| 000 | 02818nab|a22003977a|4500 | ||
|---|---|---|---|
| 999 |
_c63674 _d63666 |
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| 001 | 63674 | ||
| 003 | MX-TxCIM | ||
| 005 | 20211006085227.0 | ||
| 008 | 201209s2021||||xxk|||p|op||||00||0|eng|d | ||
| 022 | _a2045-2322 | ||
| 024 | 8 | _ahttps://doi.org/10.1038/s41598-021-83107-1 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aLópez-Malvar, A. _919779 |
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| 245 | 1 | 0 | _aAssociation mapping for maize stover yield and saccharification efficiency using a multiparent advanced generation intercross (MAGIC) population |
| 260 |
_aLondon (United Kingdom) : _bNature Publishing Group, _c2021. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aCellulosic ethanol derived from fast growing C4 grasses could become an alternative to finite fossil fuels. With the potential to generate a major source of lignocellulosic biomass, maize has gained importance as an outstanding model plant for studying the complex cell wall network and also to optimize crop breeding strategies in bioenergy grasses. A genome-wide association study (GWAS) was conducted using a subset of 408 Recombinant Inbred Lines (RILs) from a Multi-Parent Advanced Generation Intercross (MAGIC) Population in order to identify single nucleotide polymorphisms (SNPs) associated with yield and saccharification efficiency of maize stover. We identified 13 SNPs significantly associated with increased stover yield that corresponded to 13 QTL, and 2 SNPs significantly associated with improved saccharification efficiency, that could be clustered into 2 QTL. We have pointed out the most interesting SNPs to be implemented in breeding programs based on results from analyses of averaged and yearly data. Association mapping in this MAGIC population highlight genomic regions directly linked to traits that influence the final use of maize. Markers linked to these QTL could be used in genomic or marker-assisted selection programs to improve biomass quality for ethanol production. This study opens a possible optimisation path for improving the viability of second-generation biofuels. | ||
| 546 | _aText in English | ||
| 650 | 7 |
_aPlant biotechnology _2AGROVOC _98056 |
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| 650 | 7 |
_aHydrolysis _2AGROVOC _912240 |
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| 650 | 7 |
_aMaize _gAGROVOC _2 _91173 |
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| 650 | 7 |
_aGenomes _gAGROVOC _2 _91131 |
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| 700 | 1 |
_aButron, A. _919780 |
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| 700 | 1 |
_aMalvar, R.A. _919781 |
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| 700 | 1 |
_aMcQueen-Mason, S.J. _917522 |
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| 700 | 1 |
_aFaas, L. _919782 |
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| 700 | 1 |
_aGomez, L.D. _917521 |
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| 700 | 1 |
_aRevilla, P. _917116 |
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| 700 | 1 |
_aFigueroa-Garrido, D.J. _919783 |
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| 700 | 1 |
_aSantiago, R. _919784 |
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| 773 | 0 |
_gv. 11, art. 3425 _dLondon (United Kingdom) : Nature Publishing Group, 2021. _x2045-2322 _tNature Scientific Reports _wa58025 |
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| 856 | 4 |
_yClick here to access online _uhttps://doi.org/10.1038/s41598-021-83107-1 |
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| 942 |
_cJA _n0 _2ddc |
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