Dataset Information

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

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.

SUBMITTER: Bijma P

PROVIDER: S-EPMC8733421 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description: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.

Project description:PurposeAn evaluation of mortality due to infectious diseases in Poland in 1999-2012 and an analysis of standard expected years of life lost due to the above diseases.MethodsThe study material included a database created on the basis of 5,219,205 death certificates of Polish inhabitants, gathered between 1999 and 2012 and provided by the Central Statistical Office. Crude Death Rates (CDR), Standardized Death Rates (SDR) and Standard Expected Years of Life Lost (SEYLL) due to infectious and parasitic diseases were also evaluated in the study period as well as Standard Expected Years of Life Lost per living person (SEYLLp) and Standard Expected Years of Life Lost per dead person (SEYLLd). Time trends were evaluated with the application of joinpoint models and an annual percentage change in their values.ResultsDeath certificates report that 38,261 people died due to infectious diseases in Poland in the period 1999-2012, which made up 0.73% of the total number of deaths. SDR caused by these diseases decreased, particularly in the male group: Annual Percentage Change (APC = -1.05; 95% CI:-2.0 to -0.2; p<0.05). The most positive trends were observed in mortality caused by tuberculosis (A15-A19) (APC = -5.40; 95% CI:-6.3 to -4.5; p<0.05) and also meningitis, encephalitis, myelitis and encephalomyelitis (G03-G04) (APC = -3.42; 95% CI:-4.7 to -2.1; p<0.05). The most negative mortality trends were observed for intestinal infectious diseases (A00-A09) Annual Average Percentage Change (AAPC = 7.3; 95% CI:3.1 to 11.7; p<0.05). SDR substantially decreased in the first half of the study period, but then significantly increased in the second half. Infectious and parasitic diseases contributed to a loss of around 37,000 standard expected years of life in 1999 and more than 28,000 in 2012. During the study period, the SEYLLp index decreased from 9.59 to 7.39 per 10,000 population and the SEYLLd index decreased from 14.26 to 10.34 years (AAPC = 2.3; 95% CI:-2,9 to -1.7; p<0.05).ConclusionsDespite smaller numbers of deaths reported from infectious causes these diseases still represent a serious problem for Poland compared to countries in Western Europe.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure