Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement

生物 基因 计算生物学 单核苷酸多态性 数量性状位点 特质 生物技术 遗传学 计算机科学 基因型 程序设计语言
作者
Nima Khalili Samani,Zitong Li,Filomena Pettolino,Philippe Moncuquet,Antônio Reverter,Colleen P. MacMillan
出处
期刊:Frontiers in Plant Science [Frontiers Media SA]
卷期号:15
标识
DOI:10.3389/fpls.2024.1420837
摘要

Cultivated cotton plants are the world’s largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
苏玖染完成签到,获得积分10
刚刚
刚刚
过雪完成签到,获得积分10
1秒前
1秒前
ding应助小张采纳,获得10
2秒前
xlx完成签到 ,获得积分10
2秒前
3秒前
3秒前
俞思含发布了新的文献求助10
3秒前
陈伟杰发布了新的文献求助10
4秒前
天空之城完成签到,获得积分10
5秒前
小半发布了新的文献求助10
5秒前
7rey发布了新的文献求助10
6秒前
Violet发布了新的文献求助10
7秒前
KYSLCc完成签到,获得积分10
7秒前
万能图书馆应助如意蚂蚁采纳,获得10
7秒前
cckk发布了新的文献求助10
7秒前
8秒前
wb发布了新的文献求助10
8秒前
8秒前
如意立果发布了新的文献求助10
8秒前
我是老大应助一八四采纳,获得10
9秒前
miao2完成签到,获得积分10
10秒前
大贝完成签到,获得积分20
10秒前
10秒前
YY完成签到 ,获得积分10
11秒前
11秒前
ll应助温柔踏歌采纳,获得10
12秒前
12秒前
怕黑念薇发布了新的文献求助10
13秒前
ding应助大贝采纳,获得30
14秒前
自由念之发布了新的文献求助200
14秒前
暴富小羊发布了新的文献求助10
14秒前
15秒前
15秒前
沉默清炎完成签到,获得积分10
15秒前
16秒前
先林完成签到 ,获得积分10
17秒前
赘婿应助平常的如风采纳,获得10
17秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Kelsen’s Legacy: Legal Normativity, International Law and Democracy 1000
Handbook on Inequality and Social Capital 800
Conference Record, IAS Annual Meeting 1977 610
Interest Rate Modeling. Volume 3: Products and Risk Management 600
Interest Rate Modeling. Volume 2: Term Structure Models 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3546536
求助须知:如何正确求助?哪些是违规求助? 3123667
关于积分的说明 9356348
捐赠科研通 2822331
什么是DOI,文献DOI怎么找? 1551314
邀请新用户注册赠送积分活动 723326
科研通“疑难数据库(出版商)”最低求助积分说明 713699