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The development of quality control genotyping approaches : a case study using elite maize lines [Electronic Resource]

By: Jiafa Chen.
Contributor(s): Zavala Espinosa, C | Ortega, N | Petroli, C.D | Burgueño, J | Costich, D.E | Hearne, S | Franco Barrera, J.
Material type: materialTypeLabelArticlePublisher: San Francisco, USA : Public Library of Science, 2016Subject(s): Maize | Genotypes | Molecular genetics | Plant breedingOnline resources: Access only for CIMMYT Staff In: PLoS One v. 11, no. 6: e0157236Summary: Quality control (QC) of germplasmidentity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotypingmethods have been developed from a very limited panel ofmarkers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and tomeasure heterogeneity. The optimal marker selection strategies combined the selection ofmarkers with high minor allele frequency, sampling of clustered SNP in proportion tomarker cluster distance and selecting markers thatmaintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5%contamination in the entry, and approximately a 98%probability to detect a 2%contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved inmaize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species.
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Article CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

CIMMYT Staff Publications Collection Available
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Quality control (QC) of germplasmidentity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotypingmethods have been developed from a very limited panel ofmarkers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and tomeasure heterogeneity. The optimal marker selection strategies combined the selection ofmarkers with high minor allele frequency, sampling of clustered SNP in proportion tomarker cluster distance and selecting markers thatmaintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5%contamination in the entry, and approximately a 98%probability to detect a 2%contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved inmaize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species.

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