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Title: CHARACTERIZATION OF WILD MALUS POPULATIONS USING GENOTYPIC AND PHENOTYPIC TRAITS

Author
item Volk, Gayle
item Richards, Christopher
item Forsline, Philip

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/1/2006
Publication Date: 3/19/2005
Citation: Volk, G.M., C.M. Richards and P.L. Forsline. 2005. Characterization of wild malus populations using genotypic and phenotypic traits. 3rd International Rosaceae Genomics Conference. March 19-22, 2006, Napier, New Zealand. p. 34. Meeting Abstract.

Interpretive Summary: Malus sieversii, a wild relative of domestic apple, represents an important source of genetic variation for several horticulturally important traits including fruit quality and disease resistance. Collections made by the USDA in Kazakhstan have now been analyzed to determine the extent of diversity and genetic structure. The diversity and differentiation of more than 1000 M. sieversii individuals collected from 10 sites have been determined based on data collected from seven unlinked microsatellite loci and 21 quantitative traits. Bayesian assignment analyses identified 10 genetic clusters that are variously admixed among sites. These data can be displayed as a minimum spanning network that enables us to correlate genetic diversity with geographic distances. We have also identified core subsets of individuals that can be used in QTL mapping, allele mining, and other comparative genomic studies.

Technical Abstract: Malus sieversii, a wild relative of domestic apple, represents an important source of genetic variation for several horticulturally important traits including fruit quality and disease resistance. Collections made by the USDA in Kazakhstan have now been analyzed to determine the extent of diversity and genetic structure. The diversity and differentiation of more than 1000 M. sieversii individuals collected from 10 sites have been determined based on data collected from seven unlinked microsatellite loci and 21 quantitative traits. Bayesian assignment analyses identified 10 genetic clusters that are variously admixed among sites. These data can be displayed as a minimum spanning network that enables us to correlate genetic diversity with geographic distances. We have also identified core subsets of individuals that can be used in QTL mapping, allele mining, and other comparative genomic studies.