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Abstract Single‐nucleotide polymorphisms (SNPs) are preferred over microsatellite markers in many evolutionary studies, but have only recently been applied to studies of parentage. Evaluations ofSNPs and microsatellites for assigning parentage have mostly focused on special cases that require a relatively large number of heterozygous loci, such as species with low genetic diversity or with complex social structures. We developed 120SNPmarkers from a transcriptome assembled usingRNA‐sequencing of a songbird with the most common avian mating system—social monogamy. We compared the effectiveness of 97 novelSNPs and six previously described microsatellites for assigning paternity in the black‐throated blue warbler,Setophaga caerulescens. We show that the full panel of 97SNPs (meanHo = 0.19) was as powerful for assigning paternity as the panel of multiallelic microsatellites (meanHo = 0.86). Paternity assignments using the two marker types were in agreement for 92% of the offspring. Filtering individual samples by a 50% call rate andSNPs by a 75% call rate maximized the number of offspring assigned with 95% confidence usingSNPs. We also found that the 40 most heterozygousSNPs (meanHo = 0.37) had similar power to assign paternity as the full panel of 97SNPs. These findings demonstrate that a relatively small number of variableSNPs can be effective for parentage analyses in a socially monogamous species. We suggest that the development ofSNPmarkers is advantageous for studies that require high‐throughput genotyping or that plan to address a range of ecological and evolutionary questions.
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A bioinformatic pipeline for identifying informative SNP panels for parentage assignment from RAD seq data
Abstract The development of high‐throughput sequencing technologies is dramatically increasing the use of single nucleotide polymorphisms (SNPs) across the field of genetics, but most parentage studies of wild populations still rely on microsatellites. We developed a bioinformatic pipeline for identifyingSNPpanels that are informative for parentage analysis from restriction site‐associatedDNAsequencing (RADseq) data. This pipeline includes options for analysis with or without a reference genome, and provides methods to maximize genotyping accuracy and select sets of unlinked loci that have high statistical power. We test this pipeline on small populations of Mexican gray wolf and bighorn sheep, for which parentage analyses are expected to be challenging due to low genetic diversity and the presence of many closely related individuals. We compare the results of parentage analysis acrossSNPpanels generated with or without the use of a reference genome, and betweenSNPs and microsatellites. For Mexican gray wolf, we conducted parentage analyses for 30 pups from a single cohort where samples were available from 64% of possible mothers and 53% of possible fathers, and the accuracy of parentage assignments could be estimated because true identities of parents were known a priori based on field data. For bighorn sheep, we conducted maternity analyses for 39 lambs from five cohorts where 77% of possible mothers were sampled, but true identities of parents were unknown. Analyses with and without a reference genome producedSNPpanels with ≥95% parentage assignment accuracy for Mexican gray wolf, outperforming microsatellites at 78% accuracy. Maternity assignments were completely consistent across allSNPpanels for the bighorn sheep, and were 74.4% consistent with assignments from microsatellites. Accuracy and consistency of parentage analysis were not reduced when using as few as 284SNPs for Mexican gray wolf and 142SNPs for bighorn sheep, indicating our pipeline can be used to developSNPgenotyping assays for parentage analysis with relatively small numbers of loci.
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- Award ID(s):
- 1716698
- PAR ID:
- 10063570
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 18
- Issue:
- 6
- ISSN:
- 1755-098X
- Page Range / eLocation ID:
- p. 1263-1281
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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