Zeng Yanni, Navarro Pau, Fernandez-Pujals Ana M, Hall Lynsey S, Clarke Toni-Kim, Thomson Pippa A, Smith Blair H, Hocking Lynne J, Padmanabhan Sandosh, Hayward Caroline, MacIntyre Donald J, Wray Naomi R, Deary Ian J, Porteous David J, Haley Chris S, McIntosh Andrew M
Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
MRC Human Genetics Unit, University of Edinburgh, Edinburgh, United Kingdom.
Biol Psychiatry. 2017 Feb 15;81(4):336-346. doi: 10.1016/j.biopsych.2016.04.017. Epub 2016 May 2.
Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants, in particular biological pathways, may be more powerful. Regional heritability analysis can be used to detect genomic regions that contribute to disease risk.
We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested.
In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model.
These post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies.
重度抑郁症(MDD)的全基因组关联研究(GWAS)发现的显著关联较少。检测遗传变异的聚集情况,尤其是生物通路,可能更具效力。区域遗传力分析可用于检测对疾病风险有贡献的基因组区域。
我们在一个旨在识别与MDD相关通路的流程中整合了通路分析和多级区域遗传力分析。该流程应用于两个独立的GWAS样本[苏格兰世代研究:苏格兰家庭健康研究(GS:SFHS,N = 6455)和精神疾病基因组学联盟(PGC:MDD)(N = 18,759)]。创建了一个由与MDD最一致相关的通路中的单核苷酸多态性组成的多基因风险评分(PRS),并使用曲线下面积、逻辑回归和线性混合模型分析测试了其预测MDD的准确性。
在GS:SFHS中,四条通路与MDD显著相关,其中两条解释了大量的通路水平区域遗传力。在PGC:MDD中一条通路与MDD显著相关。在PGC:MDD的一个子集中,该通路的通路水平区域遗传力显著。对于两个样本,区域遗传力进一步定位到基因和子区域水平。在两个样本中,NETRIN1信号通路与MDD的关联最为一致。当使用曲线下面积统计、逻辑回归和线性混合模型时,该通路的PRS与全基因组PRS相比显示出有竞争力的预测准确性。
这些GWAS后分析突出了在多个GWAS数据上结合多种方法以识别MDD风险通路的价值。NETRIN1信号通路被确定为MDD的候选通路,应在进一步的大规模人群研究中进行探索。