Conditions Under Which Genome-Wide Association Studies Will be Positively Misleading

189 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Detecting low-complexity unobserved causes (2011, background): “A recent study in [Platt et al., 2010] investigates very similar simulated models to highlight the risk of positively misleading answers from correlation analyses.”
Natural variations and genome-wide association studies in crop plants. (2014, background): “A matter requiring attention in crop GWAS is the need to account for the massive population structure, including weighing the trade-offs of increased false-negative and decreased false-positive rates from accounting for that structure (55, 77, 85, 112).”
The advantages and limitations of trait analysis with GWAS: a review (2013, background): “Interestingly, genetic heterogeneity can lead to a noncausative marker being a better descriptor of the phenotype than a causative one [38].”
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations (2012, unknown): “Furthermore, both criteria perform appropriately in extreme scenarios where there is no detectable signal in the data, as when an external confounding variable interacts non-linearly with a single causal locus 18 .”
The ecology of subindividual variability in plants: patterns, processes, and prospects (2017, result): “Although genetic marker–trait associations are unable to provide conclusive proofs of causality (Platt et al., 2010), these results are clearly compatible with the hypothesis that differences between H.”