ArticleFrontiers in genetics2018
Correcting for Population Stratification Reduces False Positive and False Negative Results in Joint Analyses of Host and Pathogen Genomes.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Establishing standards for genome wide association studies in eusocial insects through a case study in honey bees.Scientific reports · 2026Article
- Uncovering the Genomic Basis of Infection Through Co-genomic Sequencing of Hosts and Parasites.Molecular biology and evolution · 2023Article
- Human and pathogen genotype-by-genotype interactions in the light of coevolution theory.PLoS genetics · 2023Review
- G2GSnake: a Snakemake workflow for host-pathogen genomic association studies.Bioinformatics advances · 2023Article
- A Phylogeny-aware GWAS Framework to Correct for Heritable Pathogen Effects on Infectious Disease Traits.Molecular biology and evolution · 2022Article
- The influence of human genetic variation on Epstein-Barr virus sequence diversity.Scientific reports · 2021Article
- Host-parasite co-evolution and its genomic signature.Nature reviews. Genetics · 2020Review
- Exploring the interactions between the human and viral genomes.Human genetics · 2020Review
- Article
- Correcting for Population Stratification Reduces False Positive and False Negative Results in Joint Analyses of Host and Pathogen Genomes.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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