Sex-, diet-, and cancer-dependent epistatic effects on complex traits in mice

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the "MArginal ePIstasis Test", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.

Modeling of a negative feedback mechanism explains antagonistic pleiotropy in reproduction in domesticated Caenorhabditis elegans strains

Most biological traits and common diseases have a strong but complex genetic basis, controlled by large numbers of genetic variants with small contributions to a trait or disease risk. The effect-size of most genetic variants is not absolute and is instead dependent upon multiple factors such as the age and genetic background of an organism. In order to understand the mechanistic basis of these changes, we characterized heritable trait differences between two domesticated strains of C. elegans. We previously identified a major effect locus, caused in part by a mutation in a component of the NURF chromatin remodeling complex, that regulates reproductive output in an age-dependent manner. The effect-size of this locus changes from positive to negative over the course of an animal’s reproductive lifespan. Here, we use a previously published macroscale model of the egg-laying rate in C. elegans to show that time-dependent effect-size is explained by an unequal use of sperm combined with negative feedback between sperm and ovulation rate. We validate key predictions of this model with controlled mating experiments and quantification of oogenesis and sperm use. Incorporation of this model into QTL mapping allows us to identify and partition new QTLs into specific aspects of the egg-laying process. Finally, we show how epistasis between two genetic variants is predicted by this modeling as a consequence of the unequal use of sperm. This work demonstrates how modeling of multicellular communication systems can improve our ability to predict and understand the role of genetic variation on a complex phenotype. Negative autoregulatory feedback loops, common in transcriptional regulation, could play an important role in modifying genetic architecture in other traits.

Complex traits are influenced by the individual effects of genetic variants in addition to the interactions of the variants with the environment, age, and each other. While complex genetic architectures are ubiquitous in natural traits, little is known about the causal mechanisms that create their complex genetic architectures. Here we identify an example of age-dependent genetic architecture controlling the rate and timing of reproduction in the hermaphroditic nematode C. elegans. We use computational modeling to demonstrate how age-dependent genetic architecture can arise as a consequence of two factors: hormonal feedback on oocytes mediated by major sperm protein (MSP) released by sperm stored in the spermatheca and life history differences in sperm use caused by genetic variants. Our work also suggests how antagonistic pleiotropy can emerge from multicellular feedback systems.

Armour GE, Mackay TFC, Anholt RRH. Epistatic partners of neurogenic genes modulate Drosophila olfactory behavior

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In this study, we implemented a powerful screen to quantify the extent of epistasis as well as identify candidate interacting variants using 203 inbred wild-derived lines with sequenced genomes of the Drosophila melanogaster Genetic Reference Panel (DGRP). We crossed the DGRP lines to P[GT1]-element insertion mutants in Sema-5c and neuralized (neur), two neurodevelopmental loci which affect olfactory behavior, and to their coisogenic wild-type control. We observed significant variation in olfactory responses to benzaldehyde among F1 genotypes and for the DGRP line by mutant genotype interactions for both loci, showing extensive nonadditive genetic variation. We performed genome-wide association analyses to identify the candidate modifier loci. None of these polymorphisms were in or near the focal genes; therefore, epistasis is the cause of the nonadditive genetic variance. Candidate genes could be placed in interaction networks. Several candidate modifiers are associated with neural development. Analyses of mutants of candidate epistatic partners with neur (merry-go-round (mgr), prospero (pros), CG10098, Alhambra (Alh) and CG12535) and Sema-5c (CG42540 and bruchpilot (brp)) showed aberrant olfactory responses compared with coisogenic controls. Thus, integrating genome-wide analyses of natural variants with mutations at defined genomic locations in a common coisogenic background can unmask specific epistatic modifiers of behavioral phenotypes.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

This study uses a large number of crosses between a common lab strain and vineyard-isolated strain of yeast, and estimates the phenotypic variance for various quantitative traits. Using this data set, the authors show additive quantitative trait loci (QTL) and QTL–QTL interactions to be on average 43% and 9%, respectively.

