From Galton to GWAS (and beyond): what we have learned about the genetics of human height?
Variation in quantitative traits such as human height is caused by a combination of multiple genes and environmental effects. Traditionally, since Galton in the late 1800s, the genetics of such traits has been studied using concepts that refer to the combined effect of all genes (e.g., heritability). Estimation of heritability for adult height are ~0.8, so that 80% of differences between people is due to genetic factors. Genome-wide association studies (GWAS) facilitate the dissection of heritability into individual locus effect. They have been successful in finding many SNPs associated with height and have greatly increased the number of genes involved in height variation. To date, hundreds of loci have been identified that explain in total about 1015% of heritability. Estimation of heritability explained by all common SNPs together (not just the significant ones) are ~50%, spread over all chromosomes in proportion to their length, implying that there are many more variants with effects sizes too small to be detected with sample sizes employed to date. Many SNPs are in loci that are in meaningful biological pathways (e.g., skeletal growth) and/or in genes for which major (Mendelian) mutations were already known, and there is evidence at a number of genes for multiple segregating variants. The effect sizes of individual detected SNP variants are very small, about 1 to 4 mm per allele, which means that very large sample sizes are needed to detect more variants. The International GIANT (Genetic Investigation of ANthropometric Traits) consortium is analyzing GWAS data on ~250 000 people and is likely to identify hundreds more loci that are associated with adult height. The emerging genetic architecture of human height from DNA evidence is one of many hundreds to thousands of loci scattered throughout the human genome with tiny effect sizes. The identification of hundreds of associated variants facilitates the study of biology and function, in particular when genetic data is combined with data on gene expression.
Declaration of interest: The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.
Funding: This work was supported, however funding details are unavailable.