The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R₀ and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.     

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.     

Breeding for immune responsiveness and disease resistance

Animal production efficiency, and product volume and quality can be greatly increased by reducing disease losses. Genetic variation, a prerequisite for successful selection, has been found in animals and poultry exposed to a variety of viral, bacterial and parasitic infections. Breeding for disease resistance can play a significant role alone or in combination with other control measures including disease eradication, vaccination and medication. Feasibility of simultaneously improving resistance to specific diseases and production traits has been demonstrated. However, selection for specific resistance to all diseases of animals and poultry is impossible. Development of general disease resistance through indirect selection primarily on immune response traits may be the best long-term strategy but its applicability is presently limited by insufficient understanding of resistance mechanisms. Another hindrance may be negative genetic correlations among various immune response functions: phagocytosis, cell mediated and humoral immunity. To better assess the feasibility of increasing general disease resistance by indirect selection we must obtain estimates of heritability for immune response, disease resistance, and economic production traits, as well as genetic correlations among these traits. The present level of disease resistance in farm animals resulted from natural selection and from correlated responses to selection for production traits while the influence of artificial selection for resistance was minimal. Future research should be directed towards developing and applying breeding techniques that will increase resistance to diseases without compromising production efficiency and product quality. This will require cooperation of immunogeneticists, veterinarians and animal and poultry breeders. Significant progress in the improvement of resistance to diseases may result from the application of new techniques of molecular genetics and cell manipulation.     

Breeding for immune responsiveness and disease resistance1

Animal production efficiency, and product volume and quality can be greatly increased by reducing disease losses. Genetic variation, a prerequisite for successful selection, has been found in animals and poultry exposed to a variety of viral, bacterial and parasitic infections. Breeding for disease resistance can play a significant role alone or in combination with other control measures including disease eradication, vaccination and medication. Feasibility of simultaneously improving resistance to specific diseases and production traits has been demonstrated. However, selection for specific resistance to all diseases of animals and poultry is impossible. Development of general disease resistance through indirect selection primarily on immune response traits may be the best long-term strategy but its applicability is presently limited by insufficient understanding of resistance mechanisms. Another hindrance may be negative genetic correlations among various immune response functions: phagocytosis, cell mediated and humoral immunity. To better assess the feasibility of increasing general disease resistance by indirect selection we must obtain estimates of heritability for immune response, disease resistance, and economic production traits, as well as genetic correlations among these traits. The present level of disease resistance in farm animals resulted from natural selection and from correlated responses to selection for production traits while the influence of artificial selection for resistance was minimal. Future research should be directed towards developing and applying breeding techniques that will increase resistance to diseases without compromising production efficiency and product quailty. This will require cooperation of immunogeneticists, veterinarians and animal and poultry breeders. Significant progress in the improvement of resistance to diseases may result from the application of new techniques of molecular genetics and cell manipulation.     

The impact of imitation on vaccination behavior in social contact networks

Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.     

Immunity to intracellular pathogens as a complex genetic trait

Although the role of host heredity in susceptibility to infectious diseases is significant, the genetic control of immunity to infection remains poorly understood. Advances in experimental and epidemiological analyses of complex genetic traits have led to the discoveries of novel genetic determinants of host resistance. New loci that control susceptibility to a number of intracellular pathogens have been identified using mouse models of infectious diseases. The contributions of individual loci, however, vary in quantitative and qualitative manner, depending on mechanisms of pathogen virulence and genetic background of the host. In this review, we discuss how genetic analysis of host resistance contributes to further understanding of host immunity and pathogenesis of intracellular infections.     

