What is the difference between QTL mapping and a genome-wide association study?

QTL mapping typically uses controlled crosses where known relatives let recombination be tracked directly, while a genome-wide association study scans unrelated individuals in a population, relying on historical linkage disequilibrium between markers and causal variants.

Why does population structure cause false associations?

If subgroups of a sample differ in both ancestry and the trait, any allele that merely happens to be more common in one subgroup will appear associated with the trait; correcting for ancestry removes these spurious signals.

QTL and Complex Trait Mapping

Locating the specific genomic regions that influence a continuously varying trait turns the abstract heritability of quantitative genetics into concrete map positions, using either controlled crosses or population-scale association.

Definition

QTL and complex trait mapping is the set of methods that locate the genomic regions or variants contributing to a quantitative or complex trait by detecting statistical association between genetic markers and the trait across individuals.

Scope

This topic covers quantitative trait locus (QTL) mapping in experimental crosses through linkage between markers and trait values, interval mapping and LOD scores, the move to dense molecular markers, genome-wide association studies (GWAS) in outbred populations, linkage disequilibrium and its role in association, and the confounding effect of population structure and how it is controlled. It treats the localization of loci underlying complex traits; the statistical description of the variation itself is covered in the adjacent topic.

Core questions

How does linkage between a marker and a trait reveal a quantitative trait locus in a cross?
How do genome-wide association studies detect trait-associated variants in populations?
Why is linkage disequilibrium central to association mapping?
How does unaccounted population structure produce false associations, and how is it corrected?

Key concepts

Quantitative trait loci and marker-trait linkage
Interval mapping and LOD scores
Genome-wide association studies
Linkage disequilibrium
Population structure as a confounder and its correction

Mechanisms

In a cross, a marker near a causal locus co-segregates with the trait, so a peak in the statistical signal along the chromosome marks a QTL; in populations, historical recombination leaves causal variants in linkage disequilibrium with nearby markers, allowing association scans, provided shared ancestry that correlates with both genotype and trait is modeled out.

Clinical relevance

Association mapping has identified thousands of variants linked to common human diseases and traits, informing polygenic risk scores and drug-target discovery, while controlling for population structure, as formalized in structure-inference methods, is essential to avoid spurious findings.

History

Interval QTL mapping in experimental crosses was formalized around 1989, dense marker maps then enabled finer resolution, and from the mid-2000s genome-wide association studies extended mapping to human populations; methods to infer and correct for population structure, such as the model of Falush, Stephens, and Pritchard, made these studies reliable.

Key figures

Eric Lander
Jonathan Pritchard
Trudy Mackay

Seminal works

falush2003
lynchWalsh1998

Frequently asked questions

What is the difference between QTL mapping and a genome-wide association study?: QTL mapping typically uses controlled crosses where known relatives let recombination be tracked directly, while a genome-wide association study scans unrelated individuals in a population, relying on historical linkage disequilibrium between markers and causal variants.
Why does population structure cause false associations?: If subgroups of a sample differ in both ancestry and the trait, any allele that merely happens to be more common in one subgroup will appear associated with the trait; correcting for ancestry removes these spurious signals.