Genotype by Environment Interaction for Production Traits While Accounting for Heteroscedasticity
Grazing (G) provides an alternative management system for dairy production. Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate t...
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description | Grazing (G) provides an alternative management system for dairy production. Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate the effect of HV on estimates of heritabilities and genetic correlations for mature-equivalent milk, protein, and fat yield, and lactation-average somatic cell scores of daughters, and to determine if HV affects the ability of sire's predicted transmitting ability (PTA) to predict daughter production in G and confinement (C) herds. Data consisted of 72,489 records from 35,674 cows in 366G herds from 11 states, and 117,629 records from 50,963 cows in 373C herds from the same 11 states plus 1 geographically contiguous state. Herds were divided into variance quartiles (QV1–QV4) based on milk yield. A transformation was used to reduce HV by standardizing the within-herd standard deviation to the average across-herd standard deviation of a base year for each parity, and was similar to the method used in current USDA-DHIA genetic evaluations. Regression of daughter yield on sire PTA showed that PTA overestimated production of all traits in QV1–QV3 and of milk in QV4 of G herds. For C herds, yields of milk in QV1 and QV2, and of protein and fat in QV1 were overestimated, and protein was underestimated in QV4. Reducing HV had little effect on G herds, but for C herds, regression did not differ from unity for milk and protein in QV1 and QV2. For milk, protein, and fat in G, heritabilities were approximately 0.17, 0.17, and 0.19, respectively. The heritabilities for milk, protein, and fat in C herds were approximately 0.16, 0.17, and 0.21, respectively. Genetic correlations between C and G did not suggest a G×E in 3 upper quartiles, but a possible G×E (correlation = 0.21, estimated standard error = 0.22) for the lowest quartile. Reducing HV did not affect estimates of heritabilities or genetic correlations. Results indicated that modest evidence for existence of G×E did not arise solely from HV. |
doi_str_mv | 10.3168/jds.2006-699 |
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Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate the effect of HV on estimates of heritabilities and genetic correlations for mature-equivalent milk, protein, and fat yield, and lactation-average somatic cell scores of daughters, and to determine if HV affects the ability of sire's predicted transmitting ability (PTA) to predict daughter production in G and confinement (C) herds. Data consisted of 72,489 records from 35,674 cows in 366G herds from 11 states, and 117,629 records from 50,963 cows in 373C herds from the same 11 states plus 1 geographically contiguous state. Herds were divided into variance quartiles (QV1–QV4) based on milk yield. A transformation was used to reduce HV by standardizing the within-herd standard deviation to the average across-herd standard deviation of a base year for each parity, and was similar to the method used in current USDA-DHIA genetic evaluations. Regression of daughter yield on sire PTA showed that PTA overestimated production of all traits in QV1–QV3 and of milk in QV4 of G herds. For C herds, yields of milk in QV1 and QV2, and of protein and fat in QV1 were overestimated, and protein was underestimated in QV4. Reducing HV had little effect on G herds, but for C herds, regression did not differ from unity for milk and protein in QV1 and QV2. For milk, protein, and fat in G, heritabilities were approximately 0.17, 0.17, and 0.19, respectively. The heritabilities for milk, protein, and fat in C herds were approximately 0.16, 0.17, and 0.21, respectively. Genetic correlations between C and G did not suggest a G×E in 3 upper quartiles, but a possible G×E (correlation = 0.21, estimated standard error = 0.22) for the lowest quartile. Reducing HV did not affect estimates of heritabilities or genetic correlations. Results indicated that modest evidence for existence of G×E did not arise solely from HV.