Quantitative trait loci for energy balance traits in an advanced intercross line derived from mice divergently selected for heat loss

Obesity in human populations, currently a serious health concern, is considered to be the consequence of an energy imbalance in which more energy in calories is consumed than is expended. We used interval mapping techniques to investigate the genetic basis of a number of energy balance traits in an F₁₁ advanced intercross population of mice created from an original intercross of lines selected for increased and decreased heat loss. We uncovered a total of 137 quantitative trait loci (QTLs) for these traits at 41 unique sites on 18 of the 20 chromosomes in the mouse genome, with X-linked QTLs being most prevalent. Two QTLs were found for the selection target of heat loss, one on distal chromosome 1 and another on proximal chromosome 2. The number of QTLs affecting the various traits generally was consistent with previous estimates of heritabilities in the same population, with the most found for two bone mineral traits and the least for feed intake and several body composition traits. QTLs were generally additive in their effects, and some, especially those affecting the body weight traits, were sex-specific. Pleiotropy was extensive within trait groups (body weights, adiposity and organ weight traits, bone traits) and especially between body composition traits adjusted and not adjusted for body weight at sacrifice. Nine QTLs were found for one or more of the adiposity traits, five of which appeared to be unique. The confidence intervals among all QTLs averaged 13.3 Mb, much smaller than usually observed in an F₂ cross, and in some cases this allowed us to make reasonable inferences about candidate genes underlying these QTLs. This study combined QTL mapping with genetic parameter analysis in a large segregating population, and has advanced our understanding of the genetic architecture of complex traits related to obesity.

Epistasis and quantitative traits: using model organisms to study gene-gene interactions

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

Selection for genetic markers in beef cattle reveals complex associations of thyroglobulin and casein1-s1 with carcass and meat traits

Genetic markers in casein (CSN1S1) and thyroglobulin (TG) genes have previously been associated with fat distribution in cattle. Determining the nature of these genetic associations (additive, recessive, or dominant) has been difficult, because both markers have small minor allele frequencies in most beef cattle populations. This results in few animals homozygous for the minor alleles. selection to increase the frequencies of the minor alleles for 2 SNP markers in these genes was undertaken in a composite population. The objective was to obtain better estimates of genetic effects associated with these markers and determine if there were epistatic interactions. Selection increased the frequencies of minor alleles for both SNP from <0.30 to 0.45. Bulls (n = 24) heterozygous for both SNP were used in 3 yr to produce 204 steer progeny harvested at an average age of 474 d. The combined effect of the 9 CSN1S1 × TG genotypes was associated with carcass-adjusted fat thickness (P < 0.06) and meat tenderness predicted at the abattoir by visible and near-infrared reflectance spectroscopy (P < 0.04). Genotype did not affect BW from birth through harvest, ribeye area, marbling score, slice shear force, or image-based yield grade (P > 0.10). Additive, dominance, and epistatic SNP association effects were estimated from genotypic effects for adjusted fat thickness and predicted meat tenderness. Adjusted fat thickness showed a dominance association with TG SNP (P < 0.06) and an epistatic additive CSN1S1 × additive TG association (P < 0.03). For predicted meat tenderness, heterozygous TG meat was more tender than meat from either homozygote (P < 0.002). Dominance and epistatic associations can result in different SNP allele substitution effects in populations where SNP have the same linkage disequilibrium with causal mutations but have different frequencies. Although the complex associations estimated in this study would contribute little to within-population selection response, they could be important for marker-assisted management or reciprocal selection schemes.

Epistatic Control of Mammary Cancer Susceptibility in Mice may Depend on the Dietary Environment

Recent studies have linked a high fat diet to the development of breast cancer, but any genetic basis for this association is poorly understood. We investigated this association with an epistatic analysis of seven cancer traits in a segregating population of mice with metastatic mammary cancer that were fed either a control or a high-fat diet. We used an interval mapping approach with single nucleotide polymorphisms to scan all 19 autosomes, and discovered a number of diet-independent epistatic interactions of quantitative trait loci (QTLs) affecting these traits. More importantly, we also discovered significant epistatic by diet interactions affecting some of the traits that suggested these epistatic effects varied depending on the dietary environment. An analysis of these interactions showed some were due to epistasis that occurred in mice fed only the control diet or only the high-fat diet whereas other interactions were generated by differential effects of epistasis in the two dietary environments. Some of the epistatic QTLs appeared to colocalize with cancer QTLs mapped in other mouse populations and with candidate genes identified from eQTLs previously mapped in this population, but others represented novel modifying loci affecting these cancer traits. It was concluded that these diet-dependent epistatic QTLs contribute to a genetic susceptibility of dietary effects on breast cancer, and their identification may eventually lead to a better understanding that will be needed for the design of more effective treatments for this disease.