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.     

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.     

Genetic variation in the pleiotropic association between physical activity and body weight in mice

Background

A sedentary lifestyle is often assumed to lead to increases in body weight and potentially obesity and related diseases but in fact little is known about the genetic association between physical activity and body weight. We tested for such an association between body weight and the distance, duration, and speed voluntarily run by 310 mice from the F₂ generation produced from an intercross of two inbred lines that differed dramatically in their physical activity levels.

Methods

We used a conventional interval mapping approach with SNP markers to search for QTLs that affected both body weight and activity traits. We also conducted a genome scan to search for relationship QTLs (relQTLs), or chromosomal regions that affected an activity trait variably depending on the phenotypic value of body weight.

Results

We uncovered seven quantitative trait loci (QTLs) affecting body weight, but only one co-localized with another QTL previously found for activity traits. We discovered 19 relQTLs that provided evidence for a genetic (pleiotropic) association of physical activity and body weight. The three genotypes at each of these loci typically exhibited a combination of negative, zero, and positive regressions of the activity traits on body weight, the net effect of which was to produce overall independence of body weight from physical activity. We also demonstrated that the relQTLs produced these varying associations through differential epistatic interactions with a number of other epistatic QTLs throughout the genome.

Conclusion

It was concluded that individuals with specific combinations of genotypes at the relQTLs and epiQTLs might account for some of the variation typically seen in plots of the association of physical activity with body weight.     

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.     

Genetic architecture of adiposity in the cross of LG/J and SM/J inbred mice

The genetic basis of variation in obesity in human populations is thought to be owing to many genes of relatively small effect and their interactions. The LG/J by SM/J intercross of mouse inbred strains provides an excellent model system in which to investigate multigenic obesity. We previously mapped a large number of quantitative trait loci (QTLs) affecting adult body weight in this cross. We map body composition traits, adiposity, and skeletal size, in a replicate F₂ intercross of the same two strains containing 510 individuals. Using interval-mapping methods, we located eight QTLs affecting adiposity (Adip1–8). Two of these adiposity loci also affected tail length (Adip4 and Adip6) along with seven additional tail length QTLs (Skl1–7). A further four QTLs (Wt1–4) affect adult weight but not body composition. These QTLs have relatively small effects, typically about 0.2–0.4 standard deviation units, and account for between 3% and 10% of the variance in individual characters. All QTLs participated in epistatic interactions with other QTLs. Most of these interactions were due to additive-by-additive epistasis, which can nullify the apparent effects of single loci in our population. Adip8 interacts with all the other adiposity QTLs and seems to play a central role in the genetic system affecting obesity in this cross. Only two adiposity QTLs, Adip4 and Adip6, also affect tail length, indicating largely separate genetic control of variation in adiposity and skeletal size. Body size and obesity QTLs in the same locations as those discovered here are commonly found in mapping experiments with other mouse strains. Received: 11 January 2000 / Accepted: 17 August 2000     

利用双单倍体群体剖析水稻产量及其相关性状的遗传基础

主效QTL、上位性效应和它们与环境的互作(QE)都是数量性状的重要遗传因素。利用籼粳交珍汕97／武育粳2号F1植株上的花药进行组织培养得到的190个双单倍体群体和179个微卫星标记,通过两年两重复田间试验,采用混合线性模型方法分析了9个控制水稻产量及其相关性状的遗传效应,得到57个主效QTL,41对上位性互作,8对QTL与环境的互作和7对上位性效应与环境的互作。单个主效QTL解释这些性状1．3％～25．8％的表型方差。各性状QTL的累积表型贡献率达11．5％～66．8％。大多数性状之间具有显著的表型相关性,相关性较高的性状之间常具有较多共同或紧密连锁的QTL。结果表明,基因的多效性或紧密连锁可能是性状相关的重要遗传基础。     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。     

Epistasis plays an important role as genetic basis of heterosis in rice

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Simultaneous mapping of epistatic QTL in DU6i × DBA/2 mice

We have mapped epistatic quantitative trait loci (QTL) in an F₂ cross between DU6i × DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%–128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype–phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.     

