Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm
The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records ( n = 10,607) of 2,519 individuals (which were progeny of...
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| description | The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records (
n
= 10,607) of 2,519 individuals (which were progeny of 278 rams and 1,485 ewes) were used to estimate genetic effects on growth curve traits from birth to yearling age of Zandi lambs. Using the Stochastic Approximation Expectation Maximization (SAEM) algorithm the growth curve parameters of five different mixed functions (i.e., Brody, Richards, Von Bertalanffy, Gompertz and Logistic) were obtained, then for the most appropriate model the genetic parameters were estimated using a Bayesian approach fitted multivariate animal model and ignoring or including maternal genetic effect. Except Richards model, all other mixed functions used here closely fitted actual BW records (
R
2
> 0.96). However, the Logistic function provided the best fit in every type. So, studied growth curve traits were estimated asymptotic weight which considered as mature weight (
a
), rate parameter (
b
), rate of maturing (
k
), and age (Ai)/weight (Wi) at the point of inflection. Of the fixed effects studied (i.e., gender, birth type, dam age, season and year of birth), the only non-significant relationship was the effect dam age on
b
and Ai. Based on the best-fitted model, posterior means of heritability estimates for
a
,
b
,
k
, Wi and Ai were 0.142 ± 0.036, 0.094 ± 0.029, 0.143 ± 0.063, 0.149 ± 0.039 and 0.029 ± 0.013, respectively. Posterior means of genetic correlations between mentioned traits ranged from -0.018 ± 0.069 (
b
-
k
) to 0.959 ± 0.029 (
a
-
b
), whereas the phenotypic correlation varied from -0.047 ± 0.014 (
b
-
k
) to 0.836 ± 0.007 (
a
-
b
). It was concluded that the model including only direct additive effect was sufficient to explain the variation in all investigated growth traits of Zandi lambs, selection for these traits results in slow genetic gain (due to the lack of sufficient genetic variation), but it would not be difficult to improve their mature weight and rate of maturing jointly. The results indicate that although the rate of genetic change for mature weight has been small (0.008 ± 0.003 kg year
−1
;
P
|
| doi_str_mv | 10.1007/s11250-024-04150-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3153825151</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3153825151</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-1fc6667fa2b44ac7a643ea1e3fad7c126c397f4f9769792b8ba19a61cd27755c3</originalsourceid><addsrcrecordid>eNqNkc1O3DAURi0EggH6AiwqS2y6SfH1TxwvEZoWpEEsgA0by-PcZDKaJFPbKerb1zC0SF1UrGzZ5_uu7EPIGbCvwJi-iABcsYJxWTAJeSf3yAyUFoWWotonM8akKaSW-ogcx7hmLMeq8pAcCSMEgGEz0s5j6nqXuqGlLQ6YOk-3LrgeE4ZI3VDTFHCoI-0G2obxOa2on8JPzMeuS5GODX3KVEfjCnFLp_jSlFZI7y_nt9Rt2jF0adWfkoPGbSJ-eltPyOO3-cPVdbG4-35zdbkoPK9MKqDxZVnqxvGllM5rV0qBDlA0rtYeeOmF0Y1sjC6NNnxZLR0YV4KvudZKeXFCvux6t2H8MWFMtu-ix83GDThO0QpQouIKFHwABSZZpTTL6Pk_6HqcwpAf8kopJcCoTPEd5cMYY8DGbkP-2_DLArMvxuzOmM3G7KsxK3Po81v1tOyx_hv5oygDYgfEfDW0GN5n_6f2N45OoCU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3110553195</pqid></control><display><type>article</type><title>Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Kheirabadi, Khabat</creator><creatorcontrib>Kheirabadi, Khabat</creatorcontrib><description>The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records (
n
= 10,607) of 2,519 individuals (which were progeny of 278 rams and 1,485 ewes) were used to estimate genetic effects on growth curve traits from birth to yearling age of Zandi lambs. Using the Stochastic Approximation Expectation Maximization (SAEM) algorithm the growth curve parameters of five different mixed functions (i.e., Brody, Richards, Von Bertalanffy, Gompertz and Logistic) were obtained, then for the most appropriate model the genetic parameters were estimated using a Bayesian approach fitted multivariate animal model and ignoring or including maternal genetic effect. Except Richards model, all other mixed functions used here closely fitted actual BW records (
R
2
> 0.96). However, the Logistic function provided the best fit in every type. So, studied growth curve traits were estimated asymptotic weight which considered as mature weight (
a
), rate parameter (
b
), rate of maturing (
k
), and age (Ai)/weight (Wi) at the point of inflection. Of the fixed effects studied (i.e., gender, birth type, dam age, season and year of birth), the only non-significant relationship was the effect dam age on
b
and Ai. Based on the best-fitted model, posterior means of heritability estimates for
a
,
b
,
k
, Wi and Ai were 0.142 ± 0.036, 0.094 ± 0.029, 0.143 ± 0.063, 0.149 ± 0.039 and 0.029 ± 0.013, respectively. Posterior means of genetic correlations between mentioned traits ranged from -0.018 ± 0.069 (
b
-
k
) to 0.959 ± 0.029 (
a
-
b
), whereas the phenotypic correlation varied from -0.047 ± 0.014 (
b
-
k
) to 0.836 ± 0.007 (
a
-
b
). It was concluded that the model including only direct additive effect was sufficient to explain the variation in all investigated growth traits of Zandi lambs, selection for these traits results in slow genetic gain (due to the lack of sufficient genetic variation), but it would not be difficult to improve their mature weight and rate of maturing jointly. The results indicate that although the rate of genetic change for mature weight has been small (0.008 ± 0.003 kg year
−1
;
P
< 0.05) but in the favorable direction for this breed.</description><identifier>ISSN: 0049-4747</identifier><identifier>ISSN: 1573-7438</identifier><identifier>EISSN: 1573-7438</identifier><identifier>DOI: 10.1007/s11250-024-04150-4</identifier><identifier>PMID: 39331190</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>additive effect ; Age ; Algorithms ; Animal models ; Animals ; Bayes Theorem ; Bayesian analysis ; Bayesian theory ; Biomedical and Life Sciences ; Birth ; Body weight ; Body Weight - genetics ; Breeding of animals ; Environmental effects ; Environmental impact ; Female ; gender ; Genetic diversity ; Genetic effects ; Genetic improvement ; genetic variation ; growth curves ; Heritability ; Iran ; Lamb ; Life Sciences ; Livestock ; Male ; Models, Genetic ; Parameter estimation ; phenotypic correlation ; progeny ; Regular Articles ; Sheep ; Sheep, Domestic - genetics ; Sheep, Domestic - growth & development ; Veterinary Medicine/Veterinary Science ; yearlings ; Zoology</subject><ispartof>Tropical animal health and production, 2024-11, Vol.56 (8), p.294-294, Article 294</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s), under exclusive licence to Springer Nature B.V.</rights><rights>Copyright Springer Nature B.V. Nov 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c289t-1fc6667fa2b44ac7a643ea1e3fad7c126c397f4f9769792b8ba19a61cd27755c3</cites><orcidid>0000-0002-9979-5346</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11250-024-04150-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11250-024-04150-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39331190$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kheirabadi, Khabat</creatorcontrib><title>Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm</title><title>Tropical animal health and production</title><addtitle>Trop Anim Health Prod</addtitle><addtitle>Trop Anim Health Prod</addtitle><description>The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records (
n
= 10,607) of 2,519 individuals (which were progeny of 278 rams and 1,485 ewes) were used to estimate genetic effects on growth curve traits from birth to yearling age of Zandi lambs. Using the Stochastic Approximation Expectation Maximization (SAEM) algorithm the growth curve parameters of five different mixed functions (i.e., Brody, Richards, Von Bertalanffy, Gompertz and Logistic) were obtained, then for the most appropriate model the genetic parameters were estimated using a Bayesian approach fitted multivariate animal model and ignoring or including maternal genetic effect. Except Richards model, all other mixed functions used here closely fitted actual BW records (
R
2
> 0.96). However, the Logistic function provided the best fit in every type. So, studied growth curve traits were estimated asymptotic weight which considered as mature weight (
a
), rate parameter (
b
), rate of maturing (
k
), and age (Ai)/weight (Wi) at the point of inflection. Of the fixed effects studied (i.e., gender, birth type, dam age, season and year of birth), the only non-significant relationship was the effect dam age on
b
and Ai. Based on the best-fitted model, posterior means of heritability estimates for
a
,
b
,
k
, Wi and Ai were 0.142 ± 0.036, 0.094 ± 0.029, 0.143 ± 0.063, 0.149 ± 0.039 and 0.029 ± 0.013, respectively. Posterior means of genetic correlations between mentioned traits ranged from -0.018 ± 0.069 (
b
-
k
) to 0.959 ± 0.029 (
a
-
b
), whereas the phenotypic correlation varied from -0.047 ± 0.014 (
b
-
k
) to 0.836 ± 0.007 (
a
-
b
). It was concluded that the model including only direct additive effect was sufficient to explain the variation in all investigated growth traits of Zandi lambs, selection for these traits results in slow genetic gain (due to the lack of sufficient genetic variation), but it would not be difficult to improve their mature weight and rate of maturing jointly. The results indicate that although the rate of genetic change for mature weight has been small (0.008 ± 0.003 kg year
−1
;
P
< 0.05) but in the favorable direction for this breed.</description><subject>additive effect</subject><subject>Age</subject><subject>Algorithms</subject><subject>Animal models</subject><subject>Animals</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>Biomedical and Life Sciences</subject><subject>Birth</subject><subject>Body weight</subject><subject>Body Weight - genetics</subject><subject>Breeding of animals</subject><subject>Environmental effects</subject><subject>Environmental impact</subject><subject>Female</subject><subject>gender</subject><subject>Genetic diversity</subject><subject>Genetic effects</subject><subject>Genetic improvement</subject><subject>genetic variation</subject><subject>growth curves</subject><subject>Heritability</subject><subject>Iran</subject><subject>Lamb</subject><subject>Life Sciences</subject><subject>Livestock</subject><subject>Male</subject><subject>Models, Genetic</subject><subject>Parameter estimation</subject><subject>phenotypic correlation</subject><subject>progeny</subject><subject>Regular Articles</subject><subject>Sheep</subject><subject>Sheep, Domestic - genetics</subject><subject>Sheep, Domestic - growth & development</subject><subject>Veterinary Medicine/Veterinary Science</subject><subject>yearlings</subject><subject>Zoology</subject><issn>0049-4747</issn><issn>1573-7438</issn><issn>1573-7438</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1O3DAURi0EggH6AiwqS2y6SfH1TxwvEZoWpEEsgA0by-PcZDKaJFPbKerb1zC0SF1UrGzZ5_uu7EPIGbCvwJi-iABcsYJxWTAJeSf3yAyUFoWWotonM8akKaSW-ogcx7hmLMeq8pAcCSMEgGEz0s5j6nqXuqGlLQ6YOk-3LrgeE4ZI3VDTFHCoI-0G2obxOa2on8JPzMeuS5GODX3KVEfjCnFLp_jSlFZI7y_nt9Rt2jF0adWfkoPGbSJ-eltPyOO3-cPVdbG4-35zdbkoPK9MKqDxZVnqxvGllM5rV0qBDlA0rtYeeOmF0Y1sjC6NNnxZLR0YV4KvudZKeXFCvux6t2H8MWFMtu-ix83GDThO0QpQouIKFHwABSZZpTTL6Pk_6HqcwpAf8kopJcCoTPEd5cMYY8DGbkP-2_DLArMvxuzOmM3G7KsxK3Po81v1tOyx_hv5oygDYgfEfDW0GN5n_6f2N45OoCU</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Kheirabadi, Khabat</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><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>7QL</scope><scope>7T7</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-9979-5346</orcidid></search><sort><creationdate>20241101</creationdate><title>Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm</title><author>Kheirabadi, Khabat</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-1fc6667fa2b44ac7a643ea1e3fad7c126c397f4f9769792b8ba19a61cd27755c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>additive effect</topic><topic>Age</topic><topic>Algorithms</topic><topic>Animal models</topic><topic>Animals</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian theory</topic><topic>Biomedical and Life Sciences</topic><topic>Birth</topic><topic>Body weight</topic><topic>Body Weight - genetics</topic><topic>Breeding of animals</topic><topic>Environmental effects</topic><topic>Environmental impact</topic><topic>Female</topic><topic>gender</topic><topic>Genetic diversity</topic><topic>Genetic effects</topic><topic>Genetic improvement</topic><topic>genetic variation</topic><topic>growth curves</topic><topic>Heritability</topic><topic>Iran</topic><topic>Lamb</topic><topic>Life Sciences</topic><topic>Livestock</topic><topic>Male</topic><topic>Models, Genetic</topic><topic>Parameter estimation</topic><topic>phenotypic