Disease Mechanisms in Rheumatology—Tools and Pathways: Defining Functional Genetic Variants in Autoimmune Diseases (2014)

Autoimmune diseases develop through the exposure of genetically susceptible hosts to environmental triggers. Advances in high-throughput genotyping and sequencing technologies coupled with comprehensive databases of human genetic variation and the assembly of large cohorts of case and control subjects have led to substantial progress in defining the genetic risk factors that underlie autoimmune diseases (1). The workhorse statistical methodology has been the genome-wide association study (GWAS). Studies using the GWAS approach have convincingly and reproducibly identified ∼700 genomic regions in 183 published studies of autoimmune diseases at the level of genome-wide statistical significance (P < 10−8) (www.genome.gov/gwastudies). The majority of these genetic associations are near genes that map to critical immunoregulatory pathways, illuminating genetic effects that are shared across multiple autoimmune diseases and other genetic effects that are restricted to only a few (1). GWAS studies detect most causal variants indirectly, by leveraging linkage disequilibrium (LD) throughout the human genome. Classically defined, LD is the nonrandom association of two or more loci, resulting in segments of the genome being inherited as haplotype “blocks.” Knowledge of the allele at one variant predicts with high likelihood the alleles at the other variants on the same haplotype block (2). Though LD makes locus discovery by GWAS very efficient, because only one or two variants per haplotype block need to be genotyped in order to detect association, the high correlation of variants on associated haplotypes confounds the ability of genetic association methods to distinguish causal from noncausal variants. The UBE2L3 locus associated with multiple autoimmune diseases, including systemic lupus erythematosus (SLE) (3), illustrates the problem vividly, where 34 SLE-associated variants are located within a 67-kb haplotype, but we assume that only a few are likely to be causal (3). With a situation such as this, which variants are we to choose for functional screening? No specific guidelines exist on how to answer this question. In general, the approach to overcoming the LD problem is to first comprehensively understand the genetic architecture at a given locus. Doing this in multiple ethnic populations when possible, adds significant power to our ability to discern causal variants by allowing the comparison of haplotypes across populations. Comprehensive characterization of a locus may include the following activities: 1) locus enrichment—capture for analysis all available genetic variation present on the risk haplotype, 2) locus refinement—winnow down the associated variants within a locus to a prioritized list for functional testing, and 3) functional testing—identify allele-specific differences in biologic function that support the variant's role(s) in causality. In this review, we discuss each of these steps and describe in more detail the available molecular methods that can provide the functional evidence required to assign causality to variants associated with autoimmune diseases. The objective of locus enrichment is to include in the analyses as much genomic variation as possible so that the odds of capturing the effects of all potential causal variants can be assured. This approach primarily includes the application of genetic fine mapping, resequencing, and imputation. Large-scale fine mapping is most efficient in collaborative experiments, as demonstrated in the Large Lupus Association Study 2 (LLAS2) and the ImmunoChip (IC) array. The LLAS2 fine mapped and replicated GWAS findings in SLE patients and tested for these associations in non-European SLE populations. Including non-European cohorts (subjects of African, East Asian, and native North American ancestry) facilitated the exploration of differences in LD structure across ethnic groups in what is known as transpopulation mapping (discussed below). In contrast to other consortium initiatives, the focus of LLAS2 was not to produce a single comprehensive article about the experimental results, but rather, to distribute locus-specific results to investigators with a particular interest in developing the data further. This effort led to the identification of multiple new SLE loci, including IRF8, TREM39A, and IKZF3-ZBP2 (4). The IC array was developed in 2010 by a consortium led by the Welcome Trust Case-Control Consortium, with the primary objective of providing a cost-effective platform for fine mapping loci identified in GWAS across multiple autoimmune diseases. Built on the Illumina Infinium platform, the IC contained ∼196,500 single-nucleotide polymorphisms (SNPs) selected to provide both fine mapping of existing GWAS loci from patients with autoimmune and autoinflammatory diseases and to allow for deep replication of those traits. The IC content included 186 associated regions identified in 12 autoimmune