Sexual variation in heritability and genetic correlations of morphological traits in house sparrow (Passer domesticus)

Estimates of genetic components are important for our understanding of how individual characteristics are transferred between generations. We show that the level of heritability varies between 0.12 and 0.68 in six morphological traits in house sparrows (Passer domesticus L.) in northern Norway. Positive and negative genetic correlations were present among traits, suggesting evolutionary constraints on the evolution of some of these characters. A sexual difference in the amount of heritable genetic variation was found in tarsus length, wing length, bill depth and body condition index, with generally higher heritability in females. In addition, the structure of the genetic variance-covariance matrix for the traits differed between the sexes. Genetic correlations between males and females for the morphological traits were however large and not significantly different from one, indicating that sex-specific responses to selection will be influenced by intersexual differences in selection differentials. Despite this, some traits had heritability above 0.1 in females, even after conditioning on the additive genetic covariance between sexes and the additive genetic variances in males. Moreover, a meta-analysis indicated that higher heritability in females than in males may be common in birds. Thus, this indicates sexual differences in the genetic architecture of birds. Consequently, as in house sparrows, the evolutionary responses to selection will often be larger in females than males. Hence, our results suggest that sex-specific additive genetic variances and covariances, although ignored in most studies, should be included when making predictions of evolutionary changes from standard quantitative genetic models.     

Genetic parameters for paratuberculosis infection and effect of infection on production traits in Israeli Holsteins

Paratuberculosis (Johne's disease) is an infectious enteric disease in dairy cattle and other species that causes substantial economic loss worldwide. In this study, two recursive Gaussian-threshold models were employed in order to infer the effects of Johne's disease on milk yield, fat yield, and protein yield while simultaneously estimating genetic parameters (i.e. heritability and genetic correlation) in an Israeli Holstein population. Disease diagnosis was based on ELISA serum antibody tests. Data were available for 4694 daughters of 361 sires; 3.5% were positive; and 1.6% were suspect for the disease test. Disease status was coded either as a binary character (negative vs. positive) or as an ordered categorical trait (negative, suspect, and positive) in the two recursive models and as a binary trait in a linear model. Among sires with ≥ 50 daughters, predicted probability of Mycobacterium avium ssp. paratuberculosis (MAP) infection in future daughters ranged from <1% to 16.5%. Heritability estimates for Johne's disease were near 0.15, confirming a genetic contribution to disease susceptibility. Genetic correlation estimates for Johne's disease with the three yield traits were 0.15-0.22. Residual correlations for Johne's disease with the yield traits were between -0.01 and -0.10. For the linear regression model, yield losses associated with a positive disease diagnosis during 305 days of lactation were 300 kg milk and around 10 kg for fat and protein. Yield loss estimates from the recursive models were 25-50% less than linear model estimates. Recursive modeling has theoretical advantages over linear models for these phenotypes, but the estimated genetic parameters in this study did not differ significantly between the two types of models.     

Heritability of phenotypic udder traits to improve resilience to mastitis in Texel ewes

There are no estimates of the heritability of phenotypic udder traits in suckler sheep, which produce meat lambs, and whether these are associated with resilience to mastitis. Mastitis is a common disease which damages the mammary gland and reduces productivity. The aims of this study were to investigate the feasibility of collecting udder phenotypes, their heritability and their association with mastitis in suckler ewes. Udder and teat conformation, teat lesions, intramammary masses (IMM) and litter size were recorded from 10 Texel flocks in Great Britain between 2012 and 2014; 968 records were collected. Pedigree data were obtained from an online pedigree recording system. Univariate quantitative genetic parameters were estimated using animal and sire models. Linear mixed models were used to analyse continuous traits and generalised linear mixed models were used to analyse binary traits. Continuous traits had higher heritabilities than binary with teat placement and teat length heritability (h²) highest at 0.35 (SD 0.04) and 0.42 (SD 0.04), respectively. Udder width, drop and separation heritabilities were lower and varied with udder volume. The heritabilities of IMM and teat lesions (sire model) were 0.18 (SD 0.12) and 0.17 (SD 0.11), respectively. All heritabilities were sufficiently high to be in a selection programme to increase resilience to mastitis in the population of Texel sheep. Further studies are required to investigate genetic relationships between traits and to determine whether udder traits predict IMM, and the potential benefits from including traits in a selection programme to increase resilience to chronic mastitis.     

