A bioinformatic pipeline for identifying informative SNP panels for parentage assignment from RADseq data

Area of Science:

• Population genetics
• Genomic analysis
• Bioinformatics

Background:

• High-throughput sequencing technologies are advancing genetic studies.
• Microsatellites are traditionally used for parentage studies in wild populations.
• Single nucleotide polymorphisms (SNPs) offer a powerful alternative for genetic analysis.

Purpose of the Study:

• To develop a bioinformatic pipeline for identifying informative SNP panels for parentage analysis from RADseq data.
• To assess the pipeline's performance with and without a reference genome.
• To compare SNP-based parentage analysis with microsatellite-based methods in wild populations.

Main Methods:

• Developed a bioinformatic pipeline for SNP panel identification from RADseq data.
• Included options for reference-guided and reference-free analysis.
• Tested the pipeline on Mexican gray wolf and bighorn sheep populations.
• Compared parentage assignment accuracy between SNPs and microsatellites.

Main Results:

• SNP panels achieved ≥95% parentage assignment accuracy for Mexican gray wolf, outperforming microsatellites (78%).
• Maternity assignments for bighorn sheep were highly consistent across SNP panels (74.4% consistency with microsatellites).
• Effective parentage analysis was achieved with as few as 284 SNPs (Mexican gray wolf) and 142 SNPs (bighorn sheep).

Conclusions:

• The developed pipeline efficiently identifies informative SNP panels for parentage analysis from RADseq data.
• SNP-based parentage analysis is accurate and reliable, even in populations with low genetic diversity.
• The pipeline enables the development of cost-effective SNP genotyping assays for wildlife parentage studies.