Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.     

Comparing G: multivariate analysis of genetic variation in multiple populations

The additive genetic variance–covariance matrix (G) summarizes themultivariate genetic relationships among a set of traits. The geometry of Gdescribes the distribution of multivariate genetic variance, and generates geneticconstraints that bias the direction of evolution. Determining if and how the multivariategenetic variance evolves has been limited by a number of analytical challenges incomparing G-matrices. Current methods for the comparison of G typically shareseveral drawbacks: metrics that lack a direct relationship to evolutionary theory, theinability to be applied in conjunction with complex experimental designs, difficultieswith determining statistical confidence in inferred differences and an inherentlypair-wise focus. Here, we present a cohesive and general analytical framework for thecomparative analysis of G that addresses these issues, and that incorporates andextends current methods with a strong geometrical basis. We describe the application ofrandom skewers, common subspace analysis, the 4th-order genetic covariance tensor and thedecomposition of the multivariate breeders equation, all within a Bayesian framework. Weillustrate these methods using data from an artificial selection experiment on eighttraits in Drosophila serrata, where a multi-generational pedigree was availableto estimate G in each of six populations. One method, the tensor, elegantlycaptures all of the variation in genetic variance among populations, and allows theidentification of the trait combinations that differ most in genetic variance. The tensorapproach is likely to be the most generally applicable method to the comparison ofG-matrices from any sampling or experimental design.     

Competition as a source of constraint on life history evolution in natural populations

Competition among individuals is central to our understanding of ecology and population dynamics. However, it could also have major implications for the evolution of resource-dependent life history traits (for example, growth, fecundity) that are important determinants of fitness in natural populations. This is because when competition occurs, the phenotype of each individual will be causally influenced by the phenotypes, and so the genotypes, of competitors. Theory tells us that indirect genetic effects arising from competitive interactions will give rise to the phenomenon of ‘evolutionary environmental deterioration'', and act as a source of evolutionary constraint on resource-dependent traits under natural selection. However, just how important this constraint is remains an unanswered question. This article seeks to stimulate empirical research in this area, first highlighting some patterns emerging from life history studies that are consistent with a competition-based model of evolutionary constraint, before describing several quantitative modelling strategies that could be usefully applied. A recurrent theme is that rigorous quantification of a competition''s impact on life history evolution will require an understanding of the causal pathways and behavioural processes by which genetic (co)variance structures arise. Knowledge of the G-matrix among life history traits is not, in and of itself, sufficient to identify the constraints caused by competition.     

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.     

Genome-wide Introgression Lines and their Use in Genetic and Molecular Dissection of Complex Phenotypes in Rice (Oryza sativa L.)

Tremendous efforts have been taken worldwide to develop genome-wide genetic stocks for rice functional genomic (FG) research since the rice genome was completely sequenced. To facilitate FG research of complex polygenic phenotypes in rice, we report the development of over 20 000 introgression lines (ILs) in three elite rice genetic backgrounds for a wide range of complex traits, including resistances/tolerances to many biotic and abiotic stresses, morpho-agronomic traits, physiological traits, etc., by selective introgression. ILs within each genetic background are phenotypically similar to their recurrent parent but each carries one or a few traits introgressed from a known donor. Together, these ILs contain a significant portion of loci affecting the selected complex phenotypes at which allelic diversity exists in the primary gene pool of rice. A forward genetics strategy was proposed and demonstrated with examples on how to use these ILs for large-scale FG research. Complementary to the genome-wide insertional mutants, these ILs opens a new way for highly efficient discovery, candidate gene identification and cloning of important QTLs for specific phenotypes based on convergent evidence from QTL position, expression profiling, functional and molecular diversity analyses of candidate genes, highlights the importance of genetic networks underlying complex phenotypes in rice that may ultimately lead to more complete understanding of the genetic and molecular bases of quantitative trait variation in rice. Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1007/s11103-005-8519-3     

玉米SSR遗传图谱的构建及产量性状基因定位

利用中国农大培育的高产,多抗性玉米杂交组合农大3138的F2：3家系为材料,构建了具有80对SSR标记的玉米遗传图谱,标记间平均距离25．42,覆盖玉米基因组的2033.4cm,采用随机区组田间设计,考察了230个家系的穗长,秃尖长,穗粗,穗行数,千粒重,穗重,单株粒重,利用区间作图法分析了影响各性状的数量性状基因座位（QTL）,共检测到30个QTLs,单个性状的QTLs为3－5个,QTLs解释变异量占总变异量的比例变化范围为9．5％－55．3％。     

甘蓝型油菜种胚叶绿素含量的QTL定位

种胚叶绿素含量是影响菜子油加工和销售的重要指标。关于种胚叶绿素的遗传特性还缺乏研究。本文以重庆市油菜工程中心构建的甘蓝型黄子与黑子油菜重组自交系群体为材料,2007年分别在重庆市北碚区和万州区两个实验基地种植,利用本实验室已构建的遗传连锁图谱为基础,采用复合区间作图法(C IM)分析种胚叶绿素的QTL。结果共检测到10个QTL,分别位于第1、2、5、8、21和25连锁群,单个QTL解释表型变异的4.04%～8.50%。研究结果表明胚中叶绿素表现为多基因控制的数量性状,基因表达受环境影响较大。     

Does natural selection alter genetic architecture? An evaluation of quantitative genetic variation among populations of Allonemobiussocius and A. fasciatus

To make long-term predictions using present quantitative genetic theory it is necessary to assume that the genetic variance–covariance matrix ( G ) remains constant or at least changes by a constant fraction. In this paper we examine the stability of the genetic architecture of two traits known to be subject to natural selection; femur length and ovipositor length in two species of the cricket Allonemobius. Previous studies have shown that in A. fasciatus and A. socius natural selection favours an increased body size southwards but a decreased ovipositor length. Such countergradient selection should tend to favour a change in G . In the total sample of eight populations of A. socius and one of A. fasciatus we show that there is significant variation in all genetic covariance components, i.e. V_A for body size, V_A for ovipositor length, and Cov_A. This variation results entirely from an increase in the covariances of A. fasciatus. However, although larger, these components are approximately proportionally increased, thereby leading to no statistically significant change in the genetic correlation. A proportional increase in the covariance components is consistent with changes resulting from genetic drift. On the other hand, the genetic covariance components are significantly correlated with the length of the growing season suggesting that the change in the genetic architecture is the result of selection and drift.     

Complex adaptation and system structure

The structural organization of biological systems is one of nature’s most fascinating aspects, but its origin and functional role is not yet fully understood. For instance, basic adaptational mechanisms like genetic mutation and Hebbian adaptation seem to be generic and invariant across many species and are, on their own, fairly well investigated and understood. However, it is the organism’s structure – the representations these mechanisms act upon – that bears the complex functional effects of these mechanisms. While typical technical approaches to system design require detailed problem models and suffer from the need to explicitly take care of all possible cases, the organization of biological systems seems to induce inherent adaptability, flexibility and robustness. In this discussion paper we address the concept of structured variability, particularly the role of system structure as implementing a certain representation on which basic variational mechanisms act on. The functional adaptability (or search distribution) depends crucially on this representation.