Genotype‐by‐environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications
Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to selec...
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Veröffentlicht in: | Animal genetics 2024-12, Vol.55 (6), p.871-892 |
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creator | Silva Neto, João B. Mota, Lucio F. M. Londoño‐Gil, Marisol Schmidt, Patrícia I. Rodrigues, Gustavo R. D. Ligori, Viviane A. Arikawa, Leonardo M. Magnabosco, Claudio U. Brito, Luiz F. Baldi, Fernando |
description | Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype‐by‐environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene–nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving perfor |
doi_str_mv | 10.1111/age.13483 |
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M. ; Londoño‐Gil, Marisol ; Schmidt, Patrícia I. ; Rodrigues, Gustavo R. D. ; Ligori, Viviane A. ; Arikawa, Leonardo M. ; Magnabosco, Claudio U. ; Brito, Luiz F. ; Baldi, Fernando</creator><creatorcontrib>Silva Neto, João B. ; Mota, Lucio F. M. ; Londoño‐Gil, Marisol ; Schmidt, Patrícia I. ; Rodrigues, Gustavo R. D. ; Ligori, Viviane A. ; Arikawa, Leonardo M. ; Magnabosco, Claudio U. ; Brito, Luiz F. ; Baldi, Fernando</creatorcontrib><description>Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype‐by‐environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene–nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.</description><identifier>ISSN: 0268-9146</identifier><identifier>ISSN: 1365-2052</identifier><identifier>EISSN: 1365-2052</identifier><identifier>DOI: 10.1111/age.13483</identifier><identifier>PMID: 39377556</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>animal adaptability ; Animal breeding ; animal genetics ; Animal health ; Animal husbandry ; Animal metabolism ; Animal models ; Animal populations ; Animal welfare ; Animals ; Beef ; Beef cattle ; Bos taurus indicus ; Bos taurus taurus ; Breeding ; Cattle ; Cattle - genetics ; Cattle - physiology ; Cow's milk ; Dairy cattle ; Dairying ; Disease resistance ; Efficiency ; Environmental conditions ; Environmental gradient ; environmental sensitivity ; Feed conversion ; Feed efficiency ; Fertility ; Gene expression ; Gene-Environment Interaction ; Genetic improvement ; Genomics ; Genotype ; genotype-environment interaction ; Genotype-environment interactions ; Genotypes ; Heat tolerance ; Livestock ; Livestock breeding ; Livestock production ; Meat ; meat quality ; Metabolic pathways ; Metabolism ; Milk ; milk composition ; milk yield ; Nutrigenomics ; nutrition-genotype interaction ; Phenotype ; Population genetics ; Population studies ; Populations ; Reproduction ; reproductive performance ; resilience ; Reviews ; Survival ; Transcription factors</subject><ispartof>Animal genetics, 2024-12, Vol.55 (6), p.871-892</ispartof><rights>2024 The Author(s). published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.</rights><rights>2024 The Author(s). Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.</rights><rights>2024. This article is published 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3113-7156df982ef980d2fd470232c40aff358f44a0017799556c07754ada85ced65b3</cites><orcidid>0000-0002-4307-4670 ; 0000-0001-9983-1784 ; 0000-0001-6522-5567</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fage.13483$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fage.13483$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39377556$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Silva Neto, João B.</creatorcontrib><creatorcontrib>Mota, Lucio F. M.</creatorcontrib><creatorcontrib>Londoño‐Gil, Marisol</creatorcontrib><creatorcontrib>Schmidt, Patrícia I.</creatorcontrib><creatorcontrib>Rodrigues, Gustavo R. D.</creatorcontrib><creatorcontrib>Ligori, Viviane A.</creatorcontrib><creatorcontrib>Arikawa, Leonardo M.</creatorcontrib><creatorcontrib>Magnabosco, Claudio U.</creatorcontrib><creatorcontrib>Brito, Luiz F.</creatorcontrib><creatorcontrib>Baldi, Fernando</creatorcontrib><title>Genotype‐by‐environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications</title><title>Animal genetics</title><addtitle>Anim Genet</addtitle><description>Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype‐by‐environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene–nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.</description><subject>animal adaptability</subject><subject>Animal breeding</subject><subject>animal genetics</subject><subject>Animal health</subject><subject>Animal husbandry</subject><subject>Animal metabolism</subject><subject>Animal models</subject><subject>Animal populations</subject><subject>Animal welfare</subject><subject>Animals</subject><subject>Beef</subject><subject>Beef cattle</subject><subject>Bos taurus indicus</subject><subject>Bos taurus taurus</subject><subject>Breeding</subject><subject>Cattle</subject><subject>Cattle - genetics</subject><subject>Cattle - physiology</subject><subject>Cow's milk</subject><subject>Dairy cattle</subject><subject>Dairying</subject><subject>Disease resistance</subject><subject>Efficiency</subject><subject>Environmental conditions</subject><subject>Environmental gradient</subject><subject>environmental sensitivity</subject><subject>Feed conversion</subject><subject>Feed efficiency</subject><subject>Fertility</subject><subject>Gene expression</subject><subject>Gene-Environment Interaction</subject><subject>Genetic improvement</subject><subject>Genomics</subject><subject>Genotype</subject><subject>genotype-environment interaction</subject><subject>Genotype-environment interactions</subject><subject>Genotypes</subject><subject>Heat tolerance</subject><subject>Livestock</subject><subject>Livestock breeding</subject><subject>Livestock production</subject><subject>Meat</subject><subject>meat quality</subject><subject>Metabolic pathways</subject><subject>Metabolism</subject><subject>Milk</subject><subject>milk composition</subject><subject>milk yield</subject><subject>Nutrigenomics</subject><subject>nutrition-genotype interaction</subject><subject>Phenotype</subject><subject>Population genetics</subject><subject>Population studies</subject><subject>Populations</subject><subject>Reproduction</subject><subject>reproductive performance</subject><subject>resilience</subject><subject>Reviews</subject><subject>Survival</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><sourceid>EIF</sourceid><recordid>eNqNkc1qFTEUx4Mo9lpd-AIScKOLafM5H-4upV6FghtdD7nJmTZlJhmTmVtm10dw5QP6JB7vVBeCYAgnJPmdHxz-hLzk7IzjOjfXcMalquUjsuGy1IVgWjwmGybKumi4Kk_Is5xvGWM1r_hTciIbWVValxvyfQchTssIP-6_7RcsEA4-xTBAmKgPEyRjJx9DxgvdA3TUBEed8Wmh1kxTD3SM49ybI_SObmmCg4c7Gjs6wHQTXezjtYd87Bsh5RFQeMCHGJDNYJK9OX6acey9XUXPyZPO9BlePJyn5Mv7y88XH4qrT7uPF9urwkrOZVFxXbquqQVgYU50TlVMSGEVM10ndd0pZRjjVdU0OK5lOLUyztTagiv1Xp6SN6t3TPHrDHlqB58t9L0JEOfcSq4V7oZX_4FyREXNGaKv_0Jv45wCDoKUwIC0qjRSb1fKpphzgq4dkx9MWlrO2l-5tphre8wV2VcPxnk_gPtD_g4SgfMVuPM9LP82tdvd5ar8CQvhr7M</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Silva Neto, João B.</creator><creator>Mota, Lucio F. M.</creator><creator>Londoño‐Gil, Marisol</creator><creator>Schmidt, Patrícia I.</creator><creator>Rodrigues, Gustavo R. D.</creator><creator>Ligori, Viviane A.</creator><creator>Arikawa, Leonardo M.</creator><creator>Magnabosco, Claudio U.</creator><creator>Brito, Luiz F.</creator><creator>Baldi, Fernando</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</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><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-4307-4670</orcidid><orcidid>https://orcid.org/0000-0001-9983-1784</orcidid><orcidid>https://orcid.org/0000-0001-6522-5567</orcidid></search><sort><creationdate>202412</creationdate><title>Genotype‐by‐environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications</title><author>Silva Neto, João B. ; Mota, Lucio F. M. ; Londoño‐Gil, Marisol ; Schmidt, Patrícia I. ; Rodrigues, Gustavo R. D. ; Ligori, Viviane A. ; Arikawa, Leonardo M. ; Magnabosco, Claudio U. ; Brito, Luiz F. ; Baldi, Fernando</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3113-7156df982ef980d2fd470232c40aff358f44a0017799556c07754ada85ced65b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>animal