ThistlethwaiteElDienRatcliffeEtAl2020

Reference

Thistlethwaite, F.R., El-Dien, O.G., Ratcliffe, B., Klápště, J., Porth, I., Chen, C., Stoehr, M.U., Ingvarsson, P.K., El-Kassaby, Y.A. (2020) Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species. PLoS ONE, 15(6). (Scopus )

Abstract

Background: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ∼ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. Results: As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. Conclusions: Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits. © 2020 Thistlethwaite et al.

EndNote Format

You can import this reference in EndNote.

BibTeX-CSV Format

You can import this reference in BibTeX-CSV format.

BibTeX Format

You can copy the BibTeX entry of this reference below, orimport it directly in a software like JabRef .

@ARTICLE { ThistlethwaiteElDienRatcliffeEtAl2020,
    AUTHOR = { Thistlethwaite, F.R. and El-Dien, O.G. and Ratcliffe, B. and Klápště, J. and Porth, I. and Chen, C. and Stoehr, M.U. and Ingvarsson, P.K. and El-Kassaby, Y.A. },
    JOURNAL = { PLoS ONE },
    TITLE = { Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species },
    YEAR = { 2020 },
    NOTE = { cited By 0 },
    NUMBER = { 6 },
    VOLUME = { 15 },
    ABSTRACT = { Background: The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ∼ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. Results: As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. Conclusions: Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits. © 2020 Thistlethwaite et al. },
    AFFILIATION = { Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada; Pharmacognosy Department, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt; Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand; Département des sciences du bois et de la forêt, Université Laval, Québec, QC, Canada; Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, United States; British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, BC, Canada; Linnean Centre for Plant Biology, Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden },
    ART_NUMBER = { e0232201 },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1371/journal.pone.0232201 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086356451&doi=10.1371%2fjournal.pone.0232201&partnerID=40&md5=6d2cba35b44df264aeac48943a677094 },
}

********************************************************** *************************** FRQNT ************************ **********************************************************

Un regroupement stratégique du

********************************************************** *********************** Infolettre *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ***************** Pub - Congrès Mycelium ****************** **********************************************************

Reporté en 2021

********************************************************** ***************** Pub - IWTT ****************** **********************************************************

Reporté en 2021

**********************************************************

***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...