Research linking brain volume variations to genetic risk factors for Parkinson's disease and ADHD

Unprecedented genomic analysis links variations in brain structure to genetic risk for Parkinson's disease and ADHD, offering new treatment possibilities.

study: Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores that account for variation among ancestry. Image credit: Marcin Janiec/Shutterstock.com

In a recent study published in natural geneticsA group of researchers identified genetic loci associated with intracranial volume (ICV) (the total space within the skull that contains the brain, cerebrospinal fluid, and blood) and subcortical brain volume.

It investigated its predictive value across ancestry and its association with neurodevelopmental and neuropsychiatric disorders.

background

Subcortical brain structures are critical in psychiatric, neurological, and developmental disorders, affecting key functions such as learning, memory, and motor control. ICV is also associated with neuropsychiatric characteristics.

Genome-wide association studies (GWAS) have revealed common genetic links between brain structure and behavioral traits. However, further research is needed to identify more genetic variants and clarify their role in brain structure and disease across diverse populations.

About research

This study was based on a meta-analysis of previously published data, all of which were approved by the local Institutional Review Board. We analyzed subcortical brain volume and ICV using various statistical methods, including linear regression, mixed-effects models, and GWAS.

Data used were from Enhancing Neuroimaging Genetics with Meta-Analysis (ENIGMA), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), UK Biobank, and Adolescent Brain Cognitive Development (ABCD). Obtained from research. All of these provided high quality genetic and neuroimaging data.

In total, GWAS data from 74,898 participants of European descent were analyzed to investigate the genetic architecture of nine subcortical brain structures, including the nucleus accumbens, brainstem, and amygdala.

Quality control procedures were applied to ensure data accuracy and variables such as gender, age, and brain volume were adjusted to account for differences between cohorts.

A subsample was created from the UK Biobank cohort for further GWAS analysis to enhance replication and validate the results. Additionally, we performed a sensitivity analysis to compare results with and without correction for ICV.

Functional annotation and gene prioritization analysis was performed using multimarker analysis of genome annotation (MAGMA) and transcriptome-wide association studies (TWAS) to identify genetic variations associated with brain volume.

Research results

GWAS identified 529 significant loci (P < 5 × 10^-8) are associated with ICV or subcortical brain volume, and 254 of these loci are independent and unique across brain structures. The brainstem showed the most genetic associations, while the amygdala had the least.

Heritability estimates based on single nucleotide polymorphisms (SNPs) show that common genetic variation explains a significant portion of the phenotypic variation in these brain volumes, from 17% in the amygdala to 35% in the brainstem. has become clear.

The linkage disequilibrium score regression intercept is close to 1, suggesting that polygenicity rather than population stratification is responsible for the rise and inflation of lambda in the quantile plot.

In a sensitivity analysis in the UK Biobank cohort, we examined subcortical brain volume without adjusting for ICV. The direction and magnitude of SNP effect sizes were consistent in both studies, with Pearson correlations ranging from 0.81 to 0.92.

Additionally, subsamples from the UK Biobank showed reproducibility of GWAS results for intracranial and subcortical brain volumes, with correlations between effect sizes in the two subsamples ranging from 0.67 to 0.84.

Functional annotation and gene prioritization were performed using MAGMA with several genes associated with multiple brain volumes, including forkhead box O3 (FOXO3) and geminin coiled-coil domain containing (GMNC). was executed. Genes from the Homeobox (HOX), Paired Box (PAX), and Wingless/Integrated signaling pathway (WNT) gene families were particularly associated with the ventral diencephalon, brainstem, and ICV.

Furthermore, genes involved in intracellular signaling and brain aging processes, such as oxidative resistance, autophagy, and apoptosis, were found to be involved in multiple subcortical brain volumes.

Integration of expression quantitative trait locus (eQTL) data from the Genotype Tissue Expression (GTEx) project corroborates these findings and shows that the corticotropin-releasing hormone receptor 1 (CRHR1), microtubule-associated protein tau ( Genes such as MAPT) and nucleoporin 43 (NUP43) have been identified that function as important regulators of brain volume variation.

Polygenic scores for brain volume were predictable across different ancestries, including European and non-European populations. Polygenic predictions were most accurate for participants of European ancestry, but explained significant variance in non-European ancestry groups as well.

Genetic correlations between brain volume and complex human phenotypes such as Parkinson's disease and attention-deficit hyperactivity disorder (ADHD) have been identified. Larger putamen volume was associated with a higher risk of Parkinson's disease, while larger intracranial volume was associated with a lower likelihood of ADHD and insomnia.

conclusion

In summary, the largest GWAS meta-analysis of intracranial and subcortical brain volumes was conducted using an international dataset from 19 countries. More than 254 independent genetic variants were identified as being associated with these brain volumes, including 161 new findings.

These mutations affect structures such as the brainstem, hippocampus, and amygdala. This study reproduced 39% of previously reported loci and provided insight into genes that specifically influence individual brain volume. Functional annotation and gene prioritization (including TWAS and single cell) ribonucleic acid (RNA)-seq integration reveals important pathways involved in brain development.

Polygenic scores predicted variation in brain volume across diverse ancestries and advanced our understanding of brain structural genetics.