GWAS Meta-Analysis Supports Existence of Autoimmune Clusters
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A Stanford University research team collaborated with a California hospital and clinical center to perform meta-analysis of genome-wide association studies on half a dozen autoimmune conditions, including type 1 diabetes and rheumatoid arthritis.
The team uncovered a pattern in the grouping of specific diseases based on SNP data, with certain variables that increased risk for developing some conditions while protecting against others.
SNPs are short for "single nucleotide polymorphisms — pronounced "snips." They are DNA sequence variations that occur when a single nucleotide (A,T,C,or G) in the genome sequence is altered
The SNP data led the team to suggest that there may be benefits in classifying autoimmune diseases according to shared genetic factors rather than considering them a single group.
"Maybe we should stop considering all autoimmune diseases in one lumped category," senior author Atul Butte, a pediatric and bioinformatics researcher at Stanford University and director of the Lucile Packard Children's Hospital's Center for Pediatric Bioinformatics, says in a statement. "It looks as if there may be at least two different kinds."
The team points out that all autoimmune diseases share common disease mechanisms, but that certain autoimmune diseases share more such mechanisms than do others.
Past research suggests that individuals with type 1 diabetes face greater risk of developing autoimmune diseases such as autoimmune Thyroid disease, multiple sclerosis, and celiac disease.
And, they added, at least one SNP has been discovered to have opposing effects under varying autoimmune conditions: the G allele of that SNP, called rs2076530, is more common in individuals with type 1 diabetes or rheumatoid arthritis whereas those with systematic lupus erythematosus typically show the A allele. Given the strong connection between celiac disease, diabetes and other auto-immune conditions, the data seem intriguing.
These discoveries led Butte and his colleagues to speculate about the way in which genetic factors relate to autoimmune disease clusters.
The team used meta-analysis to assess 573 SNPs in several GWAS of six autoimmune diseases — type 1 diabetes, rheumatoid arthritis, Crohn's disease, multiple sclerosis, autoimmune thyroid disease, and ankylosing spondylitis — and five non-autoimmune diseases.
By looking closely at alleles associated with each disease and determining the strength of these associations, the team crafted a so-called "genetic variation score" to evaluate connections between certain alleles and diseases across multiple genotyping platforms.
The team evaluated nearly 600 SNPs. They found nine SNPs in which one allele appears to raise individual risk for multiple sclerosis and autoimmune thyroid disease, while lowering the risk for rheumatoid arthritis and ankylosing spondylitis. The alternative alleles for these SNPs, meanwhile, showed the opposite effect.
"What was surprising was our finding that at nine locations generally associated with autoimmunity risk, where a particular chemical unit conferred a heightened risk of certain autoimmune diseases, but reduced risk of getting certain others," noted lead author Marina Sirota, who serves in a graduate capacity in Butte's Stanford University lab.
Based on their findings, the research team proposes at least two distinct groups of autoimmune diseases: one containing rheumatoid arthritis and ankylosing spondylitis and another containing multiple sclerosis and autoimmune thyroid disease.
In the mean time, the team observed, type 1 diabetes appeared to have similarities with both of groups; having some characteristics of autoimmune thyroid disease, but not of multiple sclerosis. Crohn's disease, in contrast, showed no such cluster with either group.
The results will likely help pave the way for a more complete understanding of the biological pathways at play in these autoimmune diseases. They may also give researchers a better sense of how to apply existing therapies, and even how to create new ones.
"Several of these nine interesting SNPs we've found are located in or near genes that code for molecules found on cell surfaces," Butte said, "which makes them potentially easier targets for the drugs pharmaceutical researchers are best at producing."
The team expects the number and nature of SNPs involved will likely grow as more autoimmune disease GWAS reveal new genetic variants associated with these and other diseases.
"As more genomic information becomes available on increasingly advanced platforms, this sort of analysis can be done on more diseases, possibly hundreds of them," Sirota noted.
Source: GenomeWeb News
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