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The use of weighted multiple linear regression to estimate QTL × QTL × QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines

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cris.virtual.author-orcid0000-0002-0102-0084
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cris.virtualsource.author-orcid51a5a68b-106b-4e9d-bd9b-79d15d3ec0c1
dc.abstract.enKnowledge of the magnitude of gene effects and their interactions, their nature, and contribution to determining quantitative traits is very important in conducting an effective breeding program. In traditional breeding, information on the parameter related to additive gene effect and additive-additive interaction (epistasis) and higher-order additive interactions would be useful. Although commonly overlooked in studies, higher-order interactions have a significant impact on phenotypic traits. Failure to account for the effect of triplet interactions in quantitative genetics can significantly underestimate additive QTL effects. Understanding the genetic architecture of quantitative traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–QTL interactions, and QTL–QTL–QTL interactions. This paper proposes using weighted multiple linear regression to estimate the effects of triple interaction (additive–additive–additive) quantitative trait loci (QTL–QTL–QTL). The material for the study consisted of 126 doubled haploid lines of winter wheat (Mandub × Begra cross). The lines were analyzed for 18 traits, including percentage of necrosis leaf area, percentage of leaf area covered by pycnidia, heading data, and height. The number of genes (the number of effective factors) was lower than the number of QTLs for nine traits, higher for four traits and equal for five traits. The number of triples for unweighted regression ranged from 0 to 9, while for weighted regression, it ranged from 0 to 13. The total aaagu effect ranged from − 14.74 to 15.61, while aaagw ranged from − 23.39 to 21.65. The number of detected threes using weighted regression was higher for two traits and lower for four traits. Forty-nine statistically significant threes of the additive-by-additive-by-additive interaction effects were observed. The QTL most frequently occurring in threes was 4407404 (9 times). The use of weighted regression improved (in absolute value) the assessment of QTL–QTL–QTL interaction effects compared to the assessment based on unweighted regression. The coefficients of determination for the weighted regression model were higher, ranging from 0.8 to 15.5%, than for the unweighted regression. Based on the results, it can be concluded that the QTL–QTL–QTL triple interaction had a significant effect on the expression of quantitative traits. The use of weighted multiple linear regression proved to be a useful statistical tool for estimating additive-additive-additive (aaa) interaction effects. The weighted regression also provided results closer to phenotypic evaluations than estimator values obtained using unweighted regression, which is closer to the true values.
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliation.instituteKatedra Metod Matematycznych i Statystycznych
dc.contributor.authorCyplik, Adrian
dc.contributor.authorPiaskowska, Dominika
dc.contributor.authorCzembor, Paweł
dc.contributor.authorBocianowski, Jan
dc.date.access2025-08-29
dc.date.accessioned2025-08-29T09:26:26Z
dc.date.available2025-08-29T09:26:26Z
dc.date.copyright2024-10-25
dc.date.issued2023
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>Knowledge of the magnitude of gene effects and their interactions, their nature, and contribution to determining quantitative traits is very important in conducting an effective breeding program. In traditional breeding, information on the parameter related to additive gene effect and additive-additive interaction (epistasis) and higher-order additive interactions would be useful. Although commonly overlooked in studies, higher-order interactions have a significant impact on phenotypic traits. Failure to account for the effect of triplet interactions in quantitative genetics can significantly underestimate additive QTL effects. Understanding the genetic architecture of quantitative traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–QTL interactions, and QTL–QTL–QTL interactions. This paper proposes using weighted multiple linear regression to estimate the effects of triple interaction (additive–additive–additive) quantitative trait loci (QTL–QTL–QTL). The material for the study consisted of 126 doubled haploid lines of winter wheat (Mandub × Begra cross). The lines were analyzed for 18 traits, including percentage of necrosis leaf area, percentage of leaf area covered by pycnidia, heading data, and height. The number of genes (the number of effective factors) was lower than the number of QTLs for nine traits, higher for four traits and equal for five traits. The number of triples for unweighted regression ranged from 0 to 9, while for weighted regression, it ranged from 0 to 13. The total <jats:italic>aaa</jats:italic><jats:sub><jats:italic>gu</jats:italic></jats:sub> effect ranged from − 14.74 to 15.61, while <jats:italic>aaa</jats:italic><jats:sub><jats:italic>gw</jats:italic></jats:sub> ranged from − 23.39 to 21.65. The number of detected threes using weighted regression was higher for two traits and lower for four traits. Forty-nine statistically significant threes of the additive-by-additive-by-additive interaction effects were observed. The QTL most frequently occurring in threes was 4407404 (9 times). The use of weighted regression improved (in absolute value) the assessment of QTL–QTL–QTL interaction effects compared to the assessment based on unweighted regression. The coefficients of determination for the weighted regression model were higher, ranging from 0.8 to 15.5%, than for the unweighted regression. Based on the results, it can be concluded that the QTL–QTL–QTL triple interaction had a significant effect on the expression of quantitative traits. The use of weighted multiple linear regression proved to be a useful statistical tool for estimating additive-additive-additive (<jats:italic>aaa</jats:italic>) interaction effects. The weighted regression also provided results closer to phenotypic evaluations than estimator values obtained using unweighted regression, which is closer to the true values.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,0
dc.description.number4
dc.description.points140
dc.description.versionfinal_published
dc.description.volume64
dc.identifier.doi10.1007/s13353-023-00795-3
dc.identifier.eissn2190-3883
dc.identifier.issn1234-1983
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4523
dc.identifier.weblinkhttps://link.springer.com/article/10.1007/s13353-023-00795-3#
dc.languageen
dc.pbn.affiliationagriculture and horticulture
dc.relation.ispartofJournal of Applied Genetics
dc.relation.pages679-693
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOTHER
dc.subject.enthree-way epistasis
dc.subject.enweighted regression
dc.subject.endoubled haploid lines
dc.subject.enresistance
dc.subject.enTriticum aestivum
dc.titleThe use of weighted multiple linear regression to estimate QTL × QTL × QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue4
oaire.citation.volume64