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Researchers Estimate Heritability of Childhood Autoimmune Diseases

NEW YORK (GenomeWeb) – A Children's Hospital of Philadelphia-led team of researchers has quantified the heritability of nine pediatric-onset autoimmune diseases.

Childhood autoimmune diseases have high rates of familial clustering, sibling recurrence, and twin-twin concordance both within and between diseases, suggesting that shared genetic risk factors may underlie disease etiology.

CHOP's Hakon Hakonarson and his team examined genotyping data from more than 5,000 unrelated pediatric autoimmune disease patients and 36,000 controls and found that type 1 diabetes and juvenile idiopathic arthritis had the highest levels of heritability, as they reported today in Nature Communications.

"The results from this study enable us to better understand the genetic component of these diseases and how they are genetically related to each other, thereby explaining why different autoimmune disorders often run in the same family," Hakonarson said in a statement.

Hakonarson and his colleagues applied a SNP-based estimation of heritability (SNP-h2) to a cohort of pediatric-onset autoimmune disease cases and controls. After quality control and filtering steps, this cohort included 4,956 cases representing nine conditions and 27,451 controls. They also included some 800 cases with a non-immune-mediated disease, pediatric-onset epilepsy, for comparison.

Among the pediatric autoimmune diseases with significant SNP-h2 estimates, type 1 diabetes and juvenile idiopathic arthritis were the most highly heritable, while ulcerative colitis and Crohn's disease were the least.

This, the researchers noted, is in line with estimates made in an adult cohort.

Further, they noted that juvenile idiopathic arthritis had a high SNP-h2 despite its being a heterogeneous disease — there are seven subtypes of the disease. The researchers suggested that this high SNP-h2 could be attributed to a common etiology among the disease types.

At the same time, Hakonarson and his colleagues uncovered a low SNP-h2 for lupus, which they said was consistent with the known strong effect of environmental and epigenetic factors on the disease.

As variants across the major histocompatibility complex are known to influence autoimmune diseases, Hakonarson and his colleagues gauged their contribution to SNP-h2 for each of the nine diseases. Through HLA imputation, the researchers uncovered the SNP, amino acid, or HLA allele most strongly associated with each disease and then determined the population-based heritability estimates (POP-h2) attributable to the MHC region.

Variations in these regions, they reported, accounted for nearly a third of the total autosomal SNP-h2in type 1 diabetes and about a quarter in celiac disease, but made no contribution in psoriasis, lupus, Crohn's disease, or the non-immune-related epilepsy disorder.

Through an SVM model, the researchers then examined whether common genomic variations could predict pediatric-onset autoimmune disease risk. They constructed a linear SVM model using the top GWAS signals observed in 90 percent of the samples, and tested the predictor on the remaining samples.

The predictor, they reported, was most effective for type 1 diabetes, juvenile idiopathic arthritis, and, to a lesser extent, for celiac disease.

Hakonarson and his colleagues also examined the genetic correlation between pairs of pediatric-onset autoimmune disease. The highest correlation, they reported, was between ulcerative colitis and Crohn's disease, which they noted was consistent with previous reports. In addition, they noted a link between common variable immunodeficiency disorder and juvenile idiopathic arthritis.

"[O]ur analysis suggests that there is a high heritability and disease predictability across the [pediatric-onset autoimmune diseases]," the researchers wrote in their paper. "Future studies in larger sample sizes and in adult cohorts will be helpful in validating these results and developing new and improved methods for genome-based disease prediction and for the development of novel biomarkers that can be used to predict [pediatric-onset autoimmune disease] risk."