Vivi N. Arief

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Exploring wheat landraces for rust resistance using a single marker scan

Marker-trait associations identified in diverse germplasm can be exploited in crop improvement programs. An attempt to establish such associations was made by evaluating 205 wheat landraces for stripe rust, leaf rust and stem rust responses in the field over three crop seasons. Diversity arrays technology was used to genotype the landraces and associations were identified using a single-marker scan. Sixty-eight markers were significantly associated with rust resistance. Several significantly associated loci coincided with the presence of known major genes or QTL for rust resistance. In contrast, many marker-rust response associations identified in this analysis for each of the three rust diseases uncovered new loci. Dual associations; stripe rust-leaf rust (1AL, 2BS, 2BL, 3DL, 5BS, 6BS and 7DL), leaf rust-stem rust (5BL) and stripe rust-stem rust (4BL and 6AS) resistance were also observed. These associations could enable a cost-effective targeted mapping of dual rust resistance. Some marker-trait associations identified in this study have been validated through genetic analyses and formal naming of resistance loci.

A Genomic Selection Index Applied to Simulated and Real Data.

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.

QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations—case studies in forage breeding

Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enables the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross-pollinated species. Each strategy was compared across three levels of half-sib family mean heritability (0.1, 0.5, and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.

Application of a dendrogram seriation algorithm to extract pattern from plant breeding data

A dendrogram is often used to display the results from hierarchical clustering; however, the order of objects in a standard dendrogram is arbitrary and so similarity cannot be readily interpreted. An optimized dendrogram, a dendrogram produced by re-ordering the objects using a seriation method, has a customized ordering that reflects the similarity among objects with most similar objects located closest together. Hierarchical clustering has been applied to the analysis of data from plant breeding programs to identify the patterns in breeding populations and to study genotype by environment interactions. In this paper we demonstrate the advantage of an optimized dendrogram for interpretation of plant breeding data and, given this advantage, argue that an optimized dendrogram should be used as the default whenever hierarchical clustering is used.