Although everything is a gradient, complex genetic diseases (e.g. obesity, type-2 diabetes, schizophrenia, and cancer) and quantitative traits (e.g. height, body mass index, and I.Q.) usually differ from monogenic traits and diseases (e.g. phenylketonuria, sickle-cell anemia, and tyrosinemia). Traits (phenotypes) such as drug efficacy and response to environmental toxicants likewise exhibit a gradient; many are similar to complex diseases, whereas some appear more like monogenic (caused by single gene) or oligogenic (caused by a small number of genes) traits. Moreover, all these phenotypes are influenced/modified by variations in multiple other genes and environmental factors –– explaining why (for example) “onset” and “severity” –– in two patients having the same single-nucleotide variant (SNV) –– can be so different.
Genome-wide association studies (GWAS) since ~2oo6 have successfully identified thousands of genomic regions associated with hundreds of complex traits and diseases. GWAS publications typically report “association results” as a list of loci –– distinguished from one another, for counting purposes, and labeled with an SNV and one or more (nearest-neighbor) gene names as signposts. The gene names make referring to loci easier than using genome positions or variant labels, although the genes named in GWAS reports have varying amounts of evidence supporting any role or function contributing to that trait or disease. Early GWAS were performed with less-densely-spaced sets of SNVs, so the reported variant might not have been the strongest associated variant at a locus. More recent GWAS, and GWAS meta-analyses, are much larger with sample sizes (some cohorts now approaching one million for some traits), and, although GWAS have often been performed in a single population, a growing number of trans-ancestry studies are able to combine data across populations. For most identified loci, however, the molecular and biological mechanisms remain to be determined.
Authors [see attached excellent review] analyze thoroughly the emerging complexities of molecular mechanisms at GWAS loci. Authors ask three major questions: [a] How many association signals exist at a locus? [b] What are the candidate causal variant(s)? [c] What are the target gene(s)? In each section, they provide historical context to the question, methods available for addressing it, and evidence and observations from examples of GWAS loci that have been mechanistically characterized to date. The complexity of mechanisms at GWAS loci — (including multiple signals, multiple variants, and/or multiple genes) — is discussed. Identifying mechanisms responsible for GWAS loci requires an accumulation of consistent evidence for the genes and variants that influence the trait or disease in humans. Authors conclude with future directions for researchers to consider in experimental design and interpretation of GWAS locus mechanisms.
Am J Hum Genet Nov 2o18; 103, 637–653