Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis

Area of Science:

• Genetics
• Neuroscience
• Biostatistics

Background:

• Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with a significant genetic component.
• Identifying reliable genetic risk factors for ASD is crucial for understanding its etiology and developing interventions.
• Previous genetic association studies have yielded numerous findings, but replication and validation remain challenges.

Purpose of the Study:

• To rigorously evaluate reported genetic risk factors for ASD using robust statistical methods.
• To differentiate genuine genetic associations from false positives in ASD research.
• To identify specific genetic comparisons with high probability of being true associations with ASD risk.

Main Methods:

• Systematic literature search of PubMed and the Genome-Wide Association Studies (GWAS) catalog up to August 2019.
• Re-analysis of 27 meta-analyses and 4 manually added GWAS catalog articles.
• Application of False Positive Report Probability (FPRP) and Bayesian False-Discovery Probability (BFDP) for statistical validation.

Main Results:

• Out of 31 comparisons from observational study meta-analyses, 5 showed noteworthy estimations.
• 40 out of 203 comparisons from GWAS meta-analyses met Bayesian criteria for noteworthy findings.
• 18 out of 20 comparisons from the GWAS catalog demonstrated significant Bayesian estimations.

Conclusions:

• The study identified several noteworthy genetic comparisons associated with an increased risk of ASD.
• Bayesian statistical approaches (FPRP and BFDP) are effective in verifying genetic findings for ASD.
• While multiple genetic factors are linked to ASD, careful verification of genuine associations is essential.