An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare

Genetic effects on gene expression across human tissues.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

Genetics and genomics of endometriosis

Endometriosis is an estrogen-dependent, progesterone-resistant, inflammatory disease with symptoms that include pelvic pain, infertility, and compromised quality of life in millions of women worldwide. Approximately 50% of the risk of developing endometriosis is due to genetic factors with the remaining 50% due to environmental (i.e., exposome) causes. Treatments are hormonal, surgical, or both, with limited efficacy in the long term. Diagnosis of pelvic endometriosis is through visual identification and confirmatory histopathology of lesions. Recent innovations in genomics, genetics and epigenetics, molecular and cell biology, imaging, and a worldwide effort to standardize patient phenotyping and biospecimen collection have contributed to understanding mechanisms underlying the pathogenesis and pathophysiology of endometriosis. Furthermore, integration of “big data” obtained through these technologies holds great promise for novel targeted therapies, noninvasive diagnostics, and prognostic indicators. This chapter reviews current advances in genomics, genetics, and epigenetics of endometriosis that are providing translational approaches for preventing, diagnosing, and effectively treating this enigmatic disease