Using High-Throughput Animal or Cell-Based Models to Functionally Characterize GWAS Signals

Abstract

Purpose of ReviewThe advent of genome-wide association studies (GWASs) constituted a breakthrough in our understanding of the genetic architecture of multifactorial diseases. For Alzheimer’s disease (AD), more than 20 risk loci have been identified. However, we are now facing three new challenges: (i) identifying the functional SNP or SNPs in each locus, (ii) identifying the causal gene(s) in each locus, and (iii) understanding these genes’ contribution to pathogenesis.Recent FindingsTo address these issues and thus functionally characterize GWAS signals, a number of high-throughput strategies have been implemented in cell-based and whole-animal models. Here, we review high-throughput screening, high-content screening, and the use of the Drosophila model (primarily with reference to AD).SummaryWe describe how these strategies have been successfully used to functionally characterize the genes in GWAS-defined risk loci. In the future, these strategies should help to translate GWAS data into knowledge and treatments.

Novel hypotheses emerging from GWAS in migraine?

“…Although hypothesis-free methods such as GWAS and sequencing studies are meant to provide the roadmap towards core pathophysiology of a disease, they rely on direct and well-designed ‘wet-lab’ functional follow-ups to nail down the key molecular mechanisms. As the type of assay, whether animal- or cell-based, needed for a functional follow-up very much depends on the actual variant and the gene it affects, it is not possible to give specific directions on how to go about for a particular variant (for recent reviews on technical possibilities see [25, 26]).…”

“…It is useful to keep in mind that a GWAS SNP only ‘tags’ the disease locus, implying that the identified SNP is only correlated — because of linkage disequilibrium — with the disease-causing variant, which is not ‘the end of the road’ as far as understanding the functional consequences. Efforts at combining information across phenotypes either directly at the summary statistic phase [29] or by comparative analysis of correlated phenotypes [30] as well as increasing the size of the migraine GWAS itself (leading to more implicated loci) will yield improvements on the locus side of the analysis; concurrently, considerable efforts are being focused on improving the quality of the next layer of information, which links SNPs to function, through various -omics studies assaying the genome in general [26, 31], and the improvement of these resources and better methodology will increase the statistical power on the post-hoc side of the analysis. However, it is crucial to realise that rapid progress can also be made in migraine specifically by targeted follow-ups (such as for the rs9349379/EDN1/ET-1 study) [23], given that we now have a set of well-characterised loci waiting for such detailed characterisation.…”

“…One major challenge will be to elucidate the functional consequences of the associated SNPs and identify how they may affect migraine risk at the individual level. Efforts to functionally characterise GWAS signals, for other diseases than migraine, have been considering high-throughput cell-based (e.g., induced pluripotent stem cells [iPSCs]) and animal models (e.g., Drosophila) [26]. Considerable amount of research is needed before migraine GWAS findings will show diagnostic or prognostic value and lead to the development of (personalised) treatment options.…”

Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

“…According to the World Alzheimer Report, 818 billion dollars were allocated worldwide in 2015 to support AD patients [ 2 ]. Concerning AD etiology, some high-impact risk factors were identified as genetics [ 3 - 6 ], age [ 7 - 9 ], cardiovascular [ 10 , 11 ], obesity [ 12 , 13 ] or lifestyle [ 12 ].…”

“…Rare forms of early-onset familial AD (EO-FAD) are induced by gene mutations, especially in APP, presenilin 1 (PSEN1) and presenilin 2 (PSEN 2) genes [ 42 - 44 ]. Approximately, 300 mutations occurring in PSEN1 or PSEN2 have been reported in the Dementia Mutation Database [ 6 , 45 ]. The majority of these mutations were observed in PSEN1 and over 230 mutations were reported as pathogenic in Alzforum database [ 7 , 45 ].…”