Jonathan Hess

Abstract Despite high heritability estimates, complex genetic disorders have proven difficult to predict with genetic data. Genomic research has documented polygenic inheritance, cross-disorder genetic correlations, and enrichment of risk by functional genomic annotation, but the vast potential of that combined knowledge has not yet been leveraged to build optimal risk models.

The COPDGene Study includes 10,198 current and former individuals that smoke. We identified 144 individuals who met the definition of genetic resilience, 362 genetic risk-matched individuals with COPD, and 420 individuals who met the definition of clinical resilience (Table 1). We excluded 24 individuals who met the definition of both genetic and clinical resilience.

We used paired blood and lung RNAseq data from 347 individuals from GTEx to train LungGENIE. We identified 19,304 genes that were significantly predicted by LungGENIE (R2 ≥ 0.01 and false-discovery rate adjusted p-value < 0.05). The mean cross-validation imputation accuracy for significantly imputed genes was Pearson’s r = 0.25, with an interquartile range of 0.19 to 0.32.