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

Estimating directional epistasis

Epistasis, i.e., the fact that gene effects depend on the genetic background, is a direct consequence of the complexity of genetic architectures. Despite this, most of the models used in evolutionary and quantitative genetics pay scant attention to genetic interactions. For instance, the traditional decomposition of genetic effects models epistasis as noise around the evolutionarily-relevant additive effects. Such an approach is only valid if it is assumed that there is no general pattern among interactions—a highly speculative scenario. Systematic interactions generate directional epistasis, which has major evolutionary consequences. In spite of its importance, directional epistasis is rarely measured or reported by quantitative geneticists, not only because its relevance is generally ignored, but also due to the lack of simple, operational, and accessible methods for its estimation. This paper describes conceptual and statistical tools that can be used to estimate directional epistasis from various kinds of data, including QTL mapping results, phenotype measurements in mutants, and artificial selection responses. As an illustration, I measured directional epistasis from a real-life example. I then discuss the interpretation of the estimates, showing how they can be used to draw meaningful biological inferences.

The action of stabilizing selection, mutation, and drift on epistatic quantitative traits

For a quantitative trait under stabilizing selection, the effect of epistasis on its genetic architecture and on the changes of genetic variance caused by bottlenecking were investigated using theory and simulation. Assuming empirical estimates of the rate and effects of mutations and the intensity of selection, we assessed the impact of two-locus epistasis (synergistic/antagonistic) among linked or unlinked loci on the distribution of effects and frequencies of segregating loci in populations at the mutation-selection-drift balance. Strong pervasive epistasis did not modify substantially the genetic properties of the trait and, therefore, the most likely explanation for the low amount of variation usually accounted by the loci detected in genome-wide association analyses is that many causal loci will pass undetected. We investigated the impact of epistasis on the changes in genetic variance components when large populations were subjected to successive bottlenecks of different sizes, considering the action of genetic drift, operating singly (D), or jointly with mutation (MD) and selection (MSD). An initial increase of the different components of the genetic variance, as well as a dramatic acceleration of the between-line divergence, were always associated with synergistic epistasis but were strongly constrained by selection.

Lines of least resistance and genetic architecture of house mouse (Mus musculus) mandible shape

Evolution along "lines of least resistance (LLR)" is a well-established phenomenon, which is ultimately based on variational properties of the developmental system. However, it has remained unclear which aspects of development are responsible for observed distributions of variation. This article suggests that house mouse mandible shape may present an opportunity for future research in this field. The genetic architecture of mouse mandible shape has been investigated in the laboratory, and its evolution has been surveyed in studies on natural variation. Here, I ask whether evolutionary diversification of mandible shape follows "LLR" by comparing principal directions of genetic and evolutionary variation, and I assess the potential contribution of specific genomic regions to evolutionary divergence along these directions. The role of two aspects of development, QTL number (presumably reflecting mutational target size) and canalization, is comparatively assessed. Overall, our results demonstrate a strong tendency of genetic and evolutionary systems to vary along similar directions, suggesting that mandible shape evolves along LLR at the level of populations, subspecies and species. At the level of genetic factors, effects bearing similarity to directions of evolution are significantly overrepresented, providing support for the mutational target size hypothesis. A role for canalization is not supported by a clear correlation between directions of evolution and size of genetic effects; however, the evidence for canalization remains ambiguous. These results provide some insights into how the developmental system may shape the variational properties of genetic systems and thus influence the direction of evolution.

The effect of a population bottleneck on the evolution of genetic variance/covariance structure

It is well known that standard population genetic theory predicts decreased additive genetic variance (V(a) ) following a population bottleneck and that theoretical models including interallelic and intergenic interactions indicate such loss may be avoided. However, few empirical data from multicellular model systems are available, especially regarding variance/covariance (V/CV) relationships. Here, we compare the V/CV structure of seventeen traits related to body size and composition between control (60 mating pairs/generation) and bottlenecked (2 mating pairs/generation; average F = 0.39) strains of mice. Although results for individual traits vary considerably, multivariate analysis indicates that V(a) in the bottlenecked populations is greater than expected. Traits with patterns and amounts of epistasis predictive of enhanced V(a) also show the largest deviations from additive expectations. Finally, the correlation structure of weekly weights is not significantly different between control and experimental lines but correlations between necropsy traits do differ, especially those involving the heart, kidney and tail length.

Association between polymorphisms in RAPGEF1, TP53, NRF1 and type 2 diabetes in Chinese Han population

Type 2 diabetes is a common complex disorder with environmental and genetic components. The aim of the present study was to investigate the association between the polymorphisms of RAPGEF1, TP53 and NRF1 and the risk of type 2 diabetes in the Chinese Han population. We genotyped rs11243444 (RAPGEF1), rs1042522 (TP53) and rs1882095 (NRF1) in a case-control study, including 273 type 2 diabetes and 247 healthy controls. A significant association was found in a variant of TP53 (rs1042522, odd ratio (OR)=1.28, 95% confidence interval (CI)=1.00-1.64; P=0.046), whereas polymorphisms in RAPGEF1, NRF1 were not associated with the risk of type 2 diabetes. Furthermore, a potential gene-gene interaction showed the odds of being affected with type 2 diabetes was 2.54 times greater in subjects with the TP53 (rs1042522) and RAPGEF1 (rs11243444) risk alleles than those without either (95% CI=1.34-4.81; P=0.004) and the NRF1 gene polymorphism reached significance when paired with TP53:(OR=3.87, 95% CI=1.87-8.40; P=0.0006). We demonstrated that the polymorphism in TP53 (rs1042522) was associated with type 2 diabetes, and that potential interaction of TP53 (rs1042522) and RAPGEF1 (rs11243444), or NRF1 (rs1882095) increased the risk of type 2 diabetes.

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.

Fine-mapping of obesity-related quantitative trait loci in an F9/10 advanced intercross line

Obesity develops in response to a combination of environmental effects and multiple genes of small effect. Although there has been significant progress in characterizing genes in many pathways contributing to metabolic disease, knowledge about the relationships of these genes to each other and their joint effects upon obesity lags behind. The LG,SM advanced intercross line (AIL) model of obesity has been used to characterize over 70 loci involved in fatpad weight, body weight, and organ weights. Each of these quantitative trait loci (QTLs) encompasses large regions of the genome and require fine-mapping to isolate causative sequence changes and possible mechanisms of action as indicated by the genetic architecture. In this study we fine-map QTLs first identified in the F(2) and F(2/3) populations in the combined F(9/10) advanced intercross generations. We observed significantly narrowed QTL confidence regions, identified many single QTL that resolve into multiple QTL peaks, and identified new QTLs that may have been previously masked due to opposite gene effects at closely linked loci. We also present further characterization of the pleiotropic and epistatic interactions underlying these obesity-related traits.

Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population

Background

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.

Methods/Principal Findings

Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7–5.3]; P≤0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7–3.4; P≤0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4–2.8; P≤0.0001] and 2.3 [95% CI = 1.2-4.4; P≤0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.

Conclusions

These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P≤0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.