diseases, consisting of SLE, type 1 diabetes mellitus, autoimmune thyroid disease, Crohn's disease, ulcerative colitis, psoriasis, rheumatoid arthritis, primary biliary cirrhosis, multiple sclerosis, celiac disease, IgA deficiency, and ankylosing spondylitis. The success of the IC has been driven in part by its low cost; at $39 per sample, it is far less expensive than other GWAS arrays. Through the use of IC, multiple putative causal variants have been identified in Parkinson's disease, celiac disease, psoriasis, and ankylosing spondylitis (5). Further locus enrichment with variants not directly genotyped is possible using the process of imputation, which requires a genotyped data set and a reference panel of variants configured as phased haplotypes. Public sequencing efforts, such as the HapMap Project (6) and the 1000 Genomes Project (7), have been instrumental in the development of reference panels of genetic variants and have greatly facilitated the utility of imputation, which is now a standard component of genetic analyses. Imputation programs leverage the LD between variants to populate the more sparsely genotyped data set with variants from the reference panel. For common variants, this can be done with high accuracy, thus increasing the chances of including causal variants in the analyses of association. Several programs are available for imputing genotype data, including IMPUTE 2 (8), MaCH (9), and Beagle (10). The performance of each method is influenced by multiple factors, including ancestry (population substructure), genotyping platform (selection of SNPs), and the imputation reference panel. Overall, IMPUTE 2 has demonstrated superior imputation accuracy in most comparative studies (8, 11). For quality of predictions, IMPUTE 2 and MaCH demonstrate lower error rates, from 5.16% and 5.46%, than Beagle, which is ∼6.33% (12). Importantly, the imputation error rate increases as the minor allele frequency decreases due to reduced LD of rare variants with surrounding common variants, thus limiting the use of imputation for studies focusing on rare variants. In addition, the HLA region, with its great functional diversity, is highly polymorphic and demonstrates functional importance in autoimmunity. Imputation methods have therefore been developed specifically for predicting the 4-digit HLA alleles based on the extended haplotype structure within the MHC region. These methods include HIBAG (13), HLA*IMP (14), and HLA*IMP:02 (14). The prediction accuracies of these methods for HLA alleles are comparable, with >90% for most loci. However, HIBAG has some advantages because it can be applied without the need to have access to large training data sets (unlike Beagle) or to upload data to an external website (unlike HLA*IMP and HLA*IMP:02) (14). Next-generation sequencing (NGS) platforms have ushered in new possibilities for locus enrichment. Using NGS, entire risk haplotypes, loci, or even entire genomes can be sequenced, allowing for the identification of all possible variants. There are 3 main types of NGS sequencing for genetic analysis: whole genome, whole exome, and targeted. Whole genome sequencing has the capability of providing researchers the identity of every variant contained within the genome; however, it may still be too expensive (∼$1,500–2,000 per sample for sequencing only, not including data analysis) for most large-scale studies and may generate more data than required. Ongoing improvements in sequencing chemistry and hardware are rapidly dropping the price of whole genome sequencing to the point of making it economical for some large-scale studies, but the volume of data generated from whole genome sequencing may still be challenging for most laboratories to manage. Whole exome sequencing isolates only the exonic sequences of the genome, allowing their specific capture and enrichment from noncoding sequences. The main limitation of exome sequencing is that it does not capture loci found in intergenic regions, such as long noncoding RNA or regulatory transcription factor binding sites, unless such sites are within LD blocks containing exon variants. Given that the majority (∼90%) of disease-associated or trait-associated variants are located in noncoding DNA, this method is used primarily to assess the role of rare coding variants in autoimmune disease susceptibility (15). Among these NGS methods, targeted resequencing has become the approach most frequently used to capture candidate causal variants within an established region of interest at manageable cost for large sample sizes. There are two primary methods for targeted resequencing: hybridization and amplicon. Hybridization uses oligomer “baits” tiled across regions of interest to enrich for complementary DNA sequences in the targeted region. Amplicon methods use primer walking to generate large amounts of polymerase chain reaction (PCR) products across the targeted locus in each sample. The resulting PCR products are then pooled and sequenced together. Hybridization approaches for targeted capture