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International Textbook of Diabetes Mellitus, 4th Ed., Excerpt #143: The Genetics of Type 2 Diabetes Part 5

Sep 18, 2018

The missing heritability

In spite of the large number of risk variants identified, it is estimated that they explain less than 15% of the heritability of T2DM. The unexplained heritability is an intensely discussed topic in complex genetics, some claiming it as a failure of GWA studies.There are many possible explanations for the missing heritability, including assumptions made about the genetic architecture of the disease and the definitions of heritability.The estimations of heritability explained assumes that only additive affects determine disease risk and that the risk follows the liability threshold model, that is, the genetic and environmental effects sum up to form a normal distribution of liability and that disease arises in individuals surpassing a certain threshold in the distribution [58]. If these assumptions are not true, the estimate of heritability explained will not be correct. However, there are also many other potential explanations for the missing heritability: yet unmapped common variants, distorted parent-of-origin transmission of risk alleles, rare variants, structural polymorphisms (e.g. copy number variations), gene–gene and/or gene–environment interactions (in which epigenetic effects may be important).


The genetic architecture of T2DM

There is an ongoing debate about the genetic architecture underlying common complex diseases. Some argue that disease risk is determined by a large number of very small additive effects from relatively common variants and that disease represents the extremes of a normal distribution [59] while others argue that complex diseases are rather collections of

phenocopies caused by rare, often recent, mutations [60]. One argument against common variants is that they would have been removed from the population by natural selection [8]; however, this is not a valid argument for a disease like T2DM where the penetrance of the genetic effect depends strongly on interactions with the environment, especially since this environment has changed in recent years and the genetic risk variants could have been neutral or even beneficial before the introduction of the Westernized lifestyle. Applying an approach that considers all SNPs in a GWAS could in fact explain a much larger proportion of the heritability (>50%) supporting the existence of numerous yet unidentified loci with smaller effects [13,61]. However, one should keep in mind that heritability can only be estimated from the most recent generations for which information on affection status is available, whereas most of the variants studied thus far are ancestral variants hundreds of generations old. We do not know whether these ancestral variants (which have modest effects and have escaped purifying selection) can really explain the diabetes epidemic we see in the most recent generations or whether this can be ascribed to rare variants with stronger effects. The truth may thus rather be a combination of the suggested models, at least for T2DM.

The genetic architecture could also vary within the T2DM patient group since the diagnosis may include cases of disease caused by rare, or even unique, variants with high penetrance, in parallel or combination with cases pushed over the disease threshold by their load of common risk alleles. Allelic heterogeneity can also be expected at any single disease locus, that is, there may be multiple, different susceptibility mutations at the locus conferring risk in different individuals and both common and rare variants could contribute to disease susceptibility similar to what has been found at the HNF1A and HNF4A MODY loci. Structural polymorphisms and microRNAs add a further layer of complexity and have not yet been exhaustively studied.

The rapid development of next generation sequencing tools has markedly facilitated discovery of rare variants. From being both extremely expensive and effort consuming, sequencing of the whole genome with appropriate coverage (approx. 30 times) can now be performed for <$1000 in a few days.Whole-exome sequencing can be performed for less than $400 per sample and these figures are likely to decrease over the coming years. Applying these methods in families and large population studies will hopefully answer the questions about the role of rare variants in complex diseases.

Most studies to date have been limited to SNP leaving structural polymorphisms relatively unexplored. However, since common structural variants are likely to be tagged by surrounding SNPs they are unlikely to explain a large proportion of missing heritability. A recent study [62] identified a common copy number variation (CNV), CNVR5583.1 (TSPAN8), as associated with T2DM. This association could be convincingly replicated by previously typed SNPs that tag the CNV [63].

Noncoding RNAs have recently emerged as important regulators of gene expression and function. MicroRNAs (miRNAs) naturally regulate programs of gene expression. Altered miRNA function has been shown to contribute to human disease, and manipulation of specific miRNAs is now being explored as a novel therapeutic modality [64]. The efficiency of miRNAs binding to target transcripts depends on both the sequence and the intramolecular structure of the transcript. SNPs can contribute to alterations in the structure of regions flanking them, thereby influencing the accessibility for miRNA binding. Several studies have implicated miRNAs in diabetes and inflammation and common SNPs change the target sequence of miRNAs in several T2DM susceptibility loci [65,66]. Other forms of noncoding RNAs, such as piRNAs (PIWI-interacting RNAs), snoRNAs (small nucleolar RNAs), lincRNAs (long intergenic noncoding RNAs), and lncRNAs (long noncoding RNAs), may also contribute to the development of diabetes. For example, the CDKN2A/B region on chromosome 9 is associated with T2DM, as well as cardiovascular disease and a number of other disorders. This region harbors an lncRNA, ANRIL (nonprotein coding CDKN2B-AS1 CDKN2B antisense RNA 1), which can potentially modify and explain some of these associations [67].

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