Signals of demographic expansion in Drosophila virilis

Background

The antagonistic co-evolution of hosts and their parasites is considered to be a potential driving force in maintaining host genetic variation including sexual reproduction and recombination. The examination of this hypothesis calls for information about the genetic basis of host-parasite interactions – such as how many genes are involved, how big an effect these genes have and whether there is epistasis between loci. We here examine the genetic architecture of quantitative resistance in animal and plant hosts by concatenating published studies that have identified quantitative trait loci (QTL) for host resistance in animals and plants.

Results

Collectively, these studies show that host resistance is affected by few loci. We particularly show that additional epistatic interactions, especially between loci on different chromosomes, explain a majority of the effects. Furthermore, we find that when experiments are repeated using different host or parasite genotypes under otherwise identical conditions, the underlying genetic architecture of host resistance can vary dramatically – that is, involves different QTLs and epistatic interactions. QTLs and epistatic loci vary much less when host and parasite types remain the same but experiments are repeated in different environments.

Conclusion

This pattern of variability of the genetic architecture is predicted by strong interactions between genotypes and corroborates the prevalence of varying host-parasite combinations over varying environmental conditions. Moreover, epistasis is a major determinant of phenotypic variance for host resistance. Because epistasis seems to occur predominantly between, rather than within, chromosomes, segregation and chromosome number rather than recombination via cross-over should be the major elements affecting adaptive change in host resistance.     

ApoB与UCP基因间上位效应对鸡腹脂性状影响的遗传学分析

已有研究表明, 上位效应在畜禽重要复杂经济性状的表型形成过程中发挥重要作用。文章选取对肉鸡7 周龄腹脂率有显著影响的载脂蛋白B(Apoliprotein B, ApoB)基因T123G位点与解耦连蛋白(Uncoupling protein, UCP)基因C1197A位点, 在东北农业大学肉鸡高、低腹脂双向选择品系8、9、10世代内检测这两个SNPs的多态性并利用Natural and Orthogonal InterActions (NOIA)模型分析二者之间的上位效应对7周龄腹脂率的影响。结果表明, 在高脂系内这两个位点之间存在着对7周龄腹脂率有显著影响的上位效应组分(P<0.05), 并且在连续多世代选育过程中仍能持续稳定存在; 同时, 在低脂系中二者之间各上位效应组分对腹脂率均无影响(P>0.05)。此结果意味着, 至少在高脂系内, 腹脂率的遗传受到这两个基因间上位效应的影响; 同时提示两系间脂肪性状QTL或重要候选基因功能位点之间不同的遗传互作模式可能是引起这两个品系腹脂性状巨大表型差异的重要影响因素之一。     

Characterization of the main effects,epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice

Main effects, epistatic effects and their environmental interactions of QTLs are all important genetic components of quantitative traits. In this study, we analyzed the main effects, epistatic effects of the QTLs, and QTL by environment interactions (QEs) underlying four yield traits, using a population of 240 recombinant inbred lines from a cross between two rice varieties tested in replicated field trials. A genetic linkage map with 220 DNA marker loci was constructed. A mixed linear model approach was used to detect QTLs with main effects, QTLs involved in digenic interactions and QEs. In total, 29 QTLs of main effects, and 35 digenic interactions involving 58 loci were detected for the four traits. Thirteen QTLs with main effects showed QEs; no QE was detected for the QTLs involved in epistatic interactions. The amount of variations explained by the QTLs of main effect were larger than the QTLs involved in epistatic interactions, which in turn were larger than QEs for all four traits. This study illustrates the ability of the analysis to assess the genetic components underlying the quantitative traits, and demonstrates the relative importance of the various components as the genetic basis of yield traits in this population.     

水稻对叶瘟和穗瘟部分抗性的遗传分析

在一个水稻籼籼交重组自交系群体中,选用由感病株系构成的2个亚群体和2个不同的稻瘟病菌小种,进行了水稻对叶瘟部分抗性的QTL定位,还选用由感病而且抽穗期相近的株系构成的亚群体和另一个病菌小种,进行了水稻对穗瘟部分抗性的QTL定位,将病叶面积百分比(DLA)、病斑大小(LS)和病斑数(LN)作为对叶瘟部分抗性的性状,将病斑长度(LL)和孢子量(CA)作为对穗瘟部分抗性的性状。所构建的图谱包含168个标记。应用QTLMapper 1．01b,共检测到11个表现主效应的QTL和28对双因子互作,有3个表现主效应的QTL参与对同一性状的互作。QTL的主效应对单一性状的贡献率为4．7％～38．8％,而上位性效应对单一性状的贡献率为16．0％～51．7％,QTL的主效应对大多数性状的贡献率小于互作效应,表明互作效应对于部分抗性的重要作用。对穗瘟部分抗性的两个性状LL和CA,所检测到QTL总效应的贡献率分别达到70．6％和82．6％,表明由排除了主效抗病基因的感病株系组成的亚群体适合于进行部分抗性QTL定位。     

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.     