correlation</topic><topic>progeny</topic><topic>Regular Articles</topic><topic>Sheep</topic><topic>Sheep, Domestic - genetics</topic><topic>Sheep, Domestic - growth & development</topic><topic>Veterinary Medicine/Veterinary Science</topic><topic>yearlings</topic><topic>Zoology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kheirabadi, Khabat</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Tropical animal health and production</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kheirabadi, Khabat</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm</atitle><jtitle>Tropical animal health and production</jtitle><stitle>Trop Anim Health Prod</stitle><addtitle>Trop Anim Health Prod</addtitle><date>2024-11-01</date><risdate>2024</risdate><volume>56</volume><issue>8</issue><spage>294</spage><epage>294</epage><pages>294-294</pages><artnum>294</artnum><issn>0049-4747</issn><issn>1573-7438</issn><eissn>1573-7438</eissn><abstract>The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records (
n
= 10,607) of 2,519 individuals (which were progeny of 278 rams and 1,485 ewes) were used to estimate genetic effects on growth curve traits from birth to yearling age of Zandi lambs. Using the Stochastic Approximation Expectation Maximization (SAEM) algorithm the growth curve parameters of five different mixed functions (i.e., Brody, Richards, Von Bertalanffy, Gompertz and Logistic) were obtained, then for the most appropriate model the genetic parameters were estimated using a Bayesian approach fitted multivariate animal model and ignoring or including maternal genetic effect. Except Richards model, all other mixed functions used here closely fitted actual BW records (
R
2
> 0.96). However, the Logistic function provided the best fit in every type. So, studied growth curve traits were estimated asymptotic weight which considered as mature weight (
a
), rate parameter (
b
), rate of maturing (
k
), and age (Ai)/weight (Wi) at the point of inflection. Of the fixed effects studied (i.e., gender, birth type, dam age, season and year of birth), the only non-significant relationship was the effect dam age on
b
and Ai. Based on the best-fitted model, posterior means of heritability estimates for
a
,
b
,
k
, Wi and Ai were 0.142 ± 0.036, 0.094 ± 0.029, 0.143 ± 0.063, 0.149 ± 0.039 and 0.029 ± 0.013, respectively. Posterior means of genetic correlations between mentioned traits ranged from -0.018 ± 0.069 (
b
-
k
) to 0.959 ± 0.029 (
a
-
b
), whereas the phenotypic correlation varied from -0.047 ± 0.014 (
b
-
k
) to 0.836 ± 0.007 (
a
-
b
). It was concluded that the model including only direct additive effect was sufficient to explain the variation in all investigated growth traits of Zandi lambs, selection for these traits results in slow genetic gain (due to the lack of sufficient genetic variation), but it would not be difficult to improve their mature weight and rate of maturing jointly. The results indicate that although the rate of genetic change for mature weight has been small (0.008 ± 0.003 kg year
−1
;
P
< 0.05) but in the favorable direction for this breed.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>39331190</pmid><doi>10.1007/s11250-024-04150-4</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9979-5346</orcidid></addata></record> |
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| ispartof | Tropical animal health and production, 2024-11, Vol.56 (8), p.294-294, Article 294 |
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| source | MEDLINE; SpringerLink Journals |
| subjects | additive effect Age Algorithms Animal models Animals Bayes Theorem Bayesian analysis Bayesian theory Biomedical and Life Sciences Birth Body weight Body Weight - genetics Breeding of animals Environmental effects Environmental impact Female gender Genetic diversity Genetic effects Genetic improvement genetic variation growth curves Heritability Iran Lamb Life Sciences Livestock Male Models, Genetic Parameter estimation phenotypic correlation progeny Regular Articles Sheep Sheep, Domestic - genetics Sheep, Domestic - growth & development Veterinary Medicine/Veterinary Science yearlings Zoology |
| title | Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm |
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