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Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes

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Abstract

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Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding.

Abstract

Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.

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Abbreviations

2T:

Two-trait

3T:

Three-trait

2TGS:

Two-trait genomic selection

3TGS:

Three-trait genomic selection

BLUE(s):

Best linear unbiased estimator(s)

BLUP(s):

Best linear unbiased predictor(s)

CMS:

Cytoplasmic-male sterile

DArT:

Diversity array technology

GEBV(s):

Genomic predicted breeding value(s)

GS:

Genomic selection

GY:

Grain yield

MT:

Multiple-trait

MTGS:

Multiple-trait genomic selection

NIRS:

Near-infrared reflectance spectroscopy

O-SI:

Smith–Hazel or optimum selection index

PC:

Protein content

QTL:

Quantitative trait loci

REML:

Restricted maximum likelihood

RD:

Rogers’ distance

R-SI:

Restricted selection index

SDS:

Sudden death syndrome

SEW:

Single ear weight

SI:

Selection index

ST:

Single-trait

STGS:

Single-trait genomic selection

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Acknowledgments

This research was conducted within the project “Erweiterung der genetischen Basis von Hybridroggen für Korn- und Biomasseleistung sowie Trockenheitstoleranz mittels Mehrlinienkartierung und DH-Technik” financially supported by the German Federal Ministry of Food and Agriculture via the “Fachagentur Nachwachsende Rohstoffe e.V.”, Gülzow, Germany (Grant ID: 22021711).

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    Authors and Affiliations

    1. Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany

      Albert Wilhelm Schulthess, Yu Wang, Jochen C. Reif & Yusheng Zhao

    2. State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany

      Yu Wang & Thomas Miedaner

    3. KWS LOCHOW GMBH, 29296, Bergen, Germany

      Peer Wilde

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    1. Albert Wilhelm Schulthess
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    Correspondence to Yusheng Zhao.

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    The experiments were performed according to the current laws of Germany.

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    Communicated by J. Wang.

    A. W. Schulthess and Y. Wang equally contributed to this work.

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    Schulthess, A.W., Wang, Y., Miedaner, T. et al. Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129, 273–287 (2016). https://doi.org/10.1007/s00122-015-2626-6

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