Integrated analysis of multi-omic QTLs at single cell resolution - Project Summary Novel statistical and computational tools have enabled the broad adoption of genomics technologies and served as the foundation for the modern age of human genomics. Indeed, the recent advent and popularity of single cell genomics platforms – most notably single cell RNA sequencing (scRNA-seq) – has led to a proliferation of single- cell data processing, QC, and analysis frameworks. However, to fully realize the promise of single cell genomics approaches we need to connect cell-type level regulatory phenotypes with complex disease. The most promising approach to this challenge is to identify functionally relevant genetic variation by mapping quantitative trait loci (QTLs). Indeed, studies identifying regulatory variation associated with a number of regulatory phenotypes, including but not limited to gene expression (eQTLs), DNA methylation (meQTLs), chromatin accessibility (caQTLs), and protein (pQTLs), have been carried out extensively in bulk samples. These studies have advanced our understanding of the molecular underpinnings of complex disease, but the lack of granularity provided by bulk analyses continues to hinder progress. As we move these approaches towards the cell-type level analyses enabled by single cell genomics it has become clear that the methods developed for bulk samples are not well suited to handle the complexity and specific characteristics of single cell data; or indeed to take full advantage of the resolution and richness of single cell data. Here we propose developing, validating, and deploying methods for mapping QTLs using data from single cell `omics technologies. We believe it is critically important to build these methods using relevant data obtained from primary human tissue in a disease state. Thus, we will jointly collect scRNA-seq, scATAC-seq and single cell protein levels from two tissue types: lung and peripheral blood collected from patients with pulmonary fibrosis (PF) or healthy controls. This data collection will be facilitated by our existing biorepository and build upon our expertise building tools for analyzing genomic data, mapping QTLs for regulatory phenotypes, and analyzing scRNA-seq collected from lung tissue from patients with PF. Using these data we will build methods for univariate scQTL mapping, multi-omic scQTL mapping, the identification of context specific scQTLs, and integration of scQTL results with the results from GWAS studies. These methods will be released as open-source software packages enabling broad adoption by the field. Our team brings together unique expertise in statistical genomics, computational biology, functional genomics, single cell genomics, and disease specific expertise in PF making us particularly well suited to carry out this work.
