Quantitative Genetics of Transgenic Mice: Components of Phenotypic Variation in Body Weights and Weight Gains

Transgenic mice possessing an ovine growth hormone gene were used to study the effects of elevated growth hormone on quantitative genetic variation. Males hemizygous for the transgene were mated to wild-type females to produce half- and full-sib families in which approximately half the progeny were transgenic and half were wild type. Analyses of body weights at 3-10 weeks, and weight gains from 3 to 6, and 6 to 10 weeks produced estimates of the proportion of total variance due to additive genetic effects (h(2)) and common litter effects (c(2)), and the genetic correlation between transgenic and wild-type expression of each trait. At 10 weeks, body weight of transgenics exceeded that of wild types by 26 and 49% in males and females, respectively. Estimated genetic variances in the transgenic group were significantly greater than zero for body weights at most ages and for both measurements of gain. Common litter effects accounted for a similar proportion of variation in the wild-type and transgenic groups. Additive genetic correlations between wild-type and transgenic expression of body weights tended to decline with age, indicating that a partially different array of genes may have begun to affect body weight in the transgenic group.     

Mutational Robustness Accelerates the Origin of Novel RNA Phenotypes through Phenotypic Plasticity

Novel phenotypes can originate either through mutations in existing genotypes or through phenotypic plasticity, the ability of one genotype to form multiple phenotypes. From molecules to organisms, plasticity is a ubiquitous feature of life, and a potential source of exaptations, adaptive traits that originated for nonadaptive reasons. Another ubiquitous feature is robustness to mutations, although it is unknown whether such robustness helps or hinders the origin of new phenotypes through plasticity. RNA is ideal to address this question, because it shows extensive plasticity in its secondary structure phenotypes, a consequence of their continual folding and unfolding, and these phenotypes have important biological functions. Moreover, RNA is to some extent robust to mutations. This robustness structures RNA genotype space into myriad connected networks of genotypes with the same phenotype, and it influences the dynamics of evolving populations on a genotype network. In this study I show that both effects help accelerate the exploration of novel phenotypes through plasticity. My observations are based on many RNA molecules sampled at random from RNA sequence space, and on 30 biological RNA molecules. They are thus not only a generic feature of RNA sequence space but are relevant for the molecular evolution of biological RNA.     

Mutational Robustness Accelerates the Origin of Novel RNA Phenotypes through Phenotypic Plasticity

Novel phenotypes can originate either through mutations in existing genotypes or through phenotypic plasticity, the ability of one genotype to form multiple phenotypes. From molecules to organisms, plasticity is a ubiquitous feature of life, and a potential source of exaptations, adaptive traits that originated for nonadaptive reasons. Another ubiquitous feature is robustness to mutations, although it is unknown whether such robustness helps or hinders the origin of new phenotypes through plasticity. RNA is ideal to address this question, because it shows extensive plasticity in its secondary structure phenotypes, a consequence of their continual folding and unfolding, and these phenotypes have important biological functions. Moreover, RNA is to some extent robust to mutations. This robustness structures RNA genotype space into myriad connected networks of genotypes with the same phenotype, and it influences the dynamics of evolving populations on a genotype network. In this study I show that both effects help accelerate the exploration of novel phenotypes through plasticity. My observations are based on many RNA molecules sampled at random from RNA sequence space, and on 30 biological RNA molecules. They are thus not only a generic feature of RNA sequence space but are relevant for the molecular evolution of biological RNA.     

Quantitative Genetics in the Genomics Era

The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. These analyses continue to be developed, for example in studies of natural populations. Genomic methods are having an impact on progress and prospects. Actual relationships of individuals can be estimated enabling novel quantitative analyses. Increasing precision of linkage mapping is feasible with dense marker panels and designed stocks allowing multiple generations of recombination, and large SNP panels enable the use of genome wide association analysis utilising historical recombination. Whilst such analyses are identifying many loci for disease genes and traits such as height, typically each individually contributes a small amount of the variation. Only by fitting all SNPs without regard to significance can a high proportion be accounted for, so a classical polygenic model with near infinitesimally small effects remains a useful one. Theory indicates that a high proportion of variants will have low minor allele frequency, making detection difficult. Genomic selection, based on simultaneously fitting very dense markers and incorporating these with phenotypic data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction.     

