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Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat./
作者:	Winn, Zachary James.
面頁冊數:	1 online resource (190 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29781205click for full text (PQDT)
ISBN:	9798352980705

Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat.
Winn, Zachary James.

Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat. - 1 online resource (190 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2022.

Includes bibliographical references

Genetic pest resistance is integral to ensuring food security. As the population dynamics of pest change in response to the broad deployment of genetic resistance, plant breeders must strive to identify and incorporate novel large-effect pest resistance loci to ensure effective and durable resistance in lines they release. With next generation sequencing technologies, novel approaches should be explored to optimize breeding processes and investigate the effect of quantitative trait loci (QTL) in large historical panels of breeding lines.In chapter one, I review the importance of bread wheat (Triticum aestivum L) as a food source for humanity, cover the discoveries that led to current plant breeding methods, explore methods for analysis, and then describe the genetic characteristics, life cycle, management, and available resistance QTL for Fusarium (Fusarium sp.) head blight (FHB) and Hessian fly (Mayetiola destructor Say).In chapter two, I analyze Hessian fly infestation and genome wide marker data collected on a biparental cross between the cultivar "Shirley" and "LA03136E71" and identify a novel large effect Hessian fly partial resistance locus named Qhft.nc-7D. The markers identified in this study as in high linkage with the QTL will be used to screen lines for this partial resistance locus.In chapter three, I analyze several FHB reaction traits collected on a double haploid population derived from a biparental cross of "NC13-20076" and "GA06493-13LE6". After identifying repeatable putative FHB resistance QTL through several linkage mapping techniques, I then used hierarchical clustering of sequencing data in QTL regions to estimate the frequency of the resistance haplotype of the QTL in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) and estimated their effect via historical data. QTL which appeared to produce significant resistance response in the SUWWSN and double haploid mapping population will be validated in a separate population and markers will be designed for use in marker assisted selection of these loci. In chapter four, I utilized a 2020 and 2021 panel of Kompetitive Allele Specific polymerase chain reaction (KASP) markers run in the SunGrains populations to look at ways of identifying resistant lines in early generations. I investigated how to impute or predict the QTL in earlier generations by two methods: categorization via machine learning and imputation of KASP assays to make composite QTL calls. I profiled FHB resistance QTL in early germplasm and compared the estimated means of historical QTL calls vs the estimated means of predicted QTL calls. The methods outlined in this section will be used in the SunGrains group for predicting QTL calls in lines that would not otherwise be screened with KASP markers for upwards of 60 QTL.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352980705Subjects--Topical Terms:

517247
Statistics.
Index Terms--Genre/Form:

542853
Electronic books.

Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat.
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245 1 0 $a Investigation of Fusarium Head Blight and Hessian Fly Resistance QTL and QTL Profiling via Machine Learning in Soft Red Winter Wheat.
264 0 $c 2022
300 $a 1 online resource (190 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
500 $a Advisor: Pierce, Marcela ; Murphy, Joseph.
502 $a Thesis (Ph.D.)--North Carolina State University, 2022.
504 $a Includes bibliographical references
520 $a Genetic pest resistance is integral to ensuring food security. As the population dynamics of pest change in response to the broad deployment of genetic resistance, plant breeders must strive to identify and incorporate novel large-effect pest resistance loci to ensure effective and durable resistance in lines they release. With next generation sequencing technologies, novel approaches should be explored to optimize breeding processes and investigate the effect of quantitative trait loci (QTL) in large historical panels of breeding lines.In chapter one, I review the importance of bread wheat (Triticum aestivum L) as a food source for humanity, cover the discoveries that led to current plant breeding methods, explore methods for analysis, and then describe the genetic characteristics, life cycle, management, and available resistance QTL for Fusarium (Fusarium sp.) head blight (FHB) and Hessian fly (Mayetiola destructor Say).In chapter two, I analyze Hessian fly infestation and genome wide marker data collected on a biparental cross between the cultivar "Shirley" and "LA03136E71" and identify a novel large effect Hessian fly partial resistance locus named Qhft.nc-7D. The markers identified in this study as in high linkage with the QTL will be used to screen lines for this partial resistance locus.In chapter three, I analyze several FHB reaction traits collected on a double haploid population derived from a biparental cross of "NC13-20076" and "GA06493-13LE6". After identifying repeatable putative FHB resistance QTL through several linkage mapping techniques, I then used hierarchical clustering of sequencing data in QTL regions to estimate the frequency of the resistance haplotype of the QTL in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) and estimated their effect via historical data. QTL which appeared to produce significant resistance response in the SUWWSN and double haploid mapping population will be validated in a separate population and markers will be designed for use in marker assisted selection of these loci. In chapter four, I utilized a 2020 and 2021 panel of Kompetitive Allele Specific polymerase chain reaction (KASP) markers run in the SunGrains populations to look at ways of identifying resistant lines in early generations. I investigated how to impute or predict the QTL in earlier generations by two methods: categorization via machine learning and imputation of KASP assays to make composite QTL calls. I profiled FHB resistance QTL in early germplasm and compared the estimated means of historical QTL calls vs the estimated means of predicted QTL calls. The methods outlined in this section will be used in the SunGrains group for predicting QTL calls in lines that would not otherwise be screened with KASP markers for upwards of 60 QTL.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Disease. $3 705846
650 4 $a Quantitative genetics. $3 600522
650 4 $a Wheat. $3 660846
650 4 $a Seeds. $3 573605
650 4 $a Chromosomes. $3 638948
650 4 $a Probability. $3 518898
650 4 $a Genomes. $3 592593
650 4 $a Clustering. $3 3559215
650 4 $a Decision trees. $3 827433
650 4 $a Haplotypes. $3 3680752
650 4 $a Agronomy. $3 2122783
650 4 $a Genetics. $3 530508
650 4 $a Mathematics. $3 515831
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0285
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertations Abstracts International $g 84-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29781205 $z click for full text (PQDT)