Loading…

Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi

Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (g...

Full description

Saved in:
Bibliographic Details
Published in:BMC genomics 2018-01, Vol.19 (1), p.107-107, Article 107
Main Authors: Nguyen, Nguyen H, Premachandra, H K A, Kilian, Andrzej, Knibb, Wayne
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-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703
cites cdi_FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703
container_end_page 107
container_issue 1
container_start_page 107
container_title BMC genomics
container_volume 19
creator Nguyen, Nguyen H
Premachandra, H K A
Kilian, Andrzej
Knibb, Wayne
description Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R ) from linear regression analysis ranging from 0.49 to 0.71. Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi.
doi_str_mv 10.1186/s12864-018-4493-4
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_739c73a55f044c4aaac7c7e6256d437a</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A525748179</galeid><doaj_id>oai_doaj_org_article_739c73a55f044c4aaac7c7e6256d437a</doaj_id><sourcerecordid>A525748179</sourcerecordid><originalsourceid>FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703</originalsourceid><addsrcrecordid>eNptkk9vEzEQxVcIREvhA3BBK3Ephy3-t_b6ghS1UCJVQiLhbE28443LZp3au9B8exxSqkZCPow1_r2nsf2K4i0lF5Q28mOirJGiIrSphNC8Es-KUyoUrRiV4vmT_UnxKqVbQqhqWP2yOGGaN4xpfVosr3EIG2_LbcTW29GHoZySH7ryahaX1QLvyhHtegh96HalC7HcYd-H3yP4vvyZOefTulxg9KGHsocehta_Ll446BO-eahnxY8vn5eXX6ubb9fzy9lNZWspx4papxBrayVpZcutoCsKzjKGrtEStCOW1JLnKrlqtUIKQLlUrbMriorws2J-8G0D3Jpt9BuIOxPAm7-NEDsDcfS2R6O4topDXTsihBUAYJVVKFktW8EVZK9PB6_ttNpga3EYI_RHpscng1-bLvwytdJ5KJoNzh8MYribMI1m45PNjwUDhikZqjUnlBC2n_v9Ae0gj-YHF7Kj3eNmVrNaiYYqnamL_1B5tZg_LAzofO4fCT4cCTIz4v3YwZSSmS--H7P0wNoYUoroHm9KidmHyxzCZXK4zD5cRmTNu6dP9Kj4lyb-B3H2ydU</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1993010020</pqid></control><display><type>article</type><title>Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Nguyen, Nguyen H ; Premachandra, H K A ; Kilian, Andrzej ; Knibb, Wayne</creator><creatorcontrib>Nguyen, Nguyen H ; Premachandra, H K A ; Kilian, Andrzej ; Knibb, Wayne</creatorcontrib><description>Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R ) from linear regression analysis ranging from 0.49 to 0.71. Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi.</description><identifier>ISSN: 1471-2164</identifier><identifier>EISSN: 1471-2164</identifier><identifier>DOI: 10.1186/s12864-018-4493-4</identifier><identifier>PMID: 29382299</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Animal development ; Genetic aspects ; Genetic improvement ; Genomic prediction ; Genomic selection and genotype by sequencing ; Genotype ; Identification and classification ; Kingfish ; Perciformes</subject><ispartof>BMC genomics, 2018-01, Vol.19 (1), p.107-107, Article 107</ispartof><rights>COPYRIGHT 2018 BioMed Central Ltd.</rights><rights>The Author(s). 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703</citedby><cites>FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703</cites><orcidid>0000-0002-4143-955X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791361/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791361/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,883,27907,27908,36996,53774,53776</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29382299$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nguyen, Nguyen H</creatorcontrib><creatorcontrib>Premachandra, H K A</creatorcontrib><creatorcontrib>Kilian, Andrzej</creatorcontrib><creatorcontrib>Knibb, Wayne</creatorcontrib><title>Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi</title><title>BMC genomics</title><addtitle>BMC Genomics</addtitle><description>Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R ) from linear regression analysis ranging from 0.49 to 0.71. Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi.</description><subject>Animal development</subject><subject>Genetic aspects</subject><subject>Genetic improvement</subject><subject>Genomic prediction</subject><subject>Genomic selection and genotype by sequencing</subject><subject>Genotype</subject><subject>Identification and classification</subject><subject>Kingfish</subject><subject>Perciformes</subject><issn>1471-2164</issn><issn>1471-2164</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNptkk9vEzEQxVcIREvhA3BBK3Ephy3-t_b6ghS1UCJVQiLhbE28443LZp3au9B8exxSqkZCPow1_r2nsf2K4i0lF5Q28mOirJGiIrSphNC8Es-KUyoUrRiV4vmT_UnxKqVbQqhqWP2yOGGaN4xpfVosr3EIG2_LbcTW29GHoZySH7ryahaX1QLvyhHtegh96HalC7HcYd-H3yP4vvyZOefTulxg9KGHsocehta_Ll446BO-eahnxY8vn5eXX6ubb9fzy9lNZWspx4papxBrayVpZcutoCsKzjKGrtEStCOW1JLnKrlqtUIKQLlUrbMriorws2J-8G0D3Jpt9BuIOxPAm7-NEDsDcfS2R6O4topDXTsihBUAYJVVKFktW8EVZK9PB6_ttNpga3EYI_RHpscng1-bLvwytdJ5KJoNzh8MYribMI1m45PNjwUDhikZqjUnlBC2n_v9Ae0gj-YHF7Kj3eNmVrNaiYYqnamL_1B5tZg_LAzofO4fCT4cCTIz4v3YwZSSmS--H7P0wNoYUoroHm9KidmHyxzCZXK4zD5cRmTNu6dP9Kj4lyb-B3H2ydU</recordid><startdate>20180130</startdate><enddate>20180130</enddate><creator>Nguyen, Nguyen H</creator><creator>Premachandra, H K A</creator><creator>Kilian, Andrzej</creator><creator>Knibb, Wayne</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>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4143-955X</orcidid></search><sort><creationdate>20180130</creationdate><title>Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi</title><author>Nguyen, Nguyen H ; Premachandra, H K A ; Kilian, Andrzej ; Knibb, Wayne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animal development</topic><topic>Genetic aspects</topic><topic>Genetic improvement</topic><topic>Genomic prediction</topic><topic>Genomic selection and genotype by sequencing</topic><topic>Genotype</topic><topic>Identification and classification</topic><topic>Kingfish</topic><topic>Perciformes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen, Nguyen H</creatorcontrib><creatorcontrib>Premachandra, H K A</creatorcontrib><creatorcontrib>Kilian, Andrzej</creatorcontrib><creatorcontrib>Knibb, Wayne</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen, Nguyen H</au><au>Premachandra, H K A</au><au>Kilian, Andrzej</au><au>Knibb, Wayne</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi</atitle><jtitle>BMC genomics</jtitle><addtitle>BMC Genomics</addtitle><date>2018-01-30</date><risdate>2018</risdate><volume>19</volume><issue>1</issue><spage>107</spage><epage>107</epage><pages>107-107</pages><artnum>107</artnum><issn>1471-2164</issn><eissn>1471-2164</eissn><abstract>Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R ) from linear regression analysis ranging from 0.49 to 0.71. Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>29382299</pmid><doi>10.1186/s12864-018-4493-4</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-4143-955X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1471-2164
ispartof BMC genomics, 2018-01, Vol.19 (1), p.107-107, Article 107
issn 1471-2164
1471-2164
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_739c73a55f044c4aaac7c7e6256d437a
source Publicly Available Content Database; PubMed Central
subjects Animal development
Genetic aspects
Genetic improvement
Genomic prediction
Genomic selection and genotype by sequencing
Genotype
Identification and classification
Kingfish
Perciformes
title Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T22%3A38%3A15IST&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=Genomic%20prediction%20using%20DArT-Seq%20technology%20for%20yellowtail%20kingfish%20Seriola%20lalandi&rft.jtitle=BMC%20genomics&rft.au=Nguyen,%20Nguyen%20H&rft.date=2018-01-30&rft.volume=19&rft.issue=1&rft.spage=107&rft.epage=107&rft.pages=107-107&rft.artnum=107&rft.issn=1471-2164&rft.eissn=1471-2164&rft_id=info:doi/10.1186/s12864-018-4493-4&rft_dat=%3Cgale_doaj_%3EA525748179%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c566t-1cf7ee5cc60d6d3c41b1afc22ef896a9f0c05639f0637d97e1aa1367dfcb1e703%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1993010020&rft_id=info:pmid/29382299&rft_galeid=A525748179&rfr_iscdi=true