Alopecia Areata (AA) is a highly prevalent autoimmune disease in the US with a lifetime risk of 2.1%. In AA, autoimmunity develops against the hair follicles, which leads to infiltration of immune cells around affected follicles. Among genetic risk factors in complex autoimmune diseases, variants cluster in genes regulating the immune response, as well as the target organ. AA is believed to result from both genetic and environmental factors. To identify underlying genetic drivers in AA, we analyzed AA risk genes using various sequencing techniques and analysis methods to identify causal variants and placed them in functionally relevant contexts using innovative mapping techniques. To address the role of variants in immune function, we studied the Interleukin-2 Receptor Alpha (IL2RA), which we identified as a significant locus to study genetic factors underlying immune function from our AA GWAS studies (p=1.74*10-12)11. IL2RA plays a crucial role in regulating immune tolerance and controlling activity of regulatory T cells (Treg)13. We identified significant causal variants in the IL2RA region associated with AA using GWAS, targeted resequencing, and custom capture exome sequencing approaches. We validated the expression of these variants in immune cell cluster tissue types in silico, and specifically in CD4+ T cells. The variant rs3118740 increases AA susceptibility for carriers of the C allele. Such allele specific effects could lead to a perturbation of Treg function, for example, one study in T1D where patients with the rs3118470 risk variant have Treg with IL-2 signaling defects14. These studies demonstrated that identifying causal variants may lead to an improved understating of Treg function and risk of autoimmunity in AA. Next, to study genetic susceptibility in the target organ in AA, the hair follicle (HF), the second candidate GWAS susceptibility gene we studied was peroxiredoxin 5 (PRDX5) (p= of 8.7*10-14), which is also a GWAS gene in Crohn’s disease, sarcoidosis, and psoriasis15,16. PRDX5 is a member of the family of antioxidant enzymes that are crucial for regulating oxidative stress. Our lab performed whole exome sequencing in 849 AA patients, together with selected custom capture regions of genomic sequencing. Using a test of variant enrichment, we identified variants in PRDX5 that were significant in both our GWAS and exome studies, and thus represented likely candidate causal variants. Using Bayesian fine mapping, we identified a GWAS and exome sequencing variant, rs574087, that was significantly enriched in both, and is predicted to be a causal variant in keratinocytes and melanocytes. To functionally validate PRDX5, we immunostained healthy human HF and AA affected HF, and found that PRDX5 is upregulated AA human HF. PRDX5 is expressed in cultured melanocytes by immunostaining, which is consistent with melanocytes exhibiting high levels of oxidative stress. We postulated that PRDX5 may be involved in protection from oxidative stress, and that its dysregulation may contribute to autoimmunity. Finally, along with genetic predisposition, environmental triggers such as the microbiome have emerged as potential factors contributing to pathologic immune responses in autoimmune diseases. To determine the role of the microbiome in AA pathobiology, we performed 16S rRNA sequencing of skin swabs, hair follicles, and stool samples from a cohort of 34 AA patients and 12 healthy controls (HCs). Unexpectedly, we found evidence of striking gut dysbiosis, consisting of over-representation of Firmicutes and under-representation of Bacteroides in the gut microbiome of AA patients compared with healthy subjects, but no significant differences in skin or hair follicle (HF) microbiome composition. To investigate the role of the gut microbiome in AA development in vivo, we depleted the gut microbiome in C3H/HeJ mice and found that the mice were largely protected from AA developing. These data revealed a requirement for gut microbiota in the onset of murine AA. Taken together with recent reports in the literature of reversal of AA in several patients following fecal microbiota transplant (FMT)17,18, our findings suggest that restoring homeostasis of the gut microbiome may represent an effective new treatment modality in the management of AA.

Identifying disease risk genes is a central topic of human genetics. Cost-effective exome and whole genome sequencing enabled large-scale discovery of genetic variations. However, the statistical power of finding new risk genes through rare genetic variation is fundamentally limited by sample sizes. As a result, we have an incomplete understanding of genetic architecture and molecular etiology of most of human conditions and diseases. In this thesis, I developed new computational methods that integrate functional genomics data sets, such as epigenomic profiles and single-cell transcriptomics, to improve power for identifying genetic risks and gain more insights on etiology of developmental disorders. The overall hypothesis that disease risk genes contributing to developmental disorders are bottleneck genes under normal development and subject to precise transcriptional regulations to maintain spatiotemporal specific expression during development. In this thesis I describe two major research projects. The first project, Episcore, predicts haploinsufficient genes based on a large integrated epigenomic profiles from multiple tissues and cell lines by supervised machine learning methods. The second one, A-risk, predicts plausibility of being risk genes of autism spectrum disorder based on single-cell RNA-seq data collected in human fetal midbrain and prefrontal cortex. Both methods were shown to be able to improve gene discovery in analysis of de novo mutations in developmental disorders. Overall, my thesis represents an effort to integrate functional genomics data by machine learning to facilitate both discovery and interpretation of genetic studies of human diseases. We believe that such integrative analysis can help us better understand genetic variants and disease etiology.