Prioritising Causal Genes at Type 2 Diabetes Risk Loci

In GWAS, the variant most strongly associated with disease risk is reported for each locus, though such ‘lead SNPs’ may only serve as surrogate markers for other genetic perturbations that directly contribute to disease pathology. Identifying the true causal variants can provide a direct functional link between genotype and the observed disease phenotype, especially in cases where the variant is protein altering. To identify a causal variant, or a set of likely causal variants, several strategies have been developed, including fine-mapping of disease-associated regions, experimental prioritisation, and in silico prediction tools.

Fine-mapping of a locus involves analysing SNPs in a defined region of the genome for disease association and is used to refine a GWAS association signal from the surrogate lead SNP to the actual causal variant(s). The SNPs are assayed by deep sequencing, or custom array-genotyping based on GWAS variants and imputation from extensive sequencing efforts such as the 1000 Genomes Project [14, 15]. To achieve sufficient statistical power to detect the association of the true causal variant, large sample sizes are required and the studies often include populations drawn from diverse ancestries to exploit differences in LD patterns [16].

Even so, most fine-mapping efforts uncover a large number of variants that, between them, are likely to be driving a particular association signal—a so-called credible set. In some exceptional cases, however, it is possible to narrow down the credible set to a single variant, as is the case for the melatonin receptor 1B gene (MTNR1B) [17•]. The MTNR1B locus has previously been implicated in T2D risk and the identification of the single causal variant revealed a likely, direct functional link to the causal gene [18]. The risk allele creates a binding site for the transcription factor NEUROD1 and is associated with preferential binding in human pancreatic beta cells. This additional transcription factor binding event also implicates increased FOXA2-bound enhancer activity and MTNR1B expression.

Another way to approach the search for causal variants at GWAS loci is by experimentally testing prioritised SNPs. This strategy was, for example, pursued at the JAZF1 and CDC123/CAMK1D loci [19‐21]. Variants in high LD (r ² > 0.8) with the lead GWAS SNP were selected for functional analysis based on maps of open chromatin. Effects on gene expression were tested in luciferase reporter assays, and DNA binding capability was analysed through electrophoretic mobility shift assays. The identified potential causal variants at the JAZF1 and CDC123/CAMK1D loci appear to act as part of cis-regulatory modules (CRMs). These specific regions harbour combinatorial transcription factor binding sites (TFBS), and the variants affect binding of PDX1 and FOXA1/FOXA2, respectively. However, due to practical limitations, this type of experimental studies mostly analyses a subset of regional variants, opening up the possibility of missing potential true causal variants. Further, the evidence generated is only circumstantial, since establishing functionality is necessary but not sufficient to prove causation. The emergence of new experimental lines of evidence may affect the prioritisation of the true causal variants and should ideally involve integration of different types of analyses (see section on “Integrative approach”).

To overcome the practical limitations of functional approaches for identifying causal variants, in silico prediction tools offer an alternative method based on specific assumptions regarding their properties. A recent study, for example, leveraged phylogenetic conservation of TFBS within CRMs to predict causal variants at the PPARG and FTO T2D risk loci [22, 23•]. This computational approach, termed phylogenetic module complexity analysis (PMCA), identified a clustering of homeobox TFBS at T2D risk loci, and initially proposed a potential causal variant at the PPARG locus, which allowed for a subsequent functional interpretation [22]. The risk allele at PPARG2 leads to enhanced binding of the repressive homeobox transcription factor PRRX1, and thus reduced PPARG2 expression, defective lipid handling, and insulin sensitivity. PMCA was also successfully applied to identify the causal variant and a potential disease mechanism at the obesity-associated FTO locus, a region showing the strongest genetic association in GWAS for obesity and body mass index traits [24, 25]. The proposed causal allele was shown to alter an ARID5B repressor motif, leading to activation of the distant IRX3 and IRX5 in adipocyte precursor cells, and pro-obesity consequences for adipocyte thermogenesis regulation [23•]. This work also highlights the additional complexity arising from having multiple causal genes for disease-associated haplotypes. Though post-GWAS efforts have tended to focus on the idea of a single causal gene per locus, causal variant(s) may influence any number of regional genes, and not necessarily in the same manner across different contexts.

An important aspect of the prioritisation of causal genes and variants at GWAS loci is to consider the appropriate tissue(s) and developmental stage(s), which allow any functional follow-up studies to be performed in a disease-relevant model. As the majority of T2D association signals are located in non-coding regions and exert regulatory effects, their influence on gene expression may be subject to context-specific activity [26]. Thus, studies analysing the implicated variants and genes need to consider the surrounding genomic context and expression patterns. A notable example is provided by work on the PTF1A gene, where a disease-relevant model, human pancreatic progenitor cells, was critical to elucidating a mechanism for isolated pancreatic agenesis [27•]. The identified mutations were found to disrupt an enhancer region that is selectively active in pancreatic progenitor cells and, importantly, show no activity in corresponding adult cell lines.

Recent GWAS endeavours have shifted attention towards exome-arrays and exome-sequencing to enable identification of rare and low-frequency variants with potentially larger effect sizes—and a more direct biological interpretation—than common variants [7••, 28‐30]. Missense variants in coding regions have a protein-altering effect that can directly pinpoint causal genes, offering the possibility of a straightforward and rapid translation into the clinic (Fig. 1).

The importance of coding variants for ascertaining causal mechanisms is illustrated by SLC30A8, which encodes a zinc transporter (ZnT8) that is active in the secretory vesicles of beta cells. SLC30A8 was initially identified as a T2D susceptibility gene harbouring a common missense variant [2]. Contradictory to the supposed negative impact of this risk allele, recent efforts to identify protein-truncating variants leading to loss of function (LOF) in T2D genes discovered several rare protein-truncating variants in SLC30A8 [31•]. Strikingly, the haploinsufficiency conferred by this class of variants was found to be associated with a 65% reduction in T2D risk. By discovering multiple independent coding variants at this GWAS locus, SLC30A8 has been validated with high confidence as the causal gene. Furthermore, this study highlights the importance of discovering an extended allelic series to understand functional mechanisms. More broadly, it has established reduced activity of ZnT8 as a protective disease mechanism in T2D and a potential treatment strategy based on antagonism [32].

The power to detect causal genes through coding variants can be further harnessed by performing genetic association studies in isolated populations. These populations, founded by a bottleneck event, show a higher degree of LD, less genetic complexity, and higher allelic frequencies due to genetic drift, which leads to fixation or extinction of specific alleles over time [33]. Furthermore, these studies also benefit from shared non-genetic backgrounds (e.g. common lifestyle and cultural habits), which is a potential confounding factor in larger outbred populations [34]. Exploiting these advantages of studies in isolated populations, a nonsense coding variant in TBC1D4 was discovered in the Greenlandic population with the largest effect size for a common T2D risk allele (odds ratio = 10.3) [35•]. The variant disrupts the full-length isoform of TBC1D4, which is selectively expressed in skeletal muscle, thus exerting its influence on T2D risk through insulin resistance.

Another recent study leveraging the advantages of isolated populations detected a low-frequency coding variant in AKT2 in the Finnish population [36]. The allele confers T2D risk through increased fasting plasma insulin levels and expands the allelic spectrum from the previously known rare variants in AKT2 that cause monogenic heterogeneous glycaemic diseases [37, 38]. Collectively, these studies illustrate the importance of identifying coding variants—in isolated and outbred populations—for straightforward translation into molecular mechanisms. While harnessing coding variation can offer powerful insights into causal mechanism, this approach is fundamentally limited by the occurrence of natural variation (in outbred and isolated populations) which necessitates ever-larger association studies to detect rare, coding variation. In addition, identification of a coding signal is not a guarantee for causality, and conditional analysis is often required to estimate the likelihood of a given variant being causal [39]. By design, exome-based studies analyse coding regions only, and thus require additional fine-mapping of non-coding regions to exclude the contribution of non-coding variants as drivers of the association signal.

In contrast to missense coding variants, associating GWAS signals in non-coding regions with their downstream causal gene is often a more complex challenge. To identify regulatory effects, non-coding variants can be correlated with genomic annotations to establish a functional link with their target gene (Fig. 1). Expression quantitative trait loci (eQTLs), for example, describe variants that influence gene expression in close proximity (cis-eQTL) or over a long distance (trans-eQTL), and provide an approach for directly linking a GWAS variant to its causal gene through effects on expression levels [40]. Crucial for the success of eQTL studies is the interrogation of the correct disease-relevant context(s), since gene expression is often regulated in a cell-type specific manner [41].

For T2D, a large number of disease risk loci have been found through physiological studies to affect insulin processing or secretion in the beta cell, highlighting pancreatic islets as a relevant starting point for annotation studies at these loci [42]. Up to now, islet sample availability has been limiting for large-scale studies, thereby reducing statistical power to detect associations. Nonetheless, recent studies succeeded in mapping islet cis-eQTLs and overlapping these with variants driving T2D association signals [43, 44•]. One such coincident locus is ZMIZ1, harbouring a gene that had been sparsely characterised for its role in T2D risk [44•]. A recent study confirmed ZMIZ1 as the likely causal gene at this T2D risk locus, and functional follow-up work has established a role in beta cell function for insulin secretion and exocytosis, thus giving first insights into a potential mechanism [44•, 45].

Tissue availability has so far prevented any progress in finding islet trans-eQTLs. Trans-eQTLs act over distance and the entire genome is interrogated for any variant-gene associations, thus further limiting power due to more stringent multiple-testing correction [40]. Still, efforts in adipose tissue have demonstrated the power of this approach by elucidating a trans-regulatory network of KFL14, a gene linked with both T2D and other metabolic traits [46]. As KLF14 is a transcription factor, the aim of the study was to identify trans-genes that are influenced by varied KLF14 levels through cis-eQTL variants. Several genes with genome-wide significance were discovered and the study not only connected GWAS, cis- and trans-associations for the same set of variants, but also defined important disease-related pathways.

The search for causal genes has been pushed ahead by eQTL studies, but the ability to perform large-scale studies containing correlated sets of genotype, phenotype and expression data are still limited by cost obstacles and sample availability. GWAS only measure genetic variation related to a disease phenotype, and expression studies suffer from reduced statistical power due to smaller sample sizes. Predicted expression association studies attempt to circumvent these limitations by integrating existing GWAS and eQTL data [47‐50]. This approach aims to identify disease associations based on groups of variants that influence gene expression, directly pinpointing the causal gene instead of tag SNPs. To combine limited available expression sets with large-scale GWAS data, these studies rely on predicted expression modelled from reference panels. The models then impute expression either for publically available summary GWAS data (most large-scale studies) or GWAS data with individual genotypes [47, 49]. This drastically increases power to detect genes that are predicted to show differences in genotype-dependent expression patterns in T2D, and reduces potential confounding factors like reverse causation, where the phenotype and environment influence gene expression [50]. However, similar to cis-eQTL studies, predicted expression association studies are unable to detect context-dependent effects that are not captured by the tissues and developmental stages included in the reference panel used for modelling [48, 49]. It is also not possible to exclude the possibility of pleiotropy caused by multiple, correlated effects of groups of variants on gene expression [48]. Despite such limitations, these methods offer a complementary and powerful approach for prioritisation of causal genes and predicted directions of effect.

A third way to identify genes involved in disease risk is prioritisation based on known or observed functions that are perceived to be relevant for disease pathogenesis. T2D risk variants, for instance, would be expected to affect genes involved in cellular processes relevant to disease susceptibility, such as beta cell function and insulin resistance. A gene found to regulate insulin secretion would thus have high prior odds of being the downstream mediator for a nearby T2D association signal known to impact on islet function. Though this is an indirect approach for prioritisation, the strategy benefits from focusing on the relevant processes that ultimately causes effects on disease pathology (Fig. 1). For unbiased generation of priors, all disease-relevant phenotypes should ideally be comprehensively interrogated in a genome-wide fashion. However, most post-GWAS approaches have previously focused on individual candidate genes, with experimental setups that make them poorly suited for systematic assessment of large numbers of genes across multiple tissues.