are generally preferred when targeting a large number of loci and have the capability to query up to 20 Mbp of sequence. Amplicon approaches are generally used for very specific sets of regions of <1 Mbp in total length but allow for large numbers of samples to be pooled together for high-throughput screening. Recent studies in SLE demonstrate several loci for which targeted resequencing successfully enriched functional variants and haplotypes, including TNFAIP3 (16), UBE2L3 (3), IKBKE (17), and IFIH1 (17). Three primary activities are used to refine a locus following locus enrichment: conditional haplotype analysis, transpopulation mapping (TPM), and bioinformatic annotation. Conditional haplotype–based analyses are routinely used to evaluate for the presence of multiple independent association signals that could then indicate the presence of multiple causal variants within a given locus. To perform these analyses, stepwise logistic regression analysis is applied to data after adjusting for the most significantly associated variant. If variants in the locus continue to demonstrate evidence of association after adjustment, association can then be attributed to the presence of a second independent genetic effect. The process is usually repeated with the next most significant variant and so forth until all remaining association in the locus can be accounted for. Genetic analysis programs such as Plink (18) routinely implement this type of analysis. The conditional haplotype approach was recently applied to an association between SLE and the HLA region. Using an alternative to stepwise logistic regression that allows parallel testing of different genetic models through Bayesian inference, Morris et al (19) identified 2 independent variants that were distinct from the classic HLA alleles, one in the class III region and the other in the class I region. The results suggest that there may be at least 5 independent genetic effects emanating from the HLA region that influence SLE predisposition. The TPM approach to locus refinement requires that the locus of interest be genotyped in multiple ethnic groups so that differences in the LD structure across the various populations can be leveraged to potentially eliminate segments of the associated haplotype from further consideration. TPM has not been widely applied in autoimmune diseases since most studies have concentrated on populations of European ethnicity. The LLAS2 study in SLE (described above) was really the first to facilitate the broad application of TPM in the process of locus refinement. To perform TPM, association is identified in an index population, and specific haplotype blocks are identified. Variants are then genotyped in other ethnic populations, and associations with disease and haplotype block structure are determined. The populations are then evaluated for differences in LD structure that result in the loss of association with SNPs from the index population. The region demonstrating preserved evidence of association is the one most likely to harbor the causal allele. This scenario assumes that the same causal variant is producing the signal in all associated populations. This approach has been successful in identifying causal variants in ITGAM in SLE (20) and HLA–DQA1 in type 1 diabetes mellitus (21), as well as in the refinement of the IL21 locus in SLE (22). A corollary to this approach is to compare the locus-specific haplotype structures in populations that demonstrate association with populations that demonstrate no association. Under this scenario, it is assumed that the causal variant is present only in the associated populations and is present at a frequency similar to the risk haplotype. An example of this is seen in the identification of a causal regulatory variant near TNFAIP3 (16). Once genetic approaches have refined associated regions to their smallest possible segments, bioinformatic annotation is used to determine which variants reside in regions enriched for biologic importance. This activity is relatively straightforward, with the currently available data sets generated by large publicly funded consortia (e.g., the 1000 Genomes Project 23, the Encyclopedia of DNA Elements [ENCODE] Project 24, the UK10K Project [http://www.uk10k.org/]) and a variety of bioinformatics tools designed to predict the possible impact of each variant (Table 1). To predict whether a coding variant may alter the function of a protein, several tools have been developed, including Sorting Intolerant From Tolerant (SIFT) (25), Polymorphism Phenotyping v2 (PolyPhen-2) (26), likelihood ratio test (LRT) (27), and MutationTaster (28). Each of these tools uses unique algorithmic methodology in an attempt to predict the deleterious nature of detected SNPs in protein-coding sequences. Due to their respective assumptions, they can and often do disagree as to the nature of variant damage potential. While there has been some comparison between tools, it is far from exhaustive (29). 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