Objective. To determine the stage of B cell development at which a systemic lupus erythematosus (SLE)associated DNA methylation signature originates in African American (AA) and European American (EA) subjects, and to assess whether epigenetic defects in B cell development patterns could be predictive of SLE status in individual and mixed immune cell populations.Methods. B cells from AA patients (n = 31) and EA patients (n = 49) with or without SLE were sorted using fluorescence-activated cell sorting into 5 B cell subsets. DNA methylation, measured at ~460,000 CpG sites, was interrogated in each subset. Enrichment analysis of transcription factor interaction at SLE-associated methylation sites was performed. A random forests algorithm was used to identify an epigenetic signature of SLE in the B cell subsets, which was then validated in an independent cohort of AA and EA patients and healthy controls.Results. Regression analysis across all B cell stages resulted in identification of 60 CpGs that reached genomewide significance for SLE-associated methylation differences (P ≤ 1.07 × 10 −7 ). Interrogation of ethnicity-specific CpGs associated with SLE revealed a hypomethylated pattern that was enriched for interferon (IFN)-regulated genes and binding of EBF1 in AA patients (each P < 0.001). AA patients with SLE could be distinguished from healthy controls when the predictive model developed with the transitional B cell subset was applied to other B cell subsets (mean receiver operating characteristic [ROC] area under the curve [AUC] 0.98), and when applied to CD19+ pan-B cells (mean ROC AUC 0.95) and CD4+ pan-T cells (mean ROC AUC 0.97) from the independent validation cohort.Conclusion. These results indicate that SLE-specific methylation patterns are ethnicity dependent. A pattern of epigenetic changes near IFN-regulated genes early in B cell development is a hallmark of SLE in AA female subjects. EBF1 binding sites are highly enriched for significant methylation changes, implying that this may be a potential regulator of SLE-associated epigenetic changes.

Background: Recent studies investigating longevity have revealed very few convincing genetic associations with increased lifespan. This is, in part, due to the complexity of biological aging, as well as the limited power of genome-wide association studies, which assay common single nucleotide polymorphisms (SNPs) and require several thousand subjects to achieve statistical significance. To overcome such barriers, we performed comprehensive DNA sequencing of a panel of 20 genes previously associated with phenotypic aging in a cohort of 200 individuals, half of whom were clinically defined by an "early aging" phenotype, and half of whom were clinically defined by a "late aging" phenotype based on age (65-75 years) and the ability to walk up a flight of stairs or walk for 15 min without resting. A validation cohort of 511 late agers was used to verify our results. Results: We found early agers were not enriched for more total variants in these 20 agingrelated genes than late agers. Using machine learning methods, we identified the most predictive model of aging status, both in our discovery and validation cohorts, to be a random forest model incorporating damaging exon variants [Combined Annotation-Dependent Depletion (CADD) > 15]. The most heavily weighted variants in the model were within poly(ADP-ribose) polymerase 1 (PARP1) and excision repair cross complementation group 5 (ERCC5), both of which are involved in a canonical aging pathway, DNA damage repair. Conclusion: Overall, this study implemented a framework to apply machine learning to identify sequencing variants associated with complex phenotypes such as aging. While the small sample size making up our cohort inhibits our ability to make definitive conclusions about the ability of these genes to accurately predict aging, this study offers a unique method for exploring polygenic associations with complex phenotypes.