First of all, it is important to understand that genotyping data and GWAS data are central to eQTL studies. These two types of data provide the foundational genetic information needed to understand how variations in gene expression contribute to complex traits and diseases. eQTL studies represent a powerful tool in the field of genetics, offering valuable insights into the interplay between genes and their expression levels 1. To conduct these intricate analyses, researchers need access to reliable and comprehensive sources of both genotypic and phenotypic data.
Several established databases can provide these essential datasets:
- Mouse Phenome Database: This open-source resource offers an extensive collection of mouse genetic data, making it an invaluable tool for researchers focusing on murine models. The database includes data from multiple strains of mice, providing a broad spectrum of genetic diversity.
- GWAS Central: GWAS Central is a vital platform for anyone conducting genetic association studies. It provides access to summary-level findings from numerous studies worldwide, aiding researchers in identifying potential genetic associations with various traits and diseases.
- Mouse Genomes Project: An initiative by the Wellcome Trust Sanger Institute, the Mouse Genomes Project provides high-quality genome sequences of different laboratory mouse strains. This resource aids in the identification of variants, copy number changes, and structural variants.
- MGI-Mouse Genome Informatics: As a comprehensive resource, MGI offers integrated data on genetics, genomics, and biology, thus proving invaluable for researchers studying gene functionality and disease associations in mice.
- International Mouse Phenotyping Consortium (IMPC): IMPC, with its large-scale phenotyping repository, furnishes abundant data about gene function in mice, thus aiding researchers to correlate genotypes with observable phenotypes.
Upon obtaining the data, we can incorporate it into studies of expression quantitative trait loci (eQTLs). Scholars can discern statistically significant relationships between genetic variants and gene expression levels, thereby enriching our comprehension of the genetic basis of various intricate traits [2,3].
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- Zeng, B., Lloyd-Jones, L. R., Montgomery, G. W., Metspalu, A., Esko, T., Franke, L., ... & Gibson, G. (2019). Comprehensive multiple eQTL detection and its application to GWAS interpretation. Genetics, 212(3), 905-918.
- Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022). Genetic control of RNA splicing and its distinct role in complex trait variation. Nature Genetics, 54(9), 1355-1363.
- Zhang, J., Xie, S., Gonzales, S., Liu, J., & Wang, X. (2020). A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data. Genetic epidemiology, 44(6), 550-563.