The genetic origins of 21 autoimmune diseases revealed by molecular map

[Ecoclimber]

In Autoimmune Diseases Affecting Millions, Researchers Pinpoint Genetic Risks, Cellular Culprits
New Software Tool Helps Makes Sense of Previous Genetic Data on Multiple Sclerosis, Type 1 Diabetes, Other Diseases
By Jeffrey Norris on October 29, 201

Scores of autoimmune diseases afflicting one in 12 Americans — ranging from type 1 diabetes, to multiple sclerosis (MS), to rheumatoid arthritis, to asthma — mysteriously cause the immune system to harm tissues within our own bodies. Now, a new study pinpoints the complex genetic origins for many of these diseases, a discovery that may lead to better diagnosis and ultimately to improved treatments.

A team of scientists from UC San Francisco, the Broad Institute of MIT and Harvard, and Yale School of Medicine developed a new mathematical tool to more deeply probe existing DNA databases. In so doing they discovered how certain DNA variations, when inherited, are likely to contribute to disease.

By applying their method to analyzing data from previous studies of 21 different autoimmune diseases, the research team has deepened scientific understanding of the genetic underpinnings of a wide range of these disorders. They also found the specific immune cells most responsible for the diseases. Their study is published online on October 29, 2014 in Nature.

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NATURE
Genetic and epigenetic fine mapping of causal autoimmune disease variants
Kyle Kai-How Farh, Alexander Marson, Jiang Zhu, Markus Kleinewietfeld, William J. Housley, Samantha Beik, Noam Shoresh, Holly Whitton, Russell J. H. Ryan, Alexander A. Shishkin, Meital Hatan, Marlene J. Carrasco-Alfonso, Dita Mayer, C. John Luckey, Nikolaos A. Patsopoulos, Philip L. De Jager, Vijay K. Kuchroo, Charles B. Epstein, Mark J. Daly, David A. Hafler & Bradley E. Bernstein
Nature (2014) doi:10.1038/nature13835

Abstract:
Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown.

Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4+ T-cell subsets, regulatory T cells, CD8+ T cells, B cells, and monocytes.

We find that ~90% of causal variants are non-coding, with ~60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.

 

[Snow Leopard]

Genetic and epigenetic fine mapping of causal autoimmune disease variants


http://marsonlab.ucsf.edu/sites/marsonlab.ucsf.edu/files/wysiwyg/marson and colleagues_nature.pdf

 

[alex3619]

I had always presumed that in studying snps for disease people considered regulatory elements unless the study was a small pilot study. I guess I was wrong .This was so obvious that I presumed it was being done. My bad. It should be done more often.

Of course this is stage one, the finding of candidate genetic causes. Further research will be required.

 

[Valentijn]

@alex3619 - Approximately 85% of disease-causing SNP mutations are in the coding regions, so that's where a lot of research has been focused. Whereas the regulatory regions would be outside of those regions.

Within the coding regions it's also much easier to see the direct impact of a mutation. The codons in the genetic code are spelling out what a protein should look like, and it's easy to predict the effects of a change in a codon. And that makes it a lot easier for researchers to know exactly what they are looking for and to confirm that a mutation has a physiological impact and isn't just a false positive with no impact on gene functioning.

So outside of coding regions they're usually relying on allele frequencies in patients versus healthy controls. And then the statistics get more complicated, and someone looking at thousands of SNPs is bound to find some random differences between the groups. These also tend to have VERY small effect sizes so far, compared to disease causing SNPs, so I always want to see those before believing that anything useful was found.

 

[alex3619]

These also tend to have VERY small effect sizes so far, compared to disease causing SNPs, so I always want to see those before believing that anything useful was found.

Even large effect sizes are not certain. That is why I say further research is needed. Effect sizes are an indicator though, and small effect sizes are especially dubious.