Multi Trait Research Articles

Stability and selection of trait specific genotypes of Curcuma caesia Roxb.using AMMI, BLUP, GGE, WAAS and MTSI model over three years evaluation

Curcuma caesia Roxb. is a rare and endangered plant species which has high demand due to its wide uses in medicinal as well as pharmaceutical applications. Therefore,it is imperative to conserve this species for future generation to highlight and utilize most stable genotypes for mankind.In this study, systematic analysis was performed on a panel of 135 genotypes of Curcuma caesia for three years (2018, 2019 and 2020) in replicated study to identify superior and stable genotypes which could perform well across varied environments. A total of eleven morphological and essential oil data were recorded for all the studied year. Analysis of variance based on Additive multiplicative mean interaction was performed for all the traits which indicated highly significant Genotype × Environment interaction for the characters like plant height, leaf length, leaf width, number of leaves/plant, number of tillers/plant, rhizome yield/plant and essential oil yield at P < 0.005. The principal component 1 and 2of the trait rhizome yield/plant contributed 50.7 % and 49.3 % of the total variation while in essential oil yield, the contribution of principal component 1 and 2towards total variation accounted for 65.8 % and 34.25 % respectively. The stability analysis was performed on rhizome yield/plant (RY) and essential oil yield (EO) since the ultimate goal of the breeder and researchers for varietal development programme of Curcuma caesia is associated with yield components.A total of ten and eleven most stable and superior genotypes were identified for rhizome yield/plant and essential oil yield respectively.The multivariate pattern analyses used in the study were Additive multiplicative mean interaction (AMMI), Weighted average absolute scores (WAAS), GGE biplot and Best linear unbiased predictor (BLUP). Additionally, 20 genotypes were revealed through Multi trait stability index (MTSI)which were stable and superior for both the yield traits (RY and EO). These genotypes could be used in crop breeding programme for varietal development. Moreover, this is the first stability study of Curcuma caesia germplasm using different advanced analysis which is efficient and precise for selection of trait specific genotypes.

​Plant Growth Promoting Mesorhizobia as a Potential Inoculant for Chickpea (Cicer arietinum L.): A Review

Chickpea establish symbiotic relationship with Mesorhizobium ciceri and fixes atmospheric nitrogen, which depends on the efficacy of the Mesorhizobium strains, crop management, soil and environmental factors. A multi-trait Mesorhizobium isolate having more than one Plant growth promoting (PGP) traits often stimulates plant growth directly by IAA and GA production, enhances nutrient uptake by solubilizing phosphorus and siderophore production while indirect growth promotion is by prevention of some pathogenic microorganisms by producing HCN. Several studies revealed that plant growth attributes and yield was increased with inoculation of multi trait Rhizobium isolates. The occurrence of different PGP traits of Mesorhizobium and their effects on plant growth will be discussed in this review paper.

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection.

Multi-trait multi environment analysis for stability in MABC lines of Chickpea (Cicer arietinum)

Fusarium wilt (Fusarium oxysporum f. sp. ciceris) is a major disease that can cause up to 90% yield loss in Chickpea (Cicer arietinum L.). The presence of 8 physiological races of Foc (0, 1A, 1B/C, 2, 3, 4, 5 and 6) makes it a complex task in the development of disease-resistant cultivar. Thus, Pyramiding of Foc races 1, 2, 3, 4 and 5 was undertaken using WR 315 as donor and Pusa 372 as recurrent parent through Marker assisted backcross (MABC) breeding approach. A total of 20 genotypes, including 17 MABC derived lines of Pusa 372 × WR 315, susceptible parent (Pusa 372), resistant check (WR 315) and national check (JG 16) were used. Multi-location testing of advanced MABC lines at 4 different regions (Amla, Badnapur, Sehore, IARI-New Delhi) was carried out using randomised block design (RBD) in two replications during 2020–21 winter (rabi) season. Usually, multi environment testing is performed involving a single trait, which provides lower reliability in selection of lines, compared to multi-trait analysis. The present study identifies highly stable Fusarium wilt resistant lines with higher yield advantage using MTSI (Multi trait stability index) and GGE (Genotype main effect and genotype × environment interaction) biplot methodology. From GGE biplot analyses the PC1 explains 84.97% and PC2 explains 8.96% of variability. MTSI results revealed that genotype (G) 1, 4 and 3 were stable for the multiple characters studied. But, based on GGE-mean stability value G 11, 12 and 3 were identified for higher yield and better stability values. Based on MTSI and GGE, G 3 may be considered as a stable line for multiple traits including yield superiority.

