ObjectivesJuvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA.MethodsWe performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes.ResultsThe major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes.ConclusionsThe lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.

In our study, IL1RN was the only candidate locus associated with systemic JIA. The implicated SNPs are among the strongest known determinants of IL1RN and interleukin-1 receptor antagonist levels, linking low expression with increased systemic JIA risk. Homozygous high expression alleles predicted nonresponsiveness to anakinra therapy, making them ideal candidate biomarkers to guide systemic JIA treatment. This study is an important first step toward the personalized treatment of systemic JIA.

Genetic matching between transplant donor and recipient pairs has traditionally focused on the human leukocyte antigen (HLA) regions of the genome, but recent studies suggest that matching for non‐HLA regions may be important as well. We assess four genetic matching scores for use in association analyses of transplant outcomes. These scores describe genetic ancestry distance using identity‐by‐state, or genetic incompatibility or mismatch of the two genomes and therefore may reflect different underlying biological mechanisms for donor and recipient genes to influence transplant outcomes. Our simulation studies show that jointly testing these scores with the recipient genotype is a powerful method for preliminary screening and discovery of transplant outcome related single nucleotide polymorphisms (SNPs) and gene regions. Following these joint tests with marginal testing of the recipient genotype and matching score separately can lead to further understanding of the biological mechanisms behind transplant outcomes. In addition, we present results of a liver transplant data analysis that shows joint testing can detect SNPs significantly associated with acute rejection in liver transplant.

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.