Multi-objective optimized genomic breeding strategies for sustainable food improvement.

The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss of genetic variation. The decisions at each breeding cycle involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by applying multi-objective optimization principles to breeding. The discussion in this manuscript includes the definition of several multi-objective optimized breeding approaches within the phenotypic or genomic breeding frameworks and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, independent culling and index selection. Proposed methods are demonstrated with two empirical data sets and simulations. In addition, we have described several graphical tools that can aid breeders in arriving at a compromise decision. The results show that the proposed methodology is a viable approach to answer several real breeding problems. In simulations, the newly proposed methods resulted in gains larger than the methods previously proposed including index selection: Compared to the best alternative breeding strategy, the gains from multi-objective optimized parental proportions approaches were about 20-30% higher at the end of long-term simulations of breeding cycles. In addition, the flexibility of the multi-objective optimized breeding strategies were displayed with methods and examples covering non-dominated selection, assignment of optimal parental proportions, using genomewide marker effects in producing optimal mating designs, and finally in selection of training populations for genomic prediction.

Locally epistatic models for genome-wide prediction and association by importance sampling.

BACKGROUND: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions, and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles. RESULTS: This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach. CONCLUSIONS: In this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model.

An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.

The generation of unprecedented amounts of data brings new challenges in data management, but also an opportunity to accelerate the identification of processes of multiple science disciplines. One of these challenges is the harmonization of high-dimensional unbalanced and heterogeneous data. In this manuscript, we propose a statistical approach to combine incomplete and partially-overlapping pieces of covariance matrices that come from independent experiments. We assume that the data are a random sample of partial covariance matrices sampled from Wishart distributions and we derive an expectation-maximization algorithm for parameter estimation. We demonstrate the properties of our method by (i) using simulation studies and (ii) using empirical datasets. In general, being able to make inferences about the covariance of variables not observed in the same experiment is a valuable tool for data analysis since covariance estimation is an important step in many statistical applications, such as multivariate analysis, principal component analysis, factor analysis, and structural equation modeling.

Genome-wide association mapping of Fusarium langsethiae infection and mycotoxin accumulation in oat (Avena sativa L.).

Fusarium langsethiae is a symptomless pathogen of oat panicles that produces T-2 and HT-2 mycotoxins, two of the most potent trichothecenes produced by Fusarium fungi in cereals. In the last few years, the levels of these mycotoxin in oat grain has increased and the European commission have already recommended a maximum level for of 1000 µg kg-1 for unprocessed oat for human consumption. The optimal and most sustainable way of combating infection and mycotoxin contamination is by releasing resistant oat varieties. Here the objective was to determine if we could identify any genomic loci associated with either the accumulation of F. langsethiae DNA or mycotoxins in the grain. In each of two years, field trials were conducted wherein 190 spring oat varieties were inoculated with a mixture of three isolate of the pathogen. Mycotoxins were quantified using liquid chromatography-tandem mass spectrometry. Varieties were genotyped using 16,863 genotyping by sequencing markers. Genome-wide association studies associated 5 SNPs in the linkage group Mr06 with T-2 + HT-2 mycotoxin accumulation. Markers were highly correlated, and a single QTL was identified. The marker avgbs_6K_95238.1 mapped within genes showing similarity to lipase, lipase-like or lipase precursor mRNA sequences and zinc-finger proteins. These regions have previously been shown to confer a significant increase in resistance to Fusarium species.

Design of training populations for selective phenotyping in genomic prediction.

Phenotyping is the current bottleneck in plant breeding, especially because next-generation sequencing has decreased genotyping cost more than 100.000 fold in the last 20 years. Therefore, the cost of phenotyping needs to be optimized within a breeding program. When designing the implementation of genomic selection scheme into the breeding cycle, breeders need to select the optimal method for (1) selecting training populations that maximize genomic prediction accuracy and (2) to reduce the cost of phenotyping while improving precision. In this article, we compared methods for selecting training populations under two scenarios: Firstly, when the objective is to select a training population set (TRS) to predict the remaining individuals from the same population (Untargeted), and secondly, when a test set (TS) is first defined and genotyped, and then the TRS is optimized specifically around the TS (Targeted). Our results show that optimization methods that include information from the test set (targeted) showed the highest accuracies, indicating that apriori information from the TS improves genomic predictions. In addition, predictive ability enhanced especially when population size was small which is a target to decrease phenotypic cost within breeding programs.

High-density genetic mapping of a major QTL for resistance to multiple races of loose smut in a tetraploid wheat cross.

Loose smut, caused by Ustilago tritici (Pers.) Rostr., is a systemic disease of tetraploid durum wheat (Triticum turgidum L.). Loose smut can be economically controlled by growing resistant varieties, making it important to find and deploy new sources of resistance. Blackbird, a variety of T. turgidum L. subsp. carthlicum (Nevski) A. Love & D. Love, carries a high level of resistance to loose smut. Blackbird was crossed with the loose smut susceptible durum cultivar Strongfield to produce a doubled haploid (DH) mapping population. The parents and progenies were inoculated with U. tritici races T26, T32 and T33 individually and as a mixture at Swift Current, Canada in 2011 and 2012 and loose smut incidence (LSI) was assessed. Genotyping of the DH population and parents using an Infinium iSelect 90K single nucleotide polymorphism (SNP) array identified 12,952 polymorphic SNPs. The SNPs and 426 SSRs (previously genotyped in the same population) were mapped to 16 linkage groups spanning 3008.4 cM at an average inter-marker space of 0.2 cM in a high-density genetic map. Composite interval mapping analysis revealed three significant quantitative trait loci (QTL) for loose smut resistance on chromosomes 3A, 6B and 7A. The loose smut resistance QTL on 6B (QUt.spa-6B.2) and 7A (QUt.spa-7A.2) were derived from Blackbird. Strongfield contributed the minor QTL on 3A (QUt.spa-3A.2). The resistance on 6B was a stable major QTL effective against all individual races and the mixture of the three races; it explained up to 74% of the phenotypic variation. This study is the first attempt in durum wheat to identify and map loose smut resistance QTL using a high-density genetic map. The QTL QUt.spa-6B.2 would be an effective source for breeding resistance to multiple races of the loose smut pathogen because it provides near-complete broad resistance to the predominant virulence on the Canadian prairies.

Genome-Wide Association Analysis Using R.

This chapter provides a practical overview of the statistical analysis using R [1] and genotype by sequencing (GBS) markers for genome-wide association studies (GWAS) in oats. Statistical analysis is performed by R package rrBLUP [2] and issues associated with the analysis are addressed along with the R code. The ultimate aim of this chapter is to provide a practical guideline to do GWAS analysis using R, rather than describe the theory in depth. For more details about the subject, readers are referred to the excellent resource book in GWAS [3]. A basic programming experience in R is assumed.

Chromatographic Methods to Evaluate Nutritional Quality in Oat.

Oats (A. sativa L.) have an important and positive role in human diet and health. The health benefits of oats are attributed to its multifunctional characteristic and nutritional profile, being an important source of soluble dietary fiber, well-balanced proteins, unsaturated fatty acids, vitamins, essential minerals, and a good source of natural antioxidants. These antioxidants include the avenanthramides (Avns) and avenalumic acids, which are unique to oats among cereals. High-performance liquid chromatography allows a simultaneous quantification of free amino acids and biogenic amines in oat samples as their OPA/FMOC-CL (o-phthalaldehyde/9-fluorenylmethoxycarbonyl chloride) derivatives. In addition, an ultra-performance liquid chromatography/mass spectrometry method was developed to quantify and characterize avenanthramides contained in oat samples.