Blood DNA virome associates with autoimmune diseases and COVID-19.

Aberrant immune responses to viral pathogens contribute to pathogenesis, but our understanding of pathological immune responses caused by viruses within the human virome, especially at a population scale, remains limited. We analyzed whole-genome sequencing datasets of 6,321 Japanese individuals, including patients with autoimmune diseases (psoriasis vulgaris, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), pulmonary alveolar proteinosis (PAP) or multiple sclerosis) and coronavirus disease 2019 (COVID-19), or healthy controls. We systematically quantified two constituents of the blood DNA virome, endogenous HHV-6 (eHHV-6) and anellovirus.

Genetic legacy of ancient hunter-gatherer Jomon in Japanese populations.

The tripartite ancestral structure is a recently proposed model for the genetic origin of modern Japanese, comprising indigenous Jomon hunter-gatherers and two additional continental ancestors from Northeast Asia and East Asia. To investigate the impact of the tripartite structure on genetic and phenotypic variation today, we conducted biobank-scale analyses by merging Biobank Japan (BBJ; n = 171,287) with ancient Japanese and Eurasian genomes (n = 22). We demonstrate the applicability of the tripartite model to Japanese populations throughout the archipelago, with an extremely strong correlation between Jomon ancestry and genomic variation among individuals.

Genomic and Serological Rheumatoid Arthritis Biomarkers, Promoter Variant, and Interstitial Lung Abnormalities.

Rationale: Rheumatoid arthritis (RA) has been implicated in interstitial lung disease (ILD) as majority of studies have been comprised of patients with known RA. However, it remains unclear whether an underlying risk for RA in combination with genetic risk for pulmonary fibrosis is associated with radiological markers of early lung injury and fibrosis in broader population samples.

Objective: Determine whether genetic and serological biomarkers of RA risk in combination with the (rs35705950) risk allele (T) are associated with interstitial lung abnormalities (ILA) on computed tomography (CT) scans.

Identifying genetic variants that influence the abundance of cell states in single-cell data.

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants.