ArticleBMC genomics2019
Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- The APOE ε4 allele affects the survival of patients with Parkinson's disease independent of dementia.Journal of translational medicine · 2025Article
- Neighborhood-level social determinants of suicidality in youth with schizophrenia: An EHR-based study.Schizophrenia research · 2025Article
- BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR.ArXiv · 2025Article
- Analysis of follow-up data in large biobank cohorts: a review of methodology.Frontiers in genetics · 2025Article
- Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat.International journal of molecular sciences · 2023Article
- ADuLT: An efficient and robust time-to-event GWAS.Nature communications · 2023Article
- The phenotype-genotype reference map: Improving biobank data science through replication.American journal of human genetics · 2023Article
- Improving efficiency of fitting Cox proportional hazards models for time-to-event outcomes in genome-wide association studies (GWAS).Bioinformatics advances · 2023Article
- Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint.Statistics in medicine · 2022Article
- Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS.Bioinformatics (Oxford, England) · 2022Article
- Accounting for age of onset and family history improves power in genome-wide association studies.American journal of human genetics · 2022Article
- Mammographic features are associated with cardiometabolic disease risk and mortality.European heart journal · 2021Article
- Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.Annual review of biomedical data science · 2021Review
- A novel age-informed approach for genetic association analysis in Alzheimer's disease.Alzheimer's research & therapy · 2021Article
- Set-based genetic association and interaction tests for survival outcomes based on weighted V statistics.Genetic epidemiology · 2021Article
- A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.American journal of human genetics · 2020Article
- Humanizing Big Data: Recognizing the Human Aspect of Big Data.Frontiers in oncology · 2020Review
- Making the Most of Clumping and Thresholding for Polygenic Scores.American journal of human genetics · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).
resultsIn comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression.
conclusionsAs longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.
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