Accounting for Genetics in Biomarker-Disease Association Studies: Lessons Learned from Lung Disease

• Activity Number: 441 - The Key to Integrative Analysis for Precision Medicine: Statistics!
• Type: Invited
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Katerina Kechris* and Wei Sun and Sean Jacobson and Wanda O'Neal and Russell Bowler
• Companies: University of Colorado Anschutz Medical Campus and Fred Hutchinson Cancer Research Center and National Jewish Health and University of North Carolina at Chapel Hill and National Jewish Health
• Keywords: genetics ; QTL ; biomarkers ; omics

Abstract

Omics technologies provide opportunities to discover thousands of potential biomarkers for disease diagnosis, progression and treatment. Despite this potential, only several biomarkers have been validated for successful clinical practice. In this work, through an integrated omics approach, we show how common genetic variants influence biomarker levels and that these variants are important for guiding personalized medicine. Our findings are illustrated through a large meta-analysis for chronic obstructive pulmonary disease (COPD) where we integrated genetic and protein biomarker data to identify protein quantitative trait loci (pQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and COPD clinical phenotypes were explored using conditional independence tests. Integration of DNA variants with blood biomarker levels improved the ability of predictive models to reflect biomarker-disease relationships within COPD. In summary, given the frequency of highly significant pQTLs and the large amount of variance explained by pQTL we recommend that biomarker-disease association studies take into account the potential effect of common local genetic variants.