Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes. Frontiers in Genetics
Multiple genome-wide association studies (GWAS) of clinical outcomes have identified human genetic variants that play a modulating role in infectious diseases. To explore the potential impact of human genetic diversity on infection, Bartha et al. recently proposed to integrate host and pathogen genomic data in a single analytic framework, the so-called genome-to-genome analysis (G2G).
In this work, Naret et al. aimed to explore the effects of population stratification in G2G analyses. For that, they simulated host and pathogen genetic variations using a broad array of parameters including stratification on both sides, which allowed to pinpoint the various effects of population stratification on different statistical models. They then tested for associations between genome-wide human genotypes and HIV-1 sequence diversity in a real-life dataset of 1’668 participants from the Swiss HIV Cohort Study.
They showed that correcting for both host and pathogen stratification is necessary for unbiased G2G analysis and increases power to detect real associations in a variety of tested scenarios. They confirmed the validity of the simulations by showing comparable results in an analysis of paired human and HIV genomes.