title: An unbiased estimator of gene diversity in samples containing related individuals.
abstract: Gene diversity is sometimes estimated from samples that contain inbred or related individuals. If inbred or related individuals are included in a sample, then the standard estimator for gene diversity produces a downward bias caused by an inflation of the variance of estimated allele frequencies. We develop an unbiased estimator for gene diversity that relies on kinship coefficients for pairs of individuals with known relationship and that reduces to the standard estimator when all individuals are noninbred and unrelated. Applying our estimator to data simulated based on allele frequencies observed for microsatellite loci in human populations, we find that the new estimator performs favorably compared with the standard estimator in terms of bias and similarly in terms of mean squared error. For human population-genetic data, we find that a close linear relationship previously seen between gene diversity and distance from East Africa is preserved when adjusting for the inclusion of close relatives.
authors.affiliation: Center for Computational Medicine and Biology, University of Michigan, USA. degiormi@umich.edu
authors.forename: Michael
authors.initials: M
authors.lastname: DeGiorgio
authors.forename: Noah A
authors.initials: NA
authors.lastname: Rosenberg
mesh_terms: D005190:Family; D005787:Gene Frequency; D014644:Genetic Variation; D005843:Geography; D006801:Humans; D008722:Methods; D018895:Microsatellite Repeats; D008957:Models, Genetic