The Quantitative Genetics of Phenotypic Robustness

Phenotypic robustness, or canalization, has been extensively investigated both experimentally and theoretically. However, it remains unknown to what extent robustness varies between individuals, and whether factors buffering environmental variation also buffer genetic variation. Here we introduce a quantitative genetic approach to these issues, and apply this approach to data from three species. In mice, we find suggestive evidence that for hundreds of gene expression traits, robustness is polymorphic and can be genetically mapped to discrete genomic loci. Moreover, we find that the polymorphisms buffering genetic variation are distinct from those buffering environmental variation. In fact, these two classes have quite distinct mechanistic bases: environmental buffers of gene expression are predominantly sex-specific and trans-acting, whereas genetic buffers are not sex-specific and often cis-acting. Data from studies of morphological and life-history traits in plants and yeast support the distinction between polymorphisms buffering genetic and environmental variation, and further suggest that loci buffering different types of environmental variation do overlap with one another. These preliminary results suggest that naturally occurring polymorphisms affecting phenotypic robustness could be abundant, and that these polymorphisms may generally buffer either genetic or environmental variation, but not both.     

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.     

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 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.     

小家鼠的遗传与进化研究进展

小家鼠作为重要的模式生物,近20年来对其遗传与进化领域的研究取得很大进展,通过现代遗传学的各种遗传标记（蛋白质电泳,mtDNA,rDNA,Y染色体等）,不仅更深入地探讨了共栖小家鼠间的遗传差异和系统关系以及与野生种的关系;进而还对小家鼠的分类系统进行了修正,提出小家鼠种组（species complex)由domesticus,musculus,castaneus 和bactrianus 4个共栖种以及spicilegus,macedonicus和spretus3个野生种组成;并且进一步阐明了小家鼠的起源和进化问题,目前主要有离心模型和顺序模型2种起源假说,然而对中国小家鼠的遗传分化,亚种分类尚缺乏系统的研究。     

A Novel Generalized Ridge Regression Method for Quantitative Genetics

As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithm for situations in which the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number of observations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented, and tested. The efficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis data set including 84 inbred lines and 216,130 SNPs. The computation of all the SNP effects required <10 sec using a single 2.7-GHz core. The advantage in run time makes permutation test feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNP-best linear unbiased prediction) in terms of QTL mapping, because SNP-specific shrinkage was applied instead of a common shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.     

Phenotypic Heterogeneity and the Evolution of Bacterial Life Cycles

Most bacteria live in colonies, where they often express different cell types. The ecological significance of these cell types and their evolutionary origin are often unknown. Here, we study the evolution of cell differentiation in the context of surface colonization. We particularly focus on the evolution of a ‘sticky’ cell type that is required for surface attachment, but is costly to express. The sticky cells not only facilitate their own attachment, but also that of non-sticky cells. Using individual-based simulations, we show that surface colonization rapidly evolves and in most cases leads to phenotypic heterogeneity, in which sticky and non-sticky cells occur side by side on the surface. In the presence of regulation, cell differentiation leads to a remarkable set of bacterial life cycles, in which cells alternate between living in the liquid and living on the surface. The dominant life stage is formed by the surface-attached colony that shows many complex features: colonies reproduce via fission and by producing migratory propagules; cells inside the colony divide labour; and colonies can produce filaments to facilitate expansion. Overall, our model illustrates how the evolution of an adhesive cell type goes hand in hand with the evolution of complex bacterial life cycles.     

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.     

How Important is Genetics for an Understanding of Evolution?

SYNOPSIS. The contributions of modern genetics to the understandingof evolution have been threefold. First, it has documented theextent of genetic variation that exists in populations as abasis for future evolution. In particular it has shown thatnatural selection has not destroyed all variation, as mightbe predicted, so that there must be mechanisms for the maintenanceandorigin of new selectable variation that balance the homogenizingforces of selection. Second, it has begun to provide a mechanismof the origin of genetic novelties which must be at the basisof the major features of the history of life. In doing so, ithas shown how selection fornew features is at all times historicallycontingent and that evolution is at all times at riskof fallinginto genetic dead-ends. Third, modern genetics has greatly enrichedthe diversity ofmechanisms known to cause evolutionary change.All of these mechanisms involve the conversion of variationbetween individuals into variation between populations in timeand space, but many are non-selective or even counter-selective.Natural selection is not the only mechanism of evolution.     

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.     

Convergent Phenotypic Evolution of Rhodopsin for Dim-Light Sensing across Deep-Diving Vertebrates

Rhodopsin comprises an opsin attached to a retinal chromophore and is the only visual pigment conferring dim-light vision in vertebrates. On activation by photons, the retinal group becomes detached from the opsin, which is then inactive until it is recharged. Of all vertebrate species, those that dive face unique visual challenges, experiencing rapid decreases in light level and hunting in near darkness. Here, we combine sequence analyses with functional assays to show that the rhodopsin pigments of four divergent lineages of deep-diving vertebrates have undergone convergent increases in their retinal release rate. We compare gene sequences and detect parallel amino acids between penguins and diving mammals and perform mutagenesis to show that a single critical residue fully explains the observed increases in retinal release rate in both the emperor penguin and beaked whale. At the same time, we find that other shared sites have no significant effect on retinal release, implying that convergence does not always signify adaptive significance. We propose that accelerated retinal release confers rapid rhodopsin recharging, enabling the visual systems of diving species to adjust quickly to changing light levels as they descend through the water column. This contrasts with nocturnal species, where adaptation to darkness has been attributed to slower retinal release rates.