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One Step Closer to Precision Medicine for Chronic Lung Disease Sufferers
Article

One Step Closer to Precision Medicine for Chronic Lung Disease Sufferers

One Step Closer to Precision Medicine for Chronic Lung Disease Sufferers
Article

One Step Closer to Precision Medicine for Chronic Lung Disease Sufferers

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A large biomarker-genome-wide association study (GWAS) has shown that genetic variation affects blood biomarkers in patients with chronic obstructive lung disease (COPD). Genome-wide genotyping and blood measurements of 88 biomarkers in two NIH-supported clinical cohorts of smokers (1,340 subjects) identified more than 300 novel DNA variants that influence measurement of blood protein levels. Over 30 research institutes from across the US were involved in the study led by University of North Carolina at Chapel Hill, North Carolina, and National Jewish Health, Colorado.

Precision medicine aims to improve patient healthcare by providing individualised treatment choices based on a person’s genetics, environment and lifestyle. For complex diseases such as chronic obstructive lung disease(COPD), this approach will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. Genome-wide association studies linking disease phenotypes to single nucleotide polymorphism (SNP) markers have successfully identified genes and pathways involved in complex disease phenotypes; this study provides concrete links between SNPs and known COPD blood protein biomarkers. The meta-analysis from the cohorts of current and former smokers with and without COPD (SPIROMICS and COPDGene) demonstrate the power of a study which uses a large number of subjects and includes validation cohorts. 

Data from selected biomarker blood proteins measurements, genotyping and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history and chronic bronchitis) were combined and statistically analysed to identify pQTLs with a significant relationship to the established clinical phenotypes. The team went on to demonstrate that including SNP data with blood biomarker levels can improve the ability of predictive models to reflect the variation and relationship between biomarker and disease features within COPD, taking us one step closer to precision methods for detecting and treating COPD and complex chronic diseases in general.

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