# Assessing kinship detection: single nucleotide polymorphism array density and estimator comparison in white-tailed deer

## TL;DR

This study compares SNP arrays and software for detecting genetic relationships in white-tailed deer to find the most efficient and accurate methods.

## Contribution

The study identifies optimal SNP array density and filtering thresholds, and evaluates the performance of four relatedness estimators in a wild deer population.

## Key findings

- Medium-density SNP arrays offer a favorable balance between cost, computational time, and statistical power.
- Sequoia software combined with a subset of 600 loci with no missing data provided the most efficient and accurate results.
- SNP filtering thresholds significantly impact relatedness analyses in wildlife populations.

## Abstract

Single nucleotide polymorphism (SNP) arrays have become increasingly popular due to their affordability, commercial availability, statistical power, and reproducibility. These arrays are being developed commercially for a wide range of species in various density formats. In this study, we evaluated the ability of commercially available medium-density (72,732 SNPs) and high-density SNP (702,183 SNPs) arrays for white-tailed deer (Odocoileus virginianus) to accurately identify known genetically related individuals within a wild population. We also assessed the impact of SNP filtering thresholds on relatedness analyses and compared the performance of 4 common relatedness software: KING, COLONY, Sequoia, and COANCESTRY, on these known related pairs. Our analysis revealed that the medium-density array exhibited greater tolerance to filtering and lower sensitivity to bioinformatic pipelines, making it a favorable balance between cost, computational time, and statistical power for analyses such as population structure. Additionally, we found that reducing missing data, specifically by using a subset of 600 loci with no missing data, combined with the relatedness estimator Sequoia (which allows the inclusion of life history data), yielded the most computationally efficient and accurate results. These findings offer valuable insights into the optimal SNP array size, appropriate filtering thresholds, and the most effective genetic relatedness methods for wildlife population studies.

Graphical AbstractFor image description, please refer to the figure legend and surrounding text.