The genetics of human autoimmune disease: A perspective on progress in the field and future directions

Highlights

• Hundreds of variations in HLA and non-HLA genes predispose to autoimmunity.

• For non-coding variations alterations in chromatin structure provide valuable clues.

• Expression quantitative traits can help identify causal variants.

• Pathway analyses can facilitate further understanding of autoimmune genetics.

• Functional studies and accounting for missing heredity are critical to advances.

Abstract

Progress in defining the genetics of autoimmune disease has been dramatically enhanced by large scale genetic studies. Genome-wide approaches, examining hundreds or for some diseases thousands of cases and controls, have been implemented using high throughput genotyping and appropriate algorithms to provide a wealth of data over the last decade. These studies have identified hundreds of non-HLA loci as well as further defining HLA variations that predispose to different autoimmune diseases. These studies to identify genetic risk loci are also complemented by progress in gene expression studies including definition of expression quantitative trait loci (eQTL), various alterations in chromatin structure including histone marks, DNase I sensitivity, repressed chromatin regions as well as transcript factor binding sites. Integration of this information can partially explain why particular variations can alter proclivity to autoimmune phenotypes. Despite our incomplete knowledge base with only partial definition of hereditary factors and possible functional connections, this progress has and will continue to facilitate a better understanding of critical pathways and critical changes in immunoregulation. Advances in defining and understanding functional variants potentially can lead to both novel therapeutics and personalized medicine in which therapeutic approaches are chosen based on particular molecular phenotypes and genomic alterations.

Introduction

Our understanding of which genes predispose to different autoimmune diseases has expanded rapidly over the last decade. This progress has been mostly due to genome-wide association studies (GWAS) and the development of various technical and analytic tools. However, despite this progress less than half of the heritability of most autoimmune diseases can be explained and nearly half of this identified genetic risk is due to variations within HLA. The actual functional variants that underlie statistically significant associations are with some notable exceptions are still largely unknown. In the following perspective, I will review some of the more salient advances in the field, provide examples to illustrate specific points, indicate where knowledge is sparse, and discuss the potential for future advances that I believe could further define the pathogenesis and perhaps enable application to diagnoses and therapy. More detailed aspects of the genetics for a variety of autoimmune diseases is presented by experts in the field in other sections of this special issue of the journal. A general paradigm for GWAS and sequence variant studies is shown in Fig. 1 and discussed in subsequent sections.

Epidemiological studies of most autoimmune diseases including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), multiple sclerosis (MS), and primary biliary cirrhosis (PBC) show that there is strong heritability. These include studies showing increased concordance in monozygotic compared to dizygotic twin as well as studies showing increased risk to siblings of proband cases compared to the general population. Although most of these studies are not truly population based and may have biased results, there are some caveats worth noting. First, some autoimmune diseases have much higher sibling relative risk rates than other diseases (e.g. SLE [1], [2], T1D [3], celiac disease [4], and PBC [5], [6] compared to others e.g. RA [2], and MS [7]). Second, although concordance of disease is much higher in monozygotic compared with dizygotic twins for many autoimmune diseases, the overall monozygotic concordance of disease is usually substantially less than 50% [8]. This indicates that stochastic factors including environmental variables are a strong component and although genetics can be very useful in identifying important factors in etiopathogenesis it can only partially predict phenotype. Although some specific environmental factors have been identified (e.g. smoking and rheumatoid arthritis [9], [10]) it is also possible that most of the incomplete concordance is simply chance or indefinable events.

