Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle

In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. Ho...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of animal science 2004-12, Vol.82 (12), p.3430-3439
Hauptverfasser: Reverter, A, Wang, Y.H, Byrne, K.A, Tan, S.H, Harper, G.S, Lehnert, S.A
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 3439
container_issue 12
container_start_page 3430
container_title Journal of animal science
container_volume 82
creator Reverter, A
Wang, Y.H
Byrne, K.A
Tan, S.H
Harper, G.S
Lehnert, S.A
description In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.
doi_str_mv 10.2527/2004.82123430x
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_67063683</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>67063683</sourcerecordid><originalsourceid>FETCH-LOGICAL-f324t-d2528dfe7492c1806d4eb4f88eae464b50e6c38923c0217bfbf936dc6029b0493</originalsourceid><addsrcrecordid>eNqF0btvFDEQB2ALgcgl0FKCG6Da4Nd6vWUUCA9FUEDq1ax3fHHkfWB7j1zD346jO0RJNbL06TcPE_KCs3NRi-adYEydG8GFVJLdPyIbXou6klzLx2TDmOCVMVyckNOU7hjjom7rp-SE17VsGs035PeX2U-ZwgRhn3yis6PjGrJfAlL7_usFHb2NM8QIe5ryOnhMdOfhgHYQPWQs5h4HOs4DhkRhWYIvzzzTLU6YvaV-XOK8wxFLp9KgR3TUQs4Bn5EnDkLC58d6Rm6uPvy4_FRdf_v4-fLiunJSqFwNZVczOGxUKyw3TA8Ke-WMQUClVV8z1FaaVkhbVm5617tW6sFqJtqeqVaekTeH3DLIzxVT7kafLIYAE85r6nTDtNRG_hfyRretaR7gyyNc-xGHbol-hLjv_p62gNdHAMlCcBEm69M_pyUTyqji3h7crd_e_vIRuzRCCCWWd3eQjOi46B5-t8hXB-lg7mAbS9rNd8G4ZKzVSvNW_gFdwZ_C</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>17699873</pqid></control><display><type>article</type><title>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</title><source>MEDLINE</source><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Reverter, A ; Wang, Y.H ; Byrne, K.A ; Tan, S.H ; Harper, G.S ; Lehnert, S.A</creator><creatorcontrib>Reverter, A ; Wang, Y.H ; Byrne, K.A ; Tan, S.H ; Harper, G.S ; Lehnert, S.A</creatorcontrib><description>In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</description><identifier>ISSN: 0021-8812</identifier><identifier>EISSN: 1525-3163</identifier><identifier>DOI: 10.2527/2004.82123430x</identifier><identifier>PMID: 15537761</identifier><language>eng</language><publisher>Savoy, IL: Am Soc Animal Sci</publisher><subject>algorithms ; Animal productions ; Animals ; beef cattle ; Biological and medical sciences ; Breeding ; Cattle - genetics ; complementary DNA ; DNA, Complementary ; Food industries ; Fundamental and applied biological sciences. Psychology ; gene expression ; Gene Expression Profiling - methods ; Gene Expression Profiling - veterinary ; Gene Expression Regulation ; genetic improvement ; genetic variance ; lipogenesis ; Meat and meat product industries ; microarray technology ; Multivariate Analysis ; Oligonucleotide Array Sequence Analysis - methods ; Oligonucleotide Array Sequence Analysis - veterinary ; protein metabolism ; statistical models ; Terrestrial animal productions ; Vertebrates</subject><ispartof>Journal of animal science, 2004-12, Vol.82 (12), p.3430-3439</ispartof><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=16302484$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15537761$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reverter, A</creatorcontrib><creatorcontrib>Wang, Y.H</creatorcontrib><creatorcontrib>Byrne, K.A</creatorcontrib><creatorcontrib>Tan, S.H</creatorcontrib><creatorcontrib>Harper, G.S</creatorcontrib><creatorcontrib>Lehnert, S.A</creatorcontrib><title>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</title><title>Journal of animal science</title><addtitle>J Anim Sci</addtitle><description>In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</description><subject>algorithms</subject><subject>Animal productions</subject><subject>Animals</subject><subject>beef cattle</subject><subject>Biological and medical sciences</subject><subject>Breeding</subject><subject>Cattle - genetics</subject><subject>complementary DNA</subject><subject>DNA, Complementary</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Profiling - veterinary</subject><subject>Gene Expression Regulation</subject><subject>genetic improvement</subject><subject>genetic variance</subject><subject>lipogenesis</subject><subject>Meat and meat product industries</subject><subject>microarray technology</subject><subject>Multivariate Analysis</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Oligonucleotide Array Sequence Analysis - veterinary</subject><subject>protein metabolism</subject><subject>statistical models</subject><subject>Terrestrial animal productions</subject><subject>Vertebrates</subject><issn>0021-8812</issn><issn>1525-3163</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0btvFDEQB2ALgcgl0FKCG6Da4Nd6vWUUCA9FUEDq1ax3fHHkfWB7j1zD346jO0RJNbL06TcPE_KCs3NRi-adYEydG8GFVJLdPyIbXou6klzLx2TDmOCVMVyckNOU7hjjom7rp-SE17VsGs035PeX2U-ZwgRhn3yis6PjGrJfAlL7_usFHb2NM8QIe5ryOnhMdOfhgHYQPWQs5h4HOs4DhkRhWYIvzzzTLU6YvaV-XOK8wxFLp9KgR3TUQs4Bn5EnDkLC58d6Rm6uPvy4_FRdf_v4-fLiunJSqFwNZVczOGxUKyw3TA8Ke-WMQUClVV8z1FaaVkhbVm5617tW6sFqJtqeqVaekTeH3DLIzxVT7kafLIYAE85r6nTDtNRG_hfyRretaR7gyyNc-xGHbol-hLjv_p62gNdHAMlCcBEm69M_pyUTyqji3h7crd_e_vIRuzRCCCWWd3eQjOi46B5-t8hXB-lg7mAbS9rNd8G4ZKzVSvNW_gFdwZ_C</recordid><startdate>20041201</startdate><enddate>20041201</enddate><creator>Reverter, A</creator><creator>Wang, Y.H</creator><creator>Byrne, K.A</creator><creator>Tan, S.H</creator><creator>Harper, G.S</creator><creator>Lehnert, S.A</creator><general>Am Soc Animal Sci</general><general>American Society of Animal Science</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20041201</creationdate><title>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</title><author>Reverter, A ; Wang, Y.H ; Byrne, K.A ; Tan, S.H ; Harper, G.S ; Lehnert, S.A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-f324t-d2528dfe7492c1806d4eb4f88eae464b50e6c38923c0217bfbf936dc6029b0493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>algorithms</topic><topic>Animal productions</topic><topic>Animals</topic><topic>beef cattle</topic><topic>Biological and medical sciences</topic><topic>Breeding</topic><topic>Cattle - genetics</topic><topic>complementary DNA</topic><topic>DNA, Complementary</topic><topic>Food industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Profiling - veterinary</topic><topic>Gene Expression Regulation</topic><topic>genetic improvement</topic><topic>genetic variance</topic><topic>lipogenesis</topic><topic>Meat and meat product industries</topic><topic>microarray technology</topic><topic>Multivariate Analysis</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Oligonucleotide Array Sequence Analysis - veterinary</topic><topic>protein metabolism</topic><topic>statistical models</topic><topic>Terrestrial animal productions</topic><topic>Vertebrates</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reverter, A</creatorcontrib><creatorcontrib>Wang, Y.H</creatorcontrib><creatorcontrib>Byrne, K.A</creatorcontrib><creatorcontrib>Tan, S.H</creatorcontrib><creatorcontrib>Harper, G.S</creatorcontrib><creatorcontrib>Lehnert, S.A</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of animal science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reverter, A</au><au>Wang, Y.H</au><au>Byrne, K.A</au><au>Tan, S.H</au><au>Harper, G.S</au><au>Lehnert, S.A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle</atitle><jtitle>Journal of animal science</jtitle><addtitle>J Anim Sci</addtitle><date>2004-12-01</date><risdate>2004</risdate><volume>82</volume><issue>12</issue><spage>3430</spage><epage>3439</epage><pages>3430-3439</pages><issn>0021-8812</issn><eissn>1525-3163</eissn><abstract>In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.</abstract><cop>Savoy, IL</cop><pub>Am Soc Animal Sci</pub><pmid>15537761</pmid><doi>10.2527/2004.82123430x</doi><tpages>10</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0021-8812
ispartof Journal of animal science, 2004-12, Vol.82 (12), p.3430-3439
issn 0021-8812
1525-3163
language eng
recordid cdi_proquest_miscellaneous_67063683
source MEDLINE; Oxford University Press Journals All Titles (1996-Current)
subjects algorithms
Animal productions
Animals
beef cattle
Biological and medical sciences
Breeding
Cattle - genetics
complementary DNA
DNA, Complementary
Food industries
Fundamental and applied biological sciences. Psychology
gene expression
Gene Expression Profiling - methods
Gene Expression Profiling - veterinary
Gene Expression Regulation
genetic improvement
genetic variance
lipogenesis
Meat and meat product industries
microarray technology
Multivariate Analysis
Oligonucleotide Array Sequence Analysis - methods
Oligonucleotide Array Sequence Analysis - veterinary
protein metabolism
statistical models
Terrestrial animal productions
Vertebrates
title Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T22%3A32%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Joint%20analysis%20of%20multiple%20cDNA%20microarray%20studies%20via%20multivariate%20mixed%20models%20applied%20to%20genetic%20improvement%20of%20beef%20cattle&rft.jtitle=Journal%20of%20animal%20science&rft.au=Reverter,%20A&rft.date=2004-12-01&rft.volume=82&rft.issue=12&rft.spage=3430&rft.epage=3439&rft.pages=3430-3439&rft.issn=0021-8812&rft.eissn=1525-3163&rft_id=info:doi/10.2527/2004.82123430x&rft_dat=%3Cproquest_pubme%3E67063683%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=17699873&rft_id=info:pmid/15537761&rfr_iscdi=true