NLM IRP Seminar Schedule

Abstract:

PCOS is a complex disorder manifesting as reproductive and metabolic abnormalities in women. The undefined etiology stems from our incomplete understanding of genetic aberrations contributing to the diseased phenotype. This has resulted in speculations surrounding the cause-consequence relationship between phenotypic features of PCOS and limited our knowledge of comprehensive genetic signatures of PCOS.

Pleiotropic effects are salient features of genetic regulation in mammalian genomes. The contribution of a set of regulatory elements (REs) to upregulation of a single gene, or the genetic control of multigene loci by a single RE, give rise to heterogenous phenotypes. Enhancers in particular dictate the spatio-temporal scale of gene expression by controlling transcription factor (TF) binding events. The complex traits of PCOS are suggestive of pleiotropy originating from genetic and epigenetic changes that arise from nucleotide variations in REs of the genome. Genome Wide Association Studies (GWAS) have identified ~100 risk variants associated with PCOS and implicated the involvement of several genes such as FSHB/R, THADA, DENND1A, etc. However, mechanisms by which they contribute to pathophysiology are unknown. Additionally, twin studies have implicated the heritability of PCOS to be ~80% and yet, the heritability from GWAS variants accounts for less than 10%. Experimental data may account for the ‘missing heritability problem’ specifically through rare variants that are missed in GWAS.

To address this issue, we developed a Deep Learning model of gene regulation in PCOS identifying hundreds of active REs orchestrating the activity of PCOS genes. We also established a foundation for quantifying the impact of single-nucleotide noncoding mutations on TF binding and RE activity. By combining thousands of experimental assays associated with many biological events such as tissue-specific epigenetic maps of the human genome, multi-tissue profiling of TF binding and gene expression using data from high throughput experiments, we have devised a strategy to accurately identify functionally map causative mutations in the GWAS loci of PCOS genes.