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

By Hans D. Daetwyler, B. Villanueva, J. A. Woolliams. PLoS ONE, 2008

728 citations recorded by the source corpus.

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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/eb7cb526e95f3cde2a18eca2722a0c54c0998871

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Citations in the corpus

Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers (2013, methodology): “h e M e refers to the idealized concept of having a number of independent, biallelic, and additive QTL af ecting the trait (Daetwyler et al., 2008), and M e has been proposed”
Genomewide Prediction Accuracy within 969 Maize Biparental Populations (2014, methodology): “Previous studies have calculated Me as 2NeL, where Ne is the effective population size and L is the size of the genome in Morgans (Daetwyler et al., 2008).”
Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection (2020, background): “The expected prediction accuracy can be calc ated as r = √ N h2 N h2+Me [86,87], in which r (the expected prediction accuracy) is affected by the size of the TP (N), the heritability of the trait (h2), and the effective number of independent chromosomes segments in a given population (Me) which is”
Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor (2013, neutral): “[29,30] derived formulas linking prediction R(2) to features of the trait (e.”
Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment (2018, background): “Daetwyler et al.(15) introduced the following formula for the expected predictive power of a PGS:”
Genomic selection in forest tree breeding (2011, methodology): “Deterministic approach The deterministic formulae for the predicted accuracy were presented earlier (Daetwyler et al. 2008; Resende et al. 2008) and were derived using selection index theory.”
Multi-trait analysis of genome-wide association summary statistics using MTAG (2018, neutral): “As in the calculation of the GWAS-equivalent sample size, where we assume that M e is the same in every GWAS sample and in the prediction sample, the expected predictive power of a polygenic score is where h 2 is the SNP heritability of the trait 28–30 .”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, background): “…as size of the reference data set (Meuwissen et al. 2001; VanRaden and Sullivan 2010), trait heritability, the number of loci affecting the trait (Daetwyler et al. 2008), and the degree of genetic relationships between training and validation samples (Habier et al. 2007) can greatly affect the…”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, methodology): “tively taken that var(b̂i1{bi1)&n {1 1 as previously suggested by Daetwyler et al [23].”
Computer simulations: tools for population and evolutionary genetics (2012, methodology): “Simulations are also used to infer the power of genome scan methods 62 , of genomic selection methods for domesticated plants and animals 63 and of association studies 64,65 , as well as being used to evaluate the accuracy of methods for predicting the individual genetic risk to disease 66,67 .”
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods (2010, methodology): “In Daetwyler et al. (2008), the formulae were derived by considering the regression of phenotypes on one locus at a time as loci were assumed independent.”
Genomic selection in plant breeding: from theory to practice. (2010, background): “racy due to markers in strong LD with QTL [54, 56].”