Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia (Oreochromis niloticus)

全基因组关联研究 插补(统计学) 尼罗罗非鱼 最佳线性无偏预测 单核苷酸多态性 生物 SNP公司 遗传力 基因组选择 遗传关联 遗传学 选择(遗传算法) 计算生物学 统计 基因型 俄勒冈 计算机科学 基因 数学 人工智能 渔业 缺少数据
作者
Grazyella Yoshida,Jean P. Lhorente,Katharina Correa,José Soto,Diego Salas‐Benito,José M. Yáñez
出处
期刊:G3: Genes, Genomes, Genetics [Genetics Society of America]
卷期号:9 (8): 2597-2607 被引量:67
标识
DOI:10.1534/g3.119.400116
摘要

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.
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
顺利兰完成签到 ,获得积分10
刚刚
刚刚
深情安青应助芷兰丁香采纳,获得10
1秒前
2秒前
nusiew发布了新的文献求助10
3秒前
曼波发布了新的文献求助10
3秒前
tianliyan完成签到 ,获得积分10
4秒前
BK关闭了BK文献求助
4秒前
Portafortuna完成签到,获得积分20
4秒前
Nzoth发布了新的文献求助10
5秒前
6秒前
聪聪发布了新的文献求助10
9秒前
9秒前
10秒前
西门长海完成签到,获得积分10
10秒前
bghv发布了新的文献求助10
11秒前
柚子皮发布了新的文献求助15
12秒前
13秒前
1阿完成签到,获得积分10
13秒前
谨慎鞅完成签到,获得积分10
14秒前
luyao970131发布了新的文献求助10
15秒前
yin发布了新的文献求助10
15秒前
聪聪完成签到,获得积分10
16秒前
赵保钢完成签到,获得积分10
17秒前
鱼与鱼与鱼完成签到,获得积分10
17秒前
柚子发布了新的文献求助10
18秒前
彭于晏应助默默的难破采纳,获得10
19秒前
19秒前
科研通AI5应助淡淡的浩天采纳,获得10
21秒前
21秒前
22秒前
yin完成签到,获得积分10
22秒前
22秒前
WKJiang完成签到,获得积分20
22秒前
bghv完成签到,获得积分10
24秒前
曼波完成签到,获得积分10
24秒前
学术骗子小刚完成签到,获得积分0
25秒前
善学以致用应助ting采纳,获得10
26秒前
丘比特应助Dr.c采纳,获得10
27秒前
zhang发布了新的文献求助10
28秒前
高分求助中
Encyclopedia of Mathematical Physics 2nd edition 888
Introduction to Strong Mixing Conditions Volumes 1-3 500
Tip60 complex regulates eggshell formation and oviposition in the white-backed planthopper, providing effective targets for pest control 400
Optical and electric properties of monocrystalline synthetic diamond irradiated by neutrons 320
共融服務學習指南 300
Essentials of Pharmacoeconomics: Health Economics and Outcomes Research 3rd Edition. by Karen Rascati 300
Peking Blues // Liao San 300
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3802431
求助须知:如何正确求助?哪些是违规求助? 3348058
关于积分的说明 10336202
捐赠科研通 3063960
什么是DOI,文献DOI怎么找? 1682338
邀请新用户注册赠送积分活动 808052
科研通“疑难数据库(出版商)”最低求助积分说明 763997