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GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

ABSTRACT: Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of 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 consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. 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.

SUBMITTER: Ball RL

PROVIDER: S-EPMC10903950 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

Ball Robyn L RL Bogue Molly A MA Liang Hongping H Srivastava Anuj A Ashbrook David G DG Lamoureux Anna A Gerring Matthew W MW Hatoum Alexander S AS Kim Matthew J MJ He Hao H Emerson Jake J Berger Alexander K AK Walton David O DO Sheppard Keith K El Kassaby Baha B Castellanos Francisco F Kunde-Ramamoorthy Govindarajan G Lu Lu L Bluis John J Desai Sejal S Sundberg Beth A BA Peltz Gary G Fang Zhuoqing Z Churchill Gary A GA Williams Robert W RW Agrawal Arpana A Bult Carol J CJ Philip Vivek M VM Chesler Elissa J EJ

Genome research 20240207 1

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of 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 consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome ...[more]

PMID: 38290977

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Project description:AimThe genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets.MethodsWe used two data sets for East Asian (EAS) and European (EUR) samples. SCZ data was based on the Psychiatric Genomics Consortium Asia with our own genome-wide association study for EAS and Psychiatric Genomics Consortium for EUR. Nonpsychiatric data (20 binary traits [mainly nonpsychiatric complex disorders] and 34 quantitative traits [mainly laboratory examinations and physical characteristics]) were obtained from Biobank Japan and UK Biobank for EAS and EUR samples, respectively. To evaluate genetic correlation, linkage disequilibrium score regression analysis was utilized with further meta-analysis for each result from EAS and EUR samples to obtain robust evidence. Subsequent mendelian randomization analysis was also included to examine the causal effect.ResultsA significant genetic correlation between SCZ and several metabolic syndrome (MetS) traits was detected in the combined samples (meta-analysis between EAS and EUR data) (body mass index [rg = -0.10, q-value = 1.0 × 10-9 ], high-density-lipoprotein cholesterol [rg = 0.072, q-value = 2.9 × 10-3 ], blood sugar [rg = -0.068, q-value = 1.4 × 10-2 ], triglycerides [rg = -0.052, q-value = 2.4 × 10-2 ], systolic blood pressure [rg = -0.054, q-value = 3.5 × 10-2 ], and C-reactive protein [rg = -0.076, q-value = 7.8 × 10-5 ]. However, no causal relationship on SCZ susceptibility was detected for these traits based on the mendelian randomization analysis.ConclusionOur results indicate shared genetic components between SCZ and MetS traits and C-reactive protein. Specifically, we found it interesting that the correlation between MetS traits and SCZ was the opposite of that expected from clinical studies: this genetic study suggests that SCZ susceptibility was associated with reduced MetS. This implied that MetS in patients with SCZ was not associated with genetic components but with environmental factors, including antipsychotics, lifestyle changes, poor diet, lack of exercise, and living conditions.

Project description:Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Dataset Information

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

Publications

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

Similar Datasets

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