KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/76536
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Majumdar Sayanti Guha | en_US |
dc.contributor.author | Rai Anil | en_US |
dc.contributor.author | Dwijesh Chandra Mishra | en_US |
dc.date.accessioned | 2023-03-02T04:08:27Z | - |
dc.date.available | 2023-03-02T04:08:27Z | - |
dc.date.issued | 2019-10-21 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/76536 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Genomic selection (GS) has been used globally for increasing agricultural production and productivity. It has been used for complex quantitative traits by selecting breeding material after predicting Genomic Estimated Breeding Values (GEBVs) of target species. The accuracy of GS for estimation of GEBVs depends on various factors including sampling population, genetic architecture of target species, statistical models, etc. The feature (marker) selection is one of the important steps in development of GS models. There are large numbers of models proposed in the literature for GS. However, applicability of these models is based on many factors including extent of additive and epistatic effects of breeding population. Therefore, there is strong need to evaluate the performance of these models and techniques of feature selection under different situations. In this study, performance of linear/additive effect models, viz. linear least squared regression, BLUP, LASSO, ridge regression, SpAM as well as non-linear/epistatic effect models, viz. mRMR, HSIC LASSO have been evaluated through a simulation study in R platform. In general, performance of SpAM was found to be superior for GS than all other models considered in this study in case of presence of additive effect and absence of epistatic effect. However, in case of low heritability and high epistatic effect the HSIC LASSO outperformed all models. This study will assist researcher in selection of appropriate feature selection technique for a given situation. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | BLUP, Genomic Selection, LASSO, mRMR, QTL, Regression, SpAM | en_US |
dc.title | Identification of genetic markers for increasing agricultural productivity: An empirical study | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Indian Journal of Agricultural Sciences | en_US |
dc.publication.volumeno | 89 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/94633/37971 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 6.37 | en_US |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
There are no files associated with this item.
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.