</description><identifier>ISSN: 0022-0302</identifier><identifier>EISSN: 1525-3198</identifier><identifier>DOI: 10.3168/jds.2006-699</identifier><identifier>PMID: 17639000</identifier><identifier>CODEN: JDSCAE</identifier><language>eng</language><publisher>Savoy, IL: Elsevier Inc</publisher><subject>Animal productions ; Animals ; Biological and medical sciences ; breeding value ; Cattle - genetics ; Cattle - physiology ; dairy cattle ; Dairying - methods ; daughters ; Environment ; Fats ; Female ; Food industries ; Fundamental and applied biological sciences. Psychology ; genetic correlation ; Genetic Variation - genetics ; Genotype ; genotype-environment interaction ; grazing ; heritability ; heteroscedasticity ; Lactation - genetics ; Lactation - physiology ; Linear Models ; Male ; Milk - chemistry ; Milk - cytology ; Milk and cheese industries. Ice creams ; milk composition ; Milk Proteins ; milk yield ; Phenotype ; phenotypic variation ; Statistics as Topic ; Terrestrial animal productions ; traits ; United States ; variance ; variance quartile ; Vertebrates</subject><ispartof>Journal of dairy science, 2007-08, Vol.90 (8), p.3889-3899</ispartof><rights>2007 American Dairy Science Association</rights><rights>2007 INIST-CNRS</rights><rights>Copyright American Dairy Science Association Aug 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c552t-176dd0c4f3b0202c940da59f6f077d541176c0a6804c675d064e5f04229aa6303</citedby><cites>FETCH-LOGICAL-c552t-176dd0c4f3b0202c940da59f6f077d541176c0a6804c675d064e5f04229aa6303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.3168/jds.2006-699$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18942886$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17639000$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fahey, A.G.</creatorcontrib><creatorcontrib>Schutz, M.M.</creatorcontrib><creatorcontrib>Lofgren, D.L.</creatorcontrib><creatorcontrib>Schinckel, A.P.</creatorcontrib><creatorcontrib>Stewart, T.S.</creatorcontrib><title>Genotype by Environment Interaction for Production Traits While Accounting for Heteroscedasticity</title><title>Journal of dairy science</title><addtitle>J Dairy Sci</addtitle><description>Grazing (G) provides an alternative management system for dairy production. Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate the effect of HV on estimates of heritabilities and genetic correlations for mature-equivalent milk, protein, and fat yield, and lactation-average somatic cell scores of daughters, and to determine if HV affects the ability of sire's predicted transmitting ability (PTA) to predict daughter production in G and confinement (C) herds. Data consisted of 72,489 records from 35,674 cows in 366G herds from 11 states, and 117,629 records from 50,963 cows in 373C herds from the same 11 states plus 1 geographically contiguous state. Herds were divided into variance quartiles (QV1–QV4) based on milk yield. A transformation was used to reduce HV by standardizing the within-herd standard deviation to the average across-herd standard deviation of a base year for each parity, and was similar to the method used in current USDA-DHIA genetic evaluations. Regression of daughter yield on sire PTA showed that PTA overestimated production of all traits in QV1–QV3 and of milk in QV4 of G herds. For C herds, yields of milk in QV1 and QV2, and of protein and fat in QV1 were overestimated, and protein was underestimated in QV4. Reducing HV had little effect on G herds, but for C herds, regression did not differ from unity for milk and protein in QV1 and QV2. For milk, protein, and fat in G, heritabilities were approximately 0.17, 0.17, and 0.19, respectively. The heritabilities for milk, protein, and fat in C herds were approximately 0.16, 0.17, and 0.21, respectively. Genetic correlations between C and G did not suggest a G×E in 3 upper quartiles, but a possible G×E (correlation = 0.21, estimated standard error = 0.22) for the lowest quartile. Reducing HV did not affect estimates of heritabilities or genetic correlations. Results indicated that modest evidence for existence of G×E did not arise solely from HV.</description><subject>Animal productions</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>breeding value</subject><subject>Cattle - genetics</subject><subject>Cattle - physiology</subject><subject>dairy cattle</subject><subject>Dairying - methods</subject><subject>daughters</subject><subject>Environment</subject><subject>Fats</subject><subject>Female</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>genetic correlation</subject><subject>Genetic Variation - genetics</subject><subject>Genotype</subject><subject>genotype-environment interaction</subject><subject>grazing</subject><subject>heritability</subject><subject>heteroscedasticity</subject><subject>Lactation - genetics</subject><subject>Lactation - physiology</subject><subject>Linear Models</subject><subject>Male</subject><subject>Milk - chemistry</subject><subject>Milk - cytology</subject><subject>Milk and cheese industries. 