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Assessing the performance of genome-wide association studies for predicting disease risk.

ABSTRACT: To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

SUBMITTER: Patron J

PROVIDER: S-EPMC6894795 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Assessing the performance of genome-wide association studies for predicting disease risk.

Patron Jonas J Serra-Cayuela Arnau A Han Beomsoo B Li Carin C Wishart David Scott DS

PloS one 20191205 12

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to asse ...[more]

PMID: 31805043

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Project description:BackgroundAlzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry.Recent developmentsThree new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.

Dataset Information

Assessing the performance of genome-wide association studies for predicting disease risk.

Publications

Assessing the performance of genome-wide association studies for predicting disease risk.

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