Objective. To investigate differences in genetic risk factors for rheumatoid arthritis (RA) in Han Chinese as compared with Europeans.Methods. A genome-wide association study was conducted in China with 952 patients and 943 controls, and 32 variants were followed up in 2,132 patients and 2,553 controls. A transpopulation meta-analysis with results from a large European RA study was also performed to compare the genetic architecture across the 2 ethnic remote populations.Results. Three non-major histocompatibility complex (non-MHC) loci were identified at the genomewide significance level, the effect sizes of which were larger in anti-citrullinated protein antibody (ACPA)-positive patients than in ACPA-negative patients. These

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Rheumatoid arthritis (RA) is a chronic autoimmune disease and can lead to deformities and severe disabilities, due to irreversible damage of tendons, joints, and bones. A previous study indicated that a DNA repair system was involved in the development of RA. In this study, we investigated the association of four N-methylpurine-DNA glycosylase (MPG) gene polymorphisms (rs3176364, rs710079, rs2858056, and rs2541632) with susceptibility to RA in 384 Taiwanese individuals (192 RA patients and 192 control subjects). Our data show a statistically significant difference in genotype frequency distributions at rs710079 and rs2858056 SNPs between RA patients and control groups (P = 0.040 and 0.029, respectively). Our data also indicated that individuals with the GG genotype at rs2858056 SNP may have a higher risk of developing RA. In addition, compared with the haplotype frequencies between case and control groups, individuals with the GCGC haplotype appeared to be at a greater risk of RA progression (P = 0.003, OR = 1.75; 95% CI = 1.20-1.55). Our results suggest that rs710079 and rs2858056 polymorphisms and the GCGC haplotype in the MPG gene are associated with the risk of RA progression, and thus may be used as molecular markers of RA if they are confirmed by further research.

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder characterized by substantial heterogeneity. To identify the convergence of disease pathology on common pathways, it is essential to understand the correlations among ASD candidate genes and study shared molecular pathways between them. Investigating functional interactions between ASD candidate genes in different cell types of normal human brains may shed new light on the genetic heterogeneity of ASD. Here we apply cell type-specific gene network-based analysis to analyze human brain nucleus gene expression data and identify cell type-specific ASD-associated gene modules. ASD-associated modules specific to different cell types are relevant to different gene functions, for instance, the astrocytes-specific module is involved in functions of axon and neuron projection guidance, GABAergic interneuron-specific modules are involved in functions of postsynaptic membrane, extracellular matrix structural constituent, and ion transmembrane transporter activity. Our findings can promote the study of cell type heterogeneity of ASD, providing new insights into the pathogenesis of ASD. Our method has been shown to be effective in discovering cell type-specific disease-associated gene expression patterns and can be applied to other complex diseases.

Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.