Ruiz A, Barbadilla A
Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra, Spain.
Genetics. 1995 Jan;139(1):445-55. doi: 10.1093/genetics/139.1.445.
Using Cockerham's approach of orthogonal scales, we develop genetic models for the effect of an arbitrary number of multiallelic quantitative trait loci (QTLs) or neutral marker loci (NMLs) upon any number of quantitative traits. These models allow the unbiased estimation of the contributions of a set of marker loci to the additive and dominance variances and covariances among traits in a random mating population. The method has been applied to an analysis of allozyme and quantitative data from the European oyster. The contribution of a set marker loci may either be real, when the markers are actually QTLs, or apparent, when they are NMLs that are in linkage disequilibrium with hidden QTLs. Our results show that the additive and dominance variances contributed by a set of NMLs are always minimum estimates of the corresponding variances contributed by the associated QTLs. In contrast, the apparent contribution of the NMLs to the additive and dominance covariances between two traits may be larger than, equal to or lower than the actual contributions of the QTLs. We also derive an expression for the expected variance explained by the correlation between a quantitative trait and multilocus heterozygosity. This correlation explains only a part of the genetic variance contributed by the markers, i.e., in general, a combination of additive and dominance variances and, thus, provides only very limited information relative to the method supplied here.
使用科克伦的正交尺度方法,我们开发了遗传模型,用于分析任意数量的多等位基因数量性状位点(QTL)或中性标记位点(NML)对任意数量的数量性状的影响。这些模型能够无偏估计一组标记位点对随机交配群体中各性状间加性方差、显性方差以及协方差的贡献。该方法已应用于对欧洲牡蛎的等位酶和数量数据的分析。当标记实际上是QTL时,一组标记位点的贡献可能是真实的;而当它们是与隐藏的QTL处于连锁不平衡的NML时,其贡献则是表观的。我们的结果表明,一组NML所贡献的加性方差和显性方差始终是相关QTL所贡献的相应方差的最小估计值。相比之下,NML对两个性状之间加性协方差和显性协方差的表观贡献可能大于、等于或小于QTL的实际贡献。我们还推导了一个表达式,用于表示数量性状与多位点杂合性之间的相关性所解释的期望方差。这种相关性仅解释了标记所贡献的遗传方差的一部分,即一般来说,是加性方差和显性方差的组合,因此,相对于本文所提供的方法,它提供的信息非常有限。