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. 2018 May 29;19(1):35.
doi: 10.1186/s12863-018-0613-z.

Genomic prediction of crown rust resistance in Lolium perenne

Sai Krishna Arojju  1   2 , Patrick Conaghan  3 , Susanne Barth  1 , Dan Milbourne  1 , Michael D Casler  4   5 , Trevor R Hodkinson  2 , Thibauld Michel  1 , Stephen L Byrne  6
Affiliations

Genomic prediction of crown rust resistance in Lolium perenne

Sai Krishna Arojju et al. BMC Genet. .

Abstract

Background: Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability.

Results: Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set.

Conclusion: Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

Keywords: Crown rust; GWAS; Genetic relationship; Genomic selection; Perennial ryegrass.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Phenotypic variation for crown rust resistance in the complete population, grouped according to sub-population types: cultivars (CS), ecotypes (ES), full-sibs (FS) and half-sibs (HS). Broad sense heritability (H2) in complete population and sub-populations is highlighted over the figure
Fig. 2
Fig. 2
Predictive ability in different population types. Complete population (CP), cultivars (CS), ecotypes (ES), full-sibs (FS) and half-sibs (HS) are listed on x-axis, predictive ability (left) and bias (right) on y-axis. Crown rust is in red and heading date in blue
Fig. 3
Fig. 3
Effect of training population size on predictive ability. Training population is varied from 90% (1423 individuals) to 10% (158 individuals) on x-axis and predictive ability (left), bias (right) on y-axis. Crown rust is in red and heading date in blue
Fig. 4
Fig. 4
Predictive ability of selected markers versus random markers in the complete population. Markers were selected based on the ranking from genome wide association studies and compared with random markers of similar size
Fig. 5
Fig. 5
Comparing predictive ability of selected versus random markers. Markers were selected based on the ranking from genome wide association studies in cultivars, full-sibs and IBERS material and compared with random markers of similar size
See this image and copyright information in PMC

References

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