Blog:Tools and Future of eQTL: Data Gathering and Application in Genetic Medicine

Question

[Novogene]

Emerging evidence indicates that single nucleotide polymorphisms (SNPs) linked to complex traits are frequently associated with expression Quantitative Trait Loci (eQTLs). The eQTLs technique is a key concept in genomics and genetic research, helping to bridge the gap between genotype and phenotype and providing vital insights into numerous complex traits and diseases.

This article delves into the potential applications of eQTLs, underlining their instrumental role in advancing our understanding of the genetic factors contributing to diseases and complex traits. It emphasizes the significance of genotyping and Genome-Wide Association Study (GWAS) data in conducting eQTL studies, tendering a detailed enumeration of databases serving as reliable sources for these datasets.

Furthermore, we discuss the transformative impact of these predictive tools and their role in advancing therapeutic strategies for genetic diseases. This exploration provides an enlightening illumination of the potential of eQTL studies in genetic medicine research.

Where to Get Genotyping Data/GWAS Data

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.

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 eQTLs. Researchers 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]. Consequently, these databases are instrumental in propelling genetic research forward and shedding light on the mechanisms at play in complex diseases.

Future Outlook on eQTLs: AI and Therapeutic Strategies for Genetic Diseases

Another essential aspect to grasp about this niche is its link to artificial intelligence (AI). The integration of machine learning and advanced computational methodologies has catalyzed a profound evolution in genetic research, especially in the study of eQTLs [4]. These innovative techniques allow researchers to decipher the convoluted genetic structures that underpin gene expression traits using a nuanced approach.

Leveraging comprehensive datasets that compile both genotypic and phenotypic data, machine learning algorithms, and sequence models driven by deep learning can identify intricate patterns within this information. This capability enhances the accurate prediction of eQTLs [5]. The transformative influence of these predictive tools cannot be overstated. They significantly deepen our comprehension of the interplay between genetic variances and gene expression, illuminating the functional implications of genetic variants and improving phenotype predictions’ accuracy [6].

Furthermore, these innovative methods assist in identifying novel eQTLs, empowering researchers to uncover new genetic links to diseases and traits [7] and facilitating the exploration of additional layers of gene regulation, such as interactions between eQTLs.

Beyond discovery, these tools also play a crucial role in translating findings from eQTL studies into practical therapeutic strategies for genetic diseases. Despite the complexities involved, including rigorous validation of identified eQTLs and understanding their functional consequences [8], the challenges are navigated with precision and expertise.

Significant advancements have been made in the field, with techniques like single-cell eQTL mapping leading to considerable progress in identifying cell type–specific genetic control of diseases [9].

References

1. 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.
2. 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.
3. 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.
4. Chen, J., & Nodzak, C. (2020). Statistical and machine learning methods for eQTL analysis. eQTL Analysis: Methods and Protocols, 87-104.
5. Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018). Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nature genetics, 50(8), 1171-1179.
6. Leung, M. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015). Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE, 104(1), 176-197.
7. Dang, H., Polineni, D., Pace, R. G., Stonebraker, J. R., Corvol, H., Cutting, G. R., … & Knowles, M. R. (2020). Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. PloS one, 15(11), e0239189.
8. Claussnitzer, M., & Susztak, K. (2021). Gaining insight into metabolic diseases from human genetic discoveries. Trends in Genetics, 37(12), 1081-1094.
9. Yazar, S., Alquicira-Hernandez, J., Wing, K., Senabouth, A., Gordon, M. G., Andersen, S., … & Powell, J. E. (2022). Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease. Science, 376(6589), eabf3041.