Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections

Key message Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers. Quantitative disease resistance (QDR) toward Phytop...

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Veröffentlicht in:Theoretical and applied genetics 2020-12, Vol.133 (12), p.3441-3454
Hauptverfasser: Rolling, William R., Dorrance, Anne E., McHale, Leah K.
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Dorrance, Anne E.
McHale, Leah K.
description Key message Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers. Quantitative disease resistance (QDR) toward Phytophthora sojae in soybean is a complex trait controlled by many small-effect loci throughout the genome. Along with the technical and rate-limiting challenges of phenotyping resistance to a root pathogen, the trait complexity can limit breeding efficiency. However, the application of genomic prediction to traits with complex genetic architecture, such as QDR toward P. sojae , is likely to improve breeding efficiency. We provide a novel example of genomic prediction by measuring QDR to P. sojae in two diverse panels of more than 450 plant introductions (PIs) that had previously been genotyped with the SoySNP50K chip. This research was completed in a collection of diverse germplasm and contributes to both an initial assessment of genomic prediction performance and characterization of the soybean germplasm collection. We tested six statistical models used for genomic prediction including Bayesian Ridge Regression; Bayesian LASSO; Bayes A, B, C; and reproducing kernel Hilbert spaces. We also tested how the number and distribution of SNPs included in genomic prediction altered predictive ability by varying the number of markers from less than 50 to more than 34,000 SNPs, including SNPs based on sequential sampling, random sampling, or selections from association analyses. Predictive ability was relatively independent of statistical model and marker distribution, with a diminishing return when more than 1000 SNPs were included in genomic prediction. This work estimated relative efficiency per breeding cycle between 0.57 and 0.83, which may improve the genetic gain for P. sojae QDR in soybean breeding programs.
doi_str_mv 10.1007/s00122-020-03679-w
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subjects Agriculture
Analysis
Bayes Theorem
Biochemistry
Biomedical and Life Sciences
Biotechnology
Chromosome Mapping - methods
Chromosomes, Plant - genetics
Disease Resistance - genetics
Disease Resistance - immunology
Drug resistance in microorganisms
Gene Expression Regulation, Plant
Genome, Plant
Genomics
Glycine max - genetics
Glycine max - immunology
Glycine max - parasitology
Life Sciences
Methods
Models, Statistical
Original Article
Phenotype
Phytophthora - physiology
Plant Biochemistry
Plant Breeding/Biotechnology
Plant Diseases - genetics
Plant Diseases - parasitology
Plant Genetics and Genomics
Plant Proteins - genetics
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Seeds - genetics
Seeds - immunology
Seeds - parasitology
Single nucleotide polymorphisms
Soybean
title Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections
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