Genomic prediction in hybrid breeding: I. Optimizing the training set design

Medientyp: E-Artikel
Titel: Genomic prediction in hybrid breeding: I. Optimizing the training set design
Beteiligte: Melchinger, Albrecht E.; Fernando, Rohan; Stricker, Christian; Schön, Chris-Carolin; Auinger, Hans-Jürgen
Erschienen: Springer Science and Business Media LLC, 2023
Erschienen in: Theoretical and Applied Genetics
Sprache: Englisch
DOI: 10.1007/s00122-023-04413-y
ISSN: 0040-5752; 1432-2242
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Beschreibung: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Key message</jats:title> <jats:p>Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids.</jats:p> </jats:sec><jats:sec> <jats:title>Abstract</jats:title> <jats:p>Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (<jats:italic>n</jats:italic><jats:sub>TS</jats:sub>) and crosses per parent (<jats:italic>c</jats:italic>) has received little attention. Our objective was to examine prediction accuracy (<jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula>) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of <jats:italic>n</jats:italic><jats:sub>TS</jats:sub> and <jats:italic>c</jats:italic>. In the theory, we developed estimates for <jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> of GBLUPs for hybrids: (i)<jats:inline-formula><jats:alternatives><jats:tex-math>$$\hat{r}_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> based on the expected prediction accuracy, and (ii) <jats:inline-formula><jats:alternatives><jats:tex-math>$$\tilde{r}_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> based on <jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (<jats:italic>τ</jats:italic><jats:sub>SCA</jats:sub> = 1%, 6%, 22%) of SCA variance in <jats:italic>σ</jats:italic><jats:sub><jats:italic>G</jats:italic></jats:sub><jats:sup>2</jats:sup> and heritability (<jats:italic>h</jats:italic><jats:sup>2</jats:sup> = 0.4, 0.8). Values of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\tilde{r}_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$\hat{r}_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>r</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> closely agreed with <jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> for hybrids. For given size <jats:italic>N</jats:italic><jats:sub>TS</jats:sub> = <jats:italic>n</jats:italic><jats:sub>TS</jats:sub> × <jats:italic>c</jats:italic> of TS, <jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> of H0 hybrids and GCA of I0 lines was highest for <jats:italic>c</jats:italic> = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, <jats:italic>c</jats:italic> = 1 yielded lowest <jats:inline-formula><jats:alternatives><jats:tex-math>$$r_{a}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math></jats:alternatives></jats:inline-formula> with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of <jats:italic>c</jats:italic> for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.</jats:p> </jats:sec>

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