Mendellian Randomization Services
Unlocking Causality from Genomic Data
Context
With the rise in large-scale genomic datasets, researchers face challenges in identifying causal relationships between genetic variants and diseases. Traditional observational studies are often confounded by complex interactions, making it challenging to pinpoint causal factors for a disease or trait.
Mendelian Randomization (MR) offers a powerful solution. By leveraging variation in genes with known function, MR empowers researchers like you to examine the ‘unconfounded’ causal effect estimate of a modifiable exposure to disease in observational studies.
The growing utilization of high-throughput proteomics platforms in large-scale genotyped biobanks (e.g., UKBB-PPP) provides new opportunities to obtain biological insights from GWAS data. Exploiting MR approaches can identify potentially causal effects of circulating proteins on diseases/ traits. MR helps in identifying disease risk factors, and drug targets, providing early predictions of ongoing trials, predicting adverse events, and also aiding in drug repurposing
How We Can Help
At Aganitha, we recognize that implementing MR involves navigating intricate statistical methodologies and the complexities of handling genomic data at scale. Our cross-functional team of AI/ML experts, bioinformaticians, and statistical geneticists is here to support you at every stage.
- Customizable MR Pipelines: We leverage the power of Mendelian Randomization to address a wide range of scientific questions, tailoring our pipelines to your specific needs. Whether you’re utilizing one-sample, two-sample, or bidirectional MR designs to investigate causal relationships between exposures and outcomes, we provide the expertise and tools to support your research.
- Robust Statistical Analysis at Scale: Our expertise encompasses a range of robust statistical methods to address the multifaceted nature of MR analysis, even with large-scale datasets. We control for horizontal pleiotropy, confounding variables, and instrument variable bias, ensuring the validity of your causal inferences. We also employ advanced techniques to tackle complexities such as single-locus colocalization, multi-locus heterogeneity assessment, and cis-MR or functional enrichment of IVs.
- Clear and Actionable Insights: We provide easy-to-interpret visualizations highlighting key findings, potential causal pathways, and implications for your research.