The genetic architecture (defined as the underlying genetic basis of any disease-related or quantitative trait and its variability seen in any population studied) of human traits have traditionally been classified into two major categories: complex traits arising from many low-effect common single-nucleotide variants (SNVs); and rare traits arising from high-effect monogenic SNVs. The underlying distinction between these two categories has historically been based on the presence of high-penetrant, rare, single-gene disruptive mutations causing recognizable clinical syndromes/monogenic diseases (e.g. cystic fibrosis, phenylketonuria) compared to the relative absence of such mutations in complex diseases (such as type-2 diabetes and schizophrenia). Another way to put it: clinical syndromes/monogenic diseases typically can often be diagnosed by a DNA mutation in the coding region (whole-exome sequencing) whereas complex diseases can often be “associated” with SNVs more often in noncoding regions (i.e. best discovered by whole-genome sequencing).
As these GEITP pages have often discussed, “response to a drug” or “response to any environmental agent” should be considered as having a genetic architecture and, in virtually no way different from, any disease-related phenotypic or quantitative trait.
Accumulating evidence now suggests that these two classes of phenotypes might not be so biologically distinct as previously thought, i.e. all traits likely exist as gradients (no surprise there). Thus, multiple exceptions to the ‘‘common disease, common variant’’ hypothesis have been identified for complex traits, and Mendelian disorders have also been found to be affected by multiple or common genetic variants. This suggests that there exists a spectrum of genetic architectures rather than a dichotomous (black vs white) classification.
Authors [see attached article] quantified the overlap of genes identified through large-scale genome-wide association studies (GWAS) for 62 complex traits, and diseases that have been correlated with genes having mutations known to cause 20 broad categories of Mendelian disorders. Authors identified a significant enrichment of genes linked to phenotypically-matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically-matched or related pairs (N = 50 of 92; 54%) than phenotypically unmatched pairs (N = 27 of 1,148; 2%) demonstrated significant overlap –– confirming a phenotype-specific enrichment pattern. Furthermore, they observed elevated GWAS effect-sizes near genes linked to phenotypically-matched Mendelian disorders. Authors also showed examples of GWAS variants (SNVs) localized at the transcription start site (or physically interacting with the promoters of genes linked to phenotypically-matched Mendelian disorders, which would lead to lowered expression ot that gene).
These data are consistent with the hypothesis that –– genes that are disrupted in Mendelian disorders are dysregulated by noncoding SNVs in complex traits. Moreover, the results demonstrate how leveraging findings from related Mendelian disorders, and functional genomic datasets, can prioritize genes that are putatively dysregulated by local and distal (cis and trans) noncoding SNVs.
Am J Hum Genet 4 Oct 2o18; 103: 535–552