Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes

Abstract

Background Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes. Methods Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets. Results In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases. Conclusions Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.

Effect of Genetic Factors, Age and Sex on Levels of Circulating Extracellular Vesicles and Platelets

“…Thus, EVs can be treated like any other component measured in blood, such as neurofilaments or fatty acids, whose increasingly relevant roles in disease susceptibility may provide therapeutic targets or prognostic/diagnostic markers [ 47 , 48 , 49 ]. EV assessment in large cohorts could further analyze their involvement in complex diseases through the colocalization of GWAS signals for EV levels and diseases and Mendelian randomization [ 50 ].…”

Alzheimer's polygenic risk scores are associated with cognitive phenotypes in Down syndrome

“…This observation is in line with previous studies, [41][42][43] It is well known and recognized in the field that DS is caused by a trisomy of chromosome 21, which includes APP, one of the three genes that cause ADAD. [27][28][29][30][44][45][46] In addition, there is considerable heterogeneity among the DS population in ID and the onset of memory decline and cognition, suggesting that other genetic factors beyond the APP may be involved. The latest GWAS for LOAD identified more than 74 loci associated with AD, and the genomics and pathway analyses indicate that those loci are enriched for immune and inflammatory genes.…”

“…NFL levels were measured using the SomaLogic platform in all the studies (Table S2). To ensure comparability among the different cohorts and avoid introducing batch effects, we harmonized the CSF values across cohorts as reported previously 27–30,34,35 . Briefly, duplicate samples and those with missing biomarker levels were removed.…”

Unique genetic architecture of CSF and brain metabolites pinpoints the novel targets for the traits of human wellness

“…To infer causal metabolites, we performed MR excluding highly pleiotropic regions (associated with > 5 metabolites) 44,45 . In CSF, we identi ed 38 metabolites causal for 22 traits after FDR correction (78 pairs; Supplementary Table 18).…”

“…For phenotypes les that lack effect standard error information, we used "se.from.p" r package to infer standard error. In the main analysis, we excluded variants within FADS genes region and the highly pleiotropic regions associated with at least ve 44,45 CSF or brain metabolites identi ed from MGWAS. For the stringent analysis, we excluded variants within FADS gene region and all pleiotropic regions associated with at least two CSF or brain metabolites identi ed from MGWAS.…”