Principles of Plant Genetics and Breeding. George Acquaah

the expression levels of thousands of gene simultaneously, and thereby study gene expression at both the ribonucleic acid (RNA) and protein levels as a quantitative trait.

      4.3.1 Effects of QTL on phenotype

      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.

      4.3.2 Molecular basis of quantitative variation

The causal molecular variants that affect QTL are called quantitative trait nucleotides (QTNs). The distribution of QTN allele frequencies can indicate the nature of the selective forces operating on the trait. Inference of QTN allele frequencies is limited to association mapping (see Chapter 20) designs in which all the variants in a candidate gene or gene region have been identified. QTNs allow researchers to map phenotype to genotype in the absence of biological context. QTNs consist of two components: eQTL(expression quantitative trait loci) and QTT(quantitative trait transcripts) (Figure 4.8).

Figure 4.8 Systems genetics of complex traits: An integrative framework showing the relationship between DNA sequence variation and quantitative variation for gene expression and an organismal phenotype. QTNs allow researchers to map phenotype to genotype in the absence of biological context. In order to gain this context, they need to describe the flow of information from DNA to the organismal phenotype through RNA intermediates, proteins and other molecular endophenotypes.

      The eQTL is a region of the genome containing one or more genes that affect variation in gene expression, which is identified by linkage to polymorphic marker loci. It is technically a marriage of high‐throughput expression profiling technology and QTL analysis. QTT is a transcript for which variation in its expression is correlated with variation in an organismal level quantitative trait phenotype. Numerous (even several hundreds) of QTT are believed to be associated with any single quantitative trait phenotype. Further, these QTT are genetically correlated.

4.4 Systems genetics

Systems genetics (also called genetical genomics) is considered to be the third in the developmental stages of quantitative genetics, after classical and molecular quantitative genetics in that order of development. This new paradigm is instigated by the fact that associating DNA sequence variation with variation of the phenotype of an organism skips all of the intermediate steps (e.g. the intermediate molecular phenotypes like transcript abundance which are quantitative traits and vary genetically in populations) in the chain of the causation from genetic perturbation to phenotypic variation. Systems genetics detects variation in phenotypic traits and integrates them with underlying genetic variation. According to Mackay and colleagues, this approach to QTL analysis integrates DNA sequence variation, variation in transcript abundance and other molecular phenotypes, and variation in the phenotypes of the organism in linkage or association mapping population, thereby allowing the researcher to interpret quantitative genetic variation in terms of biologically meaningful causal networks of correlated transcripts. In other words, it can be used to define biological networks and to predict molecular interactions by analyzing transcripts with expressions that co‐vary within genetic populations. The approach can be used to analyze effects of genome‐wide genetic variants on transcriptome‐wide variation in gene expression.

4.5 Predicting breeding value

      Breeding value (or genetic merit) of an individual as a genetic parent is the sum of gene effects of the individual as measured by the performance of its progeny. Statistically, it is measured as twice the deviation of the offspring from the population mean (since the individual only contributes half of the alleles to its offspring). This estimate measures the ability of an individual to produce superior offspring. This is the part of an individual's genotypic value that is due to independent gene effects and hence can be transmitted. The mean breeding value becomes zero with random mating. This estimate is of importance to breeders because it assists them in selecting the best parents to use in their programs.

      The Best Linear Unbiased Prediction(BLUP) is a common statistical method for estimating breeding values. It is unbiased because as more data are accumulated, the predicted breeding values approach the true values. BLUP is a method of estimating random effects. The context of this statistical method is the linear model

      where y = is a vector of n observable random variables; B is vector of p unknown parameters with fixed value or effects; X and Z are known matrices; u and e are vectors of q and n, respectively, unobservable random variables (random effects).

      To apply this technique, numerical scores are assigned to traits and compiled as predictions of the future. Simple traits can be most accurately and objectively measured and possibly predicted. Only one trait may be predicted in a model. This trait has to be objectively measurable with high accuracy. Further, it has to be heritable.

4.6 Genomic selection (genome‐wide selection)

      Genomic