Abstract
Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (31471188, 81573241, and 31511140285); China Postdoctoral Science Foundation (2016M602797, 2016T90902); Natural Science Basic Research Program Shaanxi Province (2016JQ3026); and the Fundamental Research Funds for the Central Universities. The study was also funded by the Grants from National Institutes of Health (P50AR055081, R01AG026564, R01AR050496, and R01AR057049).
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Yao, S., Guo, Y., Dong, SS. et al. Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis. Hum Genet 136, 963–974 (2017). https://doi.org/10.1007/s00439-017-1825-4
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DOI: https://doi.org/10.1007/s00439-017-1825-4