marker-assisted selection (MAS) and transgene in Arabidopsis and subsequently in cotton.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding.

Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

Pyramiding of High Grain Weight With Stripe Rust and Leaf Rust Resistance in Elite Indian Wheat Cultivar Using a Combination of Marker Assisted and Phenotypic Selection.

Wheat (Triticum aestivum L.) is an important cereal crop globally as well as in India and yield improvement programs encounter a strong impediment from ever-evolving rust pathogens. Hence, durable rust resistance is always a priority trait for wheat breeders globally. Grain weight, represented as thousand grain weight (TGW), is the most important yield-contributing trait in wheat. In the present study high TGW has been transferred into two elite Indian wheat cultivars PBW343 and PBW550 from a high TGW genotype, Rye selection 111, selected from local germplasm. In the background of PBW343 and PBW550, an increase in TGW upto 27.34 and 18% was observed, respectively (with respect to recipient parents), through conventional backcross breeding with phenotypic selections in 3 years replicated RBD trials. Resistance to leaf rust and stripe rust has been incorporated in the high TGW version of PBW550 through marker assisted pyramiding of stripe rust resistance gene Yr15 using marker Xuhw302, and a pair of linked leaf rust and stripe rust resistance genes Lr57-Yr40 using marker Ta5DS-2754099_kasp23. Improved versions of PBW550 with increased TGW ranging from 45.0 to 46.2 g (up to a 9% increase) and stacked genes for stripe and leaf rust resistance have been developed. This study serves as proof of utilizing conventional breeding and phenotypic selection combined with modern marker assisted selection in improvement of important wheat cultivars as a symbiont of conventional and moderan techniques.

A Weak Allele of FASCIATED EAR 2 (FEA2) Increases Maize Kernel Row Number (KRN) and Yield in Elite Maize Hybrids

Meristems are central to plant growth and development, yet evidence of directly manipulating this control to improve crop yield is scarce. Kernel row number (KRN) is an important agronomic trait that can directly affect maize (Zea mays L.) yield. However, this trait is difficult to select by phenotyping, since it is highly variable in the mixed genetic backgrounds in early selfing generations. This study sought to improve this trait by marker-assisted backcrossing (MABC) of a weak allele of FASCIATED EAR 2 that is known to affect inflorescence meristem size, but the effect of which on yield is unclear. All of the four introgressed tropical elite inbreds of different heterotic groups, which are homozygous for the fea2-1328 allele, had 2–5 more KRNs compared to their respective recurrent parents. Furthermore, one hybrid made from crosses between two introgressed parents also had KRN increases that resulted in up to 28% yield increase compared to the original hybrid across multiple yield trials. The novel negative effects of the pericentromeric fea2 and/or its linkage drag effect on plant height, seed weight, and ear length, which could prevent line improvement, were revealed in several genetic backgrounds. Integration of conventional phenotypic selection to overcome these undesirable effects was discussed. This is the first work to demonstrate the possibility to increase yield of maize varieties using a mutation in a meristem size regulator. The crossing, selection strategies, and recombinant lines in this work can be applied to other elite maize hybrids and provide a potentially straightforward, non-transgenic way to improve the yield of an existing variety by 8–28%.

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

Diseases are among the most important limiting factors for wheat production. Breeding for fungal diseases of wheat, primarily for rusts and Fusarium head blight (FHB), are major resource consuming activities in most breeding programs which prevent breeders from focusing entirely on improving yield. Breeding for these diseases is challenging because resistance is inherited mostly in a quantitative fashion and is greatly influenced by weather conditions. Recent advances in genomics, phenomics and big-data analysis provide opportunities for accelerating the development of low-cost and efficient selection methods for such complex traits. Genomic selection (GS) may provide opportunities for reducing the time and cost of making selections. By appropriately integrating GS in the breeding workflow, it is possible to select new parents purely based on genomic estimated breeding values before breeding materials are entered into nurseries and field trials. Due to reduced selection cycle time, annual genetic gain for GS is predicted to be two to threefold greater than for a conventional phenotypic selection program. In this paper, we review the recent GS studies focusing on the prediction of resistance to rusts and FHB including those that benefits from modeling multiple phenological traits correlated with the resistance. In addition, we discuss the potential of integrating phenomics and machine learning for evaluating plant disease and the integration of multiple “omics” data in genomic prediction to improve the applicability of GS for disease resistance breeding in wheat.

Empirical Comparison of Tropical Maize Hybrids Selected Through Genomic and Phenotypic Selections.

