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United States Department of Agriculture

Agricultural Research Service

Research Project: Genetic Dissection of Traits for Sugar Beet Improvement

Location: Sugarbeet and Bean Research

Title: Understanding Beta vulgaris taproot storage characteristics and relationships between biomass, sucrose, betalain and water accumulation using inbred mapping populations

Authors
item Galewski, Paul -
item McGrath, J Mitchell

Submitted to: Annual Beet Sugar Development Foundation Research Report
Publication Type: Research Notes
Publication Acceptance Date: May 1, 2014
Publication Date: June 1, 2014
Citation: Galewski, P., McGrath, J.M. 2014. Understanding Beta vulgaris taproot storage characteristics and relationships between biomass, sucrose, betalain and water accumulation using inbred mapping populations. [CD-ROM]. 2013 Annual Beet Sugar Development Foundation Research Report. Denver, Colorado: Beet Sugar Development Foundation.

Technical Abstract: Population means for B. vulgaris red beet and sugar beet parental lines are quite variable in their accumulation of sucrose, betalain, water, and biomass. The variability between populations for physiological and yield characteristics is the basis for making a cross and generating an F2-derived F7 MSR mapping population. DNA was extracted from 176 individuals of the MSR7 population, barcoded, and sequenced on two lanes of the Illumina Hi-Seq2000, resulting in ~1X genome coverage for each F7 MSR plant. In total 3,953,538 SNPs were called against the C869UK-0.1 genome. This number represents the number before filtering out monomorphic SNPs within the population as well as any SNPs that were of low quality. One potential draw back to low coverage sequencing is missing data, as the coverage at each SNP locus within the population ranged from 0.6 percent coverage to 100 percent, and only 35,888 SNP locations had coverage greater than 40 percent. For this reason, imputation programs were evaluated to generate a genotypic matrix for statistical modeling. A KNN algorithm and a mismatch matrix algorithm, NPUTE, was used to estimate missing values. The imputation accuracy in our test averaged 95 percent, in part due to the bi-allelic nature of the dataset.

Last Modified: 10/31/2014
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