scholarly journals The quantitative genetics of the endemic prevalence of infectious diseases: Indirect Genetic Effects dominate heritable variation and response to selection

2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries Hulst ◽  
Mart C. M. de Jong
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Status ◽  
Host Population ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

AbstractPathogens have profound effects on life on earth, both in nature and agriculture. Despite the availability of well-established epidemiological theory, 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 the transmission dynamics of infectious diseases for heritable variation and response to selection of the host population, our current theoretical framework of quantitative genetics does not include these dynamics. As a consequence, we do not know which genetic effects of the host population determine the prevalence of an infectious disease, and have no concepts of breeding value and heritable variation for endemic prevalence.Here we propose a quantitative genetic theory for the endemic prevalence of infectious diseases. We first identify the genetic factors that determine the prevalence of an infectious disease, using an approach founded in epidemiological theory. Subsequently we investigate the population level effects of individual genetic variation on R0 and on the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for both prevalence and individual binary disease status, and show how these parameters depend on the endemic prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases when prevalence approaches zero, while heritability of individual disease status goes to zero. We show that response of prevalence to selection accelerates considerably when prevalence goes down, in contrast to predictions based on classical genetic models. Finally, we show that most of the heritable variation in the endemic prevalence of the infection is due to indirect genetic effects, suggestion a key role for kin-group selection both in the evolutionary history of current populations and for genetic improvement strategies in animals and plants.

scholarly journals The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to Indirect Genetic Effects dominates heritable variation and response to selection

Genetics ◽  
2021 ◽  
Author(s):  
Piter Bijma ◽  
Andries D Hulst ◽  
Mart C M de Jong
Keyword(s):  
Genetic Variation ◽  
Infectious Diseases ◽  
Quantitative Genetics ◽  
Disease Status ◽  
Genetic Effects ◽  
Breeding Value ◽  
Response To Selection ◽  
Heritable Variation ◽  
Genetic Theory ◽  
Quantitative Genetic

Abstract 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.

58 Integrating Quantitative Genetics and Epidemiology: Why Selection Against Infectious Diseases Is More Promising Than We Think

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 31-32
Author(s):  
Piter Bijma ◽  
Piter Bijma
Keyword(s):  
Infectious Diseases ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Disease Status ◽  
Genetic Theory ◽  
Breeding Values ◽  
Quantitative Genetic ◽  
The Mean ◽  
Low Prevalence ◽  
Simple Average

Abstract Pathogens have profound effects on livestock. The low heritabilities of individual binary disease status suggest limited prospects for genetic improvement. However, a proper quantitative genetic theory for infectious diseases, including transmission dynamics, is currently lacking. Here we present a quantitative genetic theory for endemic infectious diseases, focussing on the genetic factors that determine the prevalence (P; the mean fraction of the population that is infected). We present simple expressions for breeding values and genetic parameters for the prevalence. Without genetic variation in infectiousness, breeding values for prevalence are a factor 1/P greater than the ordinary breeding values for individual binary disease status (0/1). Hence, even though prevalence is the simple average of individual binary disease status, breeding values for prevalence show much greater variation than our ordinary breeding values. This implies that the genetic variance that determines the potential response of prevalence to selection is largely due to indirect genetic effects (IGE), and thus hidden to ordinary genetic analysis and selection. Hence, the genetic variance that determines the potential of livestock populations to respond to selection must be much greater than currently believed, particularly at low prevalence. We evaluated this implication using simulation of endemics following standard methods in epidemiology. Results show that response of prevalence to selection increases very strongly when prevalence decreases, and is much greater than predicted by our ordinary breeding values. These results supports our theoretical findings, and show that selection against infectious diseases is much more promising than currently believed.

scholarly journals Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Genetics ◽  
2021 ◽  
Author(s):  
Andries D Hulst ◽  
Mart C M de Jong ◽  
Piter Bijma
Keyword(s):  
Infectious Diseases ◽  
Herd Immunity ◽  
Genetic Selection ◽  
Disease Status ◽  
Response To Selection ◽  
Feedback Effects ◽  
Feedback Mechanisms ◽  
Quantitative Genetic ◽  
The Common ◽  
Selection For

Abstract Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).

scholarly journals Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Thinh Tuan Chu ◽  
Mark Henryon ◽  
Just Jensen ◽  
Birgitte Ask ◽  
Ole Fredslund Christensen
Keyword(s):  
Statistical Model ◽  
Statistical Models ◽  
Genetic Effects ◽  
Response To Selection ◽  
Heritable Variation ◽  
Fixed Rate ◽  
Random Group ◽  
Breeding Programs ◽  
The Difference ◽  
Family Groups

