Accessibility navigation


Genetic dissection of heterosis using epistatic association mapping in a partial NCII mating design

Wen, J., Zhao, X., Wu, G., Xiang, D., Liu, Q., Bu, S.-H., Yi, C., Song, Q., Dunwell, J. M. ORCID: https://orcid.org/0000-0003-2147-665X, Tu, J., Zhang, T. and Zhang, Y.-M. (2015) Genetic dissection of heterosis using epistatic association mapping in a partial NCII mating design. Scientific Reports, 5. 18376. ISSN 2045-2322

[img]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

699kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1038/srep18376

Abstract/Summary

Heterosis refers to the phenomenon in which an F1 hybrid exhibits enhanced growth or agronomic performance. However, previous theoretical studies on heterosis have been based on bi-parental segregating populations instead of F1 hybrids. To understand the genetic basis of heterosis, here we used a subset of F1 hybrids, named a partial North Carolina II design, to perform association mapping for dependent variables: original trait value, general combining ability (GCA), specific combining ability (SCA) and mid-parental heterosis (MPH). Our models jointly fitted all the additive, dominance and epistatic effects. The analyses resulted in several important findings: 1) Main components are additive and additive-by-additive effects for GCA and dominance-related effects for SCA and MPH, and additive-by-dominant effect for MPH was partly identified as additive effect; 2) the ranking of factors affecting heterosis was dominance > dominance-by-dominance > over-dominance > complete dominance; and 3) increasing the proportion of F1 hybrids in the population could significantly increase the power to detect dominance-related effects, and slightly reduce the power to detect additive and additive-by-additive effects. Analyses of cotton and rapeseed datasets showed that more additive-by-additive QTL were detected from GCA than from trait phenotype, and fewer QTL were from MPH than from other dependent variables.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Centre for Food Security
Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
ID Code:49598
Publisher:Nature Publishing Group

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation