Statistical Analysis in Genetic Studies of Mental Illnesses

TL;DR

This paper reviews the challenges and statistical methods used in genetic association studies of mental illnesses, highlighting the evolution from early Mendelian studies to modern genomewide approaches.

Contribution

It provides a comprehensive overview of statistical techniques and study designs used in genetic research of mental disorders, emphasizing recent advancements.

Findings

Genetic studies have evolved from Mendelian to genomewide association methods.

Statistical challenges include complex etiologies and comorbidities.

Various study designs improve understanding of mental illness genetics.

Abstract

Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.