Abstract Background Social genetic effects (SGE) are the effects of the genotype of one animal on the phenotypes of other animals within a social group. Because SGE contribute to variation in economically important traits for pigs, the inclusion of SGE in statistical models could increase responses to selection (RS) in breeding programs. In such models, increasing the relatedness of members within groups further increases RS when using pedigree-based relationships; however, this has not been demonstrated with genomic-based relationships or with a constraint on inbreeding. In this study, we compared the use of statistical models with and without SGE and compared groups composed at random versus groups composed of families in genomic selection breeding programs with a constraint on the rate of inbreeding. Results When SGE were of a moderate magnitude, inclusion of SGE in the statistical model substantially increased RS when SGE were considered for selection. However, when SGE were included in the model but not considered for selection, the increase in RS and in accuracy of predicted direct genetic effects (DGE) depended on the correlation between SGE and DGE. When SGE were of a low magnitude, inclusion of SGE in the model did not increase RS, probably because of the poor separation of effects and convergence issues of the algorithms. Compared to a random group composition design, groups composed of families led to higher RS. The difference in RS between the two group compositions was slightly reduced when using genomic-based compared to pedigree-based relationships. Conclusions The use of a statistical model that includes SGE can substantially improve response to selection at a fixed rate of inbreeding, because it allows the heritable variation from SGE to be accounted for and capitalized on. Compared to having random groups, family groups result in greater response to selection in the presence of SGE but the advantage of using family groups decreases when genomic-based relationships are used.

scholarly journals HLA and Infectious Diseases

2009 ◽  
Vol 22 (2) ◽  
pp. 370-385 ◽  
Author(s):  
Jenefer M. Blackwell ◽  
Sarra E. Jamieson ◽  
David Burgner
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Human Leukocyte Antigen ◽  
Molecular Basis ◽  
Selective Pressure ◽  
Human Leukocyte ◽  
Acquired Immunity ◽  
Leukocyte Antigen ◽  
Extreme Variability ◽  
Classical Human Leukocyte Antigen

SUMMARY Following their discovery in the early 1970s, classical human leukocyte antigen (HLA) loci have been the prototypical candidates for genetic susceptibility to infectious disease. Indeed, the original hypothesis for the extreme variability observed at HLA loci (H-2 in mice) was the major selective pressure from infectious diseases. Now that both the human genome and the molecular basis of innate and acquired immunity are understood in greater detail, do the classical HLA loci still stand out as major genes that determine susceptibility to infectious disease? This review looks afresh at the evidence supporting a role for classical HLA loci in susceptibility to infectious disease, examines the limitations of data reported to date, and discusses current advances in methodology and technology that will potentially lead to greater understanding of their role in infectious diseases in the future.

scholarly journals Drivers of Infectious Disease Seasonality: Potential Implications for COVID-19

2021 ◽  
pp. 074873042098732
Author(s):  
N. Kronfeld-Schor ◽  
T. J. Stevenson ◽  
S. Nickbakhsh ◽  
E. S. Schernhammer ◽  
X. C. Dopico ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Multidisciplinary Approach ◽  
Preliminary Assessment ◽  
Epidemiological Evidence ◽  
Wide Range ◽  
Human Coronaviruses ◽  
Induced Changes ◽  
And Behavior ◽  
Timing Phase

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system.

scholarly journals Application of 3D bioprinting in the prevention and the therapy for human diseases

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hee-Gyeong Yi ◽  
Hyeonji Kim ◽  
Junyoung Kwon ◽  
Yeong-Jin Choi ◽  
Jinah Jang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Rapid Development ◽  
In Vitro Models ◽  
Delivery Systems ◽  
Advanced Technology ◽  
3D Bioprinting ◽  
Disease Research ◽  
Infectious Disease Research

AbstractRapid development of vaccines and therapeutics is necessary to tackle the emergence of new pathogens and infectious diseases. To speed up the drug discovery process, the conventional development pipeline can be retooled by introducing advanced in vitro models as alternatives to conventional infectious disease models and by employing advanced technology for the production of medicine and cell/drug delivery systems. In this regard, layer-by-layer construction with a 3D bioprinting system or other technologies provides a beneficial method for developing highly biomimetic and reliable in vitro models for infectious disease research. In addition, the high flexibility and versatility of 3D bioprinting offer advantages in the effective production of vaccines, therapeutics, and relevant delivery systems. Herein, we discuss the potential of 3D bioprinting technologies for the control of infectious diseases. We also suggest that 3D bioprinting in infectious disease research and drug development could be a significant platform technology for the rapid and automated production of tissue/organ models and medicines in the near future.

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