title: Genome-wide association studies of quantitative traits with related individuals: little (power) lost but much to be gained.
abstract: For complex disease genetics research in human populations, remarkable progress has been made in recent times with the publication of a number of genome-wide association scans (GWAS) and subsequent statistical replications. These studies have identified new genes and pathways implicated in disease, many of which were not known before. Given these early successes, more GWAS are being conducted and planned, both for disease and quantitative phenotypes. Many researchers and clinicians have DNA samples available on collections of families, including both cases and controls. Twin registries around the world have facilitated the collection of large numbers of families, with DNA and multiple quantitative phenotypes collected on twin pairs and their relatives. In the design of a new GWAS with a fixed budget for the number of chips, the question arises whether to include or exclude related individuals. It is commonly believed to be preferable to use unrelated individuals in the first stage of a GWAS because relatives are 'over-matched' for genotypes. In this study, we quantify that for GWAS of a quantitative phenotype, relative to a sample of unrelated individuals surprisingly little power is lost when using relatives. The advantages of using relatives are manifold, including the ability to perform more quality control, the choice to perform within-family tests of association that are robust to population stratification, and the ability to perform joint linkage and association analysis. Therefore, the advantages of using relatives in GWAS for quantitative traits may well outweigh the small disadvantage in terms of statistical power.
authors.affiliation: Genetic Epidemiology, Queensland Institute of Medical Research, Herston, Brisbane, Australia. peter.visscher@qimr.edu.au
authors.forename: Peter M
authors.initials: PM
authors.lastname: Visscher
authors.forename: Toby
authors.initials: T
authors.lastname: Andrew
authors.forename: Dale R
authors.initials: DR
authors.lastname: Nyholt
mesh_terms: D015894:Genome, Human; D006801:Humans; D040641:Quantitative Trait Loci