Estimated breeding values and association mapping for persistency and total milk yield using natural cubic smoothing splines

BackgroundFor dairy producers, a reliable description of lactation curves is a valuable tool for management and selection. From a breeding and production viewpoint, milk yield persistency and total milk yield are important traits. Understanding the genetic drivers for the phenotypic variation of both these traits could provide a means for improving these traits in commercial production.MethodsIt has been shown that Natural Cubic Smoothing Splines (NCSS) can model the features of lactation curves with greater flexibility than the traditional parametric methods. NCSS were used to model the sire effect on the lactation curves of cows. The sire solutions for persistency and total milk yield were derived using NCSS and a whole-genome approach based on a hierarchical model was developed for a large association study using single nucleotide polymorphisms (SNP).ResultsEstimated sire breeding values (EBV) for persistency and milk yield were calculated using NCSS. Persistency EBV were correlated with peak yield but not with total milk yield. Several SNP were found to be associated with both traits and these were used to identify candidate genes for further investigation.ConclusionNCSS can be used to estimate EBV for lactation persistency and total milk yield, which in turn can be used in whole-genome association studies.Klara L. Verbyla and Arunas P. Verbyl

Whole-genome analysis of multienvironment or multitrait QTL in MAGIC

Multiparent Advanced Generation Inter-Cross (MAGIC) populations are now being utilized to more accurately identify the underlying genetic basis of quantitative traits through quantitative trait loci (QTL) analyses and subsequent gene discovery. The expanded genetic diversity present in such populations and the amplified number of recombination events mean that QTL can be identified at a higher resolution. Most QTL analyses are conducted separately for each trait within a single environment. Separate analysis does not take advantage of the underlying correlation structure found in multienvironment or multitrait data. By using this information in a joint analysis-be it multienvironment or multitrait - it is possible to gain a greater understanding of genotype- or QTL-by-environment interactions or of pleiotropic effects across traits. Furthermore, this can result in improvements in accuracy for a range of traits or in a specific target environment and can influence selection decisions. Data derived from MAGIC populations allow for founder probabilities of all founder alleles to be calculated for each individual within the population. This presents an additional layer of complexity and information that can be utilized to identify QTL. A whole-genome approach is proposed for multienvironment and multitrait QTL analysis in MAGIC. The whole-genome approach simultaneously incorporates all founder probabilities at each marker for all individuals in the analysis, rather than using a genome scan. A dimension reduction technique is implemented, which allows for high-dimensional genetic data. For each QTL identified, sizes of effects for each founder allele, the percentage of genetic variance explained, and a score to reflect the strength of the QTL are found. The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population

Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth

Identification of alleles towards the selection for improved seedling vigour is a key objective of many wheat breeding programmes. A multiparent advanced generation intercross (MAGIC) population developed from four commercial spring wheat cultivars (cvv. Baxter, Chara, Westonia and Yitpi) and containing ca. 1000 F2-derived, F6:7 RILs was assessed at two contrasting soil temperatures (12 and 20 °C) for shoot length and coleoptile characteristics length and thickness. Narrow-sense heritabilities were high for coleoptile and shoot length (h2 = 0.68-0.70), indicating a strong genetic basis for the differences among progeny. Genotypic variation was large, and distributions of genotype means were approximately Gaussian with evidence for transgressive segregation for all traits. A number of significant QTL were identified for all early growth traits, and these were commonly repeatable across the different soil temperatures. The largest negative effects on coleoptile lengths were associated with Rht-B1b (-8.2%) and Rht-D1b (-10.9%) dwarfing genes varying in the population. Reduction in coleoptile length with either gene was particularly large at the warmer soil temperature. Other large QTL for coleoptile length were identified on chromosomes 1A, 2B, 4A, 5A and 6B, but these were relatively smaller than allelic effects at the Rht-B1 and Rht-D1 loci. A large coleoptile length effect allele (a = 5.3 mm at 12 °C) was identified on chromosome 1AS despite the relatively shorter coleoptile length of the donor Yitpi. Strong, positive genetic correlations for coleoptile and shoot lengths (rg = 0.85-0.90) support the co-location of QTL for these traits and suggest a common physiological basis for both. The multiparent population has enabled the identification of promising shoot and coleoptile QTL despite the potential for the confounding of large effect dwarfing gene alleles present in the commercial parents. The incidence of these alleles in commercial wheat breeding programmes should facilitate their ready implementation in selection of varieties with improved establishment and early growth

Whole‑genome QTL analysis for MAGIC

Multi-parent advanced generation inter-cross (MAGIC) populations have been developed for mice and several plant species and are useful for the genetic dissection of complex traits. The analysis of quantitative trait loci (QTL) in these populations presents some additional challenges compared with traditional mapping approaches. In particular, pedigree and marker information need to be integrated and founder genetic data needs to be incorporated into the analysis. Here, we present a method for QTL analysis that utilizes the probability of inheriting founder alleles across the whole genome simultaneously, either for intervals or markers. The probabilities can be found using three-point or Hidden Markov Model (HMM) methods. This whole-genome approach is evaluated in a simulation study and it is shown to be a powerful method of analysis. The HMM probabilities lead to low rates of false positives and low bias of estimated QTL effect sizes. An implementation of the approach is available as an R package. In addition, we illustrate the approach using a bread wheat MAGIC population

Variable selection in linear mixed models using an extended class of penalties

There is an emerging need to advance linear mixed model technology to include variable selection methods that can simultaneously choose and estimate important effects from a potentially large number of covariates. However, the complex nature of variable selection has made it difficult for it to be incorporated into mixed models. In this paper we extend the well known class of Lr penalties and show that they can be integrated succinctly into a linear mixed model setting. Under mild conditions, the estimator obtained from this mixed model penalised likelihood is shown to be consistent and asymptotically normally distributed. A simulation study reveals that the extended family of penalties achieves varying degrees of estimator shrinkage depending on the value of one of its parameters. The simulation study also shows there is a link between the number of false positives detected and the number of true coefficients when using the same penalty. This new mixed model variable selection (MMVS) technology was applied to a complex wheat quality data set to determine significant quantitative trait loci (QTL)

Transferring a Biomass Enhancement Biotechnology from Glasshouse to Field: A Case Study on Wheat GWD RNAi

In glasshouse studies we have previously shown that endosperm-specific RNAi suppression of the primary starch phosphorylation enzyme, Glucan, Water Dikinase (GWD) leads to enhanced early vigor, greater leaf biomass, and increases in both head size and yield. To confirm these affects in a field setting, trials were conducted in three Australian environments. Field results were consistent with those in the glasshouse for increased flag leaf area and rachis nodes. However, there was also a decrease in tiller number and consequently a decrease in yield for one event at two sites. These findings provide potentially important information on plant vigor enhancement and highlight the challenges of transferring the modification of complex traits from single plants in controlled environments to the field

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data

MAGIC populations in crops: current status and future prospects

The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery