{"title":"Selection index for economically important traits in Boer crossbred goats using principal component analysis.","authors":"Zeleke Tesema, Belay Deribe, Mekonnen Tilahun, Alemu Kefale, Mesfin Lakew, Tesfaye Getachew, Getachew Worku Alebachew, Solomon Gizaw","doi":"10.1371/journal.pone.0310841","DOIUrl":null,"url":null,"abstract":"<p><p>The optimal strategy for genetic selection is a selection index based on economic weight; however, in developing countries where economic weight estimation is not always evident and easy for breeders due to a lack of economic data. Thus, this study aimed to construct selection indices for crossbred goats, which could be used as an alternative to economic selection index and to explore the relationship among economically important traits. The data set contained records of birth weight (BW), weaning weight (WW), pre-weaning weight gain (ADG), pre-weaning Kleiber ratio (KR), pre-weaning relative growth rate (RGR), pre-weaning growth efficiency (GE), and pre-weaning survival (RR) of crossbred goats. Genetic parameter estimates were obtained using a single-trait animal model. General linear model, principal component analysis, and cluster procedures of SAS were also used for data analysis. Kid survival was negatively correlated with all investigated traits except BW. Traits such as KR, GE, RGR, WW, and ADG were highly and positively correlated. According to the Kaiser method, two principal components were selected from seven investigated traits. The first principal component (PC1) explained 57.71%, and the second principal component (PC2) explained 14.57% of the estimated breeding value variance, totaling 72.28% of the total genetic additive variance. PC1 explained most of the direct additive genetic variation and correlated with the estimated breeding value of WW, ADG, KR, GE, and RGR, whereas PC2 was correlated with the estimated breeding value of BW and RR. Besides, the cluster analysis categorized seven traits into two major groups. The first group includes BW and RR, whereas traits such as WW, ADG, KR, GE, and RGR were included in the second group. Therefore, two based selection indices, or principal component scores (PCS) were derived. Animals with higher PCS1 could be used to improve WW, ADG, KR, GE, and RGR, whereas animals with higher PCS2 scores could be used to improve BW and pre-weaning survival of crossbred kids. The selection of the most appropriate and specific selection index regarding the two groups of traits is determined by the breeding objectives defined for specific genetic improvement program. These selection indices could be used as an alternative approach when economic weights for traits of interests are not available to construct the economic selection index. However, further works should be done on refining the selection indices and validating them in independent datasets.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 4","pages":"e0310841"},"PeriodicalIF":2.9000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961013/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS ONE","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1371/journal.pone.0310841","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The optimal strategy for genetic selection is a selection index based on economic weight; however, in developing countries where economic weight estimation is not always evident and easy for breeders due to a lack of economic data. Thus, this study aimed to construct selection indices for crossbred goats, which could be used as an alternative to economic selection index and to explore the relationship among economically important traits. The data set contained records of birth weight (BW), weaning weight (WW), pre-weaning weight gain (ADG), pre-weaning Kleiber ratio (KR), pre-weaning relative growth rate (RGR), pre-weaning growth efficiency (GE), and pre-weaning survival (RR) of crossbred goats. Genetic parameter estimates were obtained using a single-trait animal model. General linear model, principal component analysis, and cluster procedures of SAS were also used for data analysis. Kid survival was negatively correlated with all investigated traits except BW. Traits such as KR, GE, RGR, WW, and ADG were highly and positively correlated. According to the Kaiser method, two principal components were selected from seven investigated traits. The first principal component (PC1) explained 57.71%, and the second principal component (PC2) explained 14.57% of the estimated breeding value variance, totaling 72.28% of the total genetic additive variance. PC1 explained most of the direct additive genetic variation and correlated with the estimated breeding value of WW, ADG, KR, GE, and RGR, whereas PC2 was correlated with the estimated breeding value of BW and RR. Besides, the cluster analysis categorized seven traits into two major groups. The first group includes BW and RR, whereas traits such as WW, ADG, KR, GE, and RGR were included in the second group. Therefore, two based selection indices, or principal component scores (PCS) were derived. Animals with higher PCS1 could be used to improve WW, ADG, KR, GE, and RGR, whereas animals with higher PCS2 scores could be used to improve BW and pre-weaning survival of crossbred kids. The selection of the most appropriate and specific selection index regarding the two groups of traits is determined by the breeding objectives defined for specific genetic improvement program. These selection indices could be used as an alternative approach when economic weights for traits of interests are not available to construct the economic selection index. However, further works should be done on refining the selection indices and validating them in independent datasets.
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