Estimating pairwise relatedness from dominant genetic markers.
Knowledge of the genetic relatedness between a pair of individuals is important in many research areas of quantitative genetics, conservation genetics, evolution and ecology. Many estimators have been developed to estimate such pairwise relatedness (r) using codominant markers, such as microsatellites and enzymes. In contrast, only two estimators are proposed to use dominant markers, such as random amplified polymorphic DNAs (RAPDs) and amplified fragment length polymorphisms (AFLPs), in relatedness inference. They are both biased estimators, and their statistical properties and robustness to the sampling errors in allele frequency have not been investigated. In this short paper, I propose two new pairwise relatedness estimators for dominant markers, and compare them in precision, accuracy and robustness to sampling with the two previous estimators using simulations. It was found that the new estimator based on the least squares approach is unbiased when allele frequencies are known or estimated from a sample without correcting for sampling effects. It has, however, a low precision and as a result, an intermediate overall performance among the four estimators in terms of the mean squared deviation (MSD) of estimates from actual values of r. The new estimator based on a similarity index is slightly biased but has generally the lowest MSD among the four estimators compared, regardless of the number of loci, type of actual relationships, allele frequencies known or estimated from samples. Simulations also show that the confidence intervals estimated by bootstrapping are appropriate for different estimators provided that the number of loci used in the estimation is not small.
Wang
J
J
Institute of Zoology, Zoological Society of London, London NW1 4RY, UK. jinliang.wang@ioz.ac.uk
D003198:Computer Simulation; D005787:Gene Frequency; D005799:Genes, Dominant / Q000235:genetics; D005819:Genetic Markers / Q000235:genetics; D005828:Genetics, Population / Q000379:methods*; D016018:Least-Squares Analysis; D008957:Models, Genetic; D013223:Statistics as Topic / Q000379:methods*
D005819:Genetic Markers