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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Genetic Improvement for Fruits & Vegetables Laboratory » Research » Publications at this Location » Publication #261560
Title: Development and Use of Genomic Tools in Analyzing Spatial Genetic Structure of Lowbush Blueberry Populations

Author
item Bell, Daniel
item DRUMMOND, FRANCIS - University Of Maine
item Rowland, Lisa

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 10/28/2010
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The overall aim of the Specialty Crop Research Initiative-funded project, “Generating Genomic Tools for Blueberry Improvement,” is to develop genomic tools for molecular breeding and assessing genetic diversity of blueberry. Two of the objectives are to perform deeper transcriptome sequencing of blueberry and use EST-derived markers in population genetic studies of the commercial, wild lowbush blueberry (V. angustifolium). Transcriptome sequences were generated from blueberry fruit at different stages of development, flower buds at different stages of cold acclimation, and leaves by 454 sequencing. Over 600,000 sequences were assembled into approximately 15,000 contigs. EST-derived markers were used to estimate within field spatial genetic structure (SGS) of wild lowbush blueberry in Maine. Previously, using small sample sizes (due to the constraints of agarose gels), we found significant among field differentiation but did not detect positive SGS within 4 fields in Maine. Here, we have revisited the question of within field SGS by intensively sampling 94 touching clones within a 0.35 ha area in one cultivated field. Using this contiguous design, non-parametric statistical techniques, and high throughput capillary gel electrophoresis, we analyzed 90 EST-PCR markers. Results show a strong, positive SGS signal within the first 7.5 m distance class, but not beyond. Interestingly, further local spatial autocorrelation analyses revealed that only a minority of genetically similar individuals (16/94) drive this pattern with the balance of the field being randomly distributed which is consistent with our previous study. However, this more powerful design revealed positive near neighbor SGS which our previous study did not.