Chunk #0 — Historical context and background for modeling complex traits — A brief history of the foundation of the theories underlying statistical genetics
Genetic risk prediction is rooted in complex trait theory and biometry (the application of statistics to biological measures), which emerged in the 19th century. Darwin’s concepts of selection based on continuous phenotypic variation were reconciled with Mendel’s laws of inheritance proposing discontinuous steps, which were initially interpreted as contradictory. In this context, in the mid-1870s Sir Francis Galton promoted use of twin and family studies to investigate inheritance. He recognized the utility of sum of squares based on Carl Friedrich Gauss’s and Adrien-Marie Legendre’s work at the turn of the 17th century for studies of heredity (Table S1); he applied regression to the mean, the workhorse of modern genome-wide association studies (GWAS). Karl Pearson developed fundamental statistical concepts for biometry, such as correlation and regression coefficients, and helped develop mathematical models of inheritance around the turn of the 20th century. In 1918, Ronald Fisher harmonized these concepts with the introduction of the biometrical model (Table S1) (1). Fisher’s framework introduced the infinitesimal model, in which large numbers of discrete genetic loci, each transmitted in Mendelian fashion, contribute additively to continuous phenotypic variation; thus, each individual variant explains a small fraction of heritable variation in a phenotype.