An epistatic genetic basis for fluctuating asymmetry of tooth size and shape in mice

Although there typically is little additive genetic variation for fluctuating asymmetry (FA), or variation in nondirectional differences between left and right sides of bilateral characters, several investigators have hypothesized that FA may have an epistatic genetic basis. We tested this hypothesis by conducting a whole genome scan of FA of size and shape of the mandibular molars in house mice from an F2 intercross population generated from crossing the Large (LG/J) and Small (SM/J) inbred strains. Although no individual genes (QTLs=quantitative trait loci) on any of the 19 autosomes significantly affected FA for centroid size, and only two affected shape FA, a number of pairwise combinations of QTLs exhibited significant epistasis for FA in both molar size and shape. The QTLs involved in these interactions differed for FA in molar size versus FA in molar shape, but their epistatic contributions to the total variance was nearly the same (about 20%) for FA in both molar characters. It was noted that the genetic architecture of FA in the molar characters, consisting of little or no additive genetic variance but an abundance of epistatic genetic variance, is consistent with that of other typical fitness components such as litter size.     

Little epistasis for anxiety-related measures in the DeFries strains of laboratory mice

Recent advances in methodologies for testing epistatic interactions, combined with several successes in demonstrating genetic interaction effects in animal and human genetics, have rekindled interest in the role of epistatic influences on complex traits. It has even been suggested that the unacknowledged presence of epistasis vitiates the genetic dissection of human and animal behavior. Here we report a genome-wide interaction analysis of 1636 F₂ mice to show that epistasis is of minimal importance in an animal model of anxiety. By using a sufficiently large sample of F₂ animals, we provide evidence that interaction effects between any two loci contribute less than 5% to the total phenotypic variance in multiple tests of anxiety. We conclude that interactions between loci do not necessarily vitiate the genetic analysis of behavior in at least one animal model of anxiety.     

Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus

The influence of genetic interactions (epistasis) on the genetic variance of quantitative traits is a major unresolved problem relevant to medical, agricultural, and evolutionary genetics. The additive genetic component is typically a high proportion of the total genetic variance in quantitative traits, despite that underlying genes must interact to determine phenotype. This study estimates direct and interaction effects for 11 pairs of Quantitative Trait Loci (QTLs) affecting floral traits within a single population of Mimulus guttatus. With estimates of all 9 genotypes for each QTL pair, we are able to map from QTL effects to variance components as a function of population allele frequencies, and thus predict changes in variance components as allele frequencies change. This mapping requires an analytical framework that properly accounts for bias introduced by estimation errors. We find that even with abundant interactions between QTLs, most of the genetic variance is likely to be additive. However, the strong dependency of allelic average effects on genetic background implies that epistasis is a major determinant of the additive genetic variance, and thus, the population’s ability to respond to selection.     

Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect

Epistasis, an additive-by-additive interaction between quantitative trait loci, has been defined as a deviation from the sum of independent effects of individual genes. Epistasis between QTLs assayed in populations segregating for an entire genome has been found at a frequency close to that expected by chance alone. Recently, epistatic effects have been considered by many researchers as important for complex traits. In order to understand the genetic control of complex traits, it is necessary to clarify additive-by-additive interactions among genes. Herein we compare estimates of a parameter connected with the additive gene action calculated on the basis of two models: a model excluding epistasis and a model with additive-by-additive interaction effects. In this paper two data sets were analysed: 1) 150 barley doubled haploid lines derived from the Steptoe × Morex cross, and 2) 145 DH lines of barley obtained from the Harrington × TR306 cross. The results showed that in cases when the effect of epistasis was different from zero, the coefficient of determination was larger for the model with epistasis than for the one excluding epistasis. These results indicate that epistatic interaction plays an important role in controlling the expression of complex traits.