Development of Transcriptome-Wide Predictive Models of Phenotypic Traits for Selective Breeding and Hybrid Performance Predictions in Maize

玉米选择性育种和杂交性能预测的表型性状全转录组预测模型的开发

• 批准号: 1711662
• 负责人: Ryan Sartor
• 金额: $ 21.6万
• 依托单位: Sartor Ryan C
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711662&HistoricalAwards=false
• 关键词: Development; Transcriptome; Wide; Predictive; Models

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2017. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The host institution for the fellowship is North Carolina State University and the sponsoring scientists are Dr. James Holland and Dr. Dahlia Nielsen.Plant and animal breeding is currently undergoing a revolution brought about by big data. Methods to cheaply survey entire individual genomes now exist. Using computational models, this gene sequence data can be used to create a predictive model in which a physical trait of interest can be accurately predicted given an individual's genotypic information. In addition to reading a gene's sequence, we can also measure the strength that a gene is expressed (utilized) by the cell. The major purpose of this project is to determine if such quantitative knowledge of gene expression can be used to supplement and improve current sequence-based models. This project has potential to add significantly to the impact that omics-level data has on both plant and animal breeding, allowing for generation of superior breeds in significantly shorter time while using less resources.This research will compare the predictive power of transcriptomic data for use in maize breeding to recently published methods that use genomic data. Existing public data will be used to guide a series of test crossed. Subsequent transcriptomic and phenotypic measurements will allow evaluation of two types of predictions: (i) predictions of phenotypic trait values in adult plants given either genotypic or transcriptomic data in young seedlings, (ii) predictions of hybrid performance given either genomic or transcriptomic data for the parent inbred lines. All data generated will be deposited in public repositories such as NCBI's Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Phenotypic information will be made available through the Panzea Project (http://www.panzea.org/)

该行动资助了2017财年NSF国家植物基因组计划生物学博士后研究奖学金。该研究金支持研究员在东道实验室的研究和培训计划，研究员还提出了扩大生物学参与的计划。该奖学金的主办机构是北卡罗来纳州州立大学，赞助科学家是James Holland博士和Dahlia Nielsen博士。动植物育种目前正在经历一场由大数据带来的革命。现在已经有了廉价地调查整个个体基因组的方法。使用计算模型，该基因序列数据可用于创建预测模型，其中在给定个体的基因型信息的情况下，可准确地预测感兴趣的物理性状。除了阅读基因序列外，我们还可以测量基因被细胞表达（利用）的强度。该项目的主要目的是确定基因表达的定量知识是否可以用于补充和改进当前基于序列的模型。 该项目有可能大大增加组学水平的数据对植物和动物育种的影响，允许在显著更短的时间内产生上级品种，同时使用更少的资源。本研究将比较转录组学数据用于玉米育种的预测能力，最近公布的方法，使用基因组数据。现有的公开数据将被用来指导一系列的测试交叉。随后的转录组学和表型测量将允许评估两种类型的预测：（i）在幼苗中给定基因型或转录组学数据的情况下，对成年植物中的表型性状值的预测，（ii）在亲本近交系给定基因组学或转录组学数据的情况下，对杂种性能的预测。所有生成的数据将存放在公共存储库中，如NCBI的基因表达综合库（GEO）和序列读取档案库（SRA）。将通过Panzea项目（http：//www.panzea.org/）提供表型信息。

项目成果

Identification of the expressome by machine learning on omics data

• DOI: 10.1073/pnas.1813645116
• 发表时间: 2019-09-03
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Sartor, Ryan C.;Noshay, Jaclyn;Briggs, Steven P.
• 通讯作者: Briggs, Steven P.

Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome

• DOI: 10.1261/rna.070227.118
• 发表时间: 2019-03
• 期刊: RNA
• 影响因子: 4.5
• 作者: Erin Slabaugh;Jigar S. Desai;Ryan C. Sartor;L. M. F. Lawas;S. K. Jagadish;Colleen J. Doherty