Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.
复制标题
DOI：
10.1007/s00122-013-2066-0
复制
发表时间：
2013-04
影响因子：
5.4
通讯作者：
McCouch, Susan
中科院分区：
文献类型：
--
作者：
Cobb, Joshua N.;DeClerck, Genevieve;Greenberg, Anthony;Clark, Randy;McCouch, Susan
更准确和精确的表型分析策略对于实现高分辨率连锁图谱绘制和全基因组关联研究以及在植物改良中训练基因组选择模型是必要的。在此框架内，现代表型分析的目标是在生物组织的各个层面提高表型估计的准确性、精确性和通量，同时通过自动化、遥感、改进的数据整合和实验设计来降低成本并减少劳动力。就像20世纪80年代和90年代优化基因分型的努力一样，如今设计有效的表型分析方案需要生物学家、计算机科学家、统计学家和工程师之间多方面的合作。需要强大的表型分析系统来表征在细胞、器官和组织、发育阶段、年份、环境、物种和研究项目中导致数量表型变异的全套遗传因素。新一代表型分析产生的数据比以前多得多，需要新的数据管理、访问和存储系统，更多地使用本体论来促进数据整合，以及新的统计工具来优化实验设计并从环境和实验噪声中提取有生物学意义的信号。为确保相关性，实施高效且信息丰富的表型分析实验还需要熟悉不同的种质资源、群体结构和目标环境群体。如今，表型分析正迅速成为限制遗传分析和基因组预测能力的主要操作瓶颈。下一代数量遗传学家和植物育种家面临的挑战不仅是理解复杂性状变异的遗传基础，还包括利用这些知识高效地培育21世纪的作物品种。
More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.
登录
查看更多内容
Panzea：新内容和功能的更新。
影响因子：
14.9
作者：
Canaran, Payan;Buckler, Edward S.;Glaubitz, Jeffrey C.;Stein, Lincoln;Sun, Qi;Zhao, Wei;Ware, Doreen
通讯作者：
Ware, Doreen
影响因子：
14.9
作者：
Avraham, Shulamit;Tung, Chih-Wei;Ilic, Katica;Jaiswal, Pankaj;Kellogg, Elizabeth A.;McCouch, Susan;Pujar, Anuradha;Reiser, Leonore;Rhee, Seung Y.;Sachs, Martin M.;Schaeffer, Mary;Stein, Lincoln;Stevens, Peter;Vincent, Leszek;Zapata, Felipe;Ware, Doreen
通讯作者：
Ware, Doreen
影响因子：
56.9
作者：
Buckler, Edward S.;Holland, James B.;McMullen, Michael D.
通讯作者：
McMullen, Michael D.
影响因子：
8.8
作者：
Bartlett, Megan K.;Scoffoni, Christine;Sack, Lawren
通讯作者：
Sack, Lawren
影响因子：
3.3
作者：
Calus, M P L;Meuwissen, T H E;Veerkamp, R F
通讯作者：
Veerkamp, R F