Plant Sciences Group, Department of Plant Sciences, Wageningen University and Research, Droevendaalsesteeg 1, P.O. Box 386, 6700 AJ, Wageningen, The Netherlands.
BMC Bioinformatics. 2022 Feb 14;23(1):67. doi: 10.1186/s12859-022-04607-z.
Quantitative trait locus (QTL) analysis allows to identify regions responsible for a trait and to associate alleles with their effect on phenotypes. When using biallelic markers to find these QTL regions, two alleles per QTL are modelled. This assumption might be close to reality in specific biparental crosses but is unrealistic in situations where broader genetic diversity is studied. Diversity panels used in genome-wide association studies or multi-parental populations can easily harbour multiple QTL alleles at each locus, more so in the case of polyploids that carry more than two alleles per individual. In such situations a multiallelic model would be closer to reality, allowing for different genetic effects for each potential allele in the population. To obtain such multiallelic markers we propose the usage of haplotypes, concatenations of nearby SNPs. We developed "mpQTL" an R package that can perform a QTL analysis at any ploidy level under biallelic and multiallelic models, depending on the marker type given. We tested the effect of genetic diversity on the power and accuracy difference between bi-allelic and multiallelic models using a set of simulated multiparental autotetraploid, outbreeding populations. Multiallelic models had higher detection power and were more precise than biallelic, SNP-based models, particularly when genetic diversity was higher. This confirms that moving to multi-allelic QTL models can lead to improved detection and characterization of QTLs. KEY MESSAGE: QTL detection in populations with more than two functional QTL alleles (which is likely in multiparental and/or polyploid populations) is more powerful when using multiallelic models, rather than biallelic models.
数量性状位点 (QTL) 分析可用于识别负责特定性状的区域，并将等位基因与其对表型的影响联系起来。当使用双等位基因标记来寻找这些 QTL 区域时，每个 QTL 会建模两个等位基因。在特定的双亲杂交中，这种假设可能接近现实，但在研究更广泛遗传多样性的情况下则不现实。在全基因组关联研究或多亲群体中使用的多样性面板在每个位点上很容易携带多个 QTL 等位基因，在多倍体的情况下更是如此，因为每个个体携带的等位基因超过两个。在这种情况下，多等位基因模型更接近现实，可以为群体中的每个潜在等位基因赋予不同的遗传效应。为了获得这种多等位基因标记，我们建议使用单倍型，即附近 SNP 的串联。我们开发了“mpQTL”，这是一个 R 包，可以在双等位基因和多等位基因模型下，根据给定的标记类型，在任何倍性水平上进行 QTL 分析。我们使用一组模拟的多亲体自交四倍体、异交群体，测试了遗传多样性对双等位基因和多等位基因模型之间的检测能力和准确性差异的影响。多等位基因模型比双等位基因、基于 SNP 的模型具有更高的检测能力和更高的准确性，尤其是当遗传多样性更高时。这证实了向多等位基因 QTL 模型的转变可以提高 QTL 的检测和特征描述。关键信息：在具有两个以上功能 QTL 等位基因的群体（在多亲体和/或多倍体群体中很可能存在）中，使用多等位基因模型而不是双等位基因模型可以更有效地检测 QTL。
