Image_1_Enhanced Identification of Novel Potential Variants for Appendicular Lean Mass by Leveraging Pleiotropy With Bone Mineral Density.pdf
Strong relationships have been found between appendicular lean mass (ALM) and bone mineral density (BMD). It may be due to a shared genetic basis, termed pleiotropy. By leveraging the pleiotropy with BMD, the aim of this study was to detect more potential genetic variants for ALM. Using the conditional false discovery rate (cFDR) methodology, a combined analysis of the summary statistics of two large independent genome wide association studies (GWAS) of ALM (n = 73,420) and BMD (n = 10,414) was conducted. Strong pleiotropic enrichment and 26 novel potential pleiotropic SNPs were found for ALM and BMD. We identified 156 SNPs for ALM (cFDR <0.05), of which 74 were replicates of previous GWASs and 82 were novel SNPs potentially-associated with ALM. Eleven genes annotated by 31 novel SNPs (13 pleiotropic and 18 ALM specific) were partially validated in a gene expression assay. Functional enrichment analysis indicated that genes corresponding to the novel potential SNPs were enriched in GO terms and/or KEGG pathways that played important roles in muscle development and/or BMD metabolism (adjP <0.05). In protein–protein interaction analysis, rich interactions were demonstrated among the proteins produced by the corresponding genes. In conclusion, the present study, as in other recent studies we have conducted, demonstrated superior efficiency and reliability of the cFDR methodology for enhanced detection of trait-associated genetic variants. Our findings shed novel insight into the genetic variability of ALM in addition to the shared genetic basis underlying ALM and BMD.
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References
- https://doi.org//10.1093/gerona/glu010
- https://doi.org//10.1111/j.1532-5415.2004.52014.x
- https://doi.org//10.1359/jbmr.1997.12.12.2076
- https://doi.org//10.1038/oby.2005.42
- https://doi.org//10.1016/j.ajhg.2009.02.004
- https://doi.org//10.1007/s00439-012-1236-5
- https://doi.org//10.1111/acel.12228
- https://doi.org//10.1093/ajcn/nqy272
- https://doi.org//10.1038/s41467-017-00031-7
- https://doi.org//10.1038/s42003-020-01334-0
- https://doi.org//10.1359/jbmr.070606
- https://doi.org//10.1210/er.2009-0044
- https://doi.org//10.1007/s00223-017-0314-z
- https://doi.org//10.1016/bs.pmbts.2017.11.026
- https://doi.org//10.1002/jcp.28207
- https://doi.org//10.1038/s41467-017-00108-3
- https://doi.org//10.1371/journal.pgen.1003455
- https://doi.org//10.1016/j.bone.2017.03.052
- https://doi.org//10.1016/j.bone.2017.06.016
- https://doi.org//10.1111/1753-0407.12510
- https://doi.org//10.1016/j.bone.2009.10.005
- https://doi.org//10.1016/S0166-4328(01)00297-2
- https://doi.org//10.1093/bioinformatics/btn564
- https://doi.org//10.1152/japplphysiol.00435.2011
- https://doi.org//10.1016/j.bone.2009.11.007
- https://doi.org//10.1093/nar/gkt439
- https://doi.org//10.1371/journal.pone.0021800
- https://doi.org//10.2174/1381612823666161123150032
- https://doi.org//10.1016/j.tem.2012.03.008
- https://doi.org//10.1016/j.biocel.2013.05.036
- https://doi.org//10.1128/MCB.01180-14
- https://doi.org//10.1371/journal.pone.0147388
- https://doi.org//10.1002/jcb.25164
- https://doi.org//10.1038/ng.446
- https://doi.org//10.1016/j.jbspin.2004.10.008
- https://doi.org//10.1006/geno.2000.6492
- https://doi.org//10.1042/BST20160169
- https://doi.org//10.3233/JND-150076
- https://doi.org//10.1016/j.cell.2015.05.029
- https://doi.org//10.1038/s41598-018-31853-0
- https://doi.org//10.1371/journal.pone.0200785
- https://doi.org//10.1371/journal.pgen.1003247
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- Transplantation Immunology
- Tumour Immunology
- Immunology not elsewhere classified
- Immunology
- Veterinary Immunology
- Animal Immunology
- Genetic Immunology
- Applied Immunology (incl. Antibody Engineering, Xenotransplantation and T-cell Therapies)
- Autoimmunity
- Cellular Immunology
- Humoural Immunology and Immunochemistry
- Immunogenetics (incl. Genetic Immunology)
- Innate Immunity