Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, USA.
Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
Eur J Hum Genet. 2018 Sep;26(9):1369-1377. doi: 10.1038/s41431-018-0168-5. Epub 2018 Jul 2.
A recent genome-wide association study (GWAS) of central obesity identified 27 loci, from sex-combined analysis, associated with waist-to-hip ratio adjusted for body-mass index (WHRadjBMI) in European-ancestry individuals. Nevertheless, the identified variants may not be the biological causal ones due to the presence of linkage disequilibrium (LD). To better understand the mechanisms underlying the identified loci from the GWAS meta-analysis, we first imputed summary statistics at GWAS loci to increase genetic resolution, and then we applied a Bayesian statistical fine-mapping method through PAINTOR, incorporating LD structure and functional annotations to select and prioritize the most plausible causal variants across WHRadjBMI-associated regions. Using adipose tissue- and cell-specific annotations that showed significant associations with WHRadjBMI, we identified 33 single-nucleotide polymorphisms (SNPs) from 27 sex-combined fine-mapping loci with posterior probability of causality greater than 0.9. Six of the selected 33 SNPs belong to at least one of the top five identified annotations. SNPs rs1440372 (SMAD6) and rs12608504 (JUND) are particularly important since they not only have associated functional annotations but are also GWA hits in the original study. Incorporation of functional annotations helps identify additional plausible causal variants, such as rs2213731 (DNM3-PIGC) and rs4531856 (JUND), that did not reach genome-wide significance in GWAS. Our results provide promising candidates for future functional validation experiments.
最近一项针对中心性肥胖的全基因组关联研究（GWAS）通过对性别的综合分析，鉴定出 27 个与调整体质量指数后的腰围臀围比（WHRadjBMI）相关的位点，这些位点存在于欧洲血统个体中。然而，由于连锁不平衡（LD）的存在，所鉴定的变体可能不是生物学上的因果变体。为了更好地理解 GWAS 荟萃分析中鉴定出的位点的机制，我们首先将 GWAS 位点的汇总统计数据进行了推断，以提高遗传分辨率，然后通过 PAINTOR 应用了贝叶斯统计精细映射方法，该方法结合了 LD 结构和功能注释，以选择和优先考虑与 WHRadjBMI 相关区域最合理的因果变体。使用与 WHRadjBMI 显著相关的脂肪组织和细胞特异性注释，我们从 27 个性别综合精细映射位点中鉴定出 33 个单核苷酸多态性（SNP），这些 SNP 的因果后验概率大于 0.9。在所选择的 33 个 SNP 中，有 6 个属于至少一个前五个鉴定出的注释之一。选择的 33 个 SNP 中有 6 个属于至少一个前五个鉴定出的注释之一。选择的 33 个 SNP 中有 6 个属于至少一个前五个鉴定出的注释之一。SNP rs1440372（SMAD6）和 rs12608504（JUND）尤为重要，因为它们不仅具有相关的功能注释，而且还是原始研究中的 GWAS 命中。功能注释的纳入有助于鉴定其他可能的因果变体，例如 rs2213731（DNM3-PIGC）和 rs4531856（JUND），这些变体在 GWAS 中未达到全基因组显著水平。我们的结果为未来的功能验证实验提供了有希望的候选者。