Evolutionary game theory and social learning can determine how vaccine scares unfold

Immunization programs have often been impeded by vaccine scares, as evidenced by the measles-mumps-rubella (MMR) autism vaccine scare in Britain. A "free rider" effect may be partly responsible: vaccine-generated herd immunity can reduce disease incidence to such low levels that real or imagined vaccine risks appear large in comparison, causing individuals to cease vaccinating. This implies a feedback loop between disease prevalence and strategic individual vaccinating behavior. Here, we analyze a model based on evolutionary game theory that captures this feedback in the context of vaccine scares, and that also includes social learning. Vaccine risk perception evolves over time according to an exogenously imposed curve. We test the model against vaccine coverage data and disease incidence data from two vaccine scares in England & Wales: the whole cell pertussis vaccine scare and the MMR vaccine scare. The model fits vaccine coverage data from both vaccine scares relatively well. Moreover, the model can explain the vaccine coverage data more parsimoniously than most competing models without social learning and/or feedback (hence, adding social learning and feedback to a vaccine scare model improves model fit with little or no parsimony penalty). Under some circumstances, the model can predict future vaccine coverage and disease incidence--up to 10 years in advance in the case of pertussis--including specific qualitative features of the dynamics, such as future incidence peaks and undulations in vaccine coverage due to the population's response to changing disease incidence. Vaccine scares could become more common as eradication goals are approached for more vaccine-preventable diseases. Such models could help us predict how vaccine scares might unfold and assist mitigation efforts.     

Simultaneous Discovery,Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.     

Identity-by-descent genomic selection using selective and sparse genotyping for binary traits

Background

Genomic selection (GS) allows estimation of the breeding value of individuals, even for non-phenotyped animals. The aim of the study was to examine the potential of identity-by-descent genomic selection (IBD-GS) in genomic selection for a binary, sib-evaluated trait, using different strategies of selective genotyping. This low-cost GS approach is based on linkage analysis of sparse genome-wide marker loci.

Findings

Lowly to highly heritable (h² = 0.15, 0.30 or 0.60) binary traits with varying incidences (10 to 90%) were simulated for an aquaculture-like population. Genotyping was restricted to the 30% best families according to phenotype, using three genotyping strategies for training sibs. IBD-GS increased genetic gain compared to classical pedigree-based selection; the differences were largest at incidences of 10 to 50% of the desired category (i.e. a relative increase in genetic gain greater than 20%). Furthermore, the relative advantage of IBD-GS increased as the heritability of the trait increased. Differences were small between genotyping strategies, and most of the improvement was achieved by restricting genotyping to sibs with the least common binary phenotype. Genetic gains of IBD-GS relative to pedigree-based models were highest at low to moderate (10 to 50%) incidences of the category selected for, but decreased substantially at higher incidences (80 to 90%).

Conclusions

The IBD-GS approach, combined with sparse and selective genotyping, is well suited for genetic evaluation of binary traits. Genetic gain increased considerably compared with classical pedigree-based selection. Most of the improvement was achieved by selective genotyping of the sibs with the least common (minor) binary category phenotype. Furthermore, IBD-GS had greater advantage over classical pedigree-based models at low to moderate incidences of the category selected for.     

