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      Implications of Host Genetic Variation on the Risk and Prevalence of Infectious Diseases Transmitted Through the Environment

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          Abstract

          Previous studies have shown that host genetic heterogeneity in the response to infectious challenge can affect the emergence risk and the severity of diseases transmitted through direct contact between individuals. However, there is substantial uncertainty about the degree and direction of influence owing to different definitions of genetic variation, most of which are not in line with the current understanding of the genetic architecture of disease traits. Also, the relevance of previous results for diseases transmitted through environmental sources is unclear. In this article a compartmental genetic–epidemiological model was developed to quantify the impact of host genetic diversity on epidemiological characteristics of diseases transmitted through a contaminated environment. The model was parameterized for footrot in sheep. Genetic variation was defined through continuous distributions with varying shape and degree of dispersion for different disease traits. The model predicts a strong impact of genetic heterogeneity on the disease risk and its progression and severity, as well as on observable host phenotypes, when dispersion in key epidemiological parameters is high. The impact of host variation depends on the disease trait for which variation occurs and on environmental conditions affecting pathogen survival. In particular, compared to homogeneous populations with the same average susceptibility, disease risk and severity are substantially higher in populations containing a large proportion of highly susceptible individuals, and the differences are strongest when environmental contamination is low. The implications of our results for the recording and analysis of disease data and for predicting response to selection are discussed.

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          Introduction to Quantitative Genetics

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            Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

            Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
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              Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

              Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.
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                Author and article information

                Contributors
                Role: Communicating editor
                Journal
                Genetics
                genetics
                genetics
                genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                July 2011
                July 2011
                July 2011
                : 188
                : 3
                : 683-693
                Affiliations
                [* ]Sustainable Livestock Systems Group and
                []Animal Health Group, Scottish Agricultural College, Edinburgh, EH9 3JG, United Kingdom
                []Genetics and Genomics, The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, United Kingdom
                [§ ]Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Ministerio de Ciencia e Innovación, 28040 Madrid, Spain
                Author notes
                [ 1 ] Corresponding author: Genetics and Genomics, The Roslin Institute, University of Edinburgh, Roslin, Midlothian, EH25 9PS, United Kingdom. E-mail: andrea.wilson@ 123456roslin.ed.ac.uk
                Article
                125625
                10.1534/genetics.110.125625
                3176547
                21527777
                bd6a7f77-c043-4392-a9ea-e8ec99af5a8d
                Copyright © 2011 by the Genetics Society of America

                Available freely online through the author-supported open access option.

                History
                : 3 December 2010
                : 13 April 2011
                Categories
                Investigations
                Genetics of Complex Traits

                Genetics
                Genetics

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