Objectives: The overarching goal was to employ statistical colocalization as a means to elucidate the causal genes and underlying mechanisms implicated in complex diseases. This objective involved bridging the gap between genome-wide association studies (GWAS) and biological interpretations by prioritizing variants likely to be causal, assessing genetic overlap among related traits, and discerning the presence… Continue reading A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
Tag: Bioxcelerate AI
MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates
Objectives: The research aimed to utilize Mendelian randomization (MR) as a robust epidemiological technique to investigate and estimate causal relationships between specific risk factors and their resultant outcomes. This study sought to leverage genetic variants as instrumental variables to provide a reliable framework for understanding the underlying causal mechanisms of various health conditions. Ultimately, the… Continue reading MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates
isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease
Objective: The aim of this research was to develop scalable and efficient algorithms, namely the in-silico GWAS (isGWAS) and leapfrog re-sampler (LRS), to overcome significant challenges in genome-wide association studies (GWAS). These challenges include the dependency on sensitive individual-level data, daunting computational demands arising from increasing cohort sizes and genetic variants, and limitations in traditional… Continue reading isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease
How Data Science is Impacting Drug Development: Q and A with Dr Chris Foley
Q: What role does data science have in drug development today?Data is driving change in the drug development space. Drug development is complex, resource-heavy, and very costly. Collaborating across diverse specialisms – clinicians, chemists, geneticists, epidemiologists – scientists contribute their expertise. Notably, data scientists play a critical role in research, utilizing state-of-the-art data science and… Continue reading How Data Science is Impacting Drug Development: Q and A with Dr Chris Foley