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Data from: A comparison of single nucleotide polymorphism and microsatellite markers for analysis of parentage and kinship in a cooperatively breeding bird

Cite this dataset

Weinman, Lucia R.; Solomon, Joseph W.; Rubenstein, Dustin R. (2014). Data from: A comparison of single nucleotide polymorphism and microsatellite markers for analysis of parentage and kinship in a cooperatively breeding bird [Dataset]. Dryad. https://doi.org/10.5061/dryad.jc2pj

Abstract

The development of genetic markers has revolutionized molecular studies within and among populations. Although poly-allelic microsatellites are the most commonly used genetic marker for within-population studies of free-living animals, biallelic single nucleotide polymorphisms, or SNPs, have also emerged as a viable option for use in nonmodel systems. We describe a robust method of SNP discovery from the transcriptome of a nonmodel organism that resulted in more than 99% of the markers working successfully during genotyping. We then compare the use of 102 novel SNPs with 15 previously developed microsatellites for studies of parentage and kinship in cooperatively breeding superb starlings (Lamprotornis superbus) that live in highly kin-structured groups. For 95% of the offspring surveyed, SNPs and microsatellites identified the same genetic father, but only when behavioural information about the likely parents at a nest was included to aid in assignment. Moreover, when such behavioural information was available, the number of SNPs necessary for successful parentage assignment was reduced by half. However, in a few cases where candidate fathers were highly related, SNPs did a better job at assigning fathers than microsatellites. Despite high variation between individual pairwise relatedness values, microsatellites and SNPs performed equally well in kinship analyses. This study is the first to compare SNPs and microsatellites for analyses of parentage and relatedness in a species that lives in groups with a complex social and kin structure. It should also prove informative for those interested in developing SNP loci from transcriptome data when published genomes are unavailable.

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Location

Kenya