Person:
Rosyara, U.

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Rosyara
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Rosyara, U.

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  • Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study
    (Springer, 2019) Verma, S.; Evans, K.; Guan, Y.; Luby, J.J.; Rosyara, U.; Howard, N.P.; Bassil, N.V.; Bink, M.C.A.M.; van de Weg, W.E.; Peace, C.P.
    Acidity is a critical component of the apple fruit consumption experience. In previous biparental family studies, two large-effect acidity QTLs were reported using freshly harvested fruit. Objectives of this study were to determine the number and location of QTLs for acidity variation in a large apple breeding program and ascertain the quantitative effects and breeding relevance of QTL allelic combinations at harvest and after commercially relevant periods of cold storage. Pedigree-connected germplasm of 16 full-sib families representing nine important breeding parents, genotyped for the 8K SNP array, was assessed for titratable acidity at harvest and after 10- and 20-week storage treatments, for three successive seasons. Using pedigree-based QTL mapping software, FlexQTL™, evidence was found for only two QTLs, on linkage groups 16 (the reported Ma locus) and LG 8 (here called Ma3) that jointly explained 66 ± 5% of the phenotypic variation. An additive allele dosage model for the two QTLs effectively explained most acidity variation, with an average of + 1.8 mg/L at harvest per high-acidity allele. The more high-acidity alleles, the faster the depletion with storage, with all combinations appearing to eventually converge to a common baseline. All parent cultivars and selections had one or two of the four possible high-acidity alleles. Each QTL had a rare second high-acidity allele with stronger or reduced effect. Diagnostic SNP markers were identified for QTL alleles derived from distinct sources. Combined QTL effects highlighted utility of the DNAbased information in new cultivar development for targeting desired fruit acidity levels before or after storage.
    Publication
  • Genetic contribution of synthetic hexaploid wheat to CIMMYT’s spring bread wheat breeding germplasm
    (Nature Publishing Group, 2019) Rosyara, U.; Kishii, M.; Payne, T.S.; Sansaloni, C.; Singh, R.P.; Braun, H.J.; Dreisigacker, S.
    Synthetic hexaploid (SH) wheat (AABBD’D’) is developed by artificially generating a fertile hybrid between tetraploid durum wheat (Triticum turgidum, AABB) and diploid wild goat grass (Aegilops tauschii, D’D’). Over three decades, the International Maize and Wheat Improvement Center (CIMMYT) has developed and utilized SH wheat to bridge gene transfer from Ae. tauschii and durum wheat to hexaploid bread wheat. This is a unique example of success utilizing wild relatives in mainstream breeding at large scale worldwide. Our study aimed to determine the genetic contribution of SH wheat to CIMMYT’s global spring bread wheat breeding program. We estimated the theoretical and empirical contribution of D’ to synthetic derivative lines using the ancestral pedigree and marker information using over 1,600 advanced lines and their parents. The average marker-estimated D’ contribution was 17.5% with difference in genome segments suggesting application of differential selection pressure. The pedigree-based contribution was correlated with marker-based estimates without providing chromosome segment specific variation. Results from international yield trials showed that 20% of the lines were synthetic derived with an average D’ contribution of 15.6%. Our results underline the importance of SH wheat in maintaining and enhancing genetic diversity and genetic gain over years and is important for development of a more targeted introgression strategy. The study provides retrospective view into development and utilization of SH in the CIMMYT Global Wheat Program.
    Publication
  • Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models
    (Crop Science Society of America, 2019) Basnet, B.R.; Crossa, J.; Dreisigacker, S.; Pérez-Rodríguez, P.; Yann Manes; Singh, R.P.; Rosyara, U.; Camarillo-Castillo, F.; Murua, M.
    In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1–M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat (Triticum aestivum L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments.
    Publication
  • Wheat molecular breeding laboratory
    (CIMMYT, 2017) Dreisigacker, S.; Sehgal, D.; Rosyara, U.
    Publication