Identifying causal genes for migraine by integrating the proteome and transcriptome.

While previous genome-wide association studies (GWAS) have identified multiple risk variants for migraine, there is a lack of evidence about how these variants contribute to the development of migraine. We employed an integrative pipeline to efficiently transform genetic associations to identify causal genes for migraine. We conducted a proteome-wide association study (PWAS) by combining data from the migraine GWAS data with proteomic data from the human brain and plasma to identify proteins that may play a role in the risk of developing migraine. We also combined data from GWAS of migraine with a novel joint-tissue imputation (JTI) prediction model of 17 migraine-related human tissues to conduct transcriptome-wide association studies (TWAS) together with the fine mapping method FOCUS to identify disease-associated genes. We identified 13 genes in the human brain and plasma proteome that modulate migraine risk by regulating protein abundance. In addition, 62 associated genes not reported in previous migraine TWAS studies were identified by our analysis of migraine using TWAS and fine mapping. Five genes including ICA1L, TREX1, STAT6, UFL1, and B3GNT8 showed significant associations with migraine at both the proteome and transcriptome, these genes are mainly expressed in ependymal cells, neurons, and glial cells, and are potential target genes for prevention of neuronal signaling and inflammatory responses in the pathogenesis of migraine. Our proteomic and transcriptome findings have identified disease-associated genes that may give new insights into the pathogenesis and potential therapeutic targets for migraine.

Li SJ ，Shi JJ ，Mao CY ，Zhang C ，Xu YF ，Fan Y ，Hu ZW ，Yu WK ，Hao XY ，Li MJ ，Li JD ，Ma DR ，Guo MN ，Zuo CY ，Liang YY ，Xu YM ，Wu J ，Sun SL ，Wang YG ，Shi CH ... -  《-》

Joint analysis of proteome, transcriptome, and multi-trait analysis to identify novel Parkinson's disease risk genes.

Shi JJ ，Mao CY ，Guo YZ ，Fan Y ，Hao XY ，Li SJ ，Tian J ，Hu ZW ，Li MJ ，Li JD ，Ma DR ，Guo MN ，Zuo CY ，Liang YY ，Xu YM ，Yang J ，Shi CH ... -  《Aging-US》

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

Hu T ，Parrish RL ，Dai Q ，Buchman AS ，Tasaki S ，Bennett DA ，Seyfried NT ，Epstein MP ，Yang J ... -  《-》

Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes.

Previous genome-wide association studies (GWAS) have identified numerous risk genes for lacunar stroke, but it is challenging to decipher how they confer risk for the disease. We employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for lacunar stroke. We systematically integrated lacunar stroke genome-wide association study (GWAS) (N=7338) with human brain proteomes (N=376) to perform proteome-wide association studies (PWAS), Mendelian randomization (MR), and Bayesian colocalization. We also used an independent human brain proteomic dataset (N=152) to annotate the new genes. We found that the protein abundance of seven genes (ICA1L, CAND2, ALDH2, MADD, MRVI1, CSPG4, and PTPN11) in the brain was associated with lacunar stroke. These seven genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and astrocytes. Three genes (ICA1L, CAND2, ALDH2) were causal in lacunar stroke (P < 0.05/proteins identified for PWAS; posterior probability of hypothesis 4 ≥ 75 % for Bayesian colocalization), and they were linked with lacunar stroke in confirmatory PWAS and independent MR. We also found that ICA1L is related to lacunar stroke at the brain transcriptome level. Our present proteomic findings have identified ICA1L, CAND2, and ALDH2 as compelling genes that may give key hints for future functional research and possible therapeutic targets for lacunar stroke.

Zhang C ，Qin F ，Li X ，Du X ，Li T ... -  《BMC Medicine》

Identifying novel risk genes in intracranial aneurysm by integrating human proteomes and genetics.

Genome-wide association studies (GWAS) have become increasingly popular for detecting numerous loci associated with intracranial aneurysm (IA), but how these loci function remains unclear. In this study, we employed an integrative analytical pipeline to efficiently transform genetic associations and identify novel genes for IA. Using multidimensional high-throughput data, we integrated proteome-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR) and Bayesian co-localization analyses to prioritize genes that can increase IA risk by altering their expression and protein abundances in the brain and blood. Moreover, single-cell RNA sequencing (scRNA-seq) of the circle of Willis was performed to enrich filtered genes in cells, and gene set enrichment analysis (GSEA) was conducted for each gene using bulk RNA-seq data for IA. No significant genes with cis-regulated plasma protein levels were proven to be associated with IA. The protein abundances of five genes in the brain were found to be associated with IA. According to cellular enrichment analysis, these five genes were expressed mainly in the endothelium, fibroblasts and vascular smooth muscle cells. Only three genes, CNNM2, GPRIN3 and UFL1, passed MR and Bayesian co-localization analyses. While UFL1 was not validated in confirmation PWAS as it was not profiled, it was validated in TWAS. GSEA suggested these three genes are associated with the cell cycle. In addition, the protein abundance of CNNM2 was found to be associated with IA rupture (based on PWAS, MR and co-localization analyses). Our findings indicated that CNNM2, GPRIN3 and UFL1 (CNNM2 correlated with IA rupture) are potential IA risk genes that may provide a broad hint for future research on possible mechanisms and therapeutic targets for IA.

Wu C ，Liu H ，Zuo Q ，Jiang A ，Wang C ，Lv N ，Lin R ，Wang Y ，Zong K ，Wei Y ，Huang Q ，Li Q ，Yang P ，Zhao R ，Liu J ... -  《-》