Cracking the genetic code of autoimmune disease

Introduction to Autoimmune Disease Research

The study of autoimmune diseases has evolved over time. Initially, research focused on the HLA region and genes with large effects like PTNP22. Monogenic autoimmune diseases also provided some early understanding. However, early attempts to detect variants with small contributions to risk were not very successful. Genome - wide association studies (GWAS) emerged after the sequencing of the human genome in 2003. GWAS enabled researchers to scan the entire genome for clues related to autoimmune diseases by comparing the frequency of single nucleotide polymorphisms (SNPs) in people with and without the disease. Establishing significant loci often requires large sample sizes, and many consortia have been formed for this purpose. GWAS have been very fruitful in autoimmune disease research, revealing that many genes are implicated across different conditions, suggesting shared underlying mechanisms.

GWAS Findings and Challenges

GWAS have implicated hundreds of genomic regions in autoimmune diseases. Although individual variants in these loci have small effects on risk, they cause significant molecular changes. For example, the TYK2 gene region is linked to many autoimmune diseases. However, the effects of its associated variants on risk were initially puzzling. Through experiments, it was determined that only one of the SNPs directly alters TYK2 function. The SNP that was pinned down had a strong protective effect in a mouse model of MS. Clinical trials of a drug blocking TYK2 function in psoriasis have shown promising results, and trials in other autoimmune diseases are underway. Despite these successes, GWAS also bring new problems. Disease - associated loci often contain multiple co - occurring variants, making it difficult to determine which ones are truly driving the disease. Also, most variants involved in autoimmune diseases lie in non - coding regions, and their biological consequences are hard to interpret.

Overcoming GWAS - related Difficulties

To overcome the problem of multiple co - occurring variants in loci, researchers are generating higher - quality genetic data through methods like denser sampling, combining genetic information sources, and using sophisticated statistical techniques. It has been estimated that around 90% of causal variants in autoimmune diseases are non - coding. Epigenetic sequencing technologies are being used to map these non - coding regions. For example, it was found that around 60% of causal variants lie inside immune - cell enhancers. However, regulatory machinery is context - specific, so researchers need to study the right cells at the right time. Gene editing technologies like CRISPR have simplified the study of non - coding variants. For instance, CRISPR was used to screen for enhancers regulating the IL2Ra gene, and it was found that a variant alters the timing and levels of IL2Ra expression, which has implications for therapies targeting IL2Ra.

Rare Variants and Treatment Implications

Common variants identified by GWAS have small effects, and some researchers question their value for treatment targeting. In contrast, rare monogenic conditions can provide more direct insights for treatment. For example, a rare form of lupus is caused by a loss - of - function gene, DNASE1L3. Common variants in this gene also increase risk for multiple autoimmune diseases. Understanding the function of rare variants can help target treatment pathways. There is also interest in rare polygenic - causing variants. Although they account for a small amount of population risk, their effects on individuals can be large. Some researchers believe that common and rare variants may often point in the same direction, as seen in the case of IL2Ra, where both common non - coding variants and rare mutations in the gene are being studied, and gene editing has shown potential for treating rare autoimmune conditions.

Conclusion on Autoimmune Disease Genetics

The study of autoimmune diseases has made significant progress with the advent of GWAS. These studies have revealed numerous genetic variants associated with autoimmune diseases, both common and rare. While common variants have helped in understanding the basic biology of autoimmunity, rare variants, especially those in monogenic and some polygenic cases, are being explored for their potential in treatment development. Despite challenges in interpreting non - coding variants and determining the true drivers of disease among co - occurring variants, new technologies such as epigenetic sequencing and gene editing are providing new ways to overcome these obstacles and further our understanding of autoimmune diseases at the genetic level.