Agreement among type 2 diabetes linkage studies but a poor correlation with results from genome-wide association studies

Abstract

Aims/hypothesis Little of the genetic basis for type 2 diabetes has been explained, despite numerous genetic linkage studies and the discovery of multiple genes in genome-wide association (GWA) studies. To begin to resolve the genetic component of this disease, we searched for sites at which genetic results had been corroborated in different studies, in the expectation that replication among studies should direct us to the genomic locations of causative genes with more confidence than the results of individual studies. Methods We have mapped the physical location of results from 83 linkage reports (for type 2 diabetes and diabetes precursor quantitative traits [QTs, e.g. plasma insulin levels]) and recent large GWA reports (for type 2 diabetes) onto the same human genome sequence to identify replicated results in diabetes genetic 'hot spots'. Results Genetic linkage has been found at least ten times at 18 different locations, and at least five times in 56 locations. All replication clusters contained study populations from more than one ethnic background and most contained results for both diabetes and QTs. There is no close relationship between the GWA results and linkage clusters, and the nine best replication clusters have no nearby GWA result. Conclusions/interpretation Many of the genes for type 2 diabetes remain unidentified. This analysis identifies the broad location of yet to be identified genes on 6q, 1q, 18p, 2q, 20q, 17pq, 8p, 19q and 9q. The discrepancy between the linkage and GWA studies may be explained by the presence of multiple, uncommon, mildly deleterious polymorphisms scattered throughout the regulatory and coding regions of genes for type 2 diabetes.

Accounting for a Quantitative Trait Locus for Plasma Triglyceride Levels: Utilization of Variants in Multiple Genes

“…However, in linkage analysis the assumption is that the region segregates with disease, thus if there are multiple causal variants segregating, evidence of linkage will be present but not evidence of association. Previous studies have failed to find strong relationships between linkage peaks and association peaks [10]. Others have speculated that linkage peaks results from multiple variants [41], [45], [46], [47]; our analysis supports this.…”

“…Indeed, recent findings from meta-analyses of genome-wide association studies suggest that even these large-scale efforts so far can only identify variants that together explain 5–15% of the genetic basis of a trait [6], [7], [8], [9]. This suggests that our current analysis approaches and research efforts, including genome-wide association studies, uncover novel genes contributing to disease susceptibility, but still leave large numbers of genes and variants to be uncovered that contribute to the genetic disease risk in the general population [10].…”

Genome-wide searching of rare genetic variants in WTCCC data

“…Simulation studies suggested the current method will have limited power when the number of haplotypes increases and the haplotype frequencies are too rare. Among the genes or regions reaching a p-value <10E-4, linkage evidence has been reported in these genes: PLXNA2 (plexin-A2), TRIP13 (thyroid hormone receptor interactor 13), block (42.75-42.76Mb) on chromosome 15, and block (18.259-18.259Mb) on chromosome 20(Lillioja and Wilton 2009). …”

Haplotype Analysis Improved Evidence for Candidate Genes for Intramuscular Fat Percentage from a Genome Wide Association Study of Cattle

“…One could imagine other tiers, dependent upon whether, for example, the variation deleted genes, altered splicing, transcription, or amino acid substitution, or resulted in purely neutral DNA markers [39], [40]. All of these point to an interesting feature of GWAS, that so far they have identified little of the genetic variance for most traits, accounting for amounts of variance and identities of associations that are inconsistent with previous research [41], [42], [43].…”