adaptability</topic><topic>Animal breeding</topic><topic>animal genetics</topic><topic>Animal health</topic><topic>Animal husbandry</topic><topic>Animal metabolism</topic><topic>Animal models</topic><topic>Animal populations</topic><topic>Animal welfare</topic><topic>Animals</topic><topic>Beef</topic><topic>Beef cattle</topic><topic>Bos taurus indicus</topic><topic>Bos taurus taurus</topic><topic>Breeding</topic><topic>Cattle</topic><topic>Cattle - genetics</topic><topic>Cattle - physiology</topic><topic>Cow's milk</topic><topic>Dairy cattle</topic><topic>Dairying</topic><topic>Disease resistance</topic><topic>Efficiency</topic><topic>Environmental conditions</topic><topic>Environmental gradient</topic><topic>environmental sensitivity</topic><topic>Feed conversion</topic><topic>Feed efficiency</topic><topic>Fertility</topic><topic>Gene expression</topic><topic>Gene-Environment Interaction</topic><topic>Genetic improvement</topic><topic>Genomics</topic><topic>Genotype</topic><topic>genotype-environment interaction</topic><topic>Genotype-environment interactions</topic><topic>Genotypes</topic><topic>Heat tolerance</topic><topic>Livestock</topic><topic>Livestock breeding</topic><topic>Livestock production</topic><topic>Meat</topic><topic>meat quality</topic><topic>Metabolic pathways</topic><topic>Metabolism</topic><topic>Milk</topic><topic>milk composition</topic><topic>milk yield</topic><topic>Nutrigenomics</topic><topic>nutrition-genotype interaction</topic><topic>Phenotype</topic><topic>Population genetics</topic><topic>Population studies</topic><topic>Populations</topic><topic>Reproduction</topic><topic>reproductive performance</topic><topic>resilience</topic><topic>Reviews</topic><topic>Survival</topic><topic>Transcription factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Silva Neto, João B.</creatorcontrib><creatorcontrib>Mota, Lucio F. 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M.</au><au>Londoño‐Gil, Marisol</au><au>Schmidt, Patrícia I.</au><au>Rodrigues, Gustavo R. D.</au><au>Ligori, Viviane A.</au><au>Arikawa, Leonardo M.</au><au>Magnabosco, Claudio U.</au><au>Brito, Luiz F.</au><au>Baldi, Fernando</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genotype‐by‐environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications</atitle><jtitle>Animal genetics</jtitle><addtitle>Anim Genet</addtitle><date>2024-12</date><risdate>2024</risdate><volume>55</volume><issue>6</issue><spage>871</spage><epage>892</epage><pages>871-892</pages><issn>0268-9146</issn><issn>1365-2052</issn><eissn>1365-2052</eissn><abstract>Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype‐by‐environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene–nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>39377556</pmid><doi>10.1111/age.13483</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-4307-4670</orcidid><orcidid>https://orcid.org/0000-0001-9983-1784</orcidid><orcidid>https://orcid.org/0000-0001-6522-5567</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | animal adaptability Animal breeding animal genetics Animal health Animal husbandry Animal metabolism Animal models Animal populations Animal welfare Animals Beef Beef cattle Bos taurus indicus Bos taurus taurus Breeding Cattle Cattle - genetics Cattle - physiology Cow's milk Dairy cattle Dairying Disease resistance Efficiency Environmental conditions Environmental gradient environmental sensitivity Feed conversion Feed efficiency Fertility Gene expression Gene-Environment Interaction Genetic improvement Genomics Genotype genotype-environment interaction Genotype-environment interactions Genotypes Heat tolerance Livestock Livestock breeding Livestock production Meat meat quality Metabolic pathways Metabolism Milk milk composition milk yield Nutrigenomics nutrition-genotype interaction Phenotype Population genetics Population studies Populations Reproduction reproductive performance resilience Reviews Survival Transcription factors |
title | Genotype‐by‐environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications |
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