Loading…

Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat

Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analy...

Full description

Saved in:
Bibliographic Details
Published in:BMC plant biology 2022-06, Vol.22 (1), p.288-288, Article 288
Main Authors: Wei, Jun, Fang, Yu, Jiang, Hao, Wu, Xing-Ting, Zuo, Jing-Hong, Xia, Xian-Chun, Li, Jin-Quan, Stich, Benjamin, Cao, Hong, Liu, Yong-Xiu
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23
cites cdi_FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23
container_end_page 288
container_issue 1
container_start_page 288
container_title BMC plant biology
container_volume 22
creator Wei, Jun
Fang, Yu
Jiang, Hao
Wu, Xing-Ting
Zuo, Jing-Hong
Xia, Xian-Chun
Li, Jin-Quan
Stich, Benjamin
Cao, Hong
Liu, Yong-Xiu
description Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.
doi_str_mv 10.1186/s12870-022-03677-8
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_576631ed541d4422bb5f190b308d4a02</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A707072527</galeid><doaj_id>oai_doaj_org_article_576631ed541d4422bb5f190b308d4a02</doaj_id><sourcerecordid>A707072527</sourcerecordid><originalsourceid>FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23</originalsourceid><addsrcrecordid>eNptkstu1DAUhiMEoqXwAiyQJTZlkeJbYnuDVI24jDQSAsracmwn9ZDYg53QzlPwyjgzZTSDkBexfL7_s3xyiuIlglcI8fptQpgzWEKMS0hqxkr-qDhHlKESYyweH-3PimcprSFEjFPxtDgjVS04JPy8-L0IQ-O88x34crMCg9ps5r3yBnTWW6BDae830abkggfejnch_shl1W-TS6ANEeSqcXqc66EFOkedUaPd5dPONITe6qlX8SCIts-IAWMAW2d7A5wHd7dWjc-LJ63qk33x8L0ovn94f7P4VK4-f1wurlelrgQbS8pqUVOEScNrhKFqa9Swiogmt4FoIaDRVvMGWkEaxQwjvMW0xS0khhpmMLkolnuvCWotN9ENKm5lUE7uDkLspIqj072VFatrgqypKDKUYtw0VYsEbAjkhio4u97tXZupGWy-2Y9R9SfS04p3t7ILv6TIGkRFFlw-CGL4Odk0ysElbfteeRumJHHN6qrCApKMvv4HXYcp5t-xozhGlLAjqlP5Ac63Id-rZ6m8ZjAvXGGWqav_UHkZOzgdvG1dPj8JvDkJZGa092OnppTk8tvXUxbvWR1DStG2h34gKOfxlfvxlXl85W58Jc-hV8edPET-ziv5AwIR6fA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2678214373</pqid></control><display><type>article</type><title>Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><source>PubMed Central</source><creator>Wei, Jun ; Fang, Yu ; Jiang, Hao ; Wu, Xing-Ting ; Zuo, Jing-Hong ; Xia, Xian-Chun ; Li, Jin-Quan ; Stich, Benjamin ; Cao, Hong ; Liu, Yong-Xiu</creator><creatorcontrib>Wei, Jun ; Fang, Yu ; Jiang, Hao ; Wu, Xing-Ting ; Zuo, Jing-Hong ; Xia, Xian-Chun ; Li, Jin-Quan ; Stich, Benjamin ; Cao, Hong ; Liu, Yong-Xiu</creatorcontrib><description>Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.</description><identifier>ISSN: 1471-2229</identifier><identifier>EISSN: 1471-2229</identifier><identifier>DOI: 10.1186/s12870-022-03677-8</identifier><identifier>PMID: 35698038</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Agricultural production ; Analysis ; Cereal crops ; Chromosome 7 ; Chromosomes ; Climate change ; Cloning ; Crop yield ; Crop yields ; Dormancy ; Environmental aspects ; Gene expression ; Gene mapping ; Gene regulation ; Gene sequencing ; Genes ; Genetic aspects ; Genetics ; Genomes ; Grain size ; Growth ; Inbreeding ; Kernels ; Mapping ; Methods ; Network analysis ; Pre-harvest sprouting ; Quantitative genetics ; Quantitative trait loci ; RNA sequencing ; Seed dormancy ; Seed vigor ; Seeds ; Sorghum ; Spike length ; Triticum aestivum ; WGCNA ; Wheat</subject><ispartof>BMC plant biology, 2022-06, Vol.22 (1), p.288-288, Article 288</ispartof><rights>2022. The Author(s).</rights><rights>COPYRIGHT 2022 BioMed Central Ltd.</rights><rights>2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23</citedby><cites>FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190149/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2678214373?