A Statistical Approach to Fine Mapping for the Identification of Potential Causal Variants Related to Bone Mineral Density.

Although genomewide association studies (GWASs) have been able to successfully identify dozens of genetic loci associated with bone mineral density (BMD) and osteoporosis-related traits, very few of these loci have been confirmed to be causal. This is because in a given genetic region there may exist many trait-associated SNPs that are highly correlated. Although this correlation is useful for discovering novel associations, the high degree of linkage disequilibrium that persists throughout the genome presents a major challenge to discern which among these correlated variants has a direct effect on the trait. In this study we apply a recently developed Bayesian fine-mapping method, PAINTOR, to determine the SNPs that have the highest probability of causality for femoral neck (FNK) BMD and lumbar spine (LS) BMD. The advantage of this method is that it allows for the incorporation of information about GWAS summary statistics, linkage disequilibrium, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. We present a list of the top 10 candidate SNPs for each BMD trait to be followed up in future functional validation experiments. The SNPs rs2566752 (WLS) and rs436792 (ZNF621 and CTNNB1) are particularly noteworthy because they have more than 90% probability to be causal for both FNK and LS BMD. Using this statistical fine-mapping approach we expect to gain a better understanding of the genetic determinants contributing to BMD at multiple skeletal sites.