Biology subjects

Cheverud, J. M.

Publications and source records attributed to Cheverud, J. M..

4 recordsLinked to original sources

bioRxiv2021

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N=10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

genetics↗

bioRxiv2021

Parent-of-origin effects propagate through networks to shape metabolic traits

Parent-of-origin effects are unexpectedly common in complex traits, including metabolic and neurological diseases. Parent-of-origin effects can be modified by the environment, but the architecture of these gene-by-environmental effects on phenotypes remains to be unraveled. Previously, quantitative trait loci (QTL) showing context-specific parent-of-origin effects on metabolic traits were mapped in the F16 generation of an advanced intercross between LG/J and SM/J inbred mice. However, these QTL were not enriched for known imprinted genes, suggesting another mechanism is needed to explain these parent-of-origin effects phenomena. We propose that non-imprinted genes can generate complex parent-of-origin effects on metabolic traits through interactions with imprinted genes. Here, we employ data from mouse populations at different levels of intercrossing (F0, F1, F2, F16) of the LG/J and SM/J inbred mouse lines to test this hypothesis. Using multiple populations and incorporating genetic, genomic, and physiological data, we leverage orthogonal evidence to identify networks of genes through which parent-of-origin effects propagate. We identify a network comprised of 3 imprinted and 6 non-imprinted genes that show parent-of-origin effects. This epistatic network forms a nutritional responsive pathway and the genes comprising it jointly serve cellular functions associated with growth. We focus on 2 genes, Nnat and F2r, whose interaction associates with serum glucose levels across generations in high fat-fed females. Single-cell RNAseq reveals that Nnat and F2r are negatively correlated in pre-adipocytes along an adipogenic trajectory, a result that is consistent with our observations in bulk white adipose tissue.

genomics↗

bioRxiv2019

Epistatic Networks Associated with Parent-of-Origin Effects on Metabolic Traits

Parent-of-origin effects (POE) are unexpectedly common in complex traits, including metabolic and neurological diseases. POE can also be modified by the environment, but the architecture of these gene-by-environmental effects on phenotypes remains to be unraveled. Previously, quantitative trait loci (QTL) showing context-specific POE on metabolic traits were mapped in the F16 generation of an advanced intercross between LG/J and SM/J inbred mice. However, these QTL were not enriched for known imprinted genes, suggesting another mechanism is needed to explain these POE phenomena. Here, we use a simple yet powerful F1 reciprocal cross model to test the hypothesis that non-imprinted genes can generate complex POE on metabolic traits through genetic interactions with imprinted genes. Male and female mice from a F1 reciprocal cross of LG/J and SM/J strains were fed either high or low fat diets. We generated expression profiles from three metabolically-relevant tissues: hypothalamus, white adipose, and liver. We identified two classes of parent-of-origin expression biases: genes showing parent-of-origin-dependent allele-specific expression and biallelic genes that are differentially expressed by reciprocal cross. POE patterns of both gene classes are highly tissue-and context-specific, sometimes occurring only in one sex and/or diet cohort in a particular tissue. We then constructed tissue-specific interaction networks among genes from these two classes of POE. A key subset of gene pairs show significant epistasis in the F16 LG/J x SM/J advanced intercross data in cases where the biallelic gene fell within a previously-identified metabolic POE QTL interval. We highlight one such interaction in adipose, between Nnat and Mogat1, which associates with POE on multiple adiposity traits. Both genes localize to the endoplasmic reticulum of adipocytes and play a role in adipogenesis. Additionally, expression of both genes is significantly correlated in human visceral adipose tissue. The genes and networks we present here represent a set of actionable interacting candidates that can be probed to further identify the machinery driving POE on complex traits.

systems biology↗

bioRxiv2019

The fitness cost of a congenital heart defect shapes its genetic architecture

BackgroundIn newborns, severe congenital heart defects are rarer than mild ones. The reason why is unknown, but presumably related to a liability threshold that rises with the severity of a defect. Because the same genetic mutation can cause different defects, other variables may contribute to pushing an individual past a defect-specific liability threshold. We consider here how variables in the genetic architecture of a heart defect depend upon its fitness cost, as defined by the likelihood of survival to reproductive age in natural history studies. MethodsWe phenotyped ~10,000 Nkx2-5+/- newborn mice, a model of human congenital heart disease, from two inbred strain crosses. Genome-wide association analyses detected loci that modify the risk of an atrial septal defect, membranous or muscular ventricular septal defect, or atrioventricular septal defect. The number of loci, heritability and quantitative effects on risk of pairwise (GxGNkx) and higher-order (GxGxGNkx) epistasis between the loci and Nkx2-5 mutation were examined as a function of the fitness cost of a defect. ResultsNkx2-5+/- mice have pleiotropic heart defects; about 70% have normal hearts. The model recapitulates the epidemiological relationship between the severity and incidence of a heart defect. Neither the number of modifier loci nor heritability depends upon the severity of a defect, but GxGNkx and GxGxGNkx effects on risk do. Interestingly, GxGxGNkx effects are three times more likely to suppress risk when the genotypes at the first two loci are homozygous and from the same, rather than opposite strains in a cross. Syn- and anti-homozygous genotypes at GxGxGNkx interactions can have an especially large impact on the risk of an atrioventricular septal defect. ConclusionsGiven a modestly penetrant mutation, epistasis contributes more to the risk of severe than mild congenital heart defect. Conversely, genetic compatibility between interacting genes, as indicated by the protective effects of syn-homozygosity at GxGxGNkx interactions, plays a newfound role in the robustness of cardiac development. The experimental model offers practical insights into the nature of genetic risk in congenital heart disease. The results more fundamentally address a longstanding question regarding how mutational robustness could arise from natural selection.

genetics↗