High-throughput functional genomic screening is an emerging and increasingly powerful approach that allows for highly parallel phenotypic screening to address this gap. Several screening strategies have been established that differ in their direction of modulated gene expression (gain of function (GOF) vs LOF), format (pooled vs arrayed), and gene modulation techniques (RNA interference (RNAi) vs CRISPR/Cas9 modulation) [51‐56, 57•]. Screens can either be performed genome-wide, representing an unbiased approach to detect genes that are involved in a specific phenotype, or based on selected genes of interest. A recent study by Thomsen et al. successfully pursued a small interfering RNA (siRNA) arrayed screening approach to systematically interrogate positional candidate genes at T2D GWAS loci in a human beta cell line [45]. Genes located within 1 Mb of 75 GWAS association signals were analysed for insulin secretion and cell proliferation to reflect beta cell dysfunction. This strategic approach provided 300 genes for screening and identified 45 genes at 37 GWAS loci for having a role in beta cell dysfunction, thus also pinpointing them as potential effector transcripts at these disease loci. Several prioritised genes with poorly characterised connection to beta cell function were separately validated in functional follow-up work including ARL15, THADA, and ZMIZ1. Independently of the previously described cis-eQTL study, this work thus attributed a role to ZMIZ1 in beta cell function, converging multiple lines of evidence to enhance confidence in the candidacy of this gene as causal. Importantly, the study also demonstrated a strong enrichment for known regulators of insulin secretion among significant hits, providing an internal validation that is an essential aspect of any screening strategy.

Taking a more inclusive approach, Pappalardo and colleagues recently pursued the first whole-genome siRNA screen to identify genes involved in glucose homeostasis and T2D [58]. While allowing for a more unbiased approach, performing an arrayed, genome-wide screen restricts the complexity of the phenotype(s) that can be practically measured. This screen focused on a reporter gene readout for insulin promoter activity in a rat beta cell line. The authors were able to identify several novel regulators of insulin promoter activity including Spry2, the gene in the closest proximity to a nearby T2D GWAS association signal [59]. The work thus highlights Spry2 as the likely causal gene at this locus, and follow-up work in cellular and in vivo systems including beta cell specific knockout mice discovered a potential functional mechanism. However, a link between the non-coding association signal and Spry2 remains to be investigated, ideally through integration with variant-to-gene approaches in human beta cells. This screen also provided robust internal validation by confirming the strongest hits to be known transcription factors targeting the insulin promoter.

Medium-throughput screens and systematic analysis of selected classes of genes represents a related strategy for analysing candidate genes in more depth across a larger spectrum of possible disease phenotypes. This approach was pursued by a recent study that investigated the function of 12 long non-coding RNAs (lncRNAs) in beta cell gene regulation and their potential role in T2D [60]. These lncRNA knockdown targets were selected based on criteria that included expression in a relevant model and an active chromatin profile. The study showed that the beta cell specific lncRNAs jointly regulate enhancer-cluster associated genes with known transcription factors. The lncRNA named as PLUTO was established as a regulator of its neighbouring gene PDX1, a transcription factor involved in pancreatic development and beta cell function [61]. Based on this overlapping role of lncRNAs and islet transcription factors, and the well-established involvement of the latter in T2D, the work hints at a similarly important role of lncRNAs in T2D pathology.

Future genetic screens hold the potential to play an important role in identifying causal genes for T2D. Pooled approaches are able to extend the scale of arrayed screens in a cost-effective manner and allow for simultaneous perturbation of thousands of genes to promote unbiased interrogation of candidate causal genes. The continuous development and improvement of the differentiation process of induced pluripotent stem cells into beta cells will also allow for investigations of disease-relevant phenotypes at various developmental stages [62, 63]. High-throughput screens thus offer the opportunity to facilitate the transition from T2D GWAS association signals to individual functional follow-up studies by prioritising candidate causal genes based on functional data.