Functional predictions and protein network analyses suggest a prominent role of the drug-targetable IL23/Th17 signaling pathway in the genetic etiology of sarcoidosis. Our findings reveal a substantial genetic overlap of sarcoidosis with diverse immune-mediated inflammatory disorders, which could be of relevance for the clinical application of modern therapeutics.

Leprosy, a chronic infectious disease caused by Mycobacterium leprae (M. leprae), was very common in Europe till the 16th century. Here, we perform an ancient DNA study on medieval skeletons from Denmark that show lesions specific for lepromatous leprosy (LL). First, we test the remains for M. leprae DNA to confirm the infection status of the individuals and to assess the bacterial diversity. We assemble 10 complete M. leprae genomes that all differ from each other. Second, we evaluate whether the human leukocyte antigen allele DRB1*15:01, a strong LL susceptibility factor in modern populations, also predisposed medieval Europeans to the disease. The comparison of genotype data from 69 M. leprae DNA-positive LL cases with those from contemporary and medieval controls reveals a statistically significant association in both instances. In addition, we observe that DRB1*15:01 co-occurs with DQB1*06:02 on a haplotype that is a strong risk factor for inflammatory diseases today.

Genome-wide and candidate gene studies for pulmonary sarcoidosis have highlighted several candidate variants among different populations. However, the genetic basis of functional rare variants in sarcoidosis still needs to be explored. To identify functional rare variants in sarcoidosis, we sequenced exomes of 22 sarcoidosis cases from six families. Variants were prioritized using linkage and high-penetrance approaches, and filtered to identify novel and rare variants. Functional networking and pathway analysis of identified variants was performed using gene ontology based gene-phenotype, gene-gene, and protein-protein interactions. The linkage (n = 1007-7640) and high-penetrance (n = 11,432) prioritized variants were filtered to select variants with (a) reported allele frequency < 5% in databases (1.2-3.4%) or (b) novel (0.7-2.3%). Further selection based on functional properties and validation revealed a panel of 40 functional rare variants (33 from linkage region, 6 highly penetrant and 1 shared by both approaches). Functional network analysis implicated these gene variants in immune responses, such as regulation of pro-inflammatory cytokines including production of IFN-γ and anti-inflammatory cytokine IL-10, leukocyte proliferation, bacterial defence, and vesicle-mediated transport. The KEGG pathway analysis indicated inflammatory bowel disease as most relevant. This study highlights the subsets of functional rare gene variants involved in pulmonary sarcoidosis, such as, regulations of calcium ions, G-protein-coupled receptor, and immune system including retinoic acid binding. The implicated mechanisms in etiopathogenesis of familial sarcoidosis thus include Wnt signalling, inflammation mediated by chemokine and cytokine signalling and cadherin signalling pathways.

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.