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost–benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%–23% higher grain yield under WW and 51%–52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.

Impact of Mislabeling on Genomic Selection in Cassava Breeding.

In plant breeding, humans occasionally make mistakes. Genomic selection is particularly prone to human error because it involves more steps than conventional phenotypic selection. The impact of human mistakes should be determined to evaluate the cost effectiveness of controlling human error in plant breeding. We used simulation to evaluate the impact of mislabeling, where marker scores from one plant are associated with the performance records of another plant in cassava (Manihot esculenta Crantz) breeding. Results showed that, although selection with mislabeling reduced genetic gains, scenarios including six levels of mislabeling (from 5 to 50%) persisted in achieving gain because mislabeling decreased the genetic variance lost from the population. Breeding populations with higher rates of mislabeling experienced lower selection intensity, resulting in higher genetic variance, which partially compensated for the mislabeling. For low mislabeling rates (10% or less), the increased genetic variance observed under mislabeling led to improved accuracy of the prediction model in later selection cycles. Large-scale mislabeling should therefore be prevented, but the value of preventing small-scale mislabeling depends on the effort already being invested in preventing the loss of genetic variance during the course of selection. In a program, such as the one we simulated, that makes no effort to avoid loss of genetic variance, small-scale mislabeling has a less negative effect than expected. We assume that negative effects would be greater if best practices to avoid genetic variance loss were already implemented.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Key messageEarly generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

Response and inbreeding from a genomic selection experiment in layer chickens.

BackgroundGenomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken.MethodsIn the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.ResultsBirds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line.ConclusionsThe results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

Biotechnological Approaches for Enhancing Animal Health and Production

global increase in the demand of livestock products, numerous attempts are being made to enhance the animal productivity and health. Among the various approaches, biotechnological approaches which are briefly summed up here offer promise to enhance livestock health and production. Improvement of livestock has traditionally focused on the selective breeding of individuals with superior phenotypes to allow them to perform better as well as survive and thrive in the prevailing environment. This approach has been extremely successful in increasing the quantity of agricultural output, however, recent advances in genetic technologies which not only enabled identification of genetic markers associated with the health and production traits but also led to the information of their entire genome. Marker assisted selection (MAS) which offers several merits over conventional phenotypic selection, has been integrated in the breeding program for genetic improvement of livestock. MAS strategy targets one or few alleles governing the particular traits thereby resulting in the selection of individuals with superior alleles. This strategy results in a small number of individuals having a large influence on the genepool which in turn results in the loss of genetic diversity. Recently, genomic selection which involves selection for numerous genetic markers dispersed across the genome contributing to multiple traits has been seen as an ideal approach to estimate the breeding value as well as maintaining the genetic diversity (2). Ruminants comprise major livestock species among the domesticated animals. Ruminants harbors complex microbial communities in their fore stomach known as rumen which are essential for conversion of coarse feed into digestible compounds such as volatile fatty acids and bacterial proteins, which in-turn determines the animal health and productivity (Pope et al., 2012). The rumen microbial community providing such beneficial functions is dominated by bacteria followed by anaerobic protozoa, fungi and bacteriophages (3). Recently, microbiome dynamics in response to change in the diet suggested significant impact of diet on rumen microbiome (4). These microbial dynamics suggested key role played by specific microbial communities in the degradation of fiber rich diet. Among the all rumen microbial species, less than 11% are cultivable (5). The availability of complete genome sequence of rumen microbial communities would further facilitates devising culture media for supporting the culture of these microbial species. In the near future, we hope that many of these species may hold potential to be used as probiotics to enhance digestibility and thereby production. The ability of rumen microflora to degrade the coarse feed is ascribed to their ligno-cellulolytic enzymes. These enzymes have evolved with their host depending on their feeding habitat. The ruminants are known for efficient conversion of lignocellulosic biomass to metabolic energy and hence expected to harbor enzymes with high potency so as to degrade the coarse plant fibers in few hours. These enzymes are useful for number of applications such as animal feed supplements to enhance the digestibility, for production of biofuel, fermentation industry, pulp and paper industry, textile industry, food and feed industry and production of other value-added commodities (6-8). The mining of cellulolytic enzyme from rumen microbiome has the potential to discover novel biocatalyst with high potency. Assisted reproductive technologies including embryo transfer, somatic cell nuclear transfer and transgenesis have been seen as promising approaches to introduce the novel and superior traits in the livestock such as high production traits, disease resistance, and production of biopharmaceuticals. However, these approaches require skilled manpower and expensive equipments. Further, limited success and ethical issues concerning genetically modified animals have restricted these technologies to the laboratory.

Evaluation of marker-assisted selection for the stripe rust resistance gene Yr15, introgressed from wild emmer wheat.

Stripe rust disease is caused by the fungus Puccinia striiformis f. sp. tritici and severely threatens wheat worldwide, repeatedly breaking resistance conferred by resistance genes and evolving more aggressive strains. Wild emmer wheat, Triticum dicoccoides, is an important source for novel stripe rust resistance (Yr) genes. Yr15, a major gene located on chromosome 1BS of T. dicoccoides, was previously reported to confer resistance to a broad spectrum of stripe rust isolates, at both seedling and adult plant stages. Introgressions of Yr15 into cultivated T. aestivum bread wheat and T. durum pasta wheat that began in the 1980s are widely used. In the present study, we aimed to validate SSR markers from the Yr15 region as efficient tools for marker-assisted selection (MAS) for introgression of Yr15 into wheat and to compare the outcome of gene introgression by MAS and by conventional phenotypic selection. Our findings establish the validity of MAS for introgression of Yr15 into wheat. We show that the size of the introgressed segment, defined by flanking markers, varies for both phenotypic selection and MAS. The genetic distance of the MAS marker from Yr15 and the number of backcross steps were the main factors affecting the length of the introgressed donor segments. Markers Xbarc8 and Xgwm493, which are the nearest flanking markers studied, were consistent and polymorphic in all 34 introgressions reported here and are therefore the most recommended markers for the introgression of Yr15 into wheat cultivars. Introgression directed by markers, rather than by phenotype, will facilitate simultaneous selection for multiple stripe rust resistant genes and will help to avoid escapees during the selection process.

Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi‐parental Maize Populations under Drought Stress

ABSTRACTGenomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi‐parental maize populations under managed drought stress environments. In each population, 148 to 300 F2:3 (C0) progenies were derived and crossed to a single‐cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well‐watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C0 using a phenotypic selection index and were intermated to form C1. Selections both at C1 and C2 were based on genomic estimated breeding values (GEBVs). The best lines from C0 were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single‐cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C0, C1, C2, C3, parents, F1s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha–1. The average grain yield of C3–derived hybrids was significantly higher than that of hybrids derived from C0. Hybrids derived from C3 produced 7.3% (0.176 Mg ha–1) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree‐based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.

Dissecting the genetic architecture of frost tolerance in Central European winter wheat

Abiotic stress tolerance in plants is pivotal to increase yield stability, but its genetic basis is still poorly understood. To gain insight into the genetic architecture of frost tolerance, this work evaluated a large mapping population of 1739 wheat (Triticum aestivum L.) lines and hybrids adapted to Central Europe in field trials in Germany and fingerprinted the lines with a 9000 single-nucleotide polymorphism array. Additive effects prevailed over dominance effects. A two-dimensional genome scan revealed the presence of epistatic effects. Genome-wide association mapping in combination with a robust cross-validation strategy identified one frost tolerance locus with a major effect located on chromosome 5B. This locus was not in linkage disequilibrium with the known frost loci Fr-B1 and Fr-B2. The use of the detected diagnostic markers on chromosome 5B, however, does not allow prediction of frost tolerance with high accuracy. Application of genome-wide selection approaches that take into account also loci with small effect sizes considerably improved prediction of the genetic variation of frost tolerance in wheat. The developed prediction model is valuable for improving frost tolerance because this trait displays a wide variation in occurrence across years and is therefore a difficult target for conventional phenotypic selection.

Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees

• Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency. By fitting all the genome-wide markers concurrently, GS can capture most of the 'missing heritability' of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain. Experimental support of GS is now required. • The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (N(e) = 11 and 51) genotyped with > 3000 DArT markers. Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP). • Accuracies of GS varied between 0.55 and 0.88, matching the accuracies achieved by conventional phenotypic selection. Substantial proportions (74-97%) of trait heritability were captured by fitting all genome-wide markers simultaneously. Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype × environment interaction. • GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement. Nevertheless population-specific predictive models will likely drive the initial applications of GS in forest tree breeding.

