Multi-trait selection index for superior agronomic and tuber quality traits in bush yam (Dioscorea praehensilis Benth.)

Open Access JournalDioscorea praehensilis Benth. is a semi-wild yam species and a valuable source of resistance trait genes. To access the agronomic and tuber quality performance, eleven quantitative phenotypic traits were used to discriminate and identify promising accessions among 162 accessions of D. praehensilis collected in Ghana. Significant and high genetic variability (p < 0.001) for all eleven quantitative traits was found among the evaluated accessions. Moderate broad-sense heritability (H2) (30–60%) was observed for all the evaluated quantitative traits except the response to YMV and tuber hardness. The accessions were clustered into three groups; each cluster displayed genotypes with good potentiality for the different traits evaluated. Path coefficient analysis revealed positive contributions (p < 0.01) of the number of tubers per plant, tuber length, tuber width, stem internode length, number of internodes, and tuber flesh hardness to the total tuber weight per plant. Through the multi-trait genotype–ideotype distance index (MGIDI), 24 accessions were identified from the 162 evaluated accessions as top-ranking and could be used as progenitors for trait introgression. The results of this study provide insight for future yam breeding and improvement programs in West Africa

Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods.

Incorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful

Multitrait diversification in marine diatoms in constant and warmed environments

Phytoplankton are photosynthetic marine microbes that affect food webs, nutrient cycles and climate regulation. Their roles are determined by correlated phytoplankton functional traits including cell size, chlorophyll content and cellular composition. Here, we explore patterns of evolution in interrelated trait values and correlations. Because both chance events and natural selection contribute to phytoplankton trait evolution, we used population bottlenecks to diversify six genotypes of Thalassiosirid diatoms. We then evolved them as large populations in two environments. Interspecific variation and within-species evolution were visualized for nine traits and their correlations using reduced axes (a trait-scape). Our main findings are that shifts in trait values resulted in movement of evolving populations within the trait-scape in both environments, but were more frequent when large populations evolved in a novel environment. Which trait relationships evolved was population-specific, but greater departures from ancestral trait correlations were associated with lower population growth rates. There was no single master trait that could be used to understand multi-trait evolution. Instead, repeatable multi-trait evolution occurred along a major axis of variation defined by several diatom traits and trait relationships. Because trait-scapes capture changes in trait relation-ships and values together, they offer an insightful way to study multi-trait variatio

Genomic prediction of grain yield and drought-adaptation capacity in sorghum is enhanced by multi-trait analysis

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.EEA PergaminoFil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; AustraliaFil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; AustraliaFil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaFil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holand

A Hierarchical Bayesian Approach to Multi-Trait Clinical Quantitative Trait Locus Modeling

Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits

A Penalized Multi-trait Mixed Model for Association Mapping in Pedigree-based GWAS

In genome-wide association studies (GWAS), penalization is an important approach for identifying genetic markers associated with trait while mixed model is successful in accounting for a complicated dependence structure among samples. Therefore, penalized linear mixed model is a tool that combines the advantages of penalization approach and linear mixed model. In this study, a GWAS with multiple highly correlated traits is analyzed. For GWAS with multiple quantitative traits that are highly correlated, the analysis using traits marginally inevitably lose some essential information among multiple traits. We propose a penalized-MTMM, a penalized multivariate linear mixed model that allows both the within-trait and between-trait variance components simultaneously for multiple traits. The proposed penalized-MTMM estimates variance components using an AI-REML method and conducts variable selection and point estimation simultaneously using group MCP and sparse group MCP. Best linear unbiased predictor (BLUP) is used to find predictive values and the Pearson's correlations between predictive values and their corresponding observations are used to evaluate prediction performance. Both prediction and selection performance of the proposed approach and its comparison with the uni-trait penalized-LMM are evaluated through simulation studies. We apply the proposed approach to a GWAS data from Genetic Analysis Workshop (GAW) 18

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities