Quantitative Genetic Models Research Articles

Shared environmental similarity between relatives influences heritability of reproductive timing in wild great tits.

Intraspecific variation is necessary for evolutionary change and population resilience, but the extent to which it contributes to either depends on the causes of this variation. Understanding the causes of individual variation in traits involved with reproductive timing is important in the face of environmental change, especially in systems where reproduction must coincide with seasonal resource availability. However, separating the genetic and environmental causes of variation is not straightforward, and there has been limited consideration of how small-scale environmental effects might lead to similarity between individuals that occupy similar environments, potentially biasing estimates of genetic heritability. In ecological systems, environments are often complex in spatial structure, and it may therefore be important to account for similarities in the environments experienced by individuals within a population beyond considering spatial distances alone. Here, we construct multi-matrix quantitative genetic animal models using over 11,000 breeding records (spanning 35 generations) of individually-marked great tits (Parus major) and information about breeding proximity and habitat characteristics to quantify the drivers of variability in two key seasonal reproductive timing traits. We show that the environment experienced by related individuals explains around a fifth of the variation seen in reproductive timing, and accounting for this leads to decreased estimates of heritability. Our results thus demonstrate that environmental sharing between relatives can strongly affect estimates of heritability and therefore alter our expectations of the evolutionary response to selection.

Importance and variability of the paternal component in sow reproductive traits.

Reproductive traits are an integral part of the goals of the breeding programs that contribute to the economic success of production. Reproductive phenotypes such as litter size, number of piglets born alive, or litter weight at birth are mainly attributed to females. Thus, the maternal components can be found by default in quantitative genetics' animal models. Still, paternal contribution to variance components should not be discarded. In this review, we indicate the importance of paternal effects in pig breeding by describing both the biology and genetics of boars' traits, the use of (non-)genetic service sire effects in quantitative genetic models for traits measured on females, and genes involved in male reproduction. We start by describing the important biological traits of boars that have the most important effect on their reproductive abilities, i.e., sexual maturity, sperm quality, and testes parameters. Then we move to the possible environmental effects that could affect those traits of boars (e.g., feed, temperature). The main part of the review in detail describes the genetics of boars' reproductive traits (i.e., heritability) and their direct effect on reproductive traits of females (i.e., genetic correlations). We then move to the use of both genetic and non-genetic service sire effects in quantitative models estimated as their percentage in the total variance of traits, which vary depending on the breed from 1 to 4.5% or from 1 to 2%, respectively. Finally, we focus on the description of candidate genes and confirmed mutations affecting male reproduction success: IGF2, Tgm8, ESR1, ZSWIM7, and ELMO1. In conclusion, the observed variance of paternal effects in female reproduction traits might come from various attributes of boars including biological and genetic aspects. Those attributes of boars should not be neglected as they contribute to the success of female reproductive traits.

Sharp habitat shifts, evolutionary tipping points and rescue: Quantifying the perilous path of a specialist species towards a refugium in a changing environment

Specialist species thriving under specific environmental conditions in narrow geographic ranges are widely recognized as heavily threatened by climate deregulation. Many might rely on both their potential to adapt and to disperse towards a refugium to avoid extinction. It is thus crucial to understand the influence of environmental conditions on the unfolding process of adaptation. Here, I study the eco-evolutionary dynamics of a sexually reproducing specialist species in a two-patch quantitative genetic model with moving optima. Thanks to a separation of ecological and evolutionary time scales and the phase-line study of the selection gradient, I derive the critical environmental speed for persistence, which reflects how the existence of a refugium impacts extinction patterns and how it relates to the cost of dispersal. Moreover, the analysis provides key insights about the dynamics that arise on the path towards this refugium. I show that after an initial increase of population size, there exists a critical environmental speed above which the species crosses a tipping point, resulting into an abrupt habitat switch. In addition, when selection for local adaptation is strong, this habitat switch passes through an evolutionary “death valley”, leading to a phenomenon related to evolutionary rescue, which can promote extinction for lower environmental speeds than the critical one.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Quantifying the effects of the mitochondrial genome on milk production traits in dairy cows: empirical results and modelling challenges

Significant advances in livestock traits have been achieved primarily through selection strategies targeting variation in the nuclear genome, with little attention given to mitogenome variation. We analyzed the influence of the mitogenome on milk production traits of Holstein cattle in Croatia based on strategically generated next-generation sequencing data for 109 cows pedigree-linked to 7115 milk production records (milk, fat and protein yield) from 3006 cows (first 5 lactations). Since little is known about the biology of the relationship between mitogenome variation and production traits, our quantitative genetic modeling was complex. Thus, the proportion of total variance explained by mitogenome inheritance was estimated using 5 different models: (1) cytoplasmic model with maternal lineages (CYTO), (2) haplotypic model with mitogenome sequences (HAPLO), (3) amino acid model with unique amino acid sequences (AMINO), (4) evolutionary model based on a phylogenetic analysis using Bayesian Evolutionary Analysis Sampling Trees phylogenetic analysis (EVOL), and (5) mitogenome SNP model (SNPmt). The polygenic autosomal and X chromosome additive genetic effects based on pedigree were modeled, together with the effects of herd-year-season interaction, permanent environment, location, and age at first calving. The estimated proportions of phenotypic variance explained by mitogenome in 4 different models (CYTO, HAPLO, AMINO, and SNPmt) were found to be substantial given the size of mitogenome, ranging from 5% to 7% for all 3 milk traits. At the same time, a negligible proportion of the phenotypic variance was explained by mitogenome with the EVOL model. Similarly, in all models, no proportion of phenotypic variance was explained by the X chromosome. Although our results should be confirmed in other dairy cattle populations, including a large number of sequenced mitogenomes and nuclear genomes, the potential of utilizing mitogenome information in animal breeding is promising, especially as the acquisition of complete genome sequences becomes cost-effective.

The contributions of direct and indirect selection to the evolution of mating preferences.

Many influential mathematical models of sexual selection have stressed that mating preferences evolve due to correlations that build between mating preferences and preferred display traits - that is, through indirect selection. Nevertheless, there is a perception that indirect selection should generally be overwhelmed by direct selection, for example in the form of search costs. Recent work by Fry has used quantitative genetic models to argue that in many cases, including when there are direct benefits (a fecundity advantage to mating with the preferred male), direct and indirect selection may be of similar magnitude. Here I use population genetic models, in which the strength of the genetic correlation is an emergent property of evolution at mating preference and display trait loci, to assess the relative contributions of direct and indirect selection to the evolution of mating preferences. For the cases of direct benefits and of indirect benefits with fixed and frequency-dependent search costs, I outline parameter values of fecundity benefits, preference strengths, and search costs for which indirect selection on female preferences can potentially predominate. I also analyze male mate choice under polygyny, showing that direct selection will always outweigh indirect selection except when there are direct benefits.

On the evolutionary origin of discrete phenotypic plasticity.

Phenotypic plasticity provides an attractive strategy for adapting to various environments, but the evolutionary mechanism of the underlying genetic system is poorly understood. We use a simple gene regulatory network model to explore how a species acquires phenotypic plasticity, particularly focusing on discrete phenotypic plasticity, which has been difficult to explain by quantitative genetic models. Our approach employs a population genetic framework that integrates the developmental process, where each individual undergoes growth to develop its phenotype, which subsequently becomes subject to selection pressures. Our model considers two alternative types of environments, with the gene regulatory network including a sensor gene that turns on and off depending on the type of environment. With this assumption, we demonstrate that the system gradually adapts by acquiring the ability to produce two distinct optimum phenotypes under two types of environments without changing genotype, resulting in phenotypic plasticity. We find that the resulting plasticity is often discrete after a lengthy period of evolution. Our results suggest that gene regulatory networks have a notable capacity to flexibly produce various phenotypes in response to environmental changes. This study also shows that the evolutionary dynamics of phenotype may differ significantly between mechanistic-based developmental models and quantitative genetics models, suggesting the utility of incorporating gene regulatory networks into evolutionary models.

How should we measure population-level inbreeding depression? Impacts of standing genetic associations between selfing rate and deleterious mutations.

Inbreeding depression (ID) is a major selective force during mating system evolution primarily contributed by highly to partially recessive deleterious mutations. Theories suggest that transient genetic association with fitness alleles can be important in affecting the evolution of alleles that modify the selfing rate during its sweep. Nevertheless, empirical tests often focus on the pre-existing genetic association between selfing rate and ID maintained under mutation-selection balance. Therefore, how this standing genetic association is affected by key factors and its impacts on the evolution of selfing remain unclear. I show that as the selection coefficient of deleterious mutations increases, the association between selfing rate and ID declines from positive to negative. These results predict that association between selfing and ID tends to be negative in populations with low selfing rates, while positive in highly selfing populations. Using population genetic and quantitative genetic models, I show that standing genetic associations between selfing rate and fitness alleles can significantly impact the evolution of the mean selfing rate of a population. I present better metrics of population-level ID, which can be calculated based on the correlation coefficient between individual selfing rate and the fitness of selfed and outcrossed offspring.

Salmon hatchery strays can demographically boost wild populations at the cost of diversity: quantitative genetic modelling of Alaska pink salmon.

Hatcheries are vital to many salmon fisheries, with inherent risks and rewards. While hatcheries can increase the returns of adult fish, the demographic and evolutionary consequences for natural populations interacting with hatchery fish on spawning grounds remain unclear. This study examined the impacts of stray hatchery-origin pink salmon on natural population productivity and resilience. We explored temporal assortative mating dynamics using a quantitative genetic model that assumed the only difference between hatchery- and natural-origin adults was their return timing to natural spawning grounds. This model was parameterized with empirical data from an intensive multi-generational study of hatchery-wild interactions in the world's largest pink salmon fisheries enhancement program located in Prince William Sound, Alaska. Across scenarios of increasing hatchery fish presence on spawning grounds, our findings underscore a trade-off between demographic enhancement and preservation of natural population diversity. While enhancement bolstered natural population sizes towards local carrying capacities, hatchery introgression reduced variation in adult return timing by up to 20%. Results indicated that hatchery-origin alleles can rapidly assimilate into natural populations, despite the reduced fitness of hatchery fish attributable to phenotypic mismatches. These findings elucidate the potential for long-term demographic and evolutionary consequences arising from specific hatchery-wild interactions, emphasizing the need for management strategies that balance demographic enhancement with the conservation of natural diversity.

Simulation of functional additive and non-additive genetic effects using statistical estimates from quantitative genetic models

Stochastic simulation software is commonly used to aid breeders designing cost-effective breeding programs and to validate statistical models used in genetic evaluation. An essential feature of the software is the ability to simulate populations with desired genetic and non-genetic parameters. However, this feature often fails when non-additive effects due to dominance or epistasis are modeled, as the desired properties of simulated populations are estimated from classical quantitative genetic statistical models formulated at the population level. The software simulates underlying functional effects for genotypic values at the individual level, which are not necessarily the same as effects from statistical models in which dominance and epistasis are included. This paper provides the theoretical basis and mathematical formulas for the transformation between functional and statistical effects in such simulations. The transformation is demonstrated with two statistical models analyzing individual phenotypes in a single population (common in animal breeding) and plot phenotypes of three-way hybrids involving two inbred populations (observed in some crop breeding programs). We also describe different methods for the simulation of functional effects for additive genetics, dominance, and epistasis to achieve the desired levels of variance components in classical statistical models used in quantitative genetics.

123 Phenomics enabled quantitative genetic modeling of complex traits

Abstract With the development of high-throughput technologies, particularly for genomics and phenomics, biology has become a large-scale, data-rich field. High-throughput phenotyping (HTP) is an innovative technology that integrates advances in engineering or computer science into agriculture to optimize production while ensuring sustainability. In particular, the use of image-based HTP technologies allows us to collect a large number of phenotypes in an automated manner with less human labor and reduced costs, allowing us to efficiently evaluate phenotypes that were previously difficult to measure manually. The advent of HTP, coupled with the wealth of genomic data generated by next-generation sequencing technologies, provides new and exciting resources for studying complex traits. These new technologies also bring new challenges to quantitative genetics, namely the development of robust statistical frameworks that can accommodate spatial, temporal, heterogeneous, and high-dimensional phenotypic data. However, the impact of HTP-derived traits on genetic analysis in animal breeding and genetics has been limited. In this talk some real data examples of quantitative genetic modeling of image-derived traits will be presented.

The sound of fear is heritable

Abstract The nonlinearity and fear hypothesis predicts that highly aroused vocal mammals and birds produce vocalizations (notably alarm calls and screams) which contain a variety of non-linear phenomena (NLP). Such vocalizations often sound ‘noisy’ because vocal production systems are over-blown when animals are highly aroused. While much is known about the conditions under which animals produce vocalizations containing NLP, and how species respond to them, there is little research about the heritability of such behavioral traits. Using the quantitative genetic animal model, we estimated the genetic basis of ‘noise’ in alarm calls produced by females and found significant heritability in call entropy—our measure of the noisiness. About 9% of variance in noisiness can be accounted for by genetic differences. Taken together, these findings suggest that the degree to which marmots produce noisy calls is modestly heritable and can be thus subject to further evolution via natural selection.

A quantitative genetic model of background selection in humans.

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

Comorbidity Between Inflammatory Bowel Disease and Asthma and Allergic Diseases: A Genetically Informed Study.

Little is known about shared origins between inflammatory bowel disease (IBD) and allergic diseases (asthma, allergic rhinitis, and eczema). We aimed to expand current knowledge on the etiological sources of comorbidities between these disorders using a range of genetically informed methods. Within-individual and familial co-aggregation analysis was applied to 2 873 445 individuals born in Sweden from 1987 to 2014 and their first- and second-degree relatives. Quantitative genetic modeling was applied to 38 723 twin pairs to decompose the genetic and environmental sources for comorbidity. Polygenic risk score analysis between IBD and allergic diseases was conducted in 48 186 genotyped twins, and linkage disequilibrium score regression was applied using publicly available data to explore the genetic overlap. IBD was associated with asthma (adjusted odds ratio [aOR], 1.35; 95% confidence interval [CI], 1.30 to 1.40), allergic rhinitis (aOR, 1.27; 95% CI, 1.20 to 1.34), and eczema (aOR, 1.47; 95% CI, 1.38 to 1.56), with similar estimates for ulcerative colitis or Crohn's disease. The ORs for familial co-aggregation decreased with decreasing genetic relatedness. Quantitative genetic modeling revealed little evidence of common genetic factors between IBD and allergic diseases (eg, IBD and allergic rhinitis; genetic correlation ra = 0.06; 95% CI, -0.03 to 0.15) but did reveal some evidence of unique environmental factors between IBD and eczema (re = 0.16; 95% CI, 0.00 to 0.32). Molecular genetic analyses were similarly null for IBD and allergic diseases, except for a slight association between Crohn's disease polygenic risk score and eczema (OR, 1.09; 95% CI, 1.06 to 1.12). We found little evidence to support a shared origin between IBD and any allergic disease but weak evidence for shared genetic and unique environmental components for IBD and eczema.

Large effects and the infinitesimal model

The infinitesimal model of quantitative genetics relies on the Central Limit Theorem to stipulate that under additive models of quantitative traits determined by many loci having similar effect size, the difference between an offspring’s genetic trait component and the average of their two parents’ genetic trait components is Normally distributed and independent of the parents’ values. Here, we investigate how the assumption of similar effect sizes affects the model: if, alternatively, the tail of the effect size distribution is polynomial with exponent α<2, then a different Central Limit Theorem implies that sums of effects should be well-approximated by a “stable distribution”, for which single large effects are often still important. Empirically, we first find tail exponents between 1 and 2 in effect sizes estimated by genome-wide association studies of many human disease-related traits. We then show that the independence of offspring trait deviations from parental averages in many cases implies the Gaussian aspect of the infinitesimal model, suggesting that non-Gaussian models of trait evolution must explicitly track the underlying genetics, at least for loci of large effect. We also characterize possible limiting trait distributions of the infinitesimal model with infinitely divisible noise distributions, and compare our results to simulations.

Heritability of nervous system tumors: a sibling-based design.

The contribution of genetic and environmental factors to susceptibility to nervous system tumors remains unclear. We performed a quantitative genetic study using a sibling design to estimate the heritability of nervous system tumors, as well as the proportion of the risk of these tumors, which is attributable to environmental factors. We conducted a population-based cohort study using Swedish National Register data. All individuals born in Sweden during 1950-2010 with available information on both biological parents were included. A Multi-Generation Register was used to identify family clusters, including both full- and half-siblings. Initially, one index person was randomly selected from each cluster containing only full siblings and one sibling was randomly assigned to this index person. Subsequently, within each of the remaining clusters of full- and half-siblings, an index person was randomly selected, and a half-sibling was randomly assigned to this index person. Among the randomly selected siblings, cases of nervous system tumors were identified using the cancer registry. Quantitative genetic models were used to estimate the proportion of the variance in nervous system tumors attributable to additive genetic factors, shared environment, and individual-specific environment. The heritability of nervous system tumors was estimated to be 29% (95% confidence interval (CI) = 19%-39%), while the contribution of the non-shared environment to the variance of nervous system tumors was estimated to be 71% (95% CI = 61%-81%). The shared environmental parameter was estimated as zero in the full model. The variation in susceptibility to nervous system tumors is predominantly attributable to non-shared environmental factors, followed by genetic factors.

Evolution of flowering time due to variation in the onset of pollen dispersal among individuals.

The evolution of flowering time is often attributed to variations in pollinator rates over time. This study proposes that flowering time can evolve through siring success variation among individuals caused by differential pollen dispersal timing (a result of flowering time variation). By building quantitative genetic models, I show that flowering time evolves to be earlier when the pollen removal rate is low and pollen deposition rate is high, and the fertilization ability of removed pollen declines slowly. Using evolutionary game theory, I show that the evolutionarily stable variance of flowering time is large when the pollen removal rate is either low or high, the pollen deposition rate is moderate, and the fertilization ability of removed pollen declines rapidly. Investigation of the coevolution of flower longevity and flowering time shows that under constant pollination rates, late flowering will be correlated with long-lived flowers due to nonrandom mating, which suggests that the observed correlation between late flowering and short-lived flowers is caused by other factors, such as declining pollination rates during late-stage flowering. I discuss how altered pollination rates under climate change will influence flowering time evolution and the importance of distinguishing between pollen removal and deposition rates.

Associations between sleep medication use and alcohol consumption over 36 years in Finnish twins

BackgroundSleep medication use is an indicator of underlying sleep problems that might be induced by various factors such as alcohol use. However, the longitudinal relationship between drinking and sleep problems remains poorly understood. We investigated associations between sleep medication and alcohol use throughout adulthood, and examined the role of familial and potential confounding factors contributing to these associations. MethodsWe used information of zygosity and self-report questionnaire data over a follow-up period of 36 years from the Older Finnish Twin Cohort (N=13,851). ResultsLogistic regression analyses suggested consistent associations between sleep medication use and heavy/binge drinking at all four time points (OR range =1.36-3.18, P <0.05), implying that increased drinking is associated with increased sleep medication use over time. Cross-lagged path analyses suggested that moderate/heavy and binge drinking predict sleep medication use at most time points (OR range = 1.15-1.94, P <0.05), whilst sleep medication use predicts subsequent abstaining from alcohol (OR range =2.26-2.47, P <0.05). Within-pair analyses implied that familial factors play a role, and quantitative genetic modelling estimated genetic factors to explain approximately 80% of the lifetime association of sleep medication use with moderate/heavy and binge drinking. ConclusionsDrinking is associated with sleep medication use throughout adulthood. Further, our results suggest that drinking is likely to predict sleep medication use, thereby potentially constituting a risk factor for sleep problems, and that genetic factors contribute to the association. These findings are important in terms of better understanding the development of sleep and alcohol use disorders.

Additive and non-additive genetic variance in juvenile Sitka spruce (Picea sitchensis Bong. Carr)

Many quantitative genetic models assume that all genetic variation is additive because of a lack of data with sufficient structure and quality to determine the relative contribution of additive and non-additive variation. Here the fractions of additive (fa) and non-additive (fd) genetic variation were estimated in Sitka spruce for height, bud burst and pilodyn penetration depth. Approximately 1500 offspring were produced in each of three sib families and clonally replicated across three geographically diverse sites. Genotypes from 1525 offspring from all three families were obtained by RADseq, followed by imputation using 1630 loci segregating in all families and mapped using the newly developed linkage map of Sitka spruce. The analyses employed a new approach for estimating fa and fd, which combined all available genotypic and phenotypic data with spatial modelling for each trait and site. The consensus estimate for fa increased with age for height from 0.58 at 2 years to 0.75 at 11 years, with only small overlap in 95% support intervals (I95). The estimated fa for bud burst was 0.83 (I95=[0.78, 0.90]) and 0.84 (I95=[0.77, 0.92]) for pilodyn depth. Overall, there was no evidence of family heterogeneity for height or bud burst, or site heterogeneity for pilodyn depth, and no evidence of inbreeding depression associated with genomic homozygosity, expected if dominance variance was the major component of non-additive variance. The results offer no support for the development of sublines for crossing within the species. The models give new opportunities to assess more accurately the scale of non-additive variation.

Competition-cooperation mechanism between Escherichia coli and Staphylococcus aureus based on systems mapping.

Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging. In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions. By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1. By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.