Genomic Selection for growth traits in Eucalyptus: accuracy within and across breeding populations

Background Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most QTLs for the target trait(s). GS is revolutionizing breeding practice for complex trait in domestic animals. The same approach and concepts can be readily applied to forest tree breeding. Trees also have long generation times and late expressing traits. Differently from association genetics that aims at dissecting complex traits in their discrete components, GS precludes the discovery of individual marker-trait associations and focuses on prediction of performance. By capturing the “missing heritability” of complex quantitative traits beyond the few effect variants that association genetics has so far typically identified, GS might soon cause a paradigm shift in forest tree breeding. In a prior deterministic study we assessed the impact of linkage disequilibrium (modeled by Ne and inter-marker distance), the size of the training set, trait heritability and the number of QTL on the predicted accuracy of GS [1]. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP-based phenotypic selection (0.68) was reached by GS even at a marker density ~2 markers/cM when Ne ≤30, while up to 10 markers/cM are necessary for larger Ne. Shortening the breeding cycle by 50% with GS provides an expected increase ≥100% in selection efficiency. To validate these simulation results we carried out a large multi-population proof-of-concept study of GS in tropical Eucalyptus. In this report we present results of this on-going study for two populations and three different quantitative traits.

Changing allele frequencies associated with specific resistance genes to leaf blast in backcross introgression lines of Khao Dawk Mali 105 developed from a conventional selection program

Key genomic regions associated with blast resistance against a broad spectrum of isolates could be identified in backcross introgression lines developed by conventional breeding program. In this article, eighty three BC 3F 2 backcross introgression lines (BILs) derived from a cross between IR68835-98-2-B-2-1-1 (a broad spectrum blast resistance variety) and KDML105 (a susceptible variety) were developed by phenotypic selection against a mixture of six virulent blast isolates (MXL) that are widely spread out in the rainfed lowland of the North and Northeast of Thailand. The resistance spectrum of the BILs was assessed by inoculating with 12 different Magnaporthe oryzae isolates that showed differential responses on the parental cultivars and the MXL that was used for phenotypic selection. All BILs showed highly resistant reactions (low disease score) to the MXL and to two blast isolates THL48 and THL149. Four markers, i.e., RM246, RM241, RM303 and RM164 completely favoring the IR68835 allele (shifting of allele frequencies from KDML105 to IR68835) were identified on chromosomes 1, 4 and 5, respectively. Therefore, these markers could be linked to the resistance genes functioning against the MXL, THL48 and THL149. Furthermore, significant shifting of alleles was identified at six markers located on chromosomes 2, 4, 8, 9 and 12. Seven DNA markers linked to specific resistance genes were identified, in which allele from IR68835 at 6 markers, i.e., RM6, RM205, RM211, RM252, RM273 and RM342 reduced disease score (DS), against blast isolates THL16, THL329, THL458, THL831, THL868 and THL96036 while allele from KDML105 at RM208 ( Pi-kd on chromosomes 2) reduce DS against THL84, THL191, THL557 and THL1108. In this study, the use of DNA markers enabled the identification of specific resistance genes in the backcross breeding materials developed from routine rice breeding program through the conventional phenotypic selection. In this experiment, the usefulness of breeding materials from conventional breeding program in identifying genes corresponding to the selection was illustrated. Linked markers and their genomic location provide necessary information for further use of marker-assisted selection to improve blast resistance in rice breeding program.

Best linear unbiased prediction and optimum allocation of test resources in maize breeding with doubled haploids

With best linear unbiased prediction (BLUP), information from genetically related candidates is combined to obtain more precise estimates of genotypic values of test candidates and thereby increase progress from selection. We developed and applied theory and Monte Carlo simulations implementing BLUP in 2 two-stage maize breeding schemes and various selection strategies. Our objectives were to (1) derive analytical solutions of the mixed model equations under two breeding schemes, (2) determine the optimum allocation of test resources with BLUP under different assumptions regarding the variance component ratios for grain yield in maize, (3) compare the progress from selection using BLUP and conventional phenotypic selection based on mean performance solely of the candidates, and (4) analyze the potential of BLUP for further improving the progress from selection. The breeding schemes involved selection for testcross performance either of DH lines at both stages (DHTC) or of S(1) families at the first stage and DH lines at the second stage (S(1)TC-DHTC). Our analytical solutions allowed much faster calculations of the optimum allocations and superseded matrix inversions to solve the mixed model equations. Compared to conventional phenotypic selection, the progress from selection was slightly higher with BLUP for both optimization criteria, namely the selection gain and the probability to select the best genotypes. The optimum allocation of test resources in S(1)TC-DHTC involved ≥ 10 test locations at both stages, a low number of crosses (≤ 6) each with 100-300 S(1) families at the first stage, and 500-1,000 DH lines at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5-10 times larger, whereas the number of test locations at the first stage and the number of test candidates at the second stage were strongly reduced compared to S(1)TC-DHTC.