From variants to mechanisms: Neurogenomics in the post-GWAS era.

Scientists have found thousands of genetic differences linked to autism and other brain conditions, but they still don't understand how these genetic differences actually cause problems. This review discusses new methods to figure out how genes work together to cause autism and similar conditions. The goal is to better understand what goes wrong in the brain so treatments can be developed.

This review examines the challenges of translating genetic discoveries from genome-wide association studies (GWAS) into understanding the biological mechanisms underlying neuropsychiatric disorders, including autism spectrum disorder. While GWAS have identified thousands of genetic variants associated with these conditions, determining how these variants actually cause disease remains difficult. The authors discuss the complex polygenic nature of these disorders, where multiple genetic variants contribute to risk, and most variants are located in non-coding DNA regions. They outline approaches using functional genomics, machine learning, and experimental methods to identify relevant cell types, map how variants affect genes, and find common pathways where multiple genetic effects converge.

This research framework may eventually lead to better understanding of autism's biological mechanisms and identification of therapeutic targets. However, translating genetic findings into clinical applications remains challenging and requires continued methodological advances.

As a review article, this study does not present original research data. The abstract does not specify methodological limitations of the approaches discussed or provide quantitative assessments of the techniques reviewed.

Genome-wide association studies (GWASs) have identified thousands of variants associated with neuropsychiatric disorders (NPDs), including autism spectrum disorder (ASD), schizophrenia (SCZ), and Alzheimer's disease (AD). However, deciphering the "causal" biological mechanisms and pathways through which these variants act remains a major obstacle that hinders translational understanding of NPD pathogenesis. NPDs are highly polygenic with contributions from pleiotropic variants across the allelic spectrum, most of which reside within large haplotype blocks in non-coding regions of the genome. Successful mechanistic insight requires identifying disease-relevant cell types and states, mapping variant-to-gene effects, and integrating findings across loci, at scale, to pinpoint pathways of polygenic convergence.

Here, we discuss functional genomic, machine learning, and experimental approaches to address each step of this daunting challenge. Ultimately, the convergence of results-across methodologies and within key underlying disease pathways-will be essential to realizing the promise of clinical translation for common, complex brain disorders.