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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:
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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.
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  11. Stratifying Lung Adenocarcinoma Risk With Multi-ancestry Polygenic Risk Scores In East Asian Never-smokers.
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  11. Stratifying Lung Adenocarcinoma Risk With Multi-ancestry Polygenic Risk Scores In East Asian Never-smokers.

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Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

Batel Blechter1, Xiaoyu Wang1, Jianxin Shi2,3

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.

Medrxiv : the Preprint Server for Health Sciences
|July 9, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Developing multi-ancestry polygenic risk scores (PRS) improves lung adenocarcinoma (LUAD) risk prediction in East Asian never-smokers. This approach enhances risk stratification for LUAD in this population.

Keywords:
East Asian never smokersGenome-wide association studiesLifetime absolute riskLung adenocarcinoma

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Area of Science:

  • Genetics and Genomics
  • Cancer Epidemiology
  • Personalized Medicine

Background:

  • Polygenic risk scores (PRS) are valuable for disease risk stratification.
  • Current PRS are predominantly developed in European populations, limiting their application in diverse ancestries.
  • Lung adenocarcinoma (LUAD) risk stratification in East Asian (EAS) never-smokers requires ancestry-specific or multi-ancestry PRS.

Purpose of the Study:

  • To develop and evaluate single- and multi-ancestry polygenic risk scores (PRS) for lung adenocarcinoma (LUAD) in East Asian never-smokers.
  • To assess the performance of these PRS in predicting LUAD risk within the East Asian population.
  • To explore the potential of multi-ancestry PRS for improved LUAD risk stratification.

Main Methods:

  • Utilized genome-wide association study (GWAS) summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations.
Polygenic risk scores
  • Developed single- and multi-ancestry PRS for LUAD.
  • Employed the CT-SLEB method for multi-ancestry PRS development.
  • Evaluated PRS performance using logistic regression and receiver operating characteristic (ROC) analysis.

    • Main Results:

    • A multi-ancestry PRS demonstrated a strong association with LUAD risk (OR=1.71, 95% CI: 1.61-1.82).
    • The area under the ROC curve for the multi-ancestry PRS was 0.640 (95% CI: 0.629-0.653).
    • Individuals in the highest PRS quintile had nearly four times the LUAD risk compared to the lowest quintile.
    • The 95th percentile of the PRS indicated an estimated 6.69% lifetime absolute risk, with average 10-year risk achieved decades earlier.

    Conclusions:

    • Multi-ancestry PRS approaches show significant potential for enhancing LUAD risk stratification in East Asian never-smokers.
    • Developed PRS can identify individuals at substantially elevated LUAD risk.
    • This study highlights the importance of considering ancestry in PRS development for equitable risk prediction.