Population Structure: A Key Concept for Understanding Genetic Variation
/It is common for articles to claim that “the gene for” some trait or disease has been identified. Usually they actually mean that an association has been found between an uncommon genetic variant found in, or near, a gene and some trait or disease. These kinds of articles are becoming increasingly common because Genome-Wide Association Studies (GWAS) are becoming cheaper and more common. Though GWAS yield important insights their results can be misleading because ancestral relationships between individuals in the study can create signals that can be misinterpreted as association with the trait being studied. This phenomenon is very powerful and one reason why it is important to have a diverse group of individuals in any genetic study. Underlying ancestral relationships are known as “population structure” and serious thought is required to ensure that it doesn’t skew GWAS results. The paper below is a scientific review article (in an excellent journal with exceptional authors) and not exactly easy reading, but it was written for a broad audience and worth considering the next time you see an article discussing the identification of “the genes for” something or other, even if it appears in Genome-Media.
-RPR
Population Genetics: Why structure matters
Abstract
Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.
Main text
Human height is the classic example of a quantitative trait: its distribution is continuous, presumably because it is influenced by variation at a very large number of genes, most with a small effect (Fisher, 1918). Yet height is also strongly affected by the environment: average height in many countries increased during the last century and the children of immigrants are often taller than relatives in their country of origin – in both cases presumably due to changing diet and other environmental factors (Cavalli-Sforza and Bodmer, 1971; Grasgruber et al., 2016; NCD Risk Factor Collaboration, 2016). This makes it very difficult to determine the cause of geographic patterns for height, such as the ‘latitudinal cline’ seen in Europe (Figure 1).
