The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease

Shimoyama, Mary; Pons, Jeff De; Laulederkind, Stanley J. F.; Liu, Weisong; Nigam, Rajni; Petri, Victoria; Smith, Jennifer; Tutaj, Marek; Wang, Shur-Jen; Worthey, Elizabeth A.; Dwinell, Melinda R.; Jacob, Howard J.

doi:10.1093/nar/gku1026

Cited by 214 publications

(180 citation statements)

References 30 publications

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“…Rat expression data and GO terms are from RGD downloadable files. 22 Mouse expression data and GO terms are from MGI website. 9 Zebrafish data are from ZFIN downloadable files.…”

Section: Server and Data Storingmentioning

confidence: 99%

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Wang¹,

Al‐Ouran²,

Hu³

et al. 2017

The American Journal of Human Genetics

191

146

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One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

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“…Rat expression data and GO terms are from RGD downloadable files. 22 Mouse expression data and GO terms are from MGI website. 9 Zebrafish data are from ZFIN downloadable files.…”

Section: Server and Data Storingmentioning

confidence: 99%

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Wang¹,

Al‐Ouran²,

Hu³

et al. 2017

The American Journal of Human Genetics

191

146

View full text Add to dashboard Cite

show abstract

“…Moreover,COL6A6 (chr. 3: 130,274,400,888) is in the COPD-related regions based on the Rat Genome Database (RGD) (Shimoyama et al 2015). There are two known COPD-related regions, COPD14_H (chr.…”

Section: Analysis Of Genome-wide Lung Function Datamentioning

confidence: 99%

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

et al. 2015

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The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study. In a typical GWAS, large numbers SNPs are genotyped on hundreds or thousands of subjects, and each SNP is subsequently tested, one by one, for association with the phenotype of interest. However, this traditional single-marker association test is not powerful enough to detect rare variants that confer susceptibility to complex diseases (Li and Leal 2008;Schork et al. 2009). With the recent development of sequencing technology, identification of rare susceptibility variants for complex diseases has become feasible, provided that novel statistical methods are developed to obtain optimal results.To increase the power to detect association using rare susceptibility variants, many set-based statistics have been developed that evaluate the joint effect of a group of rare genetic variants in a predefined genomic region on the phenotype of interest (Morgenthaler and Thilly 2007; Leal 2008, 2009;Madsen and Browning 2009;Han and Pan 2010;Morris and Zeggini 2010;Price et al. 2010;Lin et al. 2011Lin et al. , 2012Lin et al. , 2013Lin et al. , 2014. One commonly used method is the sequence kernel machine-based association test (SKAT) (Wu et al.

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“…Monitoring the physiology of disease is thought to be easier, and for some traits (e.g., diabetes) the rat model is thought to resemble the human disease better (1). The Rat Genome Database (RGD; Box 1) provides a central resource for information on the different rat strains and includes genetic, genomic, and phenotypic data on the different models and bioinformatics tools (62). Until recently, the production of rat models with specific mutations or the generation of knockout or conditional strains was technically challenging because of issues with embryonic stem cell manipulation.…”

Section: Some Case Studies In Cardiometabolic Traits Illustrating Sommentioning

confidence: 99%

Genome-wide association studies and contribution to cardiovascular physiology

Munroe

Tinker

2015

Physiological Genomics

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The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease

Cited by 214 publications

References 30 publications

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

Genome-wide association studies and contribution to cardiovascular physiology

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