000			03398nab|a22003857a|4500
001			69557
003			MX-TxCIM
005			20251124101717.0
008			2511202025|||||xxu||p|op||||00||0|eng|dd
024	8		_ahttp://.doi.org/10.22541/essoar.175096298.87051063/v1
040			_aMX-TxCIM
041			_aeng
100	1		_aCrain, J.L. _94131
245	1	0	_aWheat breeding with skim-sequencing for genomic selection: a comparison of marker platforms
260			_aUnited States of America : _bWiley, _c2025.
500			_aPreprint
500			_aOpen Access
520			_aThe promise of predictive genomics-assisted breeding relies on efficient, affordable, and abundant molecular markers. The quantity and quality of markers have greatly expanded, yet plant breeding programs have struggled to fully harness this power mainly using array-based genotyping, targeted amplicon sequencing platforms, or reduced representation, sequence-based genotyping including genotyping-by-sequencing (GBS). Leveraging modern sequencing technology, commercial laboratory products, and open-source software, we demonstrate how ultra-low coverage (skim-seq, 0.05-0.10x) can be a viable marker platform. We genotyped 1,709 wheat lines with GBS, a mid-density DArTAG SNP panel (TaDArTAG vs. 2.0), and skim-seq (0.07x). All skim-seq variants were identified from the pooled skim-seq data and a reference genome without the aid of high-coverage samples. STITCH software was used for imputation followed by filtering to obtain 125,682 markers. Comparing STITCH imputed values to high coverage samples resulted in the correct imputation for more than 96% of the markers. Using phenotypic data, a 5-fold cross validation was implemented for each marker platform. No one marker system performed the best in all test cases, with GBS often resulting in the highest correlation between observed and predicted values. The skim-seq correlations were typically within 0.03 of GBS, suggesting skim-seq can be a viable marker strategy for genomic prediction. As technology and computational pipelines advances, skim-seq appears to be a promising method to bridge the gap between targeted genotyping and whole-genome sequencing. The skim-seq method is highly flexible and can be optimized to a variety of program needs, potentially allowing for wide adoption by the plant breeding community.
546			_aText in English
591			_aPoland, J.A. : Not in IRS staff list but CIMMYT Affiliation
597			_dUnited States Agency for International Development (USAID) _dFoundation for Food & Agriculture Research (FFAR) _dThe Land Institute _dMalone Family Land Preservation Foundation
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aPlant breeding _2AGROVOC _91203
650		7	_aWheat _2AGROVOC _91310
650		7	_aGenotyping-by-sequencing _2AGROVOC _91135
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	0		_aLee DeHaan _940576
700	1		_aDreisigacker, S. _gGlobal Wheat Program _8INT2692 _9851
700	1		_aPoland, J.A. _92092
700	1		_aSingh, R.P. _gFormerly Global Wheat Program _8INT0610 _9825
700	1		_8001713327 _aVitale, P. _gGenetic Resources Program _931497
773	0		_tESS Open Archive _gIn press _dUnited States of America : Wiley, 2025
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/36104
942			_cJA _n0 _2ddc
999			_c69557 _d69549