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35698038$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wei, Jun</creatorcontrib><creatorcontrib>Fang, Yu</creatorcontrib><creatorcontrib>Jiang, Hao</creatorcontrib><creatorcontrib>Wu, Xing-Ting</creatorcontrib><creatorcontrib>Zuo, Jing-Hong</creatorcontrib><creatorcontrib>Xia, Xian-Chun</creatorcontrib><creatorcontrib>Li, Jin-Quan</creatorcontrib><creatorcontrib>Stich, Benjamin</creatorcontrib><creatorcontrib>Cao, Hong</creatorcontrib><creatorcontrib>Liu, Yong-Xiu</creatorcontrib><title>Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat</title><title>BMC plant biology</title><addtitle>BMC Plant Biol</addtitle><description>Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.</description><subject>Agricultural production</subject><subject>Analysis</subject><subject>Cereal crops</subject><subject>Chromosome 7</subject><subject>Chromosomes</subject><subject>Climate change</subject><subject>Cloning</subject><subject>Crop yield</subject><subject>Crop yields</subject><subject>Dormancy</subject><subject>Environmental aspects</subject><subject>Gene expression</subject><subject>Gene mapping</subject><subject>Gene regulation</subject><subject>Gene sequencing</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetics</subject><subject>Genomes</subject><subject>Grain size</subject><subject>Growth</subject><subject>Inbreeding</subject><subject>Kernels</subject><subject>Mapping</subject><subject>Methods</subject><subject>Network analysis</subject><subject>Pre-harvest sprouting</subject><subject>Quantitative genetics</subject><subject>Quantitative trait loci</subject><subject>RNA sequencing</subject><subject>Seed dormancy</subject><subject>Seed vigor</subject><subject>Seeds</subject><subject>Sorghum</subject><subject>Spike length</subject><subject>Triticum aestivum</subject><subject>WGCNA</subject><subject>Wheat</subject><issn>1471-2229</issn><issn>1471-2229</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkstu1DAUhiMEoqXwAiyQJTZlkeJbYnuDVI24jDQSAsracmwn9ZDYg53QzlPwyjgzZTSDkBexfL7_s3xyiuIlglcI8fptQpgzWEKMS0hqxkr-qDhHlKESYyweH-3PimcprSFEjFPxtDgjVS04JPy8-L0IQ-O88x34crMCg9ps5r3yBnTWW6BDae830abkggfejnch_shl1W-TS6ANEeSqcXqc66EFOkedUaPd5dPONITe6qlX8SCIts-IAWMAW2d7A5wHd7dWjc-LJ63qk33x8L0ovn94f7P4VK4-f1wurlelrgQbS8pqUVOEScNrhKFqa9Swiogmt4FoIaDRVvMGWkEaxQwjvMW0xS0khhpmMLkolnuvCWotN9ENKm5lUE7uDkLspIqj072VFatrgqypKDKUYtw0VYsEbAjkhio4u97tXZupGWy-2Y9R9SfS04p3t7ILv6TIGkRFFlw-CGL4Odk0ysElbfteeRumJHHN6qrCApKMvv4HXYcp5t-xozhGlLAjqlP5Ac63Id-rZ6m8ZjAvXGGWqav_UHkZOzgdvG1dPj8JvDkJZGa092OnppTk8tvXUxbvWR1DStG2h34gKOfxlfvxlXl85W58Jc-hV8edPET-ziv5AwIR6fA</recordid><startdate>20220613</startdate><enddate>20220613</enddate><creator>Wei, Jun</creator><creator>Fang, Yu</creator><creator>Jiang, Hao</creator><creator>Wu, Xing-Ting</creator><creator>Zuo, Jing-Hong</creator><creator>Xia, Xian-Chun</creator><creator>Li, Jin-Quan</creator><creator>Stich, Benjamin</creator><creator>Cao, Hong</creator><creator>Liu, Yong-Xiu</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20220613</creationdate><title>Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat</title><author>Wei, Jun ; Fang, Yu ; Jiang, Hao ; Wu, Xing-Ting ; Zuo, Jing-Hong ; Xia, Xian-Chun ; Li, Jin-Quan ; Stich, Benjamin ; Cao, Hong ; Liu, Yong-Xiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agricultural production</topic><topic>Analysis</topic><topic>Cereal crops</topic><topic>Chromosome 7</topic><topic>Chromosomes</topic><topic>Climate change</topic><topic>Cloning</topic><topic>Crop yield</topic><topic>Crop yields</topic><topic>Dormancy</topic><topic>Environmental aspects</topic><topic>Gene expression</topic><topic>Gene