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      Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach


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          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.


          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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          Most cited references23

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          Genome-wide genetic association of complex traits in heterogeneous stock mice.

          Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.
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            On the allelic spectrum of human disease.

            Human disease genes show enormous variation in their allelic spectra; that is, in the number and population frequency of the disease-predisposing alleles at the loci. For some genes, there are a few predominant disease alleles. For others, there is a wide range of disease alleles, each relatively rare. The allelic spectrum is important: disease genes with only a few deleterious alleles can be more readily identified and are more amenable to clinical testing. Here, we weave together strands from the human mutation and population genetics literature to provide a framework for understanding and predicting the allelic spectra of disease genes. The theory does a reasonable job for diseases where the genetic etiology is well understood. It also has bearing on the Common Disease/Common Variants (CD/CV) hypothesis, predicting that at loci where the total frequency of disease alleles is not too small, disease loci will have relatively simple spectra.
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              Bayesian LASSO for quantitative trait loci mapping.

              The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.

                Author and article information

                Role: Editor
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                14 October 2008
                : 3
                : 10
                : e3395
                [1 ]Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Roslin, Midlothian, United Kingdom
                [2 ]Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands
                [3 ]Sustainable Livestock Systems, Scottish Agriculture College, Edinburgh, United Kingdom
                Peninsula Medical School, United Kingdom
                Author notes

                Conceived and designed the experiments: HDD BV JAW. Performed the experiments: HDD JAW. Analyzed the data: HDD JAW. Wrote the paper: HDD BV JAW.

                Daetwyler et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                : 10 April 2008
                : 3 September 2008
                Page count
                Pages: 8
                Research Article
                Genetics and Genomics
                Computational Biology/Genomics
                Genetics and Genomics/Animal Genetics
                Genetics and Genomics/Complex Traits
                Genetics and Genomics/Genetics of Disease
                Genetics and Genomics/Medical Genetics
                Genetics and Genomics/Population Genetics



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