A bioinformatic pipeline for identifying informative SNP panels for parentage assignment from RADseq 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 identifying SNP panels that are informative for parentage analysis from restriction site-associated DNA sequencing (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 across SNP panels generated with or without the use of a reference genome, and between SNPs 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 produced SNP panels with ≥95% parentage assignment accuracy for Mexican gray wolf, outperforming microsatellites at 78% accuracy. Maternity assignments were completely consistent across all SNP panels 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 284 SNPs for Mexican gray wolf and 142 SNPs for bighorn sheep, indicating our pipeline can be used to develop SNP genotyping assays for parentage analysis with relatively small numbers of loci.

Quality and quantity of genetic relatedness data affect the analysis of social structure

“…This study contributes to a growing body of literature aimed at understanding the power of SNP markers and genotyping‐by‐sequencing methods to assess kinship in wild mammals (Andrews et al, ; Attard et al, ). Further, this is to our knowledge the first study for any mammalian species to investigate how the accuracy and precision of such relatedness estimation affects inference in the analysis of social structure.…”

In the surf zone: Reproductive strategy of the calico surfperch (Amphistichus koelzi) in a comparative context

“…Utilizing RADseq for paternity assignment is relatively new (Andrews et al ., ; Thrasher et al ., ). The accuracy of paternity assignment utilizing SNPs has been well documented, especially for the use in livestock and fisheries management (Anderson & Garza, ; Fernandez et al ., ; Hauser et al ., ; Jones et al ., ).…”

“…However, in most cases, a reference genome and specific loci have been developed to target specific markers that were previously determined to be variable, making paternity assignment reliable. The mating system for surfperches varies from other taxa evaluated to date because broodsizes are large, yet all offspring are either full siblings (same father) or half siblings (same mother, different father), therefore we report data using the most stringent filtering parameters (following Andrews et al ., ), indicating that all broods were sired by multiple fathers.…”

“…In DENOVO_MAP there are three main parameters: m, the minimum number of reads required to form a stack within an individual; M, the maximum number of mismatches allowed between stacks to merge them into one locus; and n, the maximum number of mismatches allowed between stacks from different individuals to merge into one locus (Catchen et al, 2013). We utilized the parameters m -3, M -3, n -1 in DENOVO_MAP following the parameter optimization studies of Mastretta-Yanes et al (2015), Andrews et al (2018) and Thrasher et al (2018). We retained 34,103 loci with 6764 variant sites remained.…”

The future is now: Amplicon sequencing and sequence capture usher in the conservation genomics era

“…Researchers new to the field of genomic analyses will still require some time learning entry level bioinformatic skills, but this process is made easier by improved pipelines, such as the stacks software (Catchen, Hohenlohe, Bassham, Amores, & Cresko, ; Paris, Stevens, & Catchen, ), which gets more and more user friendly with each update, as well as documented and user‐friendly scripts for analysis of amplicon sequencing data (Campbell et al, ). Developers continue to improve these pipelines (Andrews et al, ; O'Leary, Puritz, Willis, Hollenbeck, & Portnoy, ; Paris et al, ; Rochette & Catchen, ) and have identified important parameters that should be explored, providing guidance that minimizes the need to test the full parameter space for each new study.…”