Improvement of prediction ability by integrating multi-omic datasets in barley

生物 转录组 代谢组 计算生物学 SNP公司 表型 基因组选择 单核苷酸多态性 遗传学 生物信息学 代谢组学 基因 基因表达 基因型
作者
Po‐Ya Wu,Benjamin Stich,Marius Weisweiler,Asis Shrestha,Alexander Erban,Philipp Westhoff,Delphine Van Inghelandt
出处
期刊:BMC Genomics [BioMed Central]
卷期号:23 (1) 被引量:9
标识
DOI:10.1186/s12864-022-08337-7
摘要

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3'end mRNA sequencing of different lengths as predictors.The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3'end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
童林艳完成签到,获得积分10
刚刚
ECHO完成签到,获得积分10
2秒前
Lucas应助Fang Xianxin采纳,获得10
3秒前
xiaoyao完成签到,获得积分10
4秒前
asss完成签到,获得积分10
4秒前
Y123发布了新的文献求助30
5秒前
LOVER完成签到 ,获得积分10
6秒前
松松完成签到 ,获得积分10
6秒前
8秒前
nater4ver完成签到,获得积分10
9秒前
UU发布了新的文献求助10
11秒前
超帅鸭子完成签到,获得积分10
11秒前
LXZ完成签到,获得积分10
11秒前
依惜完成签到,获得积分10
11秒前
zhaokunfeng关注了科研通微信公众号
11秒前
赫青亦完成签到 ,获得积分10
11秒前
exy完成签到,获得积分10
12秒前
zhaohu47完成签到,获得积分10
13秒前
超帅鸭子发布了新的文献求助10
13秒前
每每反完成签到,获得积分10
15秒前
凡凡完成签到 ,获得积分10
16秒前
17秒前
呆鹅喵喵完成签到,获得积分10
17秒前
忧心的洙完成签到,获得积分10
18秒前
123完成签到,获得积分10
18秒前
青青草完成签到,获得积分10
20秒前
Fang Xianxin完成签到,获得积分20
20秒前
yue发布了新的文献求助10
20秒前
小甘看世界完成签到,获得积分0
22秒前
量子星尘发布了新的文献求助10
22秒前
张今天也要做科研呀完成签到,获得积分10
23秒前
nater3ver完成签到,获得积分10
24秒前
24秒前
自然怀梦完成签到,获得积分10
24秒前
轻歌水越完成签到 ,获得积分10
24秒前
执意完成签到 ,获得积分10
25秒前
大模型应助Coral采纳,获得10
25秒前
songyl完成签到,获得积分10
26秒前
怡然问晴发布了新的文献求助20
27秒前
24K纯帅完成签到,获得积分0
27秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038303
求助须知:如何正确求助?哪些是违规求助? 3576013
关于积分的说明 11374210
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029