SMR

Overview

About

The SMR software tool was originally developed to implement the SMR & HEIDI methods to test for pleiotropic association between the expression level of a gene and a complex trait of interest using summary-level data from GWAS and expression quantitative trait loci (eQTL) studies (Zhu et al. 2016 Nature Genetics). The SMR & HEIDI methodology can be interpreted as an analysis to test if the effect size of a SNP on the phenotype is mediated by gene expression. This tool can therefore be used to prioritize genes underlying GWAS hits for follow-up functional studies. The methods are applicable to all kinds of molecular QTL (xQTL) data, including DNA methylation QTL (mQTL) and protein abundance QTL (pQTL).

The SMR tool has subsequently been extended to include more analytical methods including SMR-multi (an extension of the SMR test to use multiple cis-xQTL SNPs; Wu et al. 2018 Nature Communications) and MeCS (a method for meta-analysis of xQTL data sets accounting for correlations among data sets; Qi et al. 2018 Nature Communications).

Credits

Futao Zhang developed original version of the the software tool and webpages with supports from Zhili Zheng, Zhihong Zhu, Ting Qi, Yang Wu, and Jian Yang.

Zhihong Zhu and Jian Yang developed the SMR and HEIDI methods.

Ting Qi and Jian Yang developed the MeCS method.

Junren Hou is currently maintaining the software.

Questions and Help Requests

Bug reports or questions to Jian Yang ([email protected]) at School of Life Sciences, Westlake University.

Citations

SMR & HEIDI methods and software tool

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM & Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature Genetics, 48:481-487.

Multi-SNP-based SMR method and omic-data-based SMR analysis

Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, Lloyd-Jones LR, Marioni RE, Martin NG, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF & Yang J (2018) Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nature Communications, 9: 918.

MeCS method

Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, Zhu Z, Kemper K, Yengo L, Zheng Z, eQTLGen Consortium, Marioni RE, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF & Yang J (2018) Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nature Communications, 9: 2282.

网址：SMR https://mxgxt.com/news/view/940307

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