Incorporating family disease history and controlling case-control imbalance for population based genetic association studies

Abstract

In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution. We propose a new method for genetic association test with family disease history, TAPE (mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation), which controls for increased phenotype correlation by adopting a two-variance-component mixed model and accounts for case-control imbalance by using empirical saddlepoint approximation. We show through simulation studies and analysis of UK-Biobank data of white British samples and KoGES data of Korean samples that the proposed method is computationally efficient and gains greater power for detection of variant-phenotype associations than common GWAS with binary traits while yielding better calibration compared to existing methods.

Genome-wide study on 72,298 Korean individuals in Korean biobank data for 76 traits identifies hundreds of novel loci

“…By incorporating the family disease history, the association test power can be improved. We used the family disease history information of disease endpoints with TAPE 18 . In two types of cancer: gastric cancer and gallbladder cancer, we identified additional 4 independent association signals that were not detected in the analysis without family disease history.…”

The HUNT study: A population-based cohort for genetic research

“…The high degree of relatedness in the HUNT Study participants has enabled analysis methods tailored to this study design. These include GWAS by proxy, 20,21 in which the phenotypes of non-genotyped family members of genotyped HUNT participants can be used to identify proxy-cases, individuals with a proportion (0.5 for first-degree relatives) of the genetic risk of cases. These proxy-cases can be appropriately modeled to increase the statistical power in GWAS.…”