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Incorporating prior knowledge to facilitate discoveries in a genome-wide association study on age-related macular degeneration.

ABSTRACT: BACKGROUND: Substantial genotyping data produced by current high-throughput technologies have brought opportunities and difficulties. With the number of single-nucleotide polymorphisms (SNPs) going into millions comes the harsh challenge of multiple-testing adjustment. However, even with the false discovery rate (FDR) control approach, a genome-wide association study (GWAS) may still fall short of discovering any true positive gene, particularly when it has a relatively small sample size. FINDINGS: To counteract such a harsh multiple-testing penalty, in this report, we incorporate findings from previous linkage and association studies to re-analyze a GWAS on age-related macular degeneration. While previous Bonferroni correction and the traditional FDR approach detected only one significant SNP (rs380390), here we have been able to detect seven significant SNPs with an easy-to-implement prioritized subset analysis (PSA) with the overall FDR controlled at 0.05. These include SNPs within three genes: CFH, CFHR4, and SGCD. CONCLUSIONS: Based on the success of this example, we advocate using the simple method of PSA to facilitate discoveries in future GWASs.

SUBMITTER: Lin WY

PROVIDER: S-EPMC2843735 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Incorporating prior knowledge to facilitate discoveries in a genome-wide association study on age-related macular degeneration.

Lin Wan-Yu WY Lee Wen-Chung WC

BMC research notes 20100128

<h4>Background</h4>Substantial genotyping data produced by current high-throughput technologies have brought opportunities and difficulties. With the number of single-nucleotide polymorphisms (SNPs) going into millions comes the harsh challenge of multiple-testing adjustment. However, even with the false discovery rate (FDR) control approach, a genome-wide association study (GWAS) may still fall short of discovering any true positive gene, particularly when it has a relatively small sample size. ...[more]

PMID: 20181037

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Project description:ObjectiveRecent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition.DesignCross-sectional study.SubjectsRetrospective dataset from the INSIGHT Health Data Research Hub at Moorfields Eye Hospital in London, United Kingdom, including 306 651 patients (602 826 eyes) with suspected retinal disease who underwent OCT imaging between January 1, 2008 and April 10, 2023.MethodsA deep learning model was trained on OCT scans to identify patients potentially eligible for GA trials, using AI-generated segmentations of retinal tissue. This method's efficacy was compared against a traditional keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value of this approach, by comparing AI predictions with expert assessments.Main outcome measuresThe primary outcomes included the positive predictive value of AI in identifying trial-eligible patients, and the secondary outcome was the intraclass correlation between GA areas segmented on FAF by experts and AI-segmented OCT scans.ResultsThe AI system shortlisted a larger number of eligible patients with greater precision (1139, positive predictive value: 63%; 95% confidence interval [CI]: 54%-71%) compared with the EHR search (693, positive predictive value: 40%; 95% CI: 39%-42%). A combined AI-EHR approach identified 604 eligible patients with a positive predictive value of 86% (95% CI: 79%-92%). Intraclass correlation of GA area segmented on FAF versus AI-segmented area on OCT was 0.77 (95% CI: 0.68-0.84) for cases meeting trial criteria. The AI also adjusts to the distinct imaging criteria from several clinical trials, generating tailored shortlists ranging from 438 to 1817 patients.ConclusionsThis study demonstrates the potential for AI in facilitating automated prescreening for clinical trials in GA, enabling site feasibility assessments, data-driven protocol design, and cost reduction. Once treatments are available, similar AI systems could also be used to identify individuals who may benefit from treatment.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Incorporating prior knowledge to facilitate discoveries in a genome-wide association study on age-related macular degeneration.

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Incorporating prior knowledge to facilitate discoveries in a genome-wide association study on age-related macular degeneration.

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