Xu Hanfei, Gupta Shreyash, Dinsmore Ian, Kollu Abbey, Cawley Anne Marie, Anwar Mohammad Y, Chen Hung-Hsin, Petty Lauren E, Seshadri Sudha, Graff Misa, Below Piper, Brody Jennifer A, Chittoor Geetha, Fisher-Hoch Susan P, Heard-Costa Nancy L, Levy Daniel, Lin Honghuang, Loos Ruth Jf, Mccormick Joseph B, Rotter Jerome I, Mirshahi Tooraj, Still Christopher D, Destefano Anita, Cupples L Adrienne, Mohlke Karen L, North Kari E, Justice Anne E, Liu Ching-Ti
Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA.
Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA.
medRxiv. 2024 Jun 12:2024.06.11.24308730. doi: 10.1101/2024.06.11.24308730.
Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (P and P, respectively) and then a correlated meta-analysis between the full summary data sets (P). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (, , , , , , and ) in five association regions. Using an independent sample of blood gene expression data, we validated results for and . We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (P<0.05 & P<P & P<P) results for in nucleus accumbens and , , , , and in liver. The identified genes help link the genetic variation at obesity risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.
全基因组关联研究（GWAS）已经确定了众多体重指数（BMI）位点。然而，从风险位点到BMI的大多数潜在机制仍不清楚。通过整合分析利用组学数据可以提供关于BMI生物途径更全面的观点。我们分析了来自弗雷明汉心脏研究（FHS）的多达5619个样本的基因型和血液基因表达数据。利用97个BMI位点的3992个单核苷酸多态性（SNP）和1 Mb内的20692个转录本，我们分别进行了转录本与BMI以及SNP与转录本的关联分析（分别为P和P），然后对完整汇总数据集进行了相关的荟萃分析（P）。我们确定了在每个组学中达到Bonferroni校正显著性、在相关荟萃分析中比每个组学更显著且在FHS数据中至少与BMI名义上相关的转录本。在308个显著的SNP - 转录本 - BMI关联中，我们在五个关联区域鉴定出七个基因（， ， ， ， ， ，和 ）。利用血液基因表达数据的独立样本，我们验证了 和 的结果。我们测试了这些关联在下丘脑、伏隔核和肝脏中的普遍性，并且在伏隔核中观察到 显著（P<0.05 & P<P & P<P）的结果，在肝脏中观察到 、 、 、 和 显著（P<0.05 & P<P & P<P）的结果。所鉴定的基因有助于将肥胖风险位点的遗传变异与生物学机制和健康结果联系起来，从而将GWAS的发现转化为功能。
