Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

Abstract

Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

Genome-wide association studies

“…Polygenicity. The extreme polygenicity of many traits 8,11,[218][219][220] can pose a challenge when attempting to uncover underlying biological mechanisms, particularly in cases where thousands of variants each have a small effect on a trait 13,221 . To avoid these issues, WES and WGS studies are increasingly being used to discover rare variants of large effect -particularly coding variants from exome sequencing -for which causal mechanisms are generally easier to elucidate 87,[222][223][224] .…”

“…The way gene sets are defined is critical; for example, a randomly chosen set of genes would not be biologically meaningful and sets created based on biological annotations rely on the accuracy of those annotations. We refer readers to a recent resource for defining gene sets 13 . Another approach is to associate genetic variants with molecular changes using trans-molQTL approaches to identify distal genes that are regulated by the GWAS locus.…”

“…For a comprehensive list of genetic data resources, see ref. 13 . GWAS, genome-wide association studies; SNP, single-nucleotide polymorphism.…”

“…Now that reliable genetic associations for various phenotypes are known, we are faced with the next big challenge: interpreting these associations in a biological and genomic context. Previous GWAS have shown that most traits are influenced by thousands of causal variants 11 that individually confer very little risk, are often associated with many other traits 8 and are correlated with causal and non-causal variants that are physically close as a result of linkage disequilibrium 12 , making direct biological, causal inferences complicated 13 . Further, genetic associations may differ across ancestries, complicating direct comparisons between groups of individuals.…”

Perspective: Quality Versus Quantity; Is It Important to Assess the Role of Enhancers in Complex Disease from an In Vivo Perspective?

“…Ever since the sequencing of the human genome and the development of gene chip technologies, GWAS has been able to scan hundreds of thousands of genetic variants in tens or even hundreds of thousands of patients to deliver a greater understanding of the genetic architecture of complex human disease. Thanks to GWAS, the number of genetic loci with significant association with complex disease ( p < 1 × 10 −6 ), known as GWAS “hits”, now number in the thousands [ 1 ]. One promising use of the data generated by these GWAS studies is in the calculation of polygenic risk scores that, by adding together the disease risk of many individual alleles, seek to predict the likelihood of developing a complex disease [ 2 ].…”