A suitable pairwise relatedness estimation is key to genetic studies. Several methods are proposed to compute relatedness in autopolyploids based on molecular data. However, unlike diploids, autopolyploids still need further studies considering scenarios with many linked molecular markers with known dosage. In this study, we provide guidelines for plant geneticists and breeders to access trustworthy pairwise relatedness estimates. To this end, we simulated populations considering different ploidy levels, meiotic pairings patterns, number of loci and alleles, and inbreeding levels. Analysis were performed to access the accuracy of distinct methods and to demonstrate the usefulness of molecular marker in practical situations. Overall, our results suggest that at least 100 effective biallelic molecular markers are required to have good pairwise relatedness estimation if methods based on correlation is used. For this number of loci, current methods based on multiallelic markers show lower performance than biallelic ones. To estimate relatedness in cases of inbreeding or close relationships (as parent-offspring, full-sibs, or half-sibs) is more challenging. Methods to estimate pairwise relatedness based on molecular markers, for different ploidy levels or pedigrees were implemented in the AGHmatrix R package.

适当的成对相关性估计是遗传研究的关键。提出了几种基于分子数据计算同源多倍体相关性的方法。然而，与二倍体不同，考虑具有许多已知剂量的链接分子标记的情况，自多倍体仍需要进一步研究。在这项研究中，我们为植物遗传学家和育种者提供了可信赖的成对相关性估计值的指南。为此，我们模拟了考虑不同倍性水平，减数分裂配对模式，基因座和等位基因数量以及近交水平的种群。进行分析以获取不同方法的准确性，并证明分子标记物在实际情况中的有用性。总体，我们的结果表明，如果使用基于相关性的方法，则至少需要100个有效的双等位基因分子标记具有良好的成对相关性估计。对于这个数目的基因座，当前基于多等位基因标记的方法显示出比双等位基因方法更低的性能。在近亲或亲密关系的情况下（例如亲子后代，同胞同父异母或同父异母同父异母），估计亲缘关系更具挑战性。在AGHmatrix R软件包中实施了基于分子标记针对不同倍性水平或谱系估计成对相关性的方法。或半同胞）更具挑战性。在AGHmatrix R软件包中实施了基于分子标记针对不同倍性水平或谱系估计成对相关性的方法。或半同胞）更具挑战性。在AGHmatrix R软件包中实施了基于分子标记针对不同倍性水平或谱系估计成对相关性的方法。