Genomic Selection for Ascochyta Blight Resistance in Pea

被引:17
作者
Carpenter, Margaret A. [1 ]
Goulden, David S. [1 ]
Woods, Carmel J. [1 ]
Thomson, Susan J. [1 ]
Kenel, Fernand [1 ]
Frew, Tonya J. [1 ]
Cooper, Rebecca D. [1 ]
Timmerman-Vaughan, Gail M. [1 ]
机构
[1] New Zealand Inst Plant & Food Res Ltd, Christchurch, New Zealand
关键词
genomic selection; ascochyta blight; pea; disease resistance; genotyping-by-sequencing; QUANTITATIVE TRAIT LOCI; PISUM-SATIVUM L; PREDICTION; ACCURACY; IDENTIFICATION; POPULATIONS; REGRESSION; SOFTWARE; MARKERS; GENES;
D O I
10.3389/fpls.2018.01878
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Genomic selection GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker x environment interactions in a genomic best linear unbiased prediction GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% i.e., missing SNP data in < 30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker x environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.
引用
收藏
页数:13
相关论文
共 59 条
[41]   Pea powdery mildew er1 resistance is associated to loss-of-function mutations at a MLO homologous locus [J].
Pavan, Stefano ;
Schiavulli, Adalgisa ;
Appiano, Michela ;
Marcotrigiano, Angelo R. ;
Cillo, Fabrizio ;
Visser, Richard G. F. ;
Bai, Yuling ;
Lotti, Concetta ;
Ricciardi, Luigi .
THEORETICAL AND APPLIED GENETICS, 2011, 123 (08) :1425-1431
[42]   Genome-Wide Regression and Prediction with the BGLR Statistical Package [J].
Perez, Paulino ;
de los Campos, Gustavo .
GENETICS, 2014, 198 (02) :483-U63
[43]   Mapping of quantitative trait loci for partial resistance to Mycosphaerella pinodes in pea (Pisum sativum L.), at the seedling and adult plant stages [J].
Prioul, S ;
Frankewitz, A ;
Deniot, G ;
Morin, G ;
Baranger, A .
THEORETICAL AND APPLIED GENETICS, 2004, 108 (07) :1322-1334
[44]  
Pritchard JK, 2000, GENETICS, V155, P945
[45]   Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.) [J].
Resende, M. F. R., Jr. ;
Munoz, P. ;
Resende, M. D. V. ;
Garrick, D. J. ;
Fernando, R. L. ;
Davis, J. M. ;
Jokela, E. J. ;
Martin, T. A. ;
Peter, G. F. ;
Kirst, M. .
GENETICS, 2012, 190 (04) :1503-+
[46]   Maximizing the Reliability of Genomic Selection by Optimizing the Calibration Set of Reference Individuals: Comparison of Methods in Two Diverse Groups of Maize Inbreds (Zea mays L.) [J].
Rincent, R. ;
Laloe, D. ;
Nicolas, S. ;
Altmann, T. ;
Brunel, D. ;
Revilla, P. ;
Rodriguez, V. M. ;
Moreno-Gonzalez, J. ;
Melchinger, A. ;
Bauer, E. ;
Schoen, C-C ;
Meyer, N. ;
Giauffret, C. ;
Bauland, C. ;
Jamin, P. ;
Laborde, J. ;
Monod, H. ;
Flament, P. ;
Charcosset, A. ;
Moreau, L. .
GENETICS, 2012, 192 (02) :715-+
[47]   Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application [J].
Scheben, Armin ;
Batley, Jacqueline ;
Edwards, David .
PLANT BIOTECHNOLOGY JOURNAL, 2017, 15 (02) :149-161
[48]   Comparisons of single-stage and two-stage approaches to genomic selection [J].
Schulz-Streeck, Torben ;
Ogutu, Joseph O. ;
Piepho, Hans-Peter .
THEORETICAL AND APPLIED GENETICS, 2013, 126 (01) :69-82
[49]   Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines [J].
Spindel, Jennifer ;
Begum, Hasina ;
Akdemir, Deniz ;
Virk, Parminder ;
Collard, Bertrand ;
Redona, Edilberto ;
Atlin, Gary ;
Jannink, Jean-Luc ;
McCouch, Susan R. .
PLOS GENETICS, 2015, 11 (02) :1-25
[50]   Measuring dementia carers' unmet need for services - an exploratory mixed method study [J].
Stirling, Christine ;
Andrews, Sharon ;
Croft, Toby ;
Vickers, James ;
Turner, Paul ;
Robinson, Andrew .
BMC HEALTH SERVICES RESEARCH, 2010, 10