Matthew R. Nelson

This study shows that plasma protein quantitative trait loci (pQTL) support therapeutic hypotheses more effectively than other genetic evidence, increasing the likelihood of drug development success.

The paper shows that human genetic evidence can identify drug side effects by linking drug targets to traits similar to the side effects.

This paper uses Mendelian randomization and colocalization to identify causal relationships between plasma proteins and disease phenotypes, finding 111 putatively causal effects.

The paper introduces PICCOLO, a method to identify colocalized genes between GWAS and xQTLs, finding 30% of associations have at least one colocalized gene-xQTL.

Exomes of 49,960 UK Biobank participants reveal 231,631 predicted loss-of-function variants (>10-fold more than imputed data); burden tests implicate PIEZO1, MEPE, and COL6A1 in disease traits.

The paper uses tensor factorization to predict successful therapeutic target-indication pairs by analyzing clinical trial data and comparing performance with other machine learning models.