全基因组广度关联研究(GWAS)分析脑白质纤维束完整性遗传关联

Genome-wide association study(GWAS)for detecting genetic associations of brain white matter tract integrity

目的对比单变量与多变量全基因组广度关联研究(GWAS)分析脑白质纤维束完整性遗传关联的效能。方法纳入英国生物样本库中34041名受试者的弥散张量成像和遗传学资料,以多变量综合统计检验分析软件对48个脑白质纤维束各向异性分数表型进行单变量和多变量GWAS分析,比较其检出独立关联信号及基因座的能力,并对新关联位点进行功能注释和富集分析。结果多变量GWAS检出462个独立关联信号和331个独立关联基因座(P<5.0×10^(-8)),明显超过单变量GWAS(130个独立关联信号和100个独立关联基因座,P<1.04×10^(-9))。单变量GWAS(P<1.04×10^(-9))检出的95个独立关联信号和68个独立关联基因座(P<5.0×10^(-8))均经多变量GWAS验证,其中23个独立关联信号和22个独立关联基因座为单变量GWAS所独有。多变量GWAS比单变量GWAS多检出331个特有的独立关联信号和254个特有的独立关联基因座。单变量与多变量GWAS共发现233个新独立关联信号和174个新独立关联基因座;功能注释及富集分析结果表明,上述新关联位点与神经发育及神经精神疾病等密切相关。结论多变量GWAS检出遗传-影像学关联的能力明显优于单变量GWAS;联合应用二者可检出更多与神经影像表型相关的遗传位点。

Objective To comparatively explore the abilities of univariate and multivariate genome-wide association studies(GWAS)for detecting genetic associations of brain white matter tract integrity.Methods Diffusion tensor imaging(DTI)and genetic data of 34041 subjects were selected from the UK Biobank.Using the software of multivariate omnibus statistical test,univariate and multivariate GWAS were conducted for 48 white matter tract fractional anisotropy phenotypes.The ability for detecting independent genetic association signals and loci was compared between these two methods,and then functional annotation and enrichment analysis were performed on new significant signals.Results Multivariate GWAS detected 462 independent association signals and 331 independent association loci(P<5.0×10^(-8)),much more than those detected with univariate GWAS(130 independent association signals and 100 independent association loci,P<1.04×10^(-9)).Totally 95 independent association signals and 68 independent association loci detected with univariate GWAS(P<1.04×10^(-9))were proved with multivariate GWAS(P<5.0×10^(-8)),including 23 independent association signals and 22 independent association loci being unique to univariate GWAS.Compared with univariate GWAS,multivariate GWAS detected 331 unique independent association signals and 254 unique independent association loci.A total of 233 new independent association signals and 174 new independent association loci were detected with univariate and multivariate GWAS.Functional annotation and enrichment analysis results indicated that the new significant signals were closely related to neurodevelopment and neuropsychiatric diseases.Conclusion The ability of multivariate GWAS for detecting genetic associations of neuroimaging phenotypes was better than that of univariate GWAS.Combining these two methods might discover more genetic variants associated with neuroimaging phenotypes.