Related Experiment Video
Updated: May 8, 2026
09:34
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Analytic Choices Shape Genomic Risk Estimates from Electronic Health Records: Coronary Heart Disease in eMERGE IV
Jingheng H Chen1, Sarah A Knerr1,2, David L Veenstra3
1Institute of Public Health Genetics, University of Washington, Seattle, WA, USA.
Summary
Analytic choices in electronic health records (EHR) studies impact coronary heart disease (CHD) risk estimates. Case definition affects relative risk, while EHR entry time influences cumulative incidence, not hazard ratios.
Area of Science:
- Genomic epidemiology
- Biostatistics
- Health informatics
Background:
- Electronic health records (EHR) are crucial for genomic studies.
- Challenges exist in accurately identifying cases and observation start times within EHR data.
- The eMERGE IV study investigated these challenges in coronary heart disease (CHD) research.
Purpose of the Study:
- To evaluate how analytical assumptions on case ascertainment and EHR entry time influence genetic risk estimations for CHD.
- To compare different methods of defining CHD cases using EHR and self-report data.
- To assess the impact of varying observation start times on time-to-event analyses.
Main Methods:
- Compared EHR-phenotyped CHD cases with survey-ascertained cases.
- Utilized multivariate relative risk regression for monogenic and polygenic risk scores (PRS) under three case definitions.
- Performed time-to-event analyses using Kaplan-Meier and Cox proportional hazards models with three entry time specifications.
Main Results:
- Agreement between EHR and self-reported CHD cases was 37.2%, with EHR showing higher accuracy.
- High PRS was associated with significantly higher relative risk (RR) for EHR-defined (2.05), self-reported (1.49), and combined (1.66) CHD.
- Alternative EHR entry times affected cumulative incidence but minimally impacted hazard ratios.
Conclusions:
- The selection of case definition significantly influences relative risk estimates in EHR and survey-based genetic studies.
- EHR entry time specifications impact absolute cumulative incidence but have minimal effect on hazard ratios.
- These findings underscore the importance of analytical choices in EHR data utilization for genomic research.
Related Concept Videos
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:29Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
01:12Statistical Software for Data Analysis and Clinical Trials
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...