LenzBeaulieuMansfieldEtAl2017

Reference

Lenz, P.R.N., Beaulieu, J., Mansfield, S.D., Clément, S., Desponts, M., Bousquet, J. (2017) Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics, 18(1):335. (URL )

Abstract

Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy.

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@ARTICLE { LenzBeaulieuMansfieldEtAl2017,
    AUTHOR = { Lenz, P.R.N. and Beaulieu, J. and Mansfield, S.D. and Clément, S. and Desponts, M. and Bousquet, J. },
    TITLE = { Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana) },
    JOURNAL = { BMC Genomics },
    YEAR = { 2017 },
    VOLUME = { 18 },
    PAGES = { 335 },
    NUMBER = { 1 },
    ABSTRACT = { Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy. },
    DOI = { 10.1186/s12864-017-3715-5 },
    ISSN = { 1471-2164 },
    URL = { http://dx.doi.org/10.1186/s12864-017-3715-5 },
}

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