Epigenomic profiling of the infrapatellar fat pad in osteoarthritis.

Osteoarthritis is a prevalent, complex disease of the joints, and affects multiple intra-articular tissues. Here, we have examined genome-wide DNA methylation profiles of primary infrapatellar fat pad and matched blood samples from 70 osteoarthritis patients undergoing total knee replacement surgery. Comparing the DNA methylation profiles between these tissues reveal widespread epigenetic differences. We produce the first genome-wide methylation quantitative trait locus (mQTL) map of fat pad, and make the resource available to the wider community. Using two-sample Mendelian randomization and colocalization analyses, we resolve osteoarthritis GWAS signals and provide insights into the molecular mechanisms underpinning disease aetiopathology. Our findings provide the first view of the epigenetic landscape of infrapatellar fat pad primary tissue in osteoarthritis.

An epigenome-wide view of osteoarthritis in primary tissues.

Osteoarthritis is a complex degenerative joint disease. Here, we investigate matched genotype and methylation profiles of primary chondrocytes from macroscopically intact (low-grade) and degraded (high-grade) osteoarthritis cartilage and from synoviocytes collected from 98 osteoarthritis-affected individuals undergoing knee replacement surgery. We perform an epigenome-wide association study of knee cartilage degeneration and report robustly replicating methylation markers, which reveal an etiologic mechanism linked to the migration of epithelial cells. Using machine learning, we derive methylation models of cartilage degeneration, which we validate with 82% accuracy in independent data. We report a genome-wide methylation quantitative trait locus (mQTL) map of articular cartilage and synovium and identify 18 disease-grade-specific mQTLs in osteoarthritis cartilage. We resolve osteoarthritis GWAS loci through causal inference and colocalization analyses and decipher the epigenetic mechanisms that mediate the effect of genotype on disease risk. Together, our findings provide enhanced insights into epigenetic mechanisms underlying osteoarthritis in primary tissues.

Genetic architecture of gene regulation in Indonesian populations identifies QTLs associated with global and local ancestries.

Lack of diversity in human genomics limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities. To map genetic effects on gene regulation in the underrepresented Indonesian population, we have integrated genotype, gene expression, and CpG methylation data from 115 participants across three island populations that capture the major sources of genomic diversity in the region. In a comparison with European datasets, we identify eQTLs shared between Indonesia and Europe as well as population-specific eQTLs that exhibit differences in allele frequencies and/or overall expression levels between populations. By combining local ancestry and archaic introgression inference with eQTLs and methylQTLs, we identify regulatory loci driven by modern Papuan ancestry as well as introgressed Denisovan and Neanderthal variation. GWAS colocalization connects QTLs detected here to hematological traits, and further comparison with European datasets reflects the poor overall transferability of GWAS statistics across diverse populations. Our findings illustrate how population-specific genetic architecture, local ancestry, and archaic introgression drive variation in gene regulation across genetically distinct and in admixed populations and highlight the need for performing association studies on non-European populations.

Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci.

RATIONALE: As the third leading cause of death in the United States, the impact of chronic obstructive pulmonary disease (COPD) makes identification of its molecular mechanisms of great importance. Genome-wide association studies (GWASs) have identified multiple genomic regions associated with COPD. However, genetic variation only explains a small fraction of the susceptibility to COPD, and sub-genome-wide significant loci may play a role in pathogenesis. OBJECTIVES: Regulatory annotation with epigenetic evidence may give priority for further investigation, particularly for GWAS associations in noncoding regions. We performed integrative genomics analyses using DNA methylation profiling and genome-wide SNP genotyping from lung tissue samples from 90 subjects with COPD and 36 control subjects. METHODS: We performed methylation quantitative trait loci (mQTL) analyses, testing for SNPs associated with percent DNA methylation and assessed the colocalization of these results with previous COPD GWAS findings using Bayesian methods in the R package coloc to highlight potential regulatory features of the loci. MEASUREMENTS AND MAIN RESULTS: We identified 942,068 unique SNPs and 33,996 unique CpG sites among the significant (5% false discovery rate) cis-mQTL results. The genome-wide significant and subthreshold (P < 10-4) GWAS SNPs were enriched in the significant mQTL SNPs (hypergeometric test P < 0.00001). We observed enrichment for sites located in CpG shores and shelves, but not CpG islands. Using Bayesian colocalization, we identified loci in regions near KCNK3, EEFSEC, PIK3CD, DCDC2C, TCERG1L, FRMD4B, and IL27. CONCLUSIONS: Colocalization of mQTL and GWAS loci provides regulatory characterization of significant and subthreshold GWAS findings, supporting a role for genetic control of methylation in COPD pathogenesis.