Neeraj Budhlakoti, Anil Rai, D. Mishra, S. Jaggi, Mukesh Kumar, A. Rao
{"title":"不同遗传结构下非参数基因组选择方法的比较研究","authors":"Neeraj Budhlakoti, Anil Rai, D. Mishra, S. Jaggi, Mukesh Kumar, A. Rao","doi":"10.31742/ijgpb.80.4.4","DOIUrl":null,"url":null,"abstract":"Genomic Selection (GS) is the most prevalent method in today’s scenario to access the genetic merit of individual under study. It selects the candidates for next breeding cycle on the basis of its genetic merit. GS has successfully been used in various plant and animal studies in last decade. Several parametric statistical models have been proposed and being used successfully in various GS studies. However, performance of parametric methods becomes very poor when we have non additive kind of genetic architecture. In such cases, generally performance of non-parametric methods are quite satisfactory as these methods do not require strict statistical assumptions. This article presents comparative performance of few most commonly used non-parametric methods for complex genetic architecture i.e. non-additive, using simulated dataset generated at different level of heritability and varying combination of population size. Among several non-parametric methods, SVM outperformed across a range of genetic architecture.","PeriodicalId":13321,"journal":{"name":"Indian Journal of Genetics and Plant Breeding","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparative study of different non-parametric genomic selection methods under diverse genetic architecture\",\"authors\":\"Neeraj Budhlakoti, Anil Rai, D. Mishra, S. Jaggi, Mukesh Kumar, A. Rao\",\"doi\":\"10.31742/ijgpb.80.4.4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genomic Selection (GS) is the most prevalent method in today’s scenario to access the genetic merit of individual under study. It selects the candidates for next breeding cycle on the basis of its genetic merit. GS has successfully been used in various plant and animal studies in last decade. Several parametric statistical models have been proposed and being used successfully in various GS studies. However, performance of parametric methods becomes very poor when we have non additive kind of genetic architecture. In such cases, generally performance of non-parametric methods are quite satisfactory as these methods do not require strict statistical assumptions. This article presents comparative performance of few most commonly used non-parametric methods for complex genetic architecture i.e. non-additive, using simulated dataset generated at different level of heritability and varying combination of population size. Among several non-parametric methods, SVM outperformed across a range of genetic architecture.\",\"PeriodicalId\":13321,\"journal\":{\"name\":\"Indian Journal of Genetics and Plant Breeding\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2020-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian Journal of Genetics and Plant Breeding\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.31742/ijgpb.80.4.4\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Genetics and Plant Breeding","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.31742/ijgpb.80.4.4","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Comparative study of different non-parametric genomic selection methods under diverse genetic architecture
Genomic Selection (GS) is the most prevalent method in today’s scenario to access the genetic merit of individual under study. It selects the candidates for next breeding cycle on the basis of its genetic merit. GS has successfully been used in various plant and animal studies in last decade. Several parametric statistical models have been proposed and being used successfully in various GS studies. However, performance of parametric methods becomes very poor when we have non additive kind of genetic architecture. In such cases, generally performance of non-parametric methods are quite satisfactory as these methods do not require strict statistical assumptions. This article presents comparative performance of few most commonly used non-parametric methods for complex genetic architecture i.e. non-additive, using simulated dataset generated at different level of heritability and varying combination of population size. Among several non-parametric methods, SVM outperformed across a range of genetic architecture.
期刊介绍:
Advance the cause of genetics and plant breeding and to encourage and promote study and research in these disciplines in the service of agriculture; to disseminate the knowledge of genetics and plant breeding; provide facilities for association and conference among students of genetics and plant breeding and for encouragement of close relationship between them and those in the related sciences; advocate policies in the interest of the nation in the field of genetics and plant breeding, and facilitate international cooperation in the field of genetics and plant breeding.