Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology

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

Please see our End User Agreement.

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

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Wang, S.-B., Feng, J.-Y., Ren, W.-L., Huang, B., Zhou, L., Wen, Y.-J., Zhang, J., Dunwell, J. M. orcid id iconORCID: https://orcid.org/0000-0003-2147-665X, Xu, S. and Zhang, Y.-M. (2016) Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Scientific Reports, 6. 19444. ISSN 2045-2322 doi: 10.1038/srep19444

Abstract/Summary

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/52425
Identification Number/DOI 10.1038/srep19444
Refereed Yes
Divisions Interdisciplinary centres and themes > Centre for Food Security
Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
Publisher Nature Publishing Group
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

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

Search Google Scholar