Using linked markers to infer the age of a mutation.
Advances in sequencing and genotyping technologies over the last decade have enabled geneticists to easily characterize genetic variation at the nucleotide level. Hundreds of genes harboring mutations associated with genetic disease have now been identified by positional cloning. Using variation at closely linked genetic markers, it is possible to predict the times in the past at which particular mutations arose. Such studies suggest that many of the rare mutations underlying human genetic disorders are relatively young. Studies of variation at genetic markers linked to particular mutations can provide insights into human geographic history, and historical patterns of natural selection and disease, that are not available from other sources. We review two approaches for estimating allele age using variation at linked genetic markers. A phylogenetic approach aims to reconstruct the gene tree underlying a sample of chromosomes carrying a particular mutation, obtaining a "direct" estimate of allele age from the age of the root of this tree. A population genetic approach relies on models of demography, mutation, and/or recombination to estimate allele age without explicitly reconstructing the gene tree. Phylogenetic methods are best suited for studies of ancient mutations, while population genetic methods are better suited for studies of recent mutations. Methods that rely on recombination to infer the ages of alleles can be fine-tuned by choosing linked markers at optimal map distances to maximize the information available about allele age. A limitation of methods that rely on recombination is the frequent lack of a fine-scale linkage map. Maximum likelihood and Bayesian methods for estimating allele age that rely on intensive numerical computation are described, as well as "composite" likelihood and moment-based methods that lead to simple estimators. The former provide more accurate estimates (particularly for large samples of chromosomes) and should be employed if computationally practical.
Rannala
B
B
Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada. brannala@ualberta.ca
Bertorelle
G
G
D000483:Alleles; D019143:Evolution, Molecular; D008040:Genetic Linkage / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics*; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D009154:Mutation / Q000235:genetics*; D010802:Phylogeny; D011995:Recombination, Genetic / Q000235:genetics; D013997:Time Factors
D005819:Genetic Markers