Mobile Menu

Human brain-derived proteome data helps elucidate a genetic causal pathway for neurological phenotypes

A team of researchers have analysed human proteome data from brain tissue to study whether targeted proteins predictively mediated the effects of genetic variants on seven neurological problems including Alzheimer’s disease, depression, insomnia, intelligence, neuroticism, amyotrophic lateral sclerosis, and depression.

The researchers of the study applied Mendelian randomisation (MR) analysis across the genome and found 43 effects between genetically predicted proteins from the dorsolateral prefrontal cortex and the above seven neurological outcomes. They found that the same causal variant at 12 of the loci was responsible for variation in both protein and neurological phenotypes because of genetic co-localisation.

They also conducted a phenome-wide MR study for each of the 12 genes to assess the pleiotropic effects on 700 complex traits and diseases. They found that genes that have been previously associated with increased risk of Alzheimer’s disease, may also influence other complex traits and diseases, such as the gene SNX32 and the potential likelihood for low levels of HDL cholesterol and high body fat percentage. However, further evaluations would be necessary to identify whether increased genetic liability toward Alzheimer’s disease risk is responsible for these additional predicted effects. It’s also possible that genetic variation at this locus may influence outcomes separately via horizontal pleiotropy, which could make these genes a less attractive target.

The same study also found other genes, such as CTSH, which is also associated with Alzheimer disease, and SARM1 which may make better targets because they do not have genetically predicted effects on any of the other phenotypes.

The findings may help to elucidate a causal pathway for the neurological phenotypes and help to prioritise targets for therapeutic intervention, but follow up studies with large-scale molecular data sets would be needed to develop insights into the mechanisms between the genetic variants and complex traits.