Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes

In a recent project, Farhad Hormozdiari and Eleazar Eskin contributed data analysis and interpretation to a project identifying new genes and genomic regions associated with metabolic function in mice. Our paper presents a comprehensive picture of the transcriptome of the mouse hypothalamus and its genetic variation and regulation. This project, which was published in eLife, was led by fellow UCLA researchers Yehudit Hasin-Brumshtein, Jake Lusis, and Desmond Smith.

Mice and humans share virtually the same set of genes; thus, mapping the mouse genome is an important step toward understanding genetic factors in common, complex human diseases such as obesity, heart disease, and diabetes. In metabolic tissues, the integration of genome-wide expression profiles with genetic and phenotypic variance can provide valuable insight into a disease’s underlying molecular mechanism. Measuring gene activity can reveal new molecules that clinical translation efforts may target to treat metabolic disorders.

Our project uses RNA-Seq to characterize transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. Mice were fed a high, high sugar diet, and all strains were comprehensively genotyped and phenotyped for 150 metabolic traits. Our study examines tissues relevant to the hypothalmus, the brain region that controls metabolism and regulates body weight and appetite.

We sequenced 285 samples from all 99 strains of the HMDP. Using methods described in our paper, we identified thousands of new isoforms and >400 new genes. The HMDP allowed us to map Quantitative Trait Loci (eQTLs) with high resolution and power, identifying both local and trans acting variants—or, variants that affect a molecule from within and from outside, respectively.

Groups of genes are associated with multiple related phenotypes in HMDP, although not necessarily enriched for GO ontology or specific pathways. For more information, see our paper.

We report numerous novel transcripts supported by proteomic analyses, as well as novel non-coding RNAs. High resolution genetic mapping of transcript levels in HMDP reveals both local and trans expression eQTLs, identifying two trans eQTL ’hotspots’ associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. We further showed that the genes associated with trans eQTL hotspots correlate to physiological phenotypes, such as HDL and triglyceride levels. This discovery provides insight into the mechanism behind correlation of these genotypes with complex traits.

Our data capture the various non-neuronal cell types, such as microglia or astrocytes, which are often overlooked in the mostly neuron focused studies of the hypothalamus. These cells are important mediators of hypothalamic inflammation and other processes induced by a high fat diet. Regulation of gene expression in these cell types impacts every aspect of metabolism, and our data provide a robust framework recapitulating transcriptional processes affecting multiple cell populations. Our approach is thus complementary to on-going cell type-specific transcriptomic efforts.

For more information, see our paper, which is available for download through eLife: https://elifesciences.org/content/5/e15614.

The full citation to our paper is: 

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See our blog post on a recent paper reviewing the HMDP data set: http://www.zarlab.xyz/the-hybrid-mouse-diversity-panel-a-resource-for-systems-genetics-analyses-of-metabolic-and-cardiovascular-traits/

Review Article: The Hybrid Mouse Diversity Panel

This year, we published a review of studies on the Hybrid Mouse Diversity Panel (HMDP) dataset, a project led by Aldons J. Lusis (David Geffen School of Medicine at UCLA). Our paper in Journal of Lipid Research describes the dataset, summarizes current discoveries facilitated by the dataset, and explains how researchers can use correlation, genetic mapping, and statistical modeling methods with HMDP data to address cardiometabolic questions.

The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated.

Since its development in 2010, studies using the HMDP have validated over a dozen novel genes underlying complex traits. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of mice subjected to various environmental conditions. These analyses have identified many novel genes and significant loci associated with disease risk relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, and fatty liver disease.

The HMDP has substantial potential to advance interdisciplinary research on genetics and computational biology. In order to make HMDP and associated methods accessible to cardiometabolic researchers, our paper includes a glossary of genetics terms and an outline of how the database can be interrogated to address certain questions using correlation, genetic mapping, and statistical modeling.

All of the published data are available and can be readily used to formulate hypotheses about genes, pathways, and interactions. For more information about HMDP, read our article: https://www.ncbi.nlm.nih.gov/pubmed/27099397

The full citation to our paper is:

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Hypothetical examples of how information from the HMDP can be utilized to explore relationships between genes (A) and traits (B) of interest. Read our paper for more information on methods for exploring their relationships with multiple layers of information.

Identifying Genes Involved in Blood Cell Traits


In this study, blood cell traits were collected from each strain in the HMDP panel which consists of 100 mouse strains. Using EMMA(10.1534/genetics.107.080101), we identified associations with these traits. The main advantage of the HMDP compared to the traditional genetic cross approach is the increase in resolution of the association.

We identified a particularly striking association with mean corpuscular volume (MCV).  The figure from the paper shows both the manhattan plot for the HMDP as well as the linkage plot from a genetic cross examining the same trait for chromosome 7.  This example clearly shows the advantge of the HMDP compared to the cross in terms of resolution of the association.  The peak is less than 1 Mb from Hbb-b1 which has been previously suggested to affect this trait.

Some reviews covering the HMDP and mouse genetics more broadly are available here.

Full Citation:
Davis, Richard C, Atila van Nas, Brian Bennett, Luz Orozco, Calvin Pan, Christoph D Rau, Eleazar Eskin, and Aldons J Lusis. 2013. Genome-wide association mapping of blood cell traits in mice. Mamm Genomedoi:10.1007/s00335-013-9448-0

Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75 Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice