Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation.

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

Association of GWAS identified INSR variants (rs2059807 & rs1799817) with polycystic ovarian syndrome in Indian women.

Polycystic ovary syndrome (PCOS), a gynaecological endocrine disorder affects 9% of Indian women and is linked to type II diabetes. The association of INSR (INSulin Receptor gene) variants (rs2059807 and rs1799817) with PCOS was established through genome-wide association studies, yet requires validation for the Indian population. This case-control study included 253 PCOS women and 308 age-matched control. The minor allele frequency of rs2059807 had an odds ratio of 13.5 and that of rs1799817 was 11.8. The cohort with rs2059807 MAF presented elevated levels of luteinising hormone [PCOS vs Control: 6.32 ± 2.26 mIU/mL vs 4.97 ± 3.27 mIU/mL], estradiol [116.01 ± 60.63 pg/mL vs 65.04 ± 44.98 pg/mL], and decreased HDL - C [50.4 ± 11.59 mg/dL vs 64 ± 15.49 mg/dL] showing disturbances in the hormonal patterns. The rs1799817 polymorphism cohort had elevated levels of serum insulin [17.99 ± 11.6 mIU/mL vs 11.67 ± 6.63 mIU/mL], blood glucose [199.15 ± 63.72 mg/dL vs 96.6 ± 24.3 mg/dL], and testosterone [0.91 ± 0.2 nmol/L vs 0.53 ± 0.16 nmol/L] thereby triggering metabolic dysfunction and predisposed to lifestyle disorder. Also, the SNPs were found to be in linkage equilibrium and contributed to the development of PCOS differentially.