Prediction of individual genetic risk to disease from genome-wide association studies.

By Naomi R. Wray, Michael E. Goddard, Peter M. Visscher. 2007

713 citations recorded by the source corpus.

Empirical studies suggest that the effect sizes of individual causal risk alleles underlying complex genetic diseases are small, with most genotype relative risks in the range of 1.1-2.0. Although the increased risk of disease for a carrier is small for any single locus, knowledge of multiple-risk alleles throughout the genome could allow the identification of individuals that are at high risk. In this study, we investigate the number and effect size of risk loci that underlie complex disease constrained by the disease parameters of prevalence and heritability. Then we quantify the value of prediction of genetic risk to disease using a range of realistic combinations of the number, size, and distribution of risk effects that underlie complex diseases. We propose an approach to assess the genetic risk of a disease in healthy individuals, based on dense genome-wide SNP panels. We test this approach using simulation. When the number of loci contributing to the disease is >50, a large case-control study is needed to identify a set of risk loci for use in predicting the disease risk of healthy people not included in the case-control study. For diseases controlled by 1000 loci of mean relative risk of only 1.04, a case-control study with 10,000 cases and controls can lead to selection of approximately 75 loci that explain >50% of the genetic variance. The 5% of people with the highest predicted risk are three to seven times more likely to suffer the disease than the population average, depending on heritability and disease prevalence. Whether an individual with known genetic risk develops the disease depends on known and unknown environmental factors.

Subfields: Genetics, Computational Biology & Bioinformatics, Medical & Health AI

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Citations in the corpus

Replication in genome-wide association studies (2009, background): “Accurate estimates of marker risks are important (even if the marker is only a surrogate for the as yet unknown causal variant), as they may be used for personalized predictive purposes [34, 68].”
Genomic selection. (2007, result): “For instance, Wray et al. (2007) found that setting a significance threshold so that only one false positive per genome was expected to give EBV highly correlated with the BV.”
Improved polygenic prediction by Bayesian multiple regression on summary statistics (2019, background): “Therefore, once a variant has been chosen to be in the model its effect is sampled from (6), 201 which is the kernel of the normal distribution with mean β̂ j and variance σ2 e /lj (see the 202 Supplemental Note for more detail).”
Genomic selection: prediction of accuracy and maximisation of long term response (2009, background): “In fact, the principles of genomic selection can be applied in humans to predict an individual’s genetic risk of a disease (Wray et al. 2007).”
Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. (2012, background): “In contrast to humans where a very large number of SNP are necessary for accurate genomic predictions due to a large effective population size (e.g., Wray et al., 2007), in modern dairy cattle breeds effective population sizes are sufficiently small that linkage disequilibrium (LD) between SNP and…”
Heritability in the genomics era — concepts and misconceptions (2008, background): “For risk prediction that is based on family history or measured genotypes, it is the heritability on the observed 0–1 risk scale, however, that is most importan”
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 .”
Heterogeneity in polygenic scores for common human traits (2017, background): “04 (14, 15) and hundreds of thousands for SNP weights to be valid for risk prediction in more complex traits (16).”
Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach (2008, neutral): “One critical difference between the two genome-wide approaches is that Wray et al. [9] set a significance threshold for the loci selected for disease prediction, whereas Meuwissen et al. [10] use all loci regardless of whether they affect or not the trait considered.”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “Successful prediction of individual disease risk has the potential to aid in disease prevention, screening, and early treatment for high-risk individuals (Wray et al., 2007; Ashley et al., 2010; Manolio, 2013).”
Increased accuracy of artificial selection by using the realized relationship matrix. (2009, background): “…QTLs aﬀecting a trait is substantially less than the eﬀective number of chromosome segments, then EBVs of higher accuracy can be obtained by methods that assume that only some markers have QTLs associated with them (Meuwissen et al ., 2001; Habier et al ., 2007; Wray et al ., 2007; Goddard, 2008).”
Power and Predictive Accuracy of Polygenic Risk Scores (2013, methodology): “Secondly and perhaps more usefully, the polygenic score can be used to predict individual trait values or risks of disease [2], potentially giving a predictor with better discrimination properties than one based on established markers only.”