Abstract
The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer’s Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.
Highlights
The diagnosis of chronic brain disorders at the present day is primarily dependent on clinical symptom assessments, which suffers from the drawback of subjectivity, symptom heterogeneity, and disease comorbidity [1]
By restricting at the genome-wide significance threshold (p < 0.05/n, where n denoted the number of tested molecules of the corresponding omics), we identified 1386 blood-based molecular markers, including
524 RNAs, 807 methylation sites, 29 proteins, seven cytokines, and 19 metabolites, which had a significant association with brain disorders (Tables S1–S5)
Summary
The diagnosis of chronic brain disorders at the present day is primarily dependent on clinical symptom assessments, which suffers from the drawback of subjectivity, symptom heterogeneity, and disease comorbidity [1]. To overcome these difficulties and aid early intervention of brain disorders, researchers have made considerable efforts to find objective diagnostic and predictive biomarkers [2]. Peripheral blood molecules, such as RNA [3], methylation site [4], and proteins [5] have gained specific attention due to the high feasibility and relatively low costs. Overfitting is one of the main challenges: a transcriptomewide analysis typically has more than 20,000 RNAs detected, whereas the sample size of test subjects is usually limited to no more than a few hundred due to the labor cost burden
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