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  5. Detection of Soybean Amino Acid QTLs and Seed Yield QTLs Using Selective Genotyping
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Detection of Soybean Amino Acid QTLs and Seed Yield QTLs Using Selective Genotyping

Date Issued
December 1, 2012
Author(s)
Fallen, Benjamin David
Advisor(s)
Vincent Pantalone
Additional Advisor(s)
Fred L. Allen
Dean A. Kopsell
Arnold Saxton
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/22452
Abstract

The U.S. Census Bureau projects the world’s population will top more than nine billion by 2050. Today, soybeans account for 56 % of the world oilseed production and 68 % of the world protein meal consumption, with U.S. soybean production accounting for 33 % of the world soybean production. So, to meet the demand of the world’s growing population and of the livestock industry improvements in both the composition and the yield of soybean is essential.


The primary objective of this project was to use molecular markers to identify genomic regions associated with amino acid composition and yield in soybean. For amino acid quantitative trait loci (QTL) detection 282 F5:9 recombinant inbred lines (RIL) developed from a cross between Essex and Williams 82 were used. The Universal Soy Linkage Panel (USLP) 1.0 of 1536 single nucleotide polymorphic markers (SNPs) was used to identify 480 polymorphic molecular genetic markers and to genotype the 282 RILs. A total of ten QTL were detected on chromosomes 5, 7, 9, 10, 13 and 20 that explained 5 to 14 % of the total phenotypic variation for a particular amino acid.

To detect yield QTL 875 F5:9 RIL developed from a cross between Essex and Williams 82 were used. The 875 RILs were divided into four groups based on maturity and each group was grown in Knoxville, TN and one other location of adaptability. Each RIL was genotyped with >50,000 SNPs of which 17,232 were polymorphic across the population. A total of forty-six yield QTLs were detected in this study, explaining 4.5 % to 11.9% of the phenotypic variation for yield. In addition, marker assisted selections (MAS) were made using only additive effects and using a yield prediction model (YPM) in each environment and across environments for each group. By including additive by additive effects in addition to additive effects into the YPM, more top yielding lines were selected than by just using only additive effects. This study provides new information concerning amino acid research in soybean and may offer some important insights into using an YPM that includes epistasis in soybean.

Subjects

additive effects

epistasis

yield predicition mod...

single nucleotide pol...

Disciplines
Agronomy and Crop Sciences
Plant Breeding and Genetics
Degree
Doctor of Philosophy
Major
Plant Sciences
Embargo Date
January 1, 2011
File(s)
Thumbnail Image
Name

BenFallenPhDDissertation.pdf

Size

1.38 MB

Format

Adobe PDF

Checksum (MD5)

25d74f656784a5b84d1be415ac1b680d


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