Screening of low-Cd accumulating early rice cultivars coupled with phytoremediation and agro-production: Bioavailability and bioaccessibility tests

Previous studies have focused on total cadmium (Cd) accumulation in rice or its transformation in soil, but only a few have examined the entire soil-rice-human system. This study investigated the Cd bioaccessibility and bioavailability for humans from grains of early rice cultivars grown in a Cd-polluted field and further combined with multi-traits to discover and evaluate the optimum safe production and phytoremediation potential cultivars. The results revealed that Cd concentration in polished rice was <0.20 mg kg−1 in 79 % of early rice cultivars, implying that Cd levels in rice might be reduced by cultivar selection. Furthermore, the higher values of root to straw translocation factor indicates the maximal accumulation of Cd in straw and with highest soil to straw accumulation factor (>1.0) in 66.67 % of cultivars. However, bioaccessibility and bioavailability varied greatly among cultivars with corresponding values ranging from 5.68 to 7.67 % and 1.87 to 5.71 ng g−1, respectively. Despite the fact that polynomial fitting revealed a statistically significant relationship between Cd content in polished rice and bioavailable Cd in humans (R2 = 0.718, P = 0.025), poor goodness of fit for bioaccessibility, bioavailability, and toxicity varied even within low-Cd accumulating cultivars. As a result of multi trait analysis and bioavailability, Zhuliangyou4024 (ER-9), Lingliangyou211 (ER-3), and Yonxian15 (ER-28) were found to be the three best early rice cultivars with higher essential nutrients, less total and bioavailable Cd, and relative high phytoremediation potential and are suitable for healthy rice production and soil remediation.

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.

Stability analysis of yield and yield related traits in ricebean [Vigna umbellata (Thunb.) Ohwi and Ohashi

A study on stability analysis was carried out in 30 diverse genotypes of ricebean (Vigna umbellata Thunb.) in eastern hill region of India. Significant differences among the genotypes over the years were observed for all the three traits studied. The first two principal components axis (IPCA1, 90.4% and IPCA2, 9.6%) could explain 100% of the total of the interaction variation. Correlation study showed that the stability parameters are highly associated with each other (p &lt; 0.01). Multi trait stability index and genotype selection along with strength and weakness view of the genotypes to the traits inferred higher productivity of BSKB 28 (G29) for number of pods/plant and seed yield/plant and Ukhrul 15 (G17) for number of seeds/pod. Different stability measures selected seven genotypes, namely, Bete 6 (G22), IC002567 (G8), Ukhrul 6 (G13), Ukhrul 14 (G16), Ukhrul 15 (G17), Bete 4 (G21) and BSKB 3 (G28) as the most desirable and stable performing with good yield over the years.

Genetic Dissection of Heat Stress Tolerance in Faba Bean (Vicia faba L.) Using GWAS.

Heat waves are expected to become more frequent and intense, which will impact faba bean cultivation globally. Conventional breeding methods are effective but take considerable time to achieve breeding goals, and, therefore, the identification of molecular markers associated with key genes controlling heat tolerance can facilitate and accelerate efficient variety development. We phenotyped 134 accessions in six open field experiments during summer seasons at Terbol, Lebanon, at Hudeiba, Sudan, and at Central Ferry, WA, USA from 2015 to 2018. These accessions were genotyped using genotyping by sequencing (GBS), and 10,794 high quality single nucleotide polymorphisms (SNPs) were discovered. These accessions were clustered in one diverse large group, although several discrete groups may exist surrounding it. Fifteen lines belonging to different botanical groups were identified as tolerant to heat. SNPs associated with heat tolerance using single-trait (ST) and multi-trait (MT) genome-wide association studies (GWASs) showed 9 and 11 significant associations, respectively. Through the annotation of the discovered significant SNPs, we found that SNPs from transcription factor helix–loop–helix bHLH143-like S-adenosylmethionine carrier, putative pentatricopeptide repeat-containing protein At5g08310, protein NLP8-like, and photosystem II reaction center PSB28 proteins are associated with heat tolerance.

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

BackgroundRecently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2.ResultsIn this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model.ConclusionsThe study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

Genomic regions associated with herbicide tolerance in a worldwide faba bean (Vicia faba L.) collection

Weeds represent one of the major constraints for faba bean crop. The identification of molecular markers associated with key genes imparting tolerance to herbicides can facilitate and fasten the efficient and effective development of herbicide tolerant cultivars. We phenotyped 140 faba bean genotypes in three open field experiments at two locations in Lebanon and Morocco against three herbicide treatments (T1 metribuzin 250 g ai/ha; T2 imazethapyr 75 g ai/ha; T3 untreated) and one in greenhouse where T1 and T3 were applied. The same set was genotyped using genotyping by sequencing (GBS) which yield 10,794 high quality single nucleotide polymorphisms (SNPs). ADMIXTURE software was used to infer the population structure which revealed two ancestral subpopulations. To identify SNPs associated with phenological and yield related traits under herbicide treatments, Single-trait (ST) and Multi-trait (MT) Genome Wide Association Studies (GWAS) were fitted using GEMMA software, showing 10 and 14 highly significant associations, respectively. Genomic sequences containing herbicide tolerance associated SNPs were aligned against the NCBI database using BLASTX tool using default parameters to annotate candidate genes underlying the causal variants. SNPs from acidic endochitinase, LRR receptor-like serine/threonine-protein kinase RCH1, probable serine/threonine-protein kinase NAK, malate dehydrogenase, photosystem I core protein PsaA and MYB-related protein P-like were significantly associated with herbicide tolerance traits.

