A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia

Integrating Proteomics and GWAS to Identify Key Tissues and Genes Underlying Human Complex Diseases

BACKGROUND

The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes.

METHODS

We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches-S-LDSC, MAGMA, and DESE-we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn's disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data.

RESULTS

Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42-0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1.

CONCLUSIONS

Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases.

Circulating Blood-Based Proteins in Psychopathology and Cognition: A Mendelian Randomization Study

Importance

Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds.

Objective

To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP).

Design, Setting, and Participants

In a 2-sample MR design, significant proteomic quantitative trait loci were used as candidate instruments, obtained from 2 large-scale plasma proteomics datasets: the UK Biobank Pharma Proteomics Project (2923 proteins per 34 557 UK individuals) and deCODE Genetics (4719 proteins per 35 559 Icelandic individuals). Data analysis was performed from November 2023 to November 2024.

Exposure

Genetic influence on circulating levels of proteins in plasma.

Main Outcomes and Measures

Outcome measures were summary statistics drawn from recent large-scale genome-wide association studies for SCZ (67 323 cases and 93 456 controls), BD (40 463 cases and 313 436 controls), MDD (166 773 cases and 507 679 controls), and CTP (215 333 individuals). MR was carried out for each phenotype, and proteins that showed statistically significant (Bonferroni-corrected P < .05) associations from MR analysis were used for pathway, protein-protein interaction, drug target enrichment, and potential druggability analysis for each outcome phenotype separately.

Results

MR analysis revealed 113 Bonferroni-corrected associations (46 novel) involving 91 proteins across the 4 outcome phenotypes. Immune-related proteins, such as interleukins and complement factors, showed pleiotropic effects across multiple outcome phenotypes. Drug target enrichment analysis provided support for repurposing of anti-inflammatory agents for SCZ, amantadine for BD, retinoic acid for MDD, and duloxetine for CTP.

Conclusions and Relevance

Identifying potentially causal effects of circulating proteins on neuropsychiatric phenotypes suggests potential biomarkers and offers insights for the development of innovative therapeutic strategies. The study also reveals pleiotropic effects of many proteins across different phenotypes, indicating shared etiology among serious psychiatric conditions and cognition.

Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders

BACKGROUND

Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear.

METHODS

We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach.

RESULTS

Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb.

CONCLUSIONS

Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations.

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance

Background

Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance.

Methods

We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes.

Results

MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance.

Conclusions

Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.

Towards Genetic Dissection of Skeletal Class III Malocclusion: A Review of Genetic Variations Underlying the Phenotype in Humans and Future Directions

INTRODUCTION

Skeletal abnormalities and malocclusions have varied features that impact populations globally, impairing aesthetics and lowering life quality. The prevalence of the Skeletal Class III disease is the lowest among all angle malocclusions, with varied prevalence across nations. Environmental, genetic, and societal factors play a role in its numerous etiologies. In this study, we conducted a thorough search across the published data relating to quantitative trait loci (QTL) and the genes associated with Class III progression in humans, discussed these findings and their limitations, and proposed future directions and strategies for studying this phenotype.

METHODS

An inclusive search of published papers in the PubMed and Google Scholar search engines using the following terms: 1. Human skeletal Class III; 2. Genetics of Human skeletal Class III; 3. QTL mapping and gene associated with human skeletal Class III; 4. enriched skeletal Class-III-malocclusion-associated pathways.

RESULTS

Our search has found 53 genes linked with skeletal Class III malocclusion reported in humans, genes associated with epigenetics and phenomena, and the top 20 enriched pathways associated with skeletal Class III malocclusion.

CONCLUSIONS

The human investigations yielded some contentious conclusions. We conducted a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNA-seq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro- and small-RNA, and long non-coding RNA analysis in tissues connected to skeletal Class III malocclusion phenotype in tissues connected with the skeletal phenotype. Finally, we invite regional, national, and international orthodontists and surgeons to join this effort by contributing human samples with skeletal Class III malocclusion following the accepted Helsinki ethical protocol to challenge these phenomena jointly.

Dissecting the Complexity of Skeletal-Malocclusion-Associated Phenotypes: Mouse for the Rescue

Skeletal deformities and malocclusions being heterogeneous traits, affect populations worldwide, resulting in compromised esthetics and function and reduced quality of life. Skeletal Class III prevalence is the least common of all angle malocclusion classes, with a frequency of 7.2%, while Class II prevalence is approximately 27% on average, varying in different countries and between ethnic groups. Orthodontic malocclusions and skeletal deformities have multiple etiologies, often affected and underlined by environmental, genetic and social aspects. Here, we have conducted a comprehensive search throughout the published data until the time of writing this review for already reported quantitative trait loci (QTL) and genes associated with the development of skeletal deformation-associated phenotypes in different mouse models. Our search has found 72 significant QTL associated with the size of the mandible, the character, shape, centroid size and facial shape in mouse models. We propose that using the collaborative cross (CC), a highly diverse mouse reference genetic population, may offer a novel venue for identifying genetic factors as a cause for skeletal deformations, which may help to better understand Class III malocclusion-associated phenotype development in mice, which can be subsequently translated to humans. We suggest that by performing a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with skeletal deformation and Class III malocclusion characterization/phenotypes, including mandibular basic bone, gum, and jaw, in the CC mouse population, we expect to better identify genetic factors and better understand the development of this disease.