Accuracy of within‐ and among‐family genomic prediction in triticale

Würschum, Tobias; Maurer, Hans Peter; Weissmann, Sigrid; Hahn, Volker; Leiser, Willmar L.

doi:10.1111/pbr.12465

Cited by 37 publications

(34 citation statements)

References 61 publications

(97 reference statements)

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“…Scenario cLe (N = 150), where the DHL in the TS did not include the DHL serving as the PS, showed similarly low r values as scenario LwL (Table 4). This result is similar to GP when combining unrelated biparental families, which yielded r values close to zero or even negative values although the TS size was increased Würschum et al 2017). Further research is warranted to investigate under which circumstances inclusion of DH lines from another landrace will improve the prediction accuracy in a combined TS.…”

Section: Gp Combining Dhl Of Landracessupporting

confidence: 71%

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

et al. 2018

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Thousands of maize landraces are stored in seed banks worldwide. Doubled-haploid libraries (DHL) produced from landraces harness their rich genetic diversity for future breeding. We investigated the prospects of genomic prediction (GP) for line per se performance in DHL from six European landraces and 53 elite flint (EF) lines by comparing four scenarios: GP within a single library (sL); GP between pairs of libraries (LwL); and GP among combined libraries, either including (cLi) or excluding (cLe) lines from the training set (TS) that belong to the same DHL as the prediction set. For scenario sL, with N = 50 lines in the TS, the prediction accuracy (r) among seven agronomic traits varied from 20.53 to 0.57 for the DHL and reached up to 0.74 for the EF lines. For LwL, r was close to zero for all DHL and traits. Whereas scenario cLi showed improved r values compared to sL, r for cLe remained at the low level observed for LwL. Forecasting r with deterministic equations yielded inflated values compared to empirical estimates of r for the DHL, but conserved the ranking. In conclusion, GP is promising within DHL, but large TS sizes (N . 100) are needed to achieve decent prediction accuracy because LD between QTL and markers is the primary source of information that can be exploited by GP. Since production of DHL from landraces is expensive, we recommend GP only for very large DHL produced from a few highly preselected landraces.

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Section: Gp Combining Dhl Of Landracessupporting

confidence: 71%

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

et al. 2018

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“…By taking into account the entire genome with both QTL with minor and major effects on resistance, genomic selection may be a useful strategy for FHB-resistance breeding, rather than simple marker-assisted selection (MAS) based on few QTL with the major effect only. Some preliminary results are available and appear promising (Arruda et al 2015(Arruda et al , 2016Galiano-Carneiro et al 2019;Steiner et al 2017;Würschum et al 2017), but other publications have concluded that genomic selection only slightly improved predictive ability compared to markerassisted selection (Miedaner et al 2017) or even led to lower accuracies than using QTL targeted markers alone (Rutkoski et al 2012). Even so, marker-assisted selection has already demonstrated its efficiency for improving FHB resistance in wheat (Anderson et al 2007;Miedaner et al 2006a, b;Salameh et al 2011;Wilde et al 2007) and could therefore be a more affordable option for triticale breeding programs in which high-density fingerprinting is not commonly implemented.…”

Section: Perspective For Triticale Breeding and Conclusionmentioning

confidence: 99%

QTL mapping and successful introgression of the spring wheat-derived QTL Fhb1 for Fusarium head blight resistance in three European triticale populations

et al. 2020

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Key message The spring wheat-derived QTL Fhb1 was successfully introgressed into triticale and resulted in significantly improved FHB resistance in the three triticale mapping populations. Abstract Fusarium head blight (FHB) is a major problem in cereal production particularly because of mycotoxin contaminations. Here we characterized the resistance to FHB in triticale breeding material harboring resistance factors from bread wheat. A highly FHB-resistant experimental line which derives from a triticale × wheat cross was crossed to several modern triticale cultivars. Three populations of recombinant inbred lines were generated and evaluated in field experiments for FHB resistance using spray inoculations during four seasons and were genotyped with genotyping-by-sequencing and SSR markers. FHB severity was assessed in the field by visual scorings and on the harvested grain samples using digital picture analysis for quantifying the whitened kernel surface (WKS). Four QTLs with major effects on FHB resistance were identified, mapping to chromosomes 2B, 3B, 5R, and 7A. Those QTLs were detectable with both Fusarium severity traits. Measuring of WKS allows easy and fast grain symptom quantification and appears as an effective scoring tool for FHB resistance. The QTL on 3B collocated with Fhb1, and the QTL on 5R with the dwarfing gene Ddw1. This is the first report demonstrating the successful introgression of Fhb1 into triticale. It comprises a significant step forward for enhancing FHB resistance in this crop.

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“…Previous studies have shown that the prediction accuracy is generally highest within full-sib families and decreases when prediction is performed in half-sib families or even unrelated families (e.g. Würschum, Maurer, Weissmann, Hahn, & Leiser, 2017). Moreover, it does not appear to be possible to preselect those individuals that allow the highest prediction accuracy within that family (Marulanda, Melchinger, & Würschum, 2015).…”

Section: Genomic Selection For Cold Tolerancementioning

confidence: 99%

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

et al. 2019

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Breeding soybeans for higher latitudes requires cultivars with an increased chilling stress tolerance, especially when flowering occurs. Phenotyping in climate chambers to select for this trait is labour‐intensive and requires an optimal allocation of resources due to limited space. We screened a diversity panel of 35 early maturity cultivars and a biparental population of 103 RILs for their cold stress tolerance at flowering stage. Pod number under control and stress conditions is highly heritable and showed only a weak correlation between the two treatments. Based on different testing scenarios, we could show that testing more genotypes with less replicates yields much higher responses to selection and hence should be pursued in such climate‐controlled experiments. We identified quantitative trait loci (QTL) for pod number under both conditions (chromosomes 7 and 13) and a cold tolerance‐specific QTL (chromosome 11). Furthermore, we performed genomic predictions using different test set scenarios and prediction models, showing that genomic prediction is a promising tool to select for cold stress tolerance, particularly if known QTL can be used as fixed effects in the model.

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Accuracy of within‐ and among‐family genomic prediction in triticale

Cited by 37 publications

References 61 publications

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

QTL mapping and successful introgression of the spring wheat-derived QTL Fhb1 for Fusarium head blight resistance in three European triticale populations

Cold stress tolerance of soybeans during flowering: QTL mapping and efficient selection strategies under controlled conditions

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