Tag: eQTL

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

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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/

Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder

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Variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits. Genome-wide characterization of gene expression is an important tool in genetic mapping studies of complex disorders, including many psychiatric disorders. Further, implicating eQTL to specific tissue types is key to understanding functional variation in disease development. Our group, in collaboration with Chiara Sabatti (Statistics, Stanford) and Nelson B. Freimer (David Geffen School of Medicine, UCLA), developed a novel approach for analyzing eQTL and applied the method to a dataset from a bipolar disorder study.

Current approaches to implicating eQTL specific to tissues lack sufficient power in large-scale studies of human brain related traits, such as bipolar disorder. Together with the University of California San Francisco, Universidad de Costa Rica, Universidad de Antioquia, Medellín, Colombia, and Tel Aviv University, our group adopted a novel approach to assess the heritability and genetic regulation of gene expression related to bipolar disorder in populations from Costa Rica and Colombia.

This project examines 786 genotyped subjects originally recruited in a study of bipolar disorder, all related within 26 extended families. While the subjects in this study were originally recruited as part of an investigation for severe bipolar disorder (BP1), we found no relationship between the observed gene expression data and BP1. Instead, we use this unique Latin American population to explore the architecture of genetic regulation. Specifically, we estimate heritability, evaluate the relative importance of local vs. distal genomic variation, identify variants with regulatory effects, and analyze the role of multiple associated SNPs in the same region.

Our group adopted a novel hierarchical testing procedure that leads to the analysis of eQTL data in a stage-wise manner with increasing levels of detail. This design allows us to compare estimates of the heritability of gene expression obtained using both traditional and genotype-based methods. First, we apply a multiscale testing strategy to identify SNPs that have regulatory effects (eSNPs) on BP1. Second, we investigate which specific probes are influenced by these eSNPs. This hierarchical testing procedure effectively controls error rates and leverages the heterogeneity across genetic variants to preserve computational power.

We use this approach to measure gene expression in lymphoblastoid cell lines (LCLs) in subjects from extended families, segregating for BP1. Our results suggest that variation in expression values is heritable and that, at least in samples including related individuals, relying on theoretical kinship coefficients or on realized genotype correlation for estimation of heritability leads to similar results.

Expression heritability and proportion of genetic variance due to local effects. For more information, see our paper. For more information, see our paper.

Variance decomposition approaches suggest that on average 30% of the genetic variance is due to local regulation. In the majority of probes under local regulation in our sample, more than one typed SNP is required to account for expression variation. This finding can be interpreted as the result of heterogeneity, but also could reflect un-typed causal variants that are tracked by more than one typed SNP.

The knowledge we acquired by studying the genetic regulatory network within these pedigrees, instead, can be used to inform our mapping studies: eSNPs might receive a higher prior probability of association, or be assigned a larger portion of the allowed global error rate when using a weighted approach to testing. We will report elsewhere on the results of these investigations.

For more information, see our paper, which is available for download through PLoS Genetics: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006046.

The full citation to our paper is: 

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Peterson, C.B., Jasinska, A.J., Gao, F., Zelaya, I., Teshiba, T.M., Bearden, C.E., Cantor, R.M., Reus, V.I., Macaya, G., López-Jaramillo, C. and Bogomolov, M., 2016. Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder. PLoS Genet, 12(5), p.e1006046.

 

Colocalization of GWAS and eQTL Signals Detects Target Genes

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Farhad Hormozdiari recently developed a method for combining genome-wide association studies (GWASs) and quantitative trait loci (eQTL) studies in a statistical framework that quantifies the probability of each variant to be causal while allowing an arbitrary number of causal variants. Together with collaborators at the University of Oxford and Broad Institute of MIT and Harvard, we present a paper in The American Journal of Human Genetics. Here, we describe eQTL and GWAS CAusal Variants Identification in Associated Regions (eCAVIAR). We apply our approach to datasets from several GWASs and eQTL studies in order to assess its accuracy and potential contributions to colocalization and fine-mapping.

Integrating GWASs and eQTL studies is a promising way to explore the mechanism of non-coding variants on diseases. Integration of GWAS and eQTL data is challenging due to the uncertainty induced by linkage disequilibrium (LD), the non-random association of alleles at different loci, and presence of loci that harbor multiple causal variants (allelic heterogeneity). Current methods assume that each locus contains a single causal variant and expect loci to be independent and associated randomly.

eCAVIAR is a novel probabilistic model for integrating GWAS and eQTL data that extends the CAVIAR (Hormozdiari et al. 2014) framework to explicitly estimate the posterior probability of the same variant being causal in both GWAS and eQTL studies, while accounting for allelic heterogeneity and LD. Our approach can quantify the strength between a causal variant and its associated signals in both studies, and it can be used to colocalize variants that pass the genome-wide significance threshold in GWAS. For any given peak variant identified in GWAS, eCAVIAR considers a collection of variants around that peak variant as one single locus.

We apply eCAVIAR to the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) dataset and GTEx dataset to detect the target gene and most relevant tissue for each GWAS risk locus. When applied to the MAGIC dataset’s 2 phenotypes, eCAVIAR identifies genetic variants that are causal in both eQTL and GWAS. Further, eCAVIAR detects a large number of loci where the GWAS causal variants are clearly distinct from the causal variants in the eQTL data. Interestingly, eCAVIAR also identifies genes that colocalize in one tissue yet can be excluded in others. For the majority of loci in which we identify a single variant causal for both GWAS and eQTL, eCAVIAR implicates more than one causal variant across the 45 tissues.

We observe that eCAVIAR outperforms existing methods even when there are different values of non-colocalization. Using simulated datasets, we compared accuracy, precision, and recall rate of eCAVIAR to RTC (Nica et al. 2010) and COLOC (Giambartolomei et al. 2014), two current methods for eQTL and GWAS colocalization. Our results show that eCAVIAR has high confidence for selecting loci to be colocalized between the GWAS and eQTL data and is conservative in selecting a locus to be colocalized.

We hope that future applications of eCAVIAR will advance identification of specific GWAS loci that share a causal variant with eQTL studies in a tissue, thus providing insight into presently unclear disease mechanisms.

Figure2

Overview of eCAVIAR.

 

eCAVIAR was created by Farhad Hormozdiari, Ayellet V. Segre, Martijn van de Bunt, Xiao Li, Jong Wha J Joo, Michael Bilow, Jae Hoon Sul, Bogdan Pasaniuc and Eleazar Eskin. The article is available at: http://www.cell.com/ajhg/abstract/S0002-9297(16)30439-6.

Visit the following page to download CAVIAR and eCAVIAR: http://genetics.cs.ucla.edu/caviar/

The full citation to our paper is:

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Our paper builds upon a method introduced in a previous publication:

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