Project description:An overall goal of functional genomics has been to measure the impact of variants on molecular endophenotypes (e.g. gene expression levels or the degree of TF binding) and relate this to organismal traits and disease phenotypes. However, all the experiments to date have been described relative to a generic reference genome, significantly hobbling their interpretation. Here, we describe a strategy for finding significant relationships between disease variation and genomic annotation via personal functional genomics, by performing personal genome sequencing and paired functional genomics experiments, on the same individual.
Project description:Here, we have profiled 386 individuals in 80 cohorts of the BXD mouse genetic reference population across two environmental states through a metabolic phenotyping program including glucose response, exercise capacity, and cold response. To understand how the observed phenotypic differences are related to genetic variance, we generated a multilayered set of molecular phenotypes—genomics, transcriptomics, proteomics, and metabolomics across all cohorts, then modeled these molecular patterns with the phenotypic variance.
Project description:Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel measurements across thousands of genes and gene products. Such high-throughput technologies have been extensively used to carry out genome-wide studies particularly in the context of diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome, and proteome of a single mammalian cell type to obtain a coherent view of the complex interplay between omes has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells, revealing hundreds of unannotated mRNA transcripts, miRNAs, pseudogenes, and noncoding RNAs. Additionally, we carried out a comparative analysis of naïve CD4+ T cells with primary resting memory CD4+ T cells, which have provided novel insights into T cell biology. Overall, our data will serve as a baseline reference of a single pure population of cells for future systems level analysis of other defined cell populations.
