ArticleGenetics2017
Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study.Journal of atherosclerosis and thrombosis · 2023Article
- Bayesian linear mixed model with multiple random effects for family-based genetic studies.Frontiers in genetics · 2023Article
- Exploiting family history in aggregation unit-based genetic association tests.European journal of human genetics : EJHG · 2022Article
- Predicting allergic diseases in children using genome-wide association study (GWAS) data and family history.The World Allergy Organization journal · 2021Article
- Potential Novel Genes for Late-Onset Alzheimer's Disease in East-Asian Descent Identified by APOE-Stratified Genome-Wide Association Study.Journal of Alzheimer's disease : JAD · 2021Article
- A Between Ethnicities Comparison of Chronic Obstructive Pulmonary Disease Genetic Risk.Frontiers in genetics · 2020Article
- Knowledge, Beliefs, and Attitudes Concerning Genetic Testing Among Young Jordanians.Journal of multidisciplinary healthcare · 2019Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.