For conservation genetic studies using non-invasively collected samples, genome-wide data may be hard to acquire. Until now, such studies have instead mostly relied on analyses of traditional genetic markers such as microsatellites (SSRs). Recently, high throughput genotyping of single nucleotide polymorphisms (SNPs) has become available, expanding the use of genomic methods to include non-model species of conservation concern. We have developed a 96-marker SNP array for use in applied conservation monitoring of the Scandinavian wolverine (Gulo gulo) population. By genotyping more than a thousand non-invasively collected samples, we were able to obtain precise estimates of different types of genotyping errors and sample dropout rates. The SNP panel significantly outperforms the SSR markers (and DBY intron markers for sexing) both in terms of precision in genotyping, sex assignment and individual identification, as well as in the proportion of samples successfully genotyped. Furthermore, SNP genotyping offers a simplified laboratory and analysis pipeline with fewer samples needed to be repeatedly genotyped in order to obtain reliable consensus data. In addition, we utilised a unique opportunity to successfully demonstrate the application of SNP genotype data for reconstructing pedigrees in wild populations, by validating the method with samples from wild individuals with known relatedness. By offering a simplified workflow with improved performance, we anticipate this methodology will facilitate the use of non-invasive samples to improve genetic management of many different types of populations that have previously been challenging to survey.

对于使用无创收集的样本进行的保护性遗传研究，全基因组范围的数据可能难以获得。到目前为止，此类研究主要依靠对传统遗传标记（如微卫星（SSR））的分析。最近，单核苷酸多态性（SNP）的高通量基因分型已变得可用，从而扩大了基因组方法的使用范围，从而包括了需要关注的非模式物种。我们开发了96标记SNP阵列，用于斯堪的纳维亚金刚狼（Gulo gulo） 人口。通过对一千多个非侵入性收集的样本进行基因分型，我们能够获得对不同类型的基因分型错误和样本漏失率的精确估计。无论是在基因分型，性别分配和个体识别方面，还是在成功进行基因分型的样本比例上，SNP指标均明显优于SSR标记（以及用于性别鉴定的DBY内含子标记）。此外，SNP基因分型提供了简化的实验室和分析流程，需要重复进行基因分型的样本较少，以获得可靠的共有数据。此外，我们利用独特的机会成功地证明了SNP基因型数据在重建野生种群中的谱系中的应用，方法是使用已知相关性的野生个体的样本对方法进行验证。