Biology subjects

Sheppard, K.

Publications and source records attributed to Sheppard, K..

3 recordsLinked to original sources

bioRxiv2023

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter (https://muster.jax.org), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

genetics↗

bioRxiv2020

Gait-level analysis of mouse open field behavior using deep learning-based pose estimation

1Gait and whole body posture are sensitive measures of the proper functioning of numerous neural circuits, and are often perturbed in many neurological, neuromuscular, and neuropsychiatric illnesses. Rodents provide a tractable model for elucidating disease mechanisms and interventions, however, studying gait and whole body posture in rodent models requires specialized methods and remains challenging. Here, we develop a simple assay that allows adoption of the commonly used open field apparatus for gait and whole body posture analysis. We leverage modern neural networks to abstract a mouse into keypoints and extract gait and whole body coordination metrics of the animal. Gait-level analysis allows us to detect every step of the animals movement and provides high resolution information about the animals behavior. We quantitate gait and whole body posture with high precision and accuracy across 62 highly visually diverse strains of mice. We apply our approach to characterize four genetic mutants with known gait deficits. In extended analysis, we demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. We conduct a large strain survey of 1898 mice, and find that gait and whole body posture measures are highly heritable in the laboratory mouse, and fall into three classes. Furthermore, the reference mouse strain, C57BL/6J, has a distinctly different gait and posture compared to other standard laboratory and wild-derived strains. We conduct a genome wide association study (GWAS) to define the genetic architecture of mouse movement in the open field. In sum, we describe a simple, sensitive, accurate, scalable, and ethologically relevant method of mouse gait and whole body posture analysis for behavioral neurogenetics. These results provide one of the largest laboratory mouse gait-level data resources for the research community and show the utility of automated machine learning approaches for deriving biological insights.

animal behavior and cognition↗

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↗