Exploring X-linked Contributions to Sex-Specific Prevalence Differences in Autism Spectrum Disorder (ASD) Open Access

Abstract

Autism spectrum disorder is a complex neurodevelopmental disorder that shows a marked sex difference with increased prevalence in males than in females. While much of the genetic risk for ASD is due to many risk genes, there is growing evidence that X-linked genes contribute prominently to the male-female difference in ASD prevalence. Thus, with respect to X-linked loci, this thesis employs a liability threshold model to better understand how X-linked loci may contribute to the sex-differentiated genetic risk of ASD. For males and females, we will derive and compare mean and variance components under three genetic models: additive, fully dominant, and fully recessive; using equations and plots to demonstrate the role of allele frequency, the effect size in a liability threshold model, as our final goal is to obtain the number of loci (n) that will need to observe the prevalence

difference.

Determining the number of loci, in an additive model, if the loci have large effect sizes, a plausible estimate is around 1 to 10 loci, whereas thousands may be necessary when the effect sizes are small. For a fully dominant model, one to five loci would be sufficient if the effect sizes are large and a hundred or more loci are needed when the effect sizes are small. However, the fully recessive model yields a valid solution for the number of loci only when the effect parameter is negative, indicating that the risk-increasing allele acts in the opposite direction compared to the additive and dominant cases. In this case, a plausible range is approximately one to 100 loci for large effect sizes and above 1000 loci when the effect sizes are small.

These results suggest that sex differentiation in the prevalence of ASD is more attributable to the unique genetic architecture of the X chromosome hemizygosity (where alleles for males are expressed unbuffered by a second X chromosome), rather than female effects in autosomes. This approach provides a quantitative framework for how allele frequency, effect size, and model influence risk for disease and applies to sex-specific genetic models and analysis of complex diseases.

Table of Contents

1 Introduction to ASD and Allele Frequency Patterns

2 Introduction to the Liability Model

3 Exploring Liability in Sex Chromosomes

4 Genetic Models and Their Impact