Genetic Testing for Complex Diseases: a Simulation Study Perspective

TL;DR

This paper reviews simulation studies on the use of genetic testing for complex diseases, discussing their parameters, assumptions, and insights into the potential and limitations of genetic risk prediction.

Contribution

It provides a comprehensive analysis of simulation-based research on genetic testing for complex diseases, highlighting key factors influencing their effectiveness.

Findings

Simulation studies help understand genetic test potential

Parameters and assumptions critically affect outcomes

Genetic contribution to disease risk remains uncertain

Abstract

It is widely recognized nowadays that complex diseases are caused by, amongst the others, multiple genetic factors. The recent advent of genome-wide association study (GWA) has triggered a wave of research aimed at discovering genetic factors underlying common complex diseases. While the number of reported susceptible genetic variants is increasing steadily, the application of such findings into diseases prognosis for the general population is still unclear, and there are doubts about whether the size of the contribution by such factors is significant. In this respect, some recent simulation-based studies have shed more light to the prospect of genetic tests. In this report, we discuss several aspects of simulation-based studies: their parameters, their assumptions, and the information they provide.