Uses of Evolutionary Theory in the Human Genome Project

The Human Genome Project (HGP) originally sought to sequence the human genome but excluded studies on genetic diversity. Now genetic diversity is a major focus, and evolutionary theory provides needed analytical tools. One type of diversity research focuses on complex traits. This is often done by screening genetic variation at candidate loci functionally related to a trait followed by gene/phenotype association tests. Linkage disequilibrium creates difficulties for association tests, but evolutionary analyses using haplotype trees can circumvent these problems and result in greater statistical power, better disease risk prediction, the elimination of some polymorphisms as causative, and physical localization of causative variation when combined with an analysis of recombination. The HGP also now proposes to map over 100,000 single nucleotide polymorphisms to test for gene/phenotype associations through linkage disequilibrium in isolated human populations affected by past founder or bottleneck events. This strategy requires prior knowledge of recent human evolutionary history and current population structure, but other evolutionary considerations dealing with disequilibrium and nonrandom mutation pose difficulties for this approach. Studies on population structure also focus upon traits of medical relevance, and an understanding of the evolutionary ultimate cause for the predisposition of some populations to certain diseases is a useful predictor for shaping public health policies. Studies on the genetic architecture of common traits reveal much epistasis and variation in norms of reaction, including drug response. Because of these interactions, context dependency and sampling bias exist in disease association studies that require population information for effective use. Overall, the population thinking of evolutionary biology is an important counterweight to naive genetic determinism in applying the results of the HGP to issues of human health and well-being.