Sensitivity of genomic selection to using different prior distributions

Genomic selection describes a selection strategy based on genomic estimated breeding values (GEBV) predicted from dense genetic markers such as single nucleotide polymorphism (SNP) data. Different Bayesian models have been suggested to derive the prediction equation, with the main difference centred around the specification of the prior distributions

Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

Background: The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information. Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling. Results: Fourteen QTL were detected at the different time points. Correlations between estimated breeding values were high between models, except when the model was used that assumed that all SNP effects came from one distribution. The model that used the selected 14 SNP found associated with QTL, gave close to unity correlations with the full parameterisations. Conclusions: Nine out of 18 QTL were detected, however the six QTL for inflection point were missed. Models for genomic selection were indicated to be fairly robust, e.g. with respect to accuracy of estimated breeding values. Still, it is worthwhile to investigate the number QTL underlying the quantitative traits, before choosing the model used for genomic selection

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

The Embedding Problem for Markov Models of Nucleotide Substitution

10.1371/journal.pone.0069187PLoS ONE87-POLN

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