5 research outputs found

    A hierarchical Bayesian model to predict APOE4 genotype and the age of Alzheimer’s disease onset - Table 3

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    <p>A hierarchical Bayesian model to predict APOE4 genotype and the age of Alzheimer’s disease onset</p> - Table

    Example of hierarchical Bayesian model to estimate APOE4 genotype of a subject based on the age and age of AD onset of the subject and his parents and grandparents.

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    <p>Example of hierarchical Bayesian model to estimate APOE4 genotype of a subject based on the age and age of AD onset of the subject and his parents and grandparents.</p

    Gompertz AD onset risk function for the general population (“baseline”), the subject’s estimated APOE status (“genetic risk”), and risk factoring in known risk factors (“factored risk”).

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    <p>Notice the risk increases steadily until approximately age 80, at which point the risk grows at a slower rate. This is because there is a probability that this individual will never have AD. The baseline risk is calculated by applying the model to the “average” person, that has APOE4 genotype probability status equal to that of the population.</p

    Theoretical Bayesian probability of APOE4 genotype based on age of AD onset.

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    <p>These functions form the basis for the P(AGE|Genotype) variable used in determining posterior probability P(Genotype|AGE). At early ages, the sum of the genetics add up to above 100% since the probability of autosomal dominant genes does not figure into the probabilities of have any of the APOE4 genotypes, which do add up to 100%. This graph is obtained using the conditional probability equation of .</p

    A hierarchical Bayesian model to predict APOE4 genotype and the age of Alzheimer’s disease onset - Table 1

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    <p>A hierarchical Bayesian model to predict APOE4 genotype and the age of Alzheimer’s disease onset</p> - Table
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