Phenotypic and genetic parameters of productive traits in Rahmani and Romanov sheep and crossbreds.

Data of 651 lambs (68 Romanov, 49 Rahmani, 151 [♀1/2 Rahmani X ♂1/2 Romanov) and 383 (♀3/4 Rahmani and 1/4♂ Romanov]) were collected from Mehalet Mousa Farm, belonging to Animal Production Research Institute from the period of 2009 to 2016 to estimate phenotypic and genetic parameters. The traits studied were birth weight (BW), body weight at four week (BW4), body weight at eight weeks (BW8) and body weight at twelve weeks (BW12) or weaning weight. Least squares analysis of variance shows significance of the effects of breed groups, gender of lambs, birth type; month of birth and year of birth on all traits studied. Rahmani lambs had heavier BW, BW4, BW8 and BW12 while Romanov lambs had the lowest ones. The first generation (♀1/2 Rhamani X ♂1/2 Romanov) had heavier body weights than Romanov and the second generation (♀3/4 Rahmani X ♂1/4 Roamnov). Gender of lambs had highly significant effect on body weights. Males were significantly (p < 0.01) heavier than females for all traits studied. Least square means of BW, BW4, BW8 and BW12 for single lambs were 2.69, 10.43, 13.53 and 16.10 kg, respectively. Least square means of BW, BW4, BW8 and BW12 for twin lambs were 2.50, 9.37, 12.5 and 15.16 kg, respectively, while least square means of BW, BW4, BW8 and BW12 for triple lambs were 2.09, 7.86, 10.83 and 13.67 kg, respectively. Estimates of direct heritability measured by single trait animal model were 0.14, 0.23, 0.25 and 0.26 for BW, BW4, BW8 and BW12, respectively, and the corresponding measured by multi trait animal model were 0.17, 0.24, 0.32 and 0.36 for the same traits, respectively. All genetic and phenotypic correlations among different traits studied are positive and significant.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40–70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50–94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine- and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and -0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine- and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine- and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Identification of drought tolerant Chickpea genotypes through multi trait stability index.

Drought is a major and constantly increasing abiotic stress factor, thus limiting chickpea production. Like other crops, Kabuli Chickpea genotypes are screened for drought stress through Multi-environment trials (METs). Although, METs analysis is generally executed taking into account only one trait, which provides less significant reliability for the recommendation of genotypes as compared to multi trait-based analysis. Multi trait-based analysis could be used to recommend genotypes across diverse environments. Hence, current research was conducted for selection of superior genotypes through multi-trait stability index (MTSI) by using mixed and fixed effect models under six diverse environments. The genotypic stability was computed for all traits individually using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype vs environment interaction (GEI) effects produced by a linear mixed-effect model index. A superiority index, WAASBY was measured to reflect the MPS (Mean performance and stability). The selection differential for the WAASBY index was 11.2%, 18.49% and 23.30% for grain yield (GY), primary branches per plant (PBP) and Stomatal Conductance (STOMA) respectively. Positive selection differential (0.80% ≤ selection differential ≤ 13.00%) were examined for traits averaged desired to be increased and negative (-0.57% ≤ selection differential ≤ -0.23%) for those traits desired to be reduced. The MTSI may be valuable to the plant breeders for the selection of genotypes based on many characters as being strong and simple selection process. Analysis of MTSI for multiple environments revealed that, the genotypes G20, G86, G31, G28, G116, G12, G105, G45, G50, G10, G30, G117, G81, G48, G85, G17, G32, G4, and G37 were the most stable and high yielding out of 120 chickpea genotypes, probably due to high MPS of selected traits under various environments. It is concluded that identified traits can be utilized as genitors in hybridization programs for the development of drought tolerant Kabuli Chickpea breeding material.

