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.
Original language | English |
---|---|
Pages (from-to) | 41-53 |
Number of pages | 13 |
Journal | Biological Psychiatry |
Volume | 89 |
Issue number | 1 |
Early online date | 27 May 2020 |
DOIs | |
Publication status | Published - 1 Jan 2021 |
Funding
This work was funded by The Netherlands Organization for Scientific Research (Grant No. NWO VICI 435–14–005 [to DP]) and NWO Gravitation: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (Grant No. 024.004.012 [to DP]), a European Research Council advanced grant (Grant No, ERC-2018-AdG GWAS2FUNC 834057 [to DP]), and a VU University research fellowship (to EU).
Funders | Funder number |
---|---|
Horizon 2020 Framework Programme | 834057 |
European Research Council | |
Vrije Universiteit Amsterdam | |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | NWO VICI 435–14–005, 024.004.012 |
Keywords
- Functional genomics
- Gene-set analysis
- Genome-wide association study
- molQTL
- Pathway analysis
- Polygenicity