Biology subjects

Churchill, G. A.

Publications and source records attributed to Churchill, G. A..

7 recordsLinked to original sources

bioRxiv2018

R/qtl2: software for mapping quantitative trait loci with high dimensional data and multi-parent populations

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multi-parent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2 comes with a test framework and is free and open source software written in the R and C++ programming languages.

genetics↗

bioRxiv2018

Accurate estimation of single cell allele-specific gene expression using all reads and combining information across cells

Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We propose a new method for ASE analysis from single cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells.

bioinformatics↗

bioRxiv2018

Genetic mapping in Diversity Outbred mice identifies a Trpa1 variant influencing late phase formalin response

Identification of genetic variants that influence susceptibility to chronic pain is key to identifying molecular mechanisms and targets for effective and safe therapeutic alternatives to opioids. To identify genes and variants associated with chronic pain, we measured late phase response to formalin injection in 275 male and female Diversity Outbred (DO) mice genotyped for over 70 thousand SNPs. One quantitative trait locus (QTL) reached genome-wide significance on chromosome 1 with a support interval of 3.1 Mb. This locus, Nociq4 (nociceptive sensitivity inflammatory QTL 4; MGI:5661503), harbors the well-known pain gene Trpa1 (transient receptor potential cation channel, subfamily A, member 1). Trpa1 is a cation channel known to play an important role in acute and chronic pain in both humans and mice. Analysis of DO founder strain allele effects revealed a significant effect of the CAST/EiJ allele at Trpa1, with CAST/EiJ carrier mice showing an early, but not late, response to formalin relative to carriers of the seven other inbred founder alleles (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, PWK/PhJ, and WSB/EiJ). We characterized possible functional consequences of sequence variants in Trpa1 by assessing channel conductance, Trpa1/Trpv1 interactions, and isoform expression. The phenotypic differences observed in CAST/EiJ relative to C57BL/6J carriers were best explained by Trpa1 isoform expression differences, implicating a splice junction variant as the causal functional variant. This study demonstrates the utility of advanced, high-precision genetic mapping populations in resolving specific molecular mechanisms of variation in pain sensitivity.

bioinformatics↗

bioRxiv2017

Hierarchical Analysis of Multi-mapping RNA-Seq Reads Improves the Accuracy of Allele-specific Expression

Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. Direct RNA sequencing (RNA-Seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. However, estimating ASE is challenging due to ambiguities in read alignment. Current approaches do not account for the hierarchy of multiple read alignments to genes, isoforms, and alleles. We have developed EMASE (Expectation-Maximization for Allele Specific Expression), an integrated approach to estimate total gene expression, ASE, and isoform usage based on hierarchical allocation of multi-mapping reads. In simulations, EMASE outperforms standard ASE estimation methods. We apply EMASE to RNA-Seq data from F1 hybrid mice where we observe widespread ASE associated with cis-acting polymorphisms and a small number of parent-of-origin effects at known imprinted genes. The EMASE software is freely available under GNU license at https://github.com/churchill-lab/emase and it can be adapted to other sequencing applications.

bioinformatics↗

bioRxiv2017

High-Resolution Maps of Mouse Reference Populations

Genetic reference panels are widely used to map complex, quantitative traits in model organisms. We have generated new high-resolution genetic maps of 259 mouse inbred strains from recombinant inbred strain panels (C57BL/6J x DBA/2J, ILS/IbgTejJ x ISS/IbgTejJ, C57BL/6J x A/J) and chromosome substitution strain panels (C57BL/6J-Chr# , C57BL/6J-Chr# , C57BL/6JChr# ). We genotyped all samples using the Affymetrix Mouse Diversity Array with an average inter-marker spacing of 4.3kb. The new genetic maps provide increased precision in the localization of recombination breakpoints compared to the previous maps. Although the strains were presumed to be fully inbred, we found residual heterozygosity in 40% of individual mice from five of the six panels. We also identified de novo deletions and duplications, in homozygous or heterozygous state, ranging in size from 21kb to 8.4Mb. Almost two-thirds (46 out of 76) of these deletions overlap exons of protein coding genes and may have phenotypic consequences. Twenty-nine putative gene conversions were identified in the chromosome substitution strains. We find that gene conversions are more likely to occur in regions where the homologous chromosomes are more similar. The raw genotyping data and genetic maps of these strain panels are available at http://churchill-lab.jax.org/website/MDA.

genetics↗

bioRxiv2017

The Effects of Sex and Diet on Physiology and Liver Gene Expression in Diversity Outbred Mice

Inter-individual variation in metabolic health and adiposity is driven by many factors. Diet composition and genetic background and the interactions between these two factors affect adiposity and related traits such as circulating cholesterol levels. In this study, we fed 850 Diversity Outbred mice, half females and half males, with either a standard chow diet or a high fat, high sucrose diet beginning at weaning and aged them to 26 weeks. We measured clinical chemistry and body composition at early and late time points during the study, and liver transcription at euthanasia. Males weighed more than females and mice on a high fat diet generally weighed more than those on chow. Many traits showed sex- or diet-specific changes as well as more complex sex by diet interactions. We mapped both the physiological and molecular traits and found that the genetic architecture of the physiological traits is complex, with many single locus associations potentially being driven by more than one polymorphism. For liver transcription, we find that local polymorphisms affect constitutive and sex-specific transcription, but that the response to diet is not affected by local polymorphisms. We identified two loci for circulating cholesterol levels. We performed mediation analysis by mapping the physiological traits, given liver transcript abundance and propose several genes that may be modifiers of the physiological traits. By including both physiological and molecular traits in our analyses, we have created deeper phenotypic profiles to identify additional significant contributors to complex metabolic outcomes such as polygenic obesity. We make the phenotype, liver transcript and genotype data publicly available as a resource for the research community.

genetics↗

bioRxiv2017

Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in Diversity Outbred mice

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. Multi-parent populations provide a resource for developing methods to understand these relationships. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Using weighted gene co-expression network analysis (WGCNA), we summarized transcript data into functional modules which we then used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects. We were also able to identify alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

genetics↗