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质量控制基因分型方法的发展:以优良玉米自交系为例的研究

The Development of Quality Control Genotyping Approaches: A Case Study Using Elite Maize Lines.

作者信息

Chen Jiafa, Zavala Cristian, Ortega Noemi, Petroli Cesar, Franco Jorge, Burgueño Juan, Costich Denise E, Hearne Sarah J

机构信息

International Maize and Wheat Improvement Center (CIMMYT); Texcoco; Edo. De Mexico; Mexico CP 56237.

Departamento de Biometría, Universidad de la Republica, Paysandú; Uruguay CP 60000.

出版信息

PLoS One. 2016 Jun 9;11(6):e0157236. doi: 10.1371/journal.pone.0157236. eCollection 2016.

DOI:10.1371/journal.pone.0157236
PMID:27280295
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4900658/
Abstract

Quality control (QC) of germplasm identity 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 genotyping methods have been developed from a very limited panel of markers 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 to measure heterogeneity. The optimal marker selection strategies combined the selection of markers with high minor allele frequency, sampling of clustered SNP in proportion to marker cluster distance and selecting markers that maintain 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 in maize 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.

摘要

种质一致性和纯度的质量控制(QC)是育种和种质保存活动的关键组成部分。单核苷酸多态性(SNP)基因分型技术以及标记物可用性的增加,为将基因分型作为这种质量控制的低成本且可靠的组成部分提供了机会。在公共部门,已从非常有限的1000至1500个标记物面板开发出低成本的SNP质量控制基因分型方法,而未广泛选择信息最丰富的SNP。除了平台选择外,最佳SNP的选择以及适当种质采样的定义,在将质量控制纳入主流时,会影响育种和种质保存应用中的后勤和资源利用考量。为了解决这些问题,我们从国际玉米小麦改良中心(CIMMYT)玉米自交系(CML)产生的大型DArTSeq数据集中评估了用于质量控制应用的SNP的选择和使用。开发了两种质量控制基因分型策略,第一种是“快速质量控制”,采用少量SNP来识别种子包装或地块的潜在错误标记,第二种是“广泛质量控制”,采用大量SNP,用于识别每个种质条目并测量异质性。最佳标记选择策略将具有高次要等位基因频率的标记选择、按标记簇距离成比例地对成簇SNP进行采样以及选择保持均匀基因组分布的标记相结合。使用这种方法选择的快速和广泛质量控制SNP面板,通过对相关再生样品的盲测评估进一步验证。评估了每个品系内采样的影响。采样192个个体将有接近100%的可能性检测到一个条目中5%的污染,以及大约98%的概率检测到一个品系2%的污染。这些结果为建立质量控制基因分型提供了一个框架。讨论了在不同平台上使用这些方法的财务和时间成本比较,为参与玉米种质保存和育种的机构评估质量控制基因分型的资源利用效率提供了一个框架。这些研究结果与现有质量控制方法相结合的应用,将确保纯合自交系种质在再生、分发和育种中的真实类型使用。这些发现还为优化其他物种质量控制基因分型的SNP选择提供了一种有效方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/1ab5a6f699eb/pone.0157236.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/a3e8155c0874/pone.0157236.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/643be1c86209/pone.0157236.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/45d4703c90ca/pone.0157236.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/5832a38caf7b/pone.0157236.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/1ab5a6f699eb/pone.0157236.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/a3e8155c0874/pone.0157236.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/a39d442d2933/pone.0157236.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/643be1c86209/pone.0157236.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/45d4703c90ca/pone.0157236.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/5832a38caf7b/pone.0157236.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ade/4900658/1ab5a6f699eb/pone.0157236.g006.jpg

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