The major advance in identifying genetic loci that predispose to autoimmune diseases has been GWAS. Although some non-major histocompatibility (HLA in humans) loci were identified prior to GWAS using linkage or candidate gene studies, and other methodologies including admixture mapping have also enabled identification of a modicum of risk loci, the exponential increase in loci (over 200 for some autoimmune diseases) has been the direct result of GWAS. The basis of GWAS is the technology enabling efficient and accurate genotyping of single base polymorphisms (SNPs) and large collaborative studies such as HapMap [11], [12] defining large numbers (hundreds of thousands) of SNPs in different populations. The success of GWAS is in large part due to practical advantage in conducting case/control design, namely the ability to recruit large numbers of cases and population controls as opposed to the difficulty in recruiting families: power for any association study is largely based on numbers. A critical aspect for these studies has been the ability to adequately control for population substructure differences using statistical methodology. Most commonly this is done by logistic regression using relevant principal components defined by principal component analyses or similar methods [13], [14], [15]. In some studies only continental population differences are accounted for, but for the most part type 2 errors (false positives) due to unrecognized stratification differences in case and control populations have been minimized. In fact, many studies have used publically available control genotypes rather than specific matched collections of controls. It is also worth noting that it may be possible to increase power (decrease Type 1 errors, false negatives) to ascertain risk variants by limiting studies to more homogenous populations and additional considerations of population substructure is discussed in subsequent sections (see sections 3.3 Rare/uncommon variants, 4 Ancestry makes a difference). However, GWAS is largely applicable to those loci that fulfill the common variant (>0.05 minor allele frequency) common disease hypothesis since this methodology relies on linkage disequilibrium (LD) between the marker (SNP) detected and the actual disease causing variant(s). The commonly used genotyping platforms (Illumina and Affymetrix chip arrays) and clustering algorithms are also most accurate for minor allele frequencies (MAFs) > 0.05, although there have been some improvements in software to enable higher accuracy genotyping.

Another important aspect of current GWAS and other association studies is the ability to accurately impute much of the sequence difference that is not directly genotyped using current genotyping platforms. Imputation is based on sophisticated algorithms that enable under certain conditions the accurate estimation of the sequence variants between genotyped markers based on reference genomes that contain the missing information [16], [17], [18], [19]. It relies on the shared loci (almost all of the SNPs in commonly used genotyping platforms are shared with the reference genomes) and the patterns of variation (http://www.1000genomes.org). With the completion of phase 3 of the 1000 genome sequencing study there is now sequence data for multiple populations that can be used to inform imputation(http://www.1000genomes.org/) [20]. This information can suggest candidate non-synonymous damaging variants and in others provides candidate non-coding variants that might be suggestive based on expression quantitative traits (eQTL), binding sites for transcription factors, DNase sensitivity or other features (e.g. histone marks) that have been elucidated in particular tissues or cell lines (discussed further in Section 6). Importantly, imputation methods can also be used to determine HLA classical determinants and amino acids.

A critical aspect for any association study is defining the phenotype of cases. Theoretically, the better the definition of phenotype the greater the likelihood that type 1 errors will be minimized since inclusion of unaffected individuals or those with a different autoimmune disease may increase heterogeneity and dilute the risk of variants for a particular phenotype. However, there may be a practical trade-off between including cases that may not have all the information (e.g. particular autoantibodies or even age of onset) that could increase the power to detect loci due to greater sample size as compared to more rigid criteria. For some autoimmune diseases particular features clearly increase the relative risk of many of the susceptibility loci. A strong example is for SLE where cases with anti-double strand (DS) DNA show substantially higher odds ratios for several lupus susceptibility alleles than those that are anti-DS DNA negative [21]. Similarly, some RA studies show stronger associations when the cases are limited to those with anti-cyclic citrullinated peptide antibodies (anti-CCP) [22]. However, in other studies when cases are limited to anti-CCP positive RA there are fewer susceptibility loci associated with disease, probably mostly due to decreased power as a function of a smaller sample size [23]. Similarly, if for example an SLE study was restricted to only those that manifested the same 4 out of 11 American College of Rheumatology criteria it is likely that many of the identified loci would have been missed due to weaker signals from decreased sample sizes.

The decision on phenotypic definition is analogous to the classic fight between the lumpers and the splitters in the definition of mammalian species [24] and was advanced many years ago with respect to the nosology of genetic disease [25]. However, for some autoimmune diseases the definition of “subtypes” may be crucial. In collaborative work by this author, the study of myasthenia gravis, it was critical to divide the disease by age-of-onset and the lack of thymomas. Here, there are major differences in which HLA genes are important in susceptibility when early onset (<45 years of age) compared to late onset (>50 years of age) are compared ([26], [27] and Seldin, Gregersen, Hammarstrom, unpublished data). In addition, the highest risk non-HLA gene, TNIP1, observed in early onset myasthenia gravis (EOMG) [26] has no effect in late onset myasthenia gravis (LOMG) (Seldin, Gregersen, Hammarstrom, unpublished data).

As the field progresses there are likely to be more studies that examine endophenotypes including the most prominent disease manifestations, disease severity and morbidity. In SLE there is a study suggesting specific loci that predispose to nephropathy [28]. Such studies could also include those individuals that have very poor outcomes or more severe phenotypes.