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Psychology</topic><topic>genetic correlation</topic><topic>Genetic Variation - genetics</topic><topic>Genotype</topic><topic>genotype-environment interaction</topic><topic>grazing</topic><topic>heritability</topic><topic>heteroscedasticity</topic><topic>Lactation - genetics</topic><topic>Lactation - physiology</topic><topic>Linear Models</topic><topic>Male</topic><topic>Milk - chemistry</topic><topic>Milk - cytology</topic><topic>Milk and cheese industries. Ice creams</topic><topic>milk composition</topic><topic>Milk Proteins</topic><topic>milk yield</topic><topic>Phenotype</topic><topic>phenotypic variation</topic><topic>Statistics as Topic</topic><topic>Terrestrial animal productions</topic><topic>traits</topic><topic>United States</topic><topic>variance</topic><topic>variance quartile</topic><topic>Vertebrates</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fahey, A.G.</creatorcontrib><creatorcontrib>Schutz, M.M.</creatorcontrib><creatorcontrib>Lofgren, D.L.</creatorcontrib><creatorcontrib>Schinckel, A.P.</creatorcontrib><creatorcontrib>Stewart, T.S.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><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>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</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>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>SIRS Editorial</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of dairy science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fahey, A.G.</au><au>Schutz, M.M.</au><au>Lofgren, D.L.</au><au>Schinckel, A.P.</au><au>Stewart, T.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genotype by Environment Interaction for Production Traits While Accounting for Heteroscedasticity</atitle><jtitle>Journal of dairy science</jtitle><addtitle>J Dairy Sci</addtitle><date>2007-08-01</date><risdate>2007</risdate><volume>90</volume><issue>8</issue><spage>3889</spage><epage>3899</epage><pages>3889-3899</pages><issn>0022-0302</issn><eissn>1525-3198</eissn><coden>JDSCAE</coden><abstract>Grazing (G) provides an alternative management system for dairy production. Heteroscedasticity (HV) of the data may bias estimates of genetic correlations of yield traits between environments, an indicator of genotype-by-environment interaction (G×E). The objective of this study was to investigate the effect of HV on estimates of heritabilities and genetic correlations for mature-equivalent milk, protein, and fat yield, and lactation-average somatic cell scores of daughters, and to determine if HV affects the ability of sire's predicted transmitting ability (PTA) to predict daughter production in G and confinement (C) herds. Data consisted of 72,489 records from 35,674 cows in 366G herds from 11 states, and 117,629 records from 50,963 cows in 373C herds from the same 11 states plus 1 geographically contiguous state. Herds were divided into variance quartiles (QV1–QV4) based on milk yield. A transformation was used to reduce HV by standardizing the within-herd standard deviation to the average across-herd standard deviation of a base year for each parity, and was similar to the method used in current USDA-DHIA genetic evaluations. Regression of daughter yield on sire PTA showed that PTA overestimated production of all traits in QV1–QV3 and of milk in QV4 of G herds. For C herds, yields of milk in QV1 and QV2, and of protein and fat in QV1 were overestimated, and protein was underestimated in QV4. Reducing HV had little effect on G herds, but for C herds, regression did not differ from unity for milk and protein in QV1 and QV2. For milk, protein, and fat in G, heritabilities were approximately 0.17, 0.17, and 0.19, respectively. The heritabilities for milk, protein, and fat in C herds were approximately 0.16, 0.17, and 0.21, respectively. Genetic correlations between C and G did not suggest a G×E in 3 upper quartiles, but a possible G×E (correlation = 0.21, estimated standard error = 0.22) for the lowest quartile. Reducing HV did not affect estimates of heritabilities or genetic correlations. Results indicated that modest evidence for existence of G×E did not arise solely from HV.</abstract><cop>Savoy, IL</cop><pub>Elsevier Inc</pub><pmid>17639000</pmid><doi>10.3168/jds.2006-699</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Animal productions Animals Biological and medical sciences breeding value Cattle - genetics Cattle - physiology dairy cattle Dairying - methods daughters Environment Fats Female Food industries Fundamental and applied biological sciences. Psychology genetic correlation Genetic Variation - genetics Genotype genotype-environment interaction grazing heritability heteroscedasticity Lactation - genetics Lactation - physiology Linear Models Male Milk - chemistry Milk - cytology Milk and cheese industries. Ice creams milk composition Milk Proteins milk yield Phenotype phenotypic variation Statistics as Topic Terrestrial animal productions traits United States variance variance quartile Vertebrates |
title | Genotype by Environment Interaction for Production Traits While Accounting for Heteroscedasticity |
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