ESTIMATING RELATEDNESS USING GENETIC MARKERS

TLDR

The authors introduce a new method for estimating genetic relatedness from markers such as protein polymorphisms. The method uses Grafen’s relatedness coefficient, interpreted as identity by descent, and supports tests of relatedness differences between samples or correlations with other variables. It eliminates downward bias in small samples, improves estimation for population subsets, allows single‑group or pairwise estimates, and while individual estimates are variable, they are useful in aggregate for nonparametric tests.

Abstract

A new method is described for estimating genetic relatedness from genetic markers such as protein polymorphisms. It is based on Grafen's (1985) relatedness coefficient and is most easily interpreted in terms of identity by descent rather than as a genetic regression. It has several advantages over methods currently in use: it eliminates a downward bias for small sample sizes; it improves estimation of relatedness for subsets of population samples; and it allows estimation of relatedness for a single group or for a single pair of individuals. Individual estimates of relatedness tend to be highly variable but, in aggregate, can still be very useful as data for nonparametric tests. Such tests allow testing for differences in relatedness between two samples or for correlating individual relatedness values with another variable.