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Genomics Assisted Ancestry Deconvolution in Grape

Overview of attention for article published in PLOS ONE, November 2013
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Title
Genomics Assisted Ancestry Deconvolution in Grape
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0080791
Pubmed ID
Authors

Jason Sawler, Bruce Reisch, Mallikarjuna K. Aradhya, Bernard Prins, Gan-Yuan Zhong, Heidi Schwaninger, Charles Simon, Edward Buckler, Sean Myles

Abstract

The genus Vitis (the grapevine) is a group of highly diverse, diploid woody perennial vines consisting of approximately 60 species from across the northern hemisphere. It is the world's most valuable horticultural crop with ~8 million hectares planted, most of which is processed into wine. To gain insights into the use of wild Vitis species during the past century of interspecific grape breeding and to provide a foundation for marker-assisted breeding programmes, we present a principal components analysis (PCA) based ancestry estimation method to calculate admixture proportions of hybrid grapes in the United States Department of Agriculture grape germplasm collection using genome-wide polymorphism data. We find that grape breeders have backcrossed to both the domesticated V. vinifera and wild Vitis species and that reasonably accurate genome-wide ancestry estimation can be performed on interspecific Vitis hybrids using a panel of fewer than 50 ancestry informative markers (AIMs). We compare measures of ancestry informativeness used in selecting SNP panels for two-way admixture estimation, and verify the accuracy of our method on simulated populations of admixed offspring. Our method of ancestry deconvolution provides a first step towards selection at the seed or seedling stage for desirable admixture profiles, which will facilitate marker-assisted breeding that aims to introgress traits from wild Vitis species while retaining the desirable characteristics of elite V. vinifera cultivars.

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Geographical breakdown

Country Count As %
Hungary 1 2%
Canada 1 2%
Unknown 63 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 18%
Researcher 10 15%
Student > Master 6 9%
Student > Bachelor 4 6%
Professor > Associate Professor 4 6%
Other 11 17%
Unknown 18 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 54%
Biochemistry, Genetics and Molecular Biology 4 6%
Computer Science 2 3%
Nursing and Health Professions 1 2%
Environmental Science 1 2%
Other 4 6%
Unknown 18 28%