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 |
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creator | Rolling, William R. 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 |
format | Article |
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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.</description><identifier>ISSN: 0040-5752</identifier><identifier>EISSN: 1432-2242</identifier><identifier>DOI: 10.1007/s00122-020-03679-w</identifier><identifier>PMID: 32960288</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>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</subject><ispartof>Theoretical and applied genetics, 2020-12, Vol.133 (12), p.3441-3454</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>COPYRIGHT 2020 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c329w-b78a34b4250cf1e211c03dabd191a6b030d75bf717f4a5d524c32fd6bda6260d3</citedby><cites>FETCH-LOGICAL-c329w-b78a34b4250cf1e211c03dabd191a6b030d75bf717f4a5d524c32fd6bda6260d3</cites><orcidid>0000-0001-6893-9872 ; 0000-0003-4138-6707 ; 0000-0003-1028-2315</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00122-020-03679-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00122-020-03679-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27926,27927,41490,42559,51321</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32960288$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rolling, William R.</creatorcontrib><creatorcontrib>Dorrance, Anne E.</creatorcontrib><creatorcontrib>McHale, Leah K.</creatorcontrib><title>Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections</title><title>Theoretical and applied genetics</title><addtitle>Theor Appl Genet</addtitle><addtitle>Theor Appl Genet</addtitle><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.</description><subject>Agriculture</subject><subject>Analysis</subject><subject>Bayes Theorem</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>Chromosome Mapping - methods</subject><subject>Chromosomes, Plant - genetics</subject><subject>Disease Resistance - genetics</subject><subject>Disease Resistance - immunology</subject><subject>Drug resistance in microorganisms</subject><subject>Gene Expression Regulation, Plant</subject><subject>Genome, Plant</subject><subject>Genomics</subject><subject>Glycine max - genetics</subject><subject>Glycine max - immunology</subject><subject>Glycine max - parasitology</subject><subject>Life Sciences</subject><subject>Methods</subject><subject>Models, Statistical</subject><subject>Original Article</subject><subject>Phenotype</subject><subject>Phytophthora - physiology</subject><subject>Plant Biochemistry</subject><subject>Plant Breeding/Biotechnology</subject><subject>Plant Diseases - genetics</subject><subject>Plant Diseases - parasitology</subject><subject>Plant Genetics and Genomics</subject><subject>Plant Proteins - genetics</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Quantitative Trait Loci</subject><subject>Seeds - genetics</subject><subject>Seeds - immunology</subject><subject>Seeds - parasitology</subject><subject>Single nucleotide polymorphisms</subject><subject>Soybean</subject><issn>0040-5752</issn><issn>1432-2242</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc9uEzEQxlcIREPhBTggS1zKYcP4z-4mx6qCghQEh3JCyJq1ZxNHu3ZqbxryVLwiTrcgISHkg63x75uxv68oXnKYc4DmbQLgQpQgoARZN8vy8KiYcSVFKYQSj4sZgIKyaipxVjxLaQsAogL5tDiTYlmDWCxmxc8bSqPzazbQuAk2MfSWpRFHl8sGezYES31ioWNr8mFwhu0iWWdGFzzrQmS3e_SjOynuiFmXCBOxSCk3QG-IjYF92RzHsNvkARFZClsk5nw-HFtCz75d90fjPLEBf7CL1fwN-0Qxfs_z4rDrMQ3MhL6n-4npefGkwz7Ri4f9vPj6_t3N1Ydy9fn649XlqjT5b4eybRYoVavyf03HSXBuQFpsLV9yrFuQYJuq7RredAorWwmVdZ2tW4u1qMHK8-Ji6ruL4XafPdKDS4b6Hj2FfdJCKbVomlqKjL6e0DX2pJ3vwhjRnHB9WctFrXi9XGZq_g8qL0vZ1OCpc7n-l0BMAhNDSpE6vYtuwHjUHPQpfz3lr3P--j5_fciiVw_P3rcD2T-S34FnQE5Aylc-W6y3YR99tvJ_bX8BXqK-qg</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Rolling, William R.</creator><creator>Dorrance, Anne E.</creator><creator>McHale, Leah K.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6893-9872</orcidid><orcidid>https://orcid.org/0000-0003-4138-6707</orcidid><orcidid>https://orcid.org/0000-0003-1028-2315</orcidid></search><sort><creationdate>20201201</creationdate><title>Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections</title><author>Rolling, William R. ; Dorrance, Anne E. ; McHale, Leah K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c329w-b78a34b4250cf1e211c03dabd191a6b030d75bf717f4a5d524c32fd6bda6260d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agriculture</topic><topic>Analysis</topic><topic>Bayes Theorem</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>Chromosome Mapping - methods</topic><topic>Chromosomes, Plant - genetics</topic><topic>Disease Resistance - genetics</topic><topic>Disease Resistance - immunology</topic><topic>Drug resistance in microorganisms</topic><topic>Gene Expression Regulation, Plant</topic><topic>Genome, Plant</topic><topic>Genomics</topic><topic>Glycine max - genetics</topic><topic>Glycine max - immunology</topic><topic>Glycine max - parasitology</topic><topic>Life Sciences</topic><topic>Methods</topic><topic>Models, Statistical</topic><topic>Original Article</topic><topic>Phenotype</topic><topic>Phytophthora - physiology</topic><topic>Plant Biochemistry</topic><topic>Plant Breeding/Biotechnology</topic><topic>Plant Diseases - genetics</topic><topic>Plant Diseases - parasitology</topic><topic>Plant Genetics and Genomics</topic><topic>Plant Proteins - genetics</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Quantitative Trait Loci</topic><topic>Seeds - genetics</topic><topic>Seeds - immunology</topic><topic>Seeds - parasitology</topic><topic>Single nucleotide polymorphisms</topic><topic>Soybean</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rolling, William R.</creatorcontrib><creatorcontrib>Dorrance, Anne E.</creatorcontrib><creatorcontrib>McHale, Leah K.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Theoretical and applied genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rolling, William R.</au><au>Dorrance, Anne E.</au><au>McHale, Leah K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections</atitle><jtitle>Theoretical and applied genetics</jtitle><stitle>Theor Appl Genet</stitle><addtitle>Theor Appl Genet</addtitle><date>2020-12-01</date><risdate>2020</risdate><volume>133</volume><issue>12</issue><spage>3441</spage><epage>3454</epage><pages>3441-3454</pages><issn>0040-5752</issn><eissn>1432-2242</eissn><abstract>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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32960288</pmid><doi>10.1007/s00122-020-03679-w</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-6893-9872</orcidid><orcidid>https://orcid.org/0000-0003-4138-6707</orcidid><orcidid>https://orcid.org/0000-0003-1028-2315</orcidid></addata></record> |
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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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