# Increasing the Discovery Power and Confidence Levels of Disease Association Studies: A Survey

## TL;DR

This survey reviews recent advances in disease association studies, highlighting methods that improve discovery power and confidence levels in identifying genetic risk factors for complex diseases like cancer.

## Contribution

It provides a comprehensive overview of the current challenges and proposed solutions in genetic disease association studies, emphasizing methodological improvements.

## Key findings

- Identification of key shortcomings in current association methods
- Summary of proposed solutions to enhance discovery power
- Discussion on integrating complementary approaches for better results

## Abstract

The majority of common diseases are influenced by multiple genetic and environmental factors such as Cancer. Even though uncovering the main causes of disease is deemed difficult due to the complexity of gene-gene and gene-environment interactions, major research efforts aim at identifying disease risk factors, especially genetic ones. Over the past decade, disease association studies have been used to uncover the susceptibility, aetiology and mechanisms of action pertaining to common diseases. In disease association studies, genetic data is analyzed in order to reveal the relationship between different types of variants, and a disease of interest. The ultimate goal of association studies is to facilitate susceptibility testing for disease prediction, early diagnosis and enhanced prognosis . Susceptibility testing and disease prediction are particularly important for diseases that can be prevented by diet, drugs or change in lifestyle. The discovered associations assist in understanding the molecular mechanisms influenced by the reported variants, and in identifying important risk factors. Current association studies suffer from several shortcomings. This report surveys the literature that addresses the shortcomings of current methods the identify genetic disease associations. In addition, it reviews the suggested solutions that either enhance some aspect of the methodologies, or complement them.