Objective

To use high-density genotyping to investigate the genetic associations of acute anterior uveitis (AAU) in patients with and those without ankylosing spondylitis (AS).

Methods

We genotyped samples from 1,711 patients with AAU (either primary or combined with AS), 2,339 AS patients without AAU, and 10,000 control subjects on an Illumina Immunochip Infinium microarray. We also used data for AS patients from previous genome-wide association studies to investigate the AS risk locus ANTXR2 for its putative effect in AAU. ANTXR2 expression in mouse eyes was investigated by real-time quantitative reverse transcription-polymerase chain reaction.

Results

A comparison between all patients with AAU and healthy control subjects showed strong association over HLA-B, corresponding to the HLA-B27 tag single-nucleotide polymorphism rs116488202. The association of 3 non-major histocompatibility complex loci, IL23R, the intergenic region 2p15, and ERAP1, reached genome-wide significance (P < 5 × 10(-8)). Five loci harboring the immune-related genes IL10-IL19, IL18R1-IL1R1, IL6R, the chromosome 1q32 locus harboring KIF21B, as well as the eye-related gene EYS, were also associated, reaching a suggestive level of significance (P < 5 × 10(-6)). Several previously confirmed AS associations demonstrated significant differences in effect size between AS patients with AAU and AS patients without AAU. ANTXR2 expression varied across eye compartments.

Conclusion

These findings of both novel AAU-specific associations and associations shared with AS demonstrate overlapping but also distinct genetic susceptibility loci for AAU and AS. The associations in IL10 and IL18R1 are shared with inflammatory bowel disease, suggesting common etiologic pathways.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1⁺ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.