東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat./
Author:	DeWitt, Noah Daniel.
Description:	1 online resource (174 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Phenology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29228748click for full text (PQDT)
ISBN:	9798841534617

Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat.
DeWitt, Noah Daniel.

Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat. - 1 online resource (174 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

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

Includes bibliographical references

For most economically important plant species, yield is the primary focus of crop improvement, as increasing it increases the amount of food that can be obtained from a production system by human activity. Growers of soft red winter wheat (Triticum aestivum L.) receive a return on investment in proportion to their per-acre yield, given that harvested grain meets quality standards. Yield is also genetically complex, integrating information on interactions between genotype and environment that occur over the course of a growing season. Yield relates to the total accumulated and reallocated resources obtained by plants over the course of their lives, and any factors that affect any aspect of plant growth and development will also affect yield. These complex interactions between genetic variation and environmental variation are difficult to untangle. By understanding important parts of those interactions, breeders and geneticists may better develop cultivars that maximize performance across a range of possible environmental conditions.Much previous work has been done to understand and breed for yield. In CHAPTER 1, I present some background on wheat, its history, and major genes that have been characterized that influence wheat phenology. I include discussion on various ways that geneticists and breeders can predict future phenotypes, including methods that take advantage of the characterization of major genes and methods that are blind to it, and discuss the yield component framework that is a major focus of the research presented here.In the following chapters, I present work that explores some parts of the genetic basis of yield variation in our germplasm. In CHAPTER 2, I attempt to break down the genetic character of traits that measure part of the variation in life histories, heading date and plant height. I find that the genetic base of both traits is fairly simple in this population, and find that models that explicitly account for major genes are more predictive than models that weigh all sites equally. In CHAPTER 3, I collect per-spike yield and its component traits. I try to extend the work of chapter 2 to understand how genetic variants for heading date and plant height affect spike yield through the effects of those phenotypes on components of yield, and to understand QTL that alter yield and not phenology. I develop models that rely on an understanding of the causal structure of the data to first map QTL then understand the mechanisms through which they alter spike yield. I find a set of yield component QTL, some known and some novel, that have large effects on the yield component space as a whole. QTL effects on total spike yield vary from environment to environment due to both altered QTL effects on trait values, and altered relationships between traits.In CHAPTERS 4 and 5, I focus in on a single yield component QTL, associated with the presence of awns, as a "model QTL" to understand the full sequence of events through which DNA sequence variation translates into environment-specific yield difference.

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

Mode of access: World Wide Web

ISBN: 9798841534617Subjects--Topical Terms:

578250
Phenology.
Index Terms--Genre/Form:

542853
Electronic books.

Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat.
LDR:04409nmm a2200361K 4500 001 2353742
005 20230313091340.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798841534617
035 $a (MiAaPQ)AAI29228748
035 $a (MiAaPQ)NCState_Univ18402039578
035 $a AAI29228748
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a DeWitt, Noah Daniel. $3 3694074
245 1 0 $a Exploring and Predicting Genetic Causes of Yield Variation in Winter Wheat.
264 0 $c 2022
300 $a 1 online resource (174 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Murphy, J. Paul; Holland, James B.; Maltecca, Christian; Dewey, Ralph.
502 $a Thesis (Ph.D.)--North Carolina State University, 2022.
504 $a Includes bibliographical references
520 $a For most economically important plant species, yield is the primary focus of crop improvement, as increasing it increases the amount of food that can be obtained from a production system by human activity. Growers of soft red winter wheat (Triticum aestivum L.) receive a return on investment in proportion to their per-acre yield, given that harvested grain meets quality standards. Yield is also genetically complex, integrating information on interactions between genotype and environment that occur over the course of a growing season. Yield relates to the total accumulated and reallocated resources obtained by plants over the course of their lives, and any factors that affect any aspect of plant growth and development will also affect yield. These complex interactions between genetic variation and environmental variation are difficult to untangle. By understanding important parts of those interactions, breeders and geneticists may better develop cultivars that maximize performance across a range of possible environmental conditions.Much previous work has been done to understand and breed for yield. In CHAPTER 1, I present some background on wheat, its history, and major genes that have been characterized that influence wheat phenology. I include discussion on various ways that geneticists and breeders can predict future phenotypes, including methods that take advantage of the characterization of major genes and methods that are blind to it, and discuss the yield component framework that is a major focus of the research presented here.In the following chapters, I present work that explores some parts of the genetic basis of yield variation in our germplasm. In CHAPTER 2, I attempt to break down the genetic character of traits that measure part of the variation in life histories, heading date and plant height. I find that the genetic base of both traits is fairly simple in this population, and find that models that explicitly account for major genes are more predictive than models that weigh all sites equally. In CHAPTER 3, I collect per-spike yield and its component traits. I try to extend the work of chapter 2 to understand how genetic variants for heading date and plant height affect spike yield through the effects of those phenotypes on components of yield, and to understand QTL that alter yield and not phenology. I develop models that rely on an understanding of the causal structure of the data to first map QTL then understand the mechanisms through which they alter spike yield. I find a set of yield component QTL, some known and some novel, that have large effects on the yield component space as a whole. QTL effects on total spike yield vary from environment to environment due to both altered QTL effects on trait values, and altered relationships between traits.In CHAPTERS 4 and 5, I focus in on a single yield component QTL, associated with the presence of awns, as a "model QTL" to understand the full sequence of events through which DNA sequence variation translates into environment-specific yield difference.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Phenology. $3 578250
650 4 $a Gene expression. $3 643979
650 4 $a Germplasm. $3 3680751
650 4 $a Plant growth. $3 3540341
650 4 $a Genotype & phenotype. $3 3561790
650 4 $a Wheat. $3 660846
650 4 $a Agronomy. $3 2122783
650 4 $a Bioinformatics. $3 553671
650 4 $a Genetics. $3 530508
650 4 $a Plant sciences. $3 3173832
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0285
690 $a 0715
690 $a 0369
690 $a 0479
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 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29228748 $z click for full text (PQDT)