Wheat breeding with skim-sequencing for genomic selection: a comparison of marker platforms
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ArticleLanguage: English Publication details: United States of America : Wiley, 2025.Subject(s): Online resources:
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ESS Open Archive United States of America : Wiley, 2025 In pressSummary: The 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.
| Item type | Current library | Collection | Status | |
|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | Available |
Preprint
Open Access
The 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.
Text in English
Poland, J.A. : Not in IRS staff list but CIMMYT Affiliation
United States Agency for International Development (USAID) Foundation for Food & Agriculture Research (FFAR) The Land Institute Malone Family Land Preservation Foundation
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