Multi trait genetic evaluation of growth traits in Nellore sheep raised on pasture in semi-arid regions of India using Bayesian approach

Abstract The main aim of this study is to estimate genetic parameters and (co) variance components for body weights at various ages in Nellore sheep born during the period from 1993 to 2016 (23 years). A total of 4822 lambs with pedigree descended from 144 sires and 1299 dams were included in this study. Bayesian analysis using Gibbs sampler multi trait animal model with and without maternal effects was studied. Traits included in this study were birth weight (BW), weaning weight (WW), six months body weight (6MW), nine months body weight (9 MW), Yearling weight (YW). Year-season of birth and sex of lamb (contemporary groups) were considered as fixed effects with ewe weight at lambing as a covariate. Estimates of direct heritability estimates for birth weight, WW, 6MW, 9MW and YW based on best model were 0.17, 0.28, 0.27, 26 and 0.23, respectively and the corresponding maternal heritability estimates were 0.12, 0.14, 0.14, 0.12 and 0.13, respectively. The direct-maternal genetic covariance was negative for all the studied traits. Strong and positive estimates of direct additive genetic correlations were observed in growth traits and they ranged from 0.16 to 0.83, whereas the direct-maternal genetic correlations were negative for all the traits and these estimates ranged from ˗0.89 to ˗0.08. Moderate estimates of heritability for the growth traits obtained in this study suggested the presence of better genetic variability and the possibility for genetic improvement of Nellore sheep through selection. Maternal genetic effects were significant in all the traits. Hence, it is recommended to incorporate these effects in the genetic analysis by including it as a genetic or environmental effect or both. The environmental variance obtained in the study suggested that the growth performance of Nellore sheep may be enhanced by improving the feeding and management aspects. Correlations among traits and effects should be considered while implementing breed improvement program.

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality.

Trait-assisted genomic prediction approach is a way to improve genetic gain by cost unit, by reducing budget allocated to phenotyping or by increasing the program's size for the same budget. This study compares different strategies of genomic prediction to optimize resource allocation in breeding schemes by using information from cheaper correlated traits to predict a more expensive trait of interest. We used bread wheat baking score (BMS) calculated for French registration as a case study. To conduct this project, 398 lines from a public breeding program were genotyped and phenotyped for BMS and correlated traits in 11 locations in France between 2000 and 2016. Single-trait (ST), multi-trait (MT) and trait-assisted (TA) strategies were compared in terms of predictive ability and cost. In MT and TA strategies, information from dough strength (W), a cheaper trait correlated with BMS (r = 0.45), was evaluated in the training population or in both the training and the validation sets, respectively. TA models allowed to reduce the budget allocated to phenotyping by up to 65% while maintaining the predictive ability of BMS. TA models also improved the predictive ability of BMS compared to ST models for a fixed budget (maximum gain: + 0.14 in cross-validation and + 0.21 in forward prediction). We also demonstrated that the budget can be further reduced by approximately one fourth while maintaining the same predictive ability by reducing the number of phenotypic records to estimate BMS adjusted means. In addition, we showed that the choice of the lines to be phenotyped can be optimized to minimize cost or maximize predictive ability. To do so, we extended the mean of the generalized coefficient of determination (CDmean) criterion to the multi-trait context (CDmulti).

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.).

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

93 Feeding behavior as predictive traits of fertility and lifetime productivity in replacement beef heifers

Abstract Fertility and longevity traits in beef cows are difficult and expensive to measure. The use of other traits to predict fertility and longevity traits in beef cows will enable selection for efficient reproduction earlier in an animal’s life. The objective of this study is to identify feeding behaviours with predictive relationships to fertility, longevity, and lifetime productivity in beef cows. This study used 421 Bos taurus commercial replacement heifers born from 2004 to 2014 and followed over 1050 mating opportunities, 11 production cycles and 5 parities. Heifers were weaned at 6–7 mo of age and developed on a 90% barley silage, 10% rolled barley diet in pens equipped with 16 electronic feed bunks to monitor feed intake and feeding behaviors. Feed intake (FI), feeding frequency (FREQ), duration (DUR), head-down time (HD) and time-to-bunk (TTB) were collected over the course of the feeding trial. Phenotypic correlations indicate that as FI increased during the heifer development stage, so did subsequent cow pre-breeding and pre-calving body condition score (BCS; r = 0.43, 0.36 respectively) and total lifetime productivity (r = 0.40). As FREQ increased, pre-breeding and pre-calving BCS decreased (r = -0.16 and -0.20, respectively), as did calf birth weight (r = -0.15). As DUR increased, weak positive correlations were observed with days in herd (r = 0.10), age at first calving (r = 0.17) and calf birth weight (0.13). Increased HD would lead to increased pre-breeding and pre-calving BCS (r = 0.16, 0.23 respectively), and greater lifetime productivity (r = 0.16). Time-to-bunk was positively correlated with days in the herd (r = 0.15), and with decreased calf birth weight (r = -0.23) and pre-calving cow weight (r = -0.33). These feeding behaviour traits, collected early in life, are worthy of further investigation into multi trait regression, genetic correlations and genomic selection.