Читать книгу Principles of Plant Genetics and Breeding - George Acquaah - Страница 198

QTLs influence quantitative trait phenotype in various ways. They can influence quantitative trait levels (quantitative trait means can be different among different genotypes). Most of the statistical methods used for studying quantitative traits are based on genotypic means. The variation in phenotypic values may also vary among genotypes. Further, QTLs also may affect the correlation among quantitative traits as well as the dynamics of traits (the change in phenotype over a period of time may be due to variations in a QTL).

QTL alleles have context‐dependent effects. Their effects may differ in magnitude or direction in different genetic backgrounds, different environments, or between male and females (i.e. genotype × genotype interaction – epistasis; genotype × environment interactions; genotype × sex interaction). These context‐dependent effects are very common and play a significant role in genetic architecture, but they are very difficult to detect and characterize. In addition to meaning the masking of genotypic effects at one locus by genotypes of another locus, epistasis in quantitative genetics also refers to any statistical interaction between the genotypes at two or more loci. It is common between mutations that affect the same quantitative trait. Epistatic effects can be as large as main QTL effects; they can also occur in opposite directions between different pairs of interacting loci and between loci with having significant main effects on the trait of interest. Epistasis has been found between closely linked QTLs and also polymorphisms at a single locus.

Pleiotropy, the effect of a gene on more than one phenotype, is important in the genetics of QTLs. In a narrow sense, pleiotropy can mean the effect of a particular allele on more than one phenotype, and is the reason for the stable genetic correlations between quantitative traits, if the effects at multiple loci affecting the same trait are in the same direction. Understanding of pleiotropic connects between quantitative traits helps in predicting the correlated responses to artificial selection and assessing the contribution of new mutations to standing variation for quantitative traits. Pleiotropy is known to occur even between traits that are not functionally related. Consequently, the pleiotropic effects of different genes that affect pairs of traits are usually not in the same direction and do not result in significant genetic correlations between traits.