A Building Block Model for Quantitative Genetics

We introduce a quantitative genetic model for multiple alleles which permits the parameterization of the degree, D, of dominance of favorable or unfavorable alleles. We assume gene effects to be random from some distribution and independent of the D's. We then fit the usual least-squares population genetic model of additive and dominance effects in an infinite equilibrium population to determine the five genetic components--additive variance sigma 2 a, dominance variance sigma 2 d, variance of homozygous dominance effects d2, covariance of additive and homozygous dominance effects d1, and the square of the inbreeding depression h--required to treat finite populations and large populations that have been through a bottleneck or in which there is inbreeding. The effects of dominance can be summarized as functions of the average, D, and the variance, sigma 2 D. An important distinction arises between symmetrical and nonsymmetrical distributions of gene effects. With symmetrical distributions d1 = -d2/2 which is always negative, and the contribution of dominance to sigma 2 a is equal to d2/2. With nonsymmetrical distributions there is an additional contribution H to sigma 2 a and -H/2 to d1, the sign of H being determined by D and the skew of the distribution. Some numerical evaluations are presented for the normal and exponential distributions of gene effects, illustrating the effects of the number of alleles and of the variation in allelic frequencies. Random additive by additive (a*a) epistatic effects contribute to sigma 2 a and to the a*a variance, sigma 2/aa, the relative contributions depending on the number of alleles and the variation in allelic frequencies.(ABSTRACT TRUNCATED AT 250 WORDS)     

Quantitative Genetics of Sperm Precedence in Drosophila Melanogaster

To assess the genetic basis of sperm competition under conditions in which it occurs, I estimated additive, dominance, homozygous and environmental variance components, the effects of inbreeding, and the weighted average dominance of segregating alleles for two measures of sperm precedence in a large, outbred laboratory population. Both first and second male precedence show significant decline on inbreeding. Second male precedence demonstrates significant dominance variance and homozygous genetic variance, but the additive variance is low and not significantly different from zero. For first male precedence, the variance among homozygous lines is again significant, and dominance variance is larger than the additive variance, but is not statistically significant. In contrast, male mating success and other fitness components in Drosophila generally exhibit significant additive variance and little or no dominance variance. Other recent experiments have shown significant genotypic variation for sperm precedence and have associated it with allelic variants of accessory-gland proteins. The contrast between sperm precedence and other male fitness traits in the structure of quantitative genetic variation suggests that different mechanisms may be responsible for the maintenance of variation in these traits. The pattern of genetic variation and inbreeding decline shown in this experiment suggests that one or a few genes with major effects on sperm precedence may be segregating in this population.     

The Effect of Isogenization on the Phenotypic Manifestation of Quantitative Traits in Drosophila melanogaster

A comparative analysis of the phenotypic values of the proximal and distal fragments of the radial wing vein was carried out in heterogeneous lines of Drosophila melanogaster and in isogenic lines derived from them with the help of a balancer line. The mean values of the traits in the isogenic lines were shown to significantly differ from the corresponding values in the parental heterogeneous lines. Apparently, the change in the trait values was caused by a double recombination exchange between the inverted and the normal chromosomes, which suggests partial crossing over suppression in the balancer lines.     

Understanding Quantitative Genetics in the Systems Biology Era

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.     

The Evolution of Epistasis and Its Links With Genetic Robustness, Complexity and Drift in a Phenotypic Model of Adaptation

The epistatic interactions among mutations have a large effect on the evolution of populations. In this article we provide a formalism under which epistatic interactions among pairs of mutations have a distribution whose mean can be modulated. We find that the mean epistasis is correlated to the effect of mutations or genetic robustness, which suggests that such formalism is in good agreement with most in silico models of evolution where the same pattern is observed. We further show that the evolution of epistasis is highly dependant on the intensity of drift and of how complex the organisms are, and that either positive or negative epistasis could be selected for, depending on the balance between the efficiency of selection and the intensity of drift.     

Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus.