Epistatic pleiotropy and the genetic architecture of covariation within early and late-developing skull trait complexes in mice

The role of epistasis as a source of trait variation is well established, but its role as a source of covariation among traits (i.e., as a source of "epistatic pleiotropy") is rarely considered. In this study we examine the relative importance of epistatic pleiotropy in producing covariation within early and late-developing skull trait complexes in a population of mice derived from an intercross of the Large and Small inbred strains. Significant epistasis was found for several pairwise combinations of the 21 quantitative trait loci (QTL) affecting early developing traits and among the 20 QTL affecting late-developing traits. The majority of the epistatic effects were restricted to single traits but epistatic pleiotropy still contributed significantly to covariances. Because of their proportionally larger effects on variances than on covariances, epistatic effects tended to reduce within-group correlations of traits and reduce their overall degree of integration. The expected contributions of single-locus and two-locus epistatic pleiotropic QTL effects to the genetic covariance between traits were analyzed using a two-locus population genetic model. The model demonstrates that, for single-locus or epistatic pleiotropy to contribute to trait covariances in the study population, both traits must show the same pattern of single-locus or epistatic effects. As a result, a large number of the cases where loci show pleiotropic effects do not contribute to the covariance between traits in this population because the loci show a different pattern of effect on the different traits. In general, covariance patterns produced by single-locus and epistatic pleiotropy predicted by the model agreed well with actual values calculated from the QTL analysis. Nearly all single-locus and epistatic pleiotropic effects contributed positive components to covariances between traits, suggesting that genetic integration in the skull is achieved by a complex combination of pleiotropic effects.     

Genetic variation in pleiotropy: differential epistasis as a source of variation in the allometric relationship between long bone lengths and body weight

Pleiotropy is an aspect of genetic architecture underlying the phenotypic covariance structure. The presence of genetic variation in pleiotropy is necessary for natural selection to shape patterns of covariation between traits. We examined the contribution of differential epistasis to variation in the intertrait relationship and the nature of this variation. Genetic variation in pleiotropy was revealed by mapping quantitative trait loci (QTLs) affecting the allometry of mouse limb and tail length relative to body weight in the mouse-inbred strain LG/J by SM/J intercross. These relationship QTLs (rQTLs) modify relationships between the traits affected by a common pleiotropic locus. We detected 11 rQTLs, mostly affecting allometry of multiple bones. We further identified epistatic interactions responsible for the observed allometric variation. Forty loci that interact epistatically with the detected rQTLs were identified. We demonstrate how these epistatic interactions differentially affect the body size variance and the covariance of traits with body size. We conclude that epistasis, by differentially affecting both the canalization and mean values of the traits of a pleiotropic domain, causes variation in the covariance structure. Variation in pleiotropy maintains evolvability of the genetic architecture, in particular the evolvability of its modular organization.     

An epistatic genetic basis for physical activity traits in mice

We recently identified several (4-8) quantitative trait loci (QTL) for 3 physical activity traits (daily distance, duration, and speed voluntarily run) in an F(2) population of mice derived from an original intercross of 2 strains that exhibited large differences in activity. These QTL cumulatively explained from 11% to 34% of the variation in these traits, but this was considerably less than their total genetic variability estimated from differences among inbred strains. We therefore decided to test whether epistatic interactions might account for additional genetic variation in these traits in this same population of mice. We conducted a full genome epistasis scan for all possible interactions of QTL between each pair of 20 chromosomes. The results of this scan revealed an abundance of epistasis, with QTL throughout the genome being involved in significant interactions. Overall, epistatic effects contributed an average of 26% of the total variation among the 3 activity traits. These results suggest that epistatic interactions of genes may play as important a role in the genetic architecture of physical activity traits as single-locus effects and need to be considered in future candidate gene identification studies.     

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.     

Quantitative trait loci for body size components in mice

Do body size components, such as weights of internal organs and long bone lengths, with different functions and different developmental histories also have different genetic architectures and pleiotropic patterns? We examine murine quantitative trait loci (QTL) for necropsy weight, four long bone lengths, and four organ weights in the LG/J × SM/J intercross. Differences between trait categories were found in number of QTL, dominance, and pleiotropic patterns. Ninety-seven QTLs for individual traits were identified: 52 for long bone lengths, 30 for organ weights, and 15 for necropsy weight. Results for long bones are typically more highly significant than for organs. Organ weights were more frequently over- or underdominant than long bone lengths or necropsy weight. The single-trait QTLs map to 35 pleiotropic loci. Long bones are much more frequently affected in groups while organs tend to be affected singly or in pairs. Organs and long bones are found at the same locus in only 11 cases, 8 of which also include necropsy weight. Our results suggest mainly separate genetic modules for organ weights and long bone lengths, with a few loci that affect overall body size. Antagonistic pleiotropy, in which a locus has opposite effects on different characteristics, is uncommon.     

Pleiotropic effects on mandibular morphology II: differential epistasis and genetic variation in morphological integration

The evolution of morphological modularity through the sequestration of pleiotropy to sets of functionally and developmentally related traits requires genetic variation in the relationships between traits. Genetic variation in relationships between traits can result from differential epistasis, where epistatic relationships for pairs of loci are different for different traits. This study maps relationship quantitative trait loci (QTLs), specifically QTLs that affect the relationship between individual mandibular traits and mandible length, across the genome in an F2 intercross of the LG/J and SM/J inbred mouse strains (N = 1045). We discovered 23 relationship QTLs scattered throughout the genome. All mandibular traits were involved in one or more relationship QTL. When multiple traits were affected at a relationship QTL, the traits tended to come from a developmentally restricted region of the mandible, either the muscular processes or the alveolus. About one-third of the relationship QTLs correspond to previously located trait QTLs affecting the same traits. These results comprise examples of genetic variation necessary for an evolutionary response to selection on the range of pleiotropic effects.     

Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse

The extent to which epistasis contributes to adaptation, population differentiation, and speciation is a long-standing and important problem in evolutionary genetics. Using recombinant inbred (RI) lines of Arabidopsis thaliana grown under natural field conditions, we have examined the genetic architecture of fitness-correlated traits with respect to epistasis; we identified both single-locus additive and two-locus epistatic QTL for natural variation in fruit number, germination, and seed length and width. For fruit number, we found seven significant epistatic interactions, but only two additive QTL. For seed germination, length, and width, there were from two to four additive QTL and from five to eight epistatic interactions. The epistatic interactions were both positive and negative. In each case, the magnitude of the epistatic effects was roughly double that of the effects of the additive QTL, varying from -41% to +29% for fruit number and from -5% to +4% for seed germination, length, and width. A number of the QTL that we describe participate in more than one epistatic interaction, and some loci identified as additive also may participate in an epistatic interaction; the genetic architecture for fitness traits may be a network of additive and epistatic effects. We compared the map positions of the additive and epistatic QTL for germination, seed width, and seed length from plants grown in both the field and the greenhouse. While the total number of significant additive and epistatic QTL was similar under the two growth conditions, the map locations were largely different. We found a small number of significant epistatic QTL x environment effects when we tested directly for them. Our results support the idea that epistatic interactions are an important part of natural genetic variation and reinforce the need for caution in comparing results from greenhouse-grown and field-grown plants.     

Mapping the epistatic network underlying murine reproductive fatpad variation

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F(10) generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F(2) loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.     

Genetic dissection of femur breaking strength in a large population (MRL/MpJ x SJL/J) of F2 Mice: single QTL effects, epistasis, and pleiotropy

Bone breaking strength is an ultimate measurement of the risk of fracture. For a practical reason, bone mineral density (BMD) has been commonly used for predicting the risk instead. To identify genetic loci influencing femur-breaking strength (FBS), which was measured by three-point bending using an Instron DynaMight Low-Force Testing System, the whole-genome scan was carried out using 119 polymorphic markers in 633 (MRLxSJL) F2 female mice. We identified six significant quantitative trait loci (QTL) affecting bone breaking strength on chromosomes 1, 2, 8, 9, 10, and 17, which together explained 23% of F2 variance. Of those, the QTL on chromosomes 2, 8, and 10 seem to be unique to bone breaking strength, whereas the remaining three QTL are concordant with femur BMD QTL. Genetic analysis suggests that, of these six FBS QTL, three influence BMD, two influence bone quality, and one influences bone size. We detected multiple significant epistatic interactions for FBS, which accounts for half (14.6%) of F2 variance compared with significant single QTL effects. We found evidence that pleiotropic effect might represent a common genetic mechanism to coordinately regulate bone-related phenotypes. Pleiotropic analysis also suggests that our current threshold level for significant QTL may be too high to detect biologically significant QTL with small effect. Together with epistatic interactions, these undetected small QTL could explain 30% of genetic variance that remains unaccounted for in this study (heritability estimate for FBS is 68%). Our findings in single QTL effects, epistasis, and pleiotropy demonstrate that partially overlapped but distinct combinations of genetic loci in MRL/MpJ and SJL/J inbred strains of mice regulate bone strength and bone density. Identification of the genes unique to FBS may have an impact on prediction of osteoporosis in human.     

Epistasis and the evolutionary dynamics of measured genotypic values during simulated serial bottlenecks

The evolutionary effects of epistasis have been primarily explored analytically and most empirical studies have utilized yeast, viral and bacterial populations. Empirical analyses in multi‐cellular organisms are rare because of experimental constraints. Here, we report the results of a genome‐wide scan for two‐way epistasis in 16 traits related to body size and composition in F₂ mice from the LG/J by SM/J intercross. We analyze two‐locus genotypic values at quantitative trait loci (QTL), which provides an especially detailed view of epistatic architectures, to evaluate their predicted evolutionary consequences via Monte Carlo simulations. Epistatic profiles vary, but all traits show complicated genetic architectures which are largely hidden in single locus QTL scans. On average, detected epistatic effects are comparable in size to marginal effects. Simulations demonstrate an expected preservation, and often inflation, of heritable variance across several generations of small effective population size for many identified epistatic pairs over a range of starting allele frequencies.     

玉米开花期相关性状的QTL分析

利用玉米强优势组合(Mo17×黄早四)自交衍生的191个F_2单株构建了由SSR和AFLP标记组成的分子连锁图谱,用F2进一步自交产生的184个F_(2:3)家系调查散粉期、吐丝期和开花-吐丝间隔期(ASI)的表型值,采用基于混合线性模型的复合区间作图法和相应的作图软件QTLmapper/V2.0,在两个生长环境下定位了与散粉期、吐丝期和ASI相关的QTL数目分别为13、7和5个,检测到3对控制散粉期、17对控制吐丝期和5对控制ASI的上位性效应位点;同时发现了与环境存在显著互作的3个散粉期、3个吐丝期和2个ASI单位点标记区域以及1对散粉期、3对吐丝期和2对ASI上位性效应区域.对玉米散粉期、吐丝期和ASI遗传基础中遗传因素相对作用大小分析表明,加性效应、部分显性效应和上位性效应是玉米开花期相关性状的重要遗传基础.     

The genetic architecture of domestication in the chicken: effects of pleiotropy and linkage

The extent of pleiotropy and epistasis in quantitative traits remains equivocal. In the case of pleiotropy, multiple quantitative trait loci are often taken to be pleiotropic if their confidence intervals overlap, without formal statistical tests being used to ascertain if these overlapping loci are statistically significantly pleiotropic. Additionally, the degree to which the genetic correlations between phenotypic traits are reflected in these pleiotropic quantitative trait loci is often variable, especially in the case of antagonistic pleiotropy. Similarly, the extent of epistasis in various morphological, behavioural and life-history traits is also debated, with a general problem being the sample sizes required to detect such effects. Domestication involves a large number of trade-offs, which are reflected in numerous behavioural, morphological and life-history traits which have evolved as a consequence of adaptation to selective pressures exerted by humans and captivity. The comparison between wild and domestic animals allows the genetic analysis of the traits that differ between these population types, as well as being a general model of evolution. Using a large F(2) intercross between wild and domesticated chickens, in combination with a dense SNP and microsatellite marker map, both pleiotropy and epistasis were analysed. The majority of traits were found to segregate in 11 tight 'blocks' and reflected the trade-offs associated with domestication. These blocks were shown to have a pleiotropic 'core' surrounded by more loosely linked loci. In contrast, epistatic interactions were almost entirely absent, with only six pairs identified over all traits analysed. These results give insights both into the extent of such blocks in evolution and the development of domestication itself.     

Epistatic interactions of genes influence within-individual variation of physical activity traits in mice

A number of quantitative trait loci (QTLs) recently have been discovered that affect various activity traits in mice, but their collective impact does not appear to explain the consistently moderate to high heritabilities for these traits. We previously suggested interactions of genes, or epistasis, might account for additional genetic variability of activity, and tested this for the average distance, duration and speed run by mice during a 3 week period. We found abundant evidence for epistasis affecting these traits, although, recognized that epistatic effects may well vary within individuals over time. We therefore conducted a full genome scan for epistatic interactions affecting these traits in each of seven three-day intervals. Our intent was to assess the extent and trends in epistasis affecting these traits in each of the intervals. We discovered a number of epistatic interactions of QTLs that influenced the activity traits in the mice, the majority of which were not previously found and appeared to affect the activity traits (especially distance and speed) primarily in the early or in the late age intervals. The overall impact of epistasis was considerable, its contribution to the total phenotypic variance varying from an average of 22–35% in the three traits across all age intervals. It was concluded that epistasis is more important than single-locus effects of genes on activity traits at specific ages and it is therefore an essential component of the genetic architecture of physical activity.     

High throughput analyses of epistasis for swine body dimensions and organ weights

High throughput analyses were performed to detect epistatic QTL in 17 body dimension and organ weight traits from a large F₂ pig population derived from a White Duroc and Erhualian intercross. The analyses used a nested test framework to handle multiple tests and a combined search algorithm to map epistatic QTL with empirical genome‐wide thresholds derived via prior permutation. Alternative statistical models (e.g. including vs. excluding carcass weight as a covariate) were tested to develop an in‐depth understanding of the role of epistasis in these kinds of traits. Epistasis signals were detected in only two or three traits under each statistical model studied. The interaction component of each pair of epistatic QTL explained a small proportion (0.7 to 2.1%) of the phenotypic variance in general. About half of the detected epistatic QTL pairs involved one of the two major QTL on porcine chromosomes 7 and 4. In those traits, the Erhualian allele consistently increased the phenotypes for the chromosome 7 QTL but decreased them for the chromosome 4 QTL. Models including carcass weight as covariate detected epistasis in body dimension traits whereas those excluding carcass weight found epistasis in organ weight traits. In addition, the epistasis results suggested that a QTL on chromosome 14 could be important for a number of organ weight traits. Using the high‐throughput analysis tool to examine different statistical models was essential for the generation of a complete picture of epistasis in a whole category of traits.     

Both additivity and epistasis control the genetic variation for fruit quality traits in tomato

The effect of a gene involved in the variation of a quantitative trait may change due to epistatic interactions with the overall genetic background or with other genes through digenic interactions. The classical populations used to map quantitative trait loci (QTL) are poorly efficient to detect epistasis. To assess the importance of epistasis in the genetic control of fruit quality traits, we compared 13 tomato lines having the same genetic background except for one to five chromosome fragments introgressed from a distant line. Six traits were assessed: fruit soluble solid content, sugar content and titratable acidity, fruit weight, locule number and fruit firmness. Except for firmness, a large part of the variation of the six traits was under additive control, but interactions between QTL leading to epistasis effects were common. In the lines cumulating several QTL regions, all the significant epistatic interactions had a sign opposite to the additive effects, suggesting less than additive epistasis. Finally the re-examination of the segregating population initially used to map the QTL confirmed the extent of epistasis, which frequently involved a region where main effect QTL have been detected in this progeny or in other studies.     

Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype-phenotype map

The genotype-phenotype (GP) map consists of developmental and physiological mechanisms mapping genetic onto phenotypic variation. It determines the distribution of heritable phenotypic variance on which selection can act. Comparative studies of morphology as well as of gene regulatory networks show that the GP map itself evolves, yet little is known about the actual evolutionary mechanisms involved. The study of such mechanisms requires exploring the variation in GP maps at the population level, which presently is easier to quantify by statistical genetic methods rather than by regulatory network structures. We focus on the evolution of pleiotropy, a major structural aspect of the GP map. Pleiotropic genes affect multiple traits and underlie genetic covariance between traits, often causing evolutionary constraints. Previous quantitative genetic studies have demonstrated population-level variation in pleiotropy in the form of loci, at which genotypes differ in the genetic covariation between traits. This variation can potentially fuel evolution of the GP map under selection and/or drift. Here, we propose a developmental mechanism underlying population genetic variation in covariance and test its predictions. Specifically, the mechanism predicts that the loci identified as responsible for genetic variation in pleiotropy are involved in trait-specific epistatic interactions. We test this prediction for loci affecting allometric relationships between traits in an advanced intercross between inbred mouse strains. The results consistently support the prediction. We further find a high degree of sign epistasis in these interactions, which we interpret as an indication of adaptive gene complexes within the diverged parental lines.     

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F₂ populations of 150 F_2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R ² increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.     

Quantitative trait loci mapping of floral and leaf morphology traits in Arabidopsis thaliana: evidence for modular genetic architecture

Summary Morphological variation within organisms is integrated and often modular in nature. That is to say, the size and shape of traits tend to vary in a coordinated and structured manner across sets of organs or parts of an organism. The genetic basis of this morphological integration is largely unknown. Here, we report on quantitative trait loci (QTL) analysis of leaf and floral organ size in Arabidopsis thaliana. We evaluate patterns of genetic correlations among traits and perform whole-genome scans using QTL mapping methods. We detected significant genetic variation for the size and shape of each floral and leaf trait in our study. Moreover, we found large positive genetic correlations among sets of either flower or leaf traits, but low and generally nonsignificant genetic correlations between flower and leaf traits. These results support the hypothesis of independent floral and vegetative modules. We consider co-localization of QTL for different traits as support for a pleiotropic basis of morphological integration and modularity. A total of eight QTL affecting flower and three QTL affecting leaf traits were identified. Most QTL affected either floral or leaf traits, providing a general explanation for high correlations within and low correlations between modules. Only two genomic locations affected both flower and leaf growth. These results are discussed in the context of the evolution of modules, pleiotropy, and the putative homologous relationship between leaves and flowers.     

Identification of QTL for maize resistance to common smut by using recombinant inbred lines developed from the Chinese hybrid Yuyu22

Common smut in maize, caused by Ustilago maydis, reduces grain yield greatly. Agronomic and chemical approaches to control such diseases are often impractical or ineffective. Resistance breeding could be an efficient approach to minimize the losses caused by common smut. In this study, quantitative trait loci (QTL) for resistance to common smut in maize were identified. In 2005, a recombinant inbred line (RIL) population along with the resistant (Zong 3) and susceptible (87-1) parents were planted in Beijing and Zhengzhou. Significant genotypic variation in resistance to common smut was observed at both locations after artificial inoculation by injecting inoculum into the whorl of plants with a modified hog vaccinator. Basing on a genetic map containing 246 polymorphic SSR markers with an average linkage distance of 9.11 cM, resistance QTL were analysed by composite interval mapping. Six additive-effect QTL associated with resistance to common smut were identified on chromosomes 3 (three QTL), 5 (one QTL) and 8 (two QTL), and explained 3.2% to 12.4% of the phenotypic variation. Among the 6 QTL, 4 showed significant QTL x environment (Q x E) interaction effects, which accounted for 1.2% to 2.5% of the phenotypic variation. Nine pairs of epistatic interactions were also detected, involving 18 loci distributed on all chromosomes except 2, 6 and 10, which contributed 0.8% to 3.0% of the observed phenotypic variation. However, no significant epistasis x environment interactions were detected. In total, additive QTL effects and Q x E interactions explained 38.8% and 8.0% of the phenotypic variation, respectively. Epistatic effects contributed 15% of the phenotypic variation. The results showed that besides the additive QTL, both epistasis and Q x E interactions formed an important genetic basis for the resistance to Ustilago maydis in maize.