Evidence map›Paper›PMID 29895260›Full record

ArticleBMC genomics2018

Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study.

Xiongming Du, Shouye Liu, Junling Sun, Gengyun Zhang, Yinhua Jia, Zhaoe Pan, Haitao Xiang, Shoupu He, Qiuju Xia, Songhua Xiao and 12 more

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Xiongming DuInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China. dxm630723@163.com.
Shouye LiuInstitute of crop science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Junling SunInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Gengyun ZhangShenzhen Huada Gene Research Institute, Shenzhen, 518031, People's Republic of China.
Yinhua JiaInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Zhaoe PanInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Haitao XiangShenzhen Huada Gene Research Institute, Shenzhen, 518031, People's Republic of China.
Shoupu HeInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Qiuju XiaShenzhen Huada Gene Research Institute, Shenzhen, 518031, People's Republic of China.
Songhua XiaoInstitute of industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, People's Republic of China.
Weijun ShiEconomic Crop Research Institute, Xinjiang Academy of Agricultural Science, Urumqi, 830002, People's Republic of China.
Zhiwu QuanShenzhen Huada Gene Research Institute, Shenzhen, 518031, People's Republic of China.
Jianguang LiuInstitute of industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, People's Republic of China.
Jun MaEconomic Crop Research Institute, Xinjiang Academy of Agricultural Science, Urumqi, 830002, People's Republic of China.
Baoyin PangInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Liru WangInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Gaofei SunDepartment of Computer Science and Information Engineering, Anyang Institute of Technology, Anyang, 455000, People's Republic of China.
Wenfang GongInstitute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS), State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, 455000, People's Republic of China.
Johnie N JenkinsUSDA-ARS at Mississippi State, Jackson, MS, 39762-5367, USA.
Xiangyang LouDepartment of Pediatrics, Biostatistics Division Arkansas Children's Hospital Research Institute School of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, 72202, USA.
Jun ZhuInstitute of crop science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Haiming XuInstitute of crop science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, People's Republic of China. hmxu@zju.edu.cn.ORCID http://orcid.org/0000-0002-0675-4087

Funding

the National Key Research and Development Program of China 2016YFD0100203The National Natural Science Foundation of China 31671570 and 31470083
6 · The paper itself

Abstract

backgroundCottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement.

resultsA genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines.

conclusionsThis study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

Indexed as

Fatty AcidsGenes, PlantGenome-Wide Association StudyGenotypeGossypiumPlant BreedingPlant ProteinsQuantitative Trait, HeritableSeedsFatty AcidsPlant ProteinsAssociation mappingComplex traitsCottonseed traitsEpistasisGene by environment interactionGWASMixed linear modelMolecular breeding by design

Identifiers

PMID29895260
PMCPMC5998501

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.