Originally published as Genetics Published Articles Ahead of Print on February 16, 2009.

Genetics, Vol. 181, 1579-1594, April 2009, Copyright © 2009
doi:10.1534/genetics.108.100214

Parentage and Sibship Inference From Multilocus Genotype Data Under Polygamy

J. Wang1 and A. W. Santure

Institute of Zoology, Zoological Society of London, London NW1 4RY, United Kingdom

1 Corresponding author: Institute of Zoology, Regent's Park, London NW1 4RY, United Kingdom.
E-mail: jinliang.wang{at}ioz.ac.uk

Likelihood methods have been developed to partition individuals in a sample into sibling clusters using genetic marker data without parental information. Most of these methods assume either both sexes are monogamous to infer full sibships only or only one sex is polygamous to infer full sibships and paternal or maternal (but not both) half sibships. We extend our previous method to the more general case of both sexes being polygamous to infer full sibships, paternal half sibships, and maternal half sibships and to the case of a two-generation sample of individuals to infer parentage jointly with sibships. The extension not only expands enormously the scope of application of the method, but also increases its statistical power. The method is implemented for both diploid and haplodiploid species and for codominant and dominant markers, with mutations and genotyping errors accommodated. The performance and robustness of the method are evaluated by analyzing both simulated and empirical data sets. Our method is shown to be much more powerful than pairwise methods in both parentage and sibship assignments because of the more efficient use of marker information. It is little affected by inbreeding in parents and is moderately robust to nonrandom mating and linkage of markers. We also show that individually much less informative markers, such as SNPs or AFLPs, can reach the same power for parentage and sibship inferences as the highly informative marker simple sequence repeats (SSRs), as long as a sufficient number of loci are employed in the analysis.