Tail colour signals performance in blue tit nestlings

Indirect sexual selection arises when reproductive individuals choose their mates based on heritable ornaments that are genetically correlated to fitness. Evidence for genetic associations between ornamental colouration and fitness remains scarce. In this study, we investigate the quantitative genetic relationship between different aspects of tail structural colouration (brightness, hue and UV chroma) and performance (cell‐mediated immunity, body mass and wing length) in blue tit (Cyanistes caeruleus) nestlings. In line with previous studies, we find low heritability for structural colouration and moderate heritability for performance measures. Multivariate animal models show positive genetic correlations between the three measures of performance, indicating quantitative genetic variation for overall performance, and tail brightness and UV chroma, two genetically independent colour measures, are genetically correlated with performance (positively and negatively, respectively). Our results suggest that mate choice based on independent aspects of tail colouration can have fitness payoffs in blue tits and provide support for the indirect benefits hypothesis. However, low heritability of tail structural colouration implies that indirect sexual selection on mate choice for this ornament will be a weak evolutionary force.     

Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

Quantitative genetic analyses have been increasingly used to estimate the genetic basis of life‐history traits in natural populations. Imperfect detection of individuals is inherent to studies that monitor populations in the wild, yet it is seldom accounted for by quantitative genetic studies, perhaps leading to flawed inference. To facilitate the inclusion of imperfect detection of individuals in such studies, we develop a method to estimate additive genetic variance and assess heritability for binary traits such as survival, using capture–recapture (CR) data. Our approach combines mixed‐effects CR models with a threshold model to incorporate discrete data in a standard ‘animal model’ approach. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data from a wild population of blue tits (Cyanistes caeruleus) and present the first estimate of heritability of adult survival in the wild. In agreement with the prediction that selection should deplete additive genetic variance in fitness, we found that survival had low heritability. Because the detection process is incorporated, capture–recapture animal models (CRAM) provide unbiased quantitative genetics analyses of longitudinal data collected in the wild.     

On the definition and utilization of heritable variation among hosts in reproduction ratio R0 for infectious diseases

Infectious diseases have a major role in evolution by natural selection and pose a worldwide concern in livestock. Understanding quantitative genetics of infectious diseases, therefore, is essential both for understanding the consequences of natural selection and for designing artificial selection schemes in agriculture. The basic reproduction ratio, R₀, is the key parameter determining risk and severity of infectious diseases. Genetic improvement for control of infectious diseases in host populations should therefore aim at reducing R₀. This requires definitions of breeding value and heritable variation for R₀, and understanding of mechanisms determining response to selection. This is challenging, as R₀ is an emergent trait arising from interactions among individuals in the population. Here we show how to define breeding value and heritable variation for R₀ for genetically heterogeneous host populations. Furthermore, we identify mechanisms determining utilization of heritable variation for R₀. Using indirect genetic effects, next-generation matrices and a SIR (Susceptible, Infected and Recovered) model, we show that an individual''s breeding value for R₀ is a function of its own allele frequencies for susceptibility and infectivity and of population average susceptibility and infectivity. When interacting individuals are unrelated, selection for individual disease status captures heritable variation in susceptibility only, yielding limited response in R₀. With related individuals, however, there is a secondary selection process, which also captures heritable variation in infectivity and additional variation in susceptibility, yielding substantially greater response. This shows that genetic variation in susceptibility represents an indirect genetic effect. As a consequence, response in R₀ increased substantially when interacting individuals were genetically related.     

不同水分条件下春小麦种群中个体大小不整齐性及遗传学分析不同水分条件下春小麦种群中个体大小不整齐性及遗传学分析

 种群内个体大小不整齐性是种群数量结构的主要指标。本文研究了不同水分条件下,3个品种春小麦种群个体大小不整齐性的建立及变化规律。对春小麦种群不整齐性的遗传学分析表明：遗传结构与随机环境修饰对种群数量结构形成的相对重要性,因水分条件不同而异。种群不整齐性在自然选择中的作用可用下列简单模型表示：CSo＝SH×hSH2 CSo：自然选择强度;SH：大小不整齐性;hSH2：不整齐性的遗传力。     

A unifying theory for genetic epidemiological analysis of binary disease data

Background

Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses.

Results

Our simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required.

Conclusions

We have derived a genetic-epidemiological function for quantitative genetic analyses of binary infectious disease data, which, unlike current approaches, takes infection dynamics into account and allows for variation in host susceptibility and infectiousness.     

