Abstract
Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell’s method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.
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Acknowledgements
We thank all the study subjects for volunteering to participate in the study. We thank Genetic Factors for Osteoporosis Consortium provided the BMD replication dataset to download for us. Also, we thank Shin SY et al. to provide metabolomics GWAS results for download.
Funding
This study was supported in part by Natural Science Foundation of China (NSFC; 81570807, 30900810, 31271344 and 31071097), Hunan Provincial Construct Program of the Key Discipline in Ecology (0713) and the Cooperative Innovation Center of Engineering and New Products for Developmental Biology of Hunan Province (20134486). The investigators of this work were also partially supported by grants from the NIH (R01 AR069055, U19 AG055373, R01 MH104680, R01 AR059781 and P20 GM109036), and the Edward G. Schlieder Endowment as well as the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University.
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Meng, XH., Shen, H., Chen, XD. et al. Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies. Hum Genet 137, 247–255 (2018). https://doi.org/10.1007/s00439-018-1876-1
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DOI: https://doi.org/10.1007/s00439-018-1876-1