Abstract
Studies of host genetic determinants of pathogen sequence variations can identify sites of genomic conflicts, by highlighting variants that are implicated in immune response on the host side and adaptive escape on the pathogen side. However, systematic genetic differences in host and pathogen populations can lead to inflated type I (false positive) and type II (false negative) error rates in genome-wide association analyses. Here, we demonstrate through a simulation that correcting for both host and pathogen stratification reduces spurious signals and increases power to detect real associations in a variety of tested scenarios. We confirm the validity of the simulations by showing comparable results in an analysis of paired human and HIV genomes.
Highlights
Important inter-individual differences can be observed in human responses to infections, and in recent years researchers have started to explore the genetic underpinning of this phenotypic diversity (Prugnolle et al, 2005; Vannberg et al, 2011; Chapman and Hill, 2012; Rausell and Telenti, 2014; McLaren and Carrington, 2015)
One of the most prominent examples is the strong association between human leukocyte antigen (HLA) variation and HIV-1 control (Fellay et al, 2007; Thomas R. et al, 2009; Apps et al, 2013)
Host restriction factors can be uncovered by identifying the escape mutations that accumulate in the pathogen genome in response to selection pressure exerted by host genetic variants
Summary
Important inter-individual differences can be observed in human responses to infections, and in recent years researchers have started to explore the genetic underpinning of this phenotypic diversity (Prugnolle et al, 2005; Vannberg et al, 2011; Chapman and Hill, 2012; Rausell and Telenti, 2014; McLaren and Carrington, 2015). Multiple genome-wide association studies (GWAS) of clinical outcomes have identified human genetic variants that play a modulating role in infectious diseases To further explore the potential impact of human genetic diversity on infection, we recently proposed to integrate host and pathogen genomic data in a single analytic framework (which we called genome-to-genome analysis, or G2G; Bartha et al, 2013). Host restriction factors can be uncovered by identifying the escape mutations that accumulate in the pathogen genome in response to selection pressure exerted by host genetic variants
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