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

Wang, S.-B., Feng, J.-Y., Ren, W.-L., Huang, B., Zhou, L., Wen, Y.-J., Zhang, J., Dunwell, J. M. ORCID:, 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

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To link to this item DOI: 10.1038/srep19444


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.

Item Type:Article
Divisions:Interdisciplinary centres and themes > Centre for Food Security
Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
ID Code:52425
Publisher:Nature Publishing Group


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