An association weight matrix identified biological pathways associated with bull fertility traits in a multi‐breed population
Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple lis...
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Veröffentlicht in: | Animal genetics 2024-08, Vol.55 (4), p.495-510 |
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description | Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with ‘regulation of neurotransmitter secretion’ and ‘chromatin formation’. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further. |
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We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with ‘regulation of neurotransmitter secretion’ and ‘chromatin formation’. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.</description><identifier>ISSN: 0268-9146</identifier><identifier>ISSN: 1365-2052</identifier><identifier>EISSN: 1365-2052</identifier><identifier>DOI: 10.1111/age.13431</identifier><identifier>PMID: 38692842</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>association weight matrix ; beef cattle ; Biosynthesis ; Chromatin ; Enrichment ; Fertility ; FOXO4 protein ; Foxp3 protein ; GATA-1 protein ; Gene regulation ; Genes ; Genotypes ; GWAS ; Information theory ; LRRK2 protein ; Males ; network analysis ; p53 Protein ; Pentose ; Pentose phosphate pathway ; Phenotypes ; Population genetics ; Regulatory sequences ; reproductive traits ; Signal transduction ; Single-nucleotide polymorphism ; sperm morphology ; Transcription factors</subject><ispartof>Animal genetics, 2024-08, Vol.55 (4), p.495-510</ispartof><rights>2024 The Authors. published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.</rights><rights>2024 The Authors. 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We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with ‘regulation of neurotransmitter secretion’ and ‘chromatin formation’. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.</description><subject>association weight matrix</subject><subject>beef cattle</subject><subject>Biosynthesis</subject><subject>Chromatin</subject><subject>Enrichment</subject><subject>Fertility</subject><subject>FOXO4 protein</subject><subject>Foxp3 protein</subject><subject>GATA-1 protein</subject><subject>Gene regulation</subject><subject>Genes</subject><subject>Genotypes</subject><subject>GWAS</subject><subject>Information theory</subject><subject>LRRK2 protein</subject><subject>Males</subject><subject>network analysis</subject><subject>p53 Protein</subject><subject>Pentose</subject><subject>Pentose phosphate pathway</subject><subject>Phenotypes</subject><subject>Population genetics</subject><subject>Regulatory sequences</subject><subject>reproductive traits</subject><subject>Signal transduction</subject><subject>Single-nucleotide polymorphism</subject><subject>sperm morphology</subject><subject>Transcription factors</subject><issn>0268-9146</issn><issn>1365-2052</issn><issn>1365-2052</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp10cFuFCEcBnBiNHZbPfgChsSLPUwLA8Mwx01T2yZNvOh5Agzs_htmGIHJdk_6CD6jT1LarT2YyIUQfnxAPoQ-UHJGyzhXG3tGGWf0FVpRJpqqJk39Gq1ILWTVUS6O0HFKd4QQSVv6Fh0xKbpa8nqFfq4nrFIKBlSGMOGdhc0241HlCPcYBjtlcGAHrCH4sAGjPJ5V3u7UPr0cLNs7yFusF--xszGDh7zHOSrICUO5AY-Lz_Dn128dbdFzmBf_dOE79MYpn-z75_kEff9y-e3iurr9enVzsb6tDOOSVtIxpWXTGcM60zEqXPmi4I53vLakEwNrjNNOa0VoWRsha9t1TAz10LbacnaCPh9y5xh-LDblfoRkrPdqsmFJPSMNoa1ghBb66R96F5Y4ldcV1XIpJKdNUacHZWJIKVrXzxFGFfc9Jf1jK31ppX9qpdiPz4mLHu3wIv_WUMD5AezA2_3_k_r11eUh8gExcZj7</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Tan, Wei Liang Andre</creator><creator>Hudson, Nicholas James</creator><creator>Porto Neto, Laercio Ribeiro</creator><creator>Reverter, Antonio</creator><creator>Afonso, Juliana</creator><creator>Fortes, Marina Rufino Salinas</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3549-9396</orcidid><orcidid>https://orcid.org/0000-0003-0960-9923</orcidid><orcidid>https://orcid.org/0000-0002-4435-5386</orcidid><orcidid>https://orcid.org/0000-0002-7254-1960</orcidid></search><sort><creationdate>202408</creationdate><title>An association weight matrix identified biological pathways associated with bull fertility traits in a multi‐breed population</title><author>Tan, Wei Liang Andre ; Hudson, Nicholas James ; Porto Neto, Laercio Ribeiro ; Reverter, Antonio ; Afonso, Juliana ; Fortes, Marina Rufino Salinas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3481-8f3ab859cc39c9316f20564f4942e096d35cfbfbba01e09c682e9936d2d77be43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>association weight matrix</topic><topic>beef cattle</topic><topic>Biosynthesis</topic><topic>Chromatin</topic><topic>Enrichment</topic><topic>Fertility</topic><topic>FOXO4 protein</topic><topic>Foxp3 protein</topic><topic>GATA-1 protein</topic><topic>Gene regulation</topic><topic>Genes</topic><topic>Genotypes</topic><topic>GWAS</topic><topic>Information theory</topic><topic>LRRK2 protein</topic><topic>Males</topic><topic>network analysis</topic><topic>p53 Protein</topic><topic>Pentose</topic><topic>Pentose phosphate pathway</topic><topic>Phenotypes</topic><topic>Population genetics</topic><topic>Regulatory sequences</topic><topic>reproductive traits</topic><topic>Signal transduction</topic><topic>Single-nucleotide polymorphism</topic><topic>sperm morphology</topic><topic>Transcription factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tan, Wei Liang Andre</creatorcontrib><creatorcontrib>Hudson, Nicholas James</creatorcontrib><creatorcontrib>Porto Neto, Laercio Ribeiro</creatorcontrib><creatorcontrib>Reverter, Antonio</creatorcontrib><creatorcontrib>Afonso, Juliana</creatorcontrib><creatorcontrib>Fortes, Marina Rufino Salinas</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Animal genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tan, Wei Liang Andre</au><au>Hudson, Nicholas James</au><au>Porto Neto, Laercio Ribeiro</au><au>Reverter, Antonio</au><au>Afonso, Juliana</au><au>Fortes, Marina Rufino Salinas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An association weight matrix identified biological pathways associated with bull fertility traits in a multi‐breed population</atitle><jtitle>Animal genetics</jtitle><addtitle>Anim Genet</addtitle><date>2024-08</date><risdate>2024</risdate><volume>55</volume><issue>4</issue><spage>495</spage><epage>510</epage><pages>495-510</pages><issn>0268-9146</issn><issn>1365-2052</issn><eissn>1365-2052</eissn><abstract>Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with ‘regulation of neurotransmitter secretion’ and ‘chromatin formation’. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>38692842</pmid><doi>10.1111/age.13431</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-3549-9396</orcidid><orcidid>https://orcid.org/0000-0003-0960-9923</orcidid><orcidid>https://orcid.org/0000-0002-4435-5386</orcidid><orcidid>https://orcid.org/0000-0002-7254-1960</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | association weight matrix beef cattle Biosynthesis Chromatin Enrichment Fertility FOXO4 protein Foxp3 protein GATA-1 protein Gene regulation Genes Genotypes GWAS Information theory LRRK2 protein Males network analysis p53 Protein Pentose Pentose phosphate pathway Phenotypes Population genetics Regulatory sequences reproductive traits Signal transduction Single-nucleotide polymorphism sperm morphology Transcription factors |
title | An association weight matrix identified biological pathways associated with bull fertility traits in a multi‐breed population |
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