mapping</topic><topic>Gene regulation</topic><topic>Gene sequencing</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetics</topic><topic>Genomes</topic><topic>Grain size</topic><topic>Growth</topic><topic>Inbreeding</topic><topic>Kernels</topic><topic>Mapping</topic><topic>Methods</topic><topic>Network analysis</topic><topic>Pre-harvest sprouting</topic><topic>Quantitative genetics</topic><topic>Quantitative trait loci</topic><topic>RNA sequencing</topic><topic>Seed dormancy</topic><topic>Seed vigor</topic><topic>Seeds</topic><topic>Sorghum</topic><topic>Spike length</topic><topic>Triticum aestivum</topic><topic>WGCNA</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Jun</creatorcontrib><creatorcontrib>Fang, Yu</creatorcontrib><creatorcontrib>Jiang, Hao</creatorcontrib><creatorcontrib>Wu, Xing-Ting</creatorcontrib><creatorcontrib>Zuo, Jing-Hong</creatorcontrib><creatorcontrib>Xia, Xian-Chun</creatorcontrib><creatorcontrib>Li, Jin-Quan</creatorcontrib><creatorcontrib>Stich, Benjamin</creatorcontrib><creatorcontrib>Cao, Hong</creatorcontrib><creatorcontrib>Liu, Yong-Xiu</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Science in Context</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC plant biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wei, Jun</au><au>Fang, Yu</au><au>Jiang, Hao</au><au>Wu, Xing-Ting</au><au>Zuo, Jing-Hong</au><au>Xia, Xian-Chun</au><au>Li, Jin-Quan</au><au>Stich, Benjamin</au><au>Cao, Hong</au><au>Liu, Yong-Xiu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat</atitle><jtitle>BMC plant biology</jtitle><addtitle>BMC Plant Biol</addtitle><date>2022-06-13</date><risdate>2022</risdate><volume>22</volume><issue>1</issue><spage>288</spage><epage>288</epage><pages>288-288</pages><artnum>288</artnum><issn>1471-2229</issn><eissn>1471-2229</eissn><abstract>Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population. QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL. A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>35698038</pmid><doi>10.1186/s12870-022-03677-8</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1471-2229
ispartof BMC plant biology, 2022-06, Vol.22 (1), p.288-288, Article 288
issn 1471-2229
1471-2229
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_576631ed541d4422bb5f190b308d4a02
source Publicly Available Content Database (Proquest) (PQ_SDU_P3); PubMed Central
subjects Agricultural production
Analysis
Cereal crops
Chromosome 7
Chromosomes
Climate change
Cloning
Crop yield
Crop yields
Dormancy
Environmental aspects
Gene expression
Gene mapping
Gene regulation
Gene sequencing
Genes
Genetic aspects
Genetics
Genomes
Grain size
Growth
Inbreeding
Kernels
Mapping
Methods
Network analysis
Pre-harvest sprouting
Quantitative genetics
Quantitative trait loci
RNA sequencing
Seed dormancy
Seed vigor
Seeds
Sorghum
Spike length
Triticum aestivum
WGCNA
Wheat
title Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T09%3A42%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Combining%20QTL%20mapping%20and%20gene%20co-expression%20network%20analysis%20for%20prediction%20of%20candidate%20genes%20and%20molecular%20network%20related%20to%20yield%20in%20wheat&rft.jtitle=BMC%20plant%20biology&rft.au=Wei,%20Jun&rft.date=2022-06-13&rft.volume=22&rft.issue=1&rft.spage=288&rft.epage=288&rft.pages=288-288&rft.artnum=288&rft.issn=1471-2229&rft.eissn=1471-2229&rft_id=info:doi/10.1186/s12870-022-03677-8&rft_dat=%3Cgale_doaj_%3EA707072527%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c597t-476964123b86120af61b7539b6773c990dcec8b0e93ba7d738f24f2f03d4d7d23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2678214373&rft_id=info:pmid/35698038&rft_galeid=A707072527&rfr_iscdi=true