Employing world-class statistical genomics methods, we navigate from broad, national-scale studies to precise investigations into rare diseases. Our robust approach spans from single variant associations in large populations to intricate gene-burden and protein pathway analyses in small cohorts, crucial for unravelling complex genetic associations with diseases.
From Single Variants to Protein Pathway Analysis
We have developed and emply world class statistical genomics methods for both large national-scale population data and for small rare disease cohorts. This process encompasses a broad range of methodologies from single variant association studies typical of GWAS to more complex gene-burden and protein pathway analyses. This multi-tiered approach is crucial in identifying associations between genetic variants and phenotypes, for complex mechanisms in large cohorts and for rare diseases with small cohort sizes.
Key Stages in Statistical Genomics
-
Single Variant Analysis (GWAS): This foundational method focuses on the association of individual genetic variants with disease traits. It’s particularly effective in large cohorts where common variants have minor effects on disease susceptibility.
-
Variant collapse and Gene-Burden Testing: Moving beyond single variants, variant set association testing (sometimes referred to by its misnomer gene-burden testing) evaluates the cumulative impact of multiple variants within a single gene. This method increases the power to detect associations in cases where a single variant alone may not be significant or when the mechanisms in more complex.
-
Protein Pathway Analysis with Variant Collapse: This advanced approach involves aggregating variants across multiple genes within the same biological pathway. By collapsing these variants, researchers can uncover associations with phenotypes that might be missed when looking at single genes or variants. This technique is especially valuable in rare disease research where cohorts are smaller and variant effects are often more pronounced.
After identifying new associtaions within a cohort, interpretation remains a challenging step. We treat each result with as much care a as a single-case clinical diagnosis. Therefore, out GuRu interpretation tool and Heracles reporting tool are flexible enough to interpret the results of joint statistical genomics analysis.
Interpretation of Genomic Associations
-
Complexity of Interpretation: After identifying new associations within a cohort, interpretation remains a challenging step. Each result is treated with the same meticulous care as a single-case clinical diagnosis, underscoring the complexity and importance of accurate genomic interpretation.
-
Flexibility of Interpretation Tools: To address these challenges, our tools, including the GuRu interpretation tool and the Heracles reporting system, are designed to be flexible enough to handle the results of joint statistical genomics analysis. This adaptability is crucial for accurate and meaningful interpretation of complex genetic data.
-
Reporting Spectrum: We report findings from broad to narrow, ensuring a comprehensive understanding of the data:
- Pathway-Level Insights: If a pathway is found to be associated, we explain its known roles and mechanisms in relation to the phenotype. This helps in understanding the broader biological impact.
- Gene and Protein Contributions: When specific genes or proteins are involved, each contributing variant is assessed individually to pinpoint its potential impact.
- Individual Genomic Profiles: Despite sharing a phenotype with the cohort, every individual’s unique genomic profile is considered to ensure personalized interpretation. This approach highlights the significance of each variant within the broader context of the patient’s genetic landscape.
By employing a structured approach to statistical genomics, we can systematically dissect the genetic underpinnings of complex diseases. From the broad scope of GWAS to the detailed scrutiny of protein pathways, each methodological layer adds depth to our understanding, guiding therapeutic strategies and advancing personalized medicine.