How does selfing affect the genetic variance of quantitative traits? An updated meta‐analysis on empirical results in angiosperm species

Most theoretical works predict that selfing should reduce the level of additive genetic variance available for quantitative traits within natural populations. Despite a growing number of quantitative genetic studies undertaken during the last two decades, this prediction is still not well supported empirically. To resolve this issue and confirm or reject theoretical predictions, we reviewed quantitative trait heritability estimates from natural plant populations with different rates of self‐fertilization and carried out a meta‐analysis. In accordance with models of polygenic traits under stabilizing selection, we found that the fraction of additive genetic variance is negatively correlated with the selfing rate. Although the mating system explains a moderate fraction of the variance, the mean reduction of narrow‐sense heritability values between strictly allogamous and predominantly selfing populations is strong, around 60%. Because some nonadditive components of genetic variance become selectable under inbreeding, we determine whether self‐fertilization affects the relative contribution of these components to genetic variance by comparing narrow‐sense heritability estimates from outcrossing populations with broad‐sense heritability estimated in autogamous populations. Results suggest that these nonadditive components of variance may restore some genetic variance in predominantly selfing populations; it remains, however, uncertain how these nonadditive components will contribute to adaptation.     

EVOLUTION OF FLORAL TRAITS IN A HERMAPHRODITIC PLANT: FIELD MEASUREMENTS OF HERITABILITIES AND GENETIC CORRELATIONS

Genetic variances, heritabilities, and genetic correlations of floral traits were measured in the monocarpic perennial Ipomopsis aggregata (Polemoniaceae). A paternal half-sib design was employed to generate seeds in each of four years, and seeds were planted back in the field near the parental site. The progeny were followed for up to eight years to estimate quantitative genetic parameters subject to natural levels of environmental variation over the entire life cycle. Narrow-sense heritabilities of 0.2–0.8 were detected for the morphometric traits of corolla length, corolla width, stigma position, and anther position. The proportion of time spent by the protandrous flowers in the pistillate phase (“proportion pistillate”) also exhibited detectable heritability of near 0.3. In contrast, heritability estimates for nectar reward traits were low and not significantly different from zero, due to high environmental variance between and within flowering years. The estimates of genetic parameters were combined with phenotypic selection gradients to predict evolutionary responses to selection mediated by the hummingbird pollinators. One trait, corolla width, showed the potential for a rapid response to ongoing selection through male function, as it experienced both direct selection, by influencing pollen export, and relatively high heritability. Predicted responses were lower for proportion pistillate and corolla length, even though these traits also experienced direct selection. Stigma position was expected to respond positively to indirect selection of proportion pistillate but negatively to selection of corolla length, with the net effect sensitive to variation in the selection estimates. Anther position also was not directly selected but could respond to indirect selection of genetically correlated traits.     

The battle of two genomes: genetics of bacterial host/pathogen interactions in mice

Genetic factors strongly determine the outcome of infectious diseases caused by various pathogens. The molecular mechanisms of resistance and susceptibility in humans, however, remains largely unknown. Complex interactions of multiple genes that control the host response to a pathogen further complicate the picture. Animal models have a tremendous potential to dissect the complex genetic system of host–pathogen interaction into single components. This is particularly true for the mouse, which will continue to develop into an invaluable tool in the identification and cloning of host resistance genes. Three main approaches have been taken to establish mouse models for human infectious diseases: 1) Production of mouse mutants by gene targeting; 2) positional cloning of host-resistance genes in mutant mice; and 3) mapping and characterization of quantitative trait loci (QTL) controlling the complex aspects of host–pathogen interactions. The contribution of all three methods to the understanding of infectious diseases in humans will be reviewed in this work, with a special emphasis on the studies of resistance/susceptibility mechanism in bacterial infections. Received: 7 September 2000 / Accepted: 23 November 2000