| 000 | 03695nab|a22004097a|4500 | ||
|---|---|---|---|
| 001 | 64428 | ||
| 003 | MX-TxCIM | ||
| 005 | 20230711184811.0 | ||
| 008 | 202109s2021||||sz |||p|op||||00||0|eng|d | ||
| 022 | _a1664-8021 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.3389/fgene.2021.710485 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aTomar, V. _911316 |
|
| 245 | 1 | 0 | _aEvaluations of genomic prediction and identification of new loci for resistance to stripe rust disease in wheat (Triticum aestivum L.) |
| 260 |
_aSwitzerland : _bFrontiers, _c2021. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aStripe rust is one of the most destructive diseases of wheat (Triticum aestivum L.), caused by Puccinia striiformis f. sp. tritici (Pst), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (Q.Yr.bisa-2A.1, Q.Yr.bisa-2D, Q.Yr.bisa-5B.2, and Q.Yr.bisa-7A) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for Pst resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs. | ||
| 546 | _aText in English | ||
| 650 | 7 |
_aMarker-assisted selection _2AGROVOC _910737 |
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| 650 | 7 |
_aQuantitative Trait Loci _2AGROVOC _91853 |
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| 650 | 7 |
_aRusts _91251 _2AGROVOC |
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| 650 | 7 |
_aGenotyping _2AGROVOC _922057 |
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| 650 | 7 |
_aTriticum aestivum _2AGROVOC _91296 |
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| 700 | 1 |
_aDhillon, G.S. _918242 |
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| 700 | 1 |
_aSingh, D. _93851 |
|
| 700 | 1 |
_aSingh, R.P. _gGlobal Wheat Program _9825 _8INT0610 |
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| 700 | 1 |
_aPoland, J.A. _92092 |
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| 700 | 1 |
_aChaudhary, A.A. _924332 |
|
| 700 | 0 |
_aBhati, P.K. _97677 |
|
| 700 | 1 |
_aJoshi, A.K. _gGlobal Wheat Program _9873 _8INT2917 |
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| 700 | 1 |
_aKumar, U. _gFormerly Borlaug Institute for South Asia (BISA) _8INT3331 _9921 |
|
| 773 | 0 |
_tFrontiers in Genetics _gv. 12, art. 710485 _dSwitzerland : Frontiers, 2021. _x1664-8021 _w58093 |
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| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21707 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c64428 _d64420 |
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