Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Inclusion of Significant Markers Identified With GWAS Into Prediction Models

Version 1 : Received: 1 May 2023 / Approved: 2 May 2023 / Online: 2 May 2023 (02:33:16 CEST)

How to cite: Ayegbidun, O. Inclusion of Significant Markers Identified With GWAS Into Prediction Models. Preprints 2023, 2023050043. https://doi.org/10.20944/preprints202305.0043.v1 Ayegbidun, O. Inclusion of Significant Markers Identified With GWAS Into Prediction Models. Preprints 2023, 2023050043. https://doi.org/10.20944/preprints202305.0043.v1

Abstract

Advancement in biotechnology and genomics research have promoted access to DNA markers and their use in breeding programs. Genome-wide association study (GWAS), Genomic selection (GS) and Marker-Assisted Selection (MAS) are some of the applications of DNA markers in plant breeding. Researchers have suggested combining these individual applications for better selection accuracies. This study examines the potential advantages of incorporating GWAS-results into MAS and GS as well as the validity of the different methods for combining these approaches. From this study, it was concluded that number of QTNs have greater effects on prediction accuracies compared to heritability estimates. Also, the increase in prediction accuracy from the invalid method of incorporating GWAS results into GS and MAS model is similar to results recorded with using the valid approach. However, greater difference may be observed in another scenario which can lead to spurious results when used to make breeding decisions.

Keywords

Genome-Wide Association Study; Genomic Selection, Marker-Assisted Selection

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Comments (0)

Comment 1
Received: 2 May 2023
Commenter: Oloorire
The commenter has declared there is no conflict of interests.
Comment: Good effort there! Writing is one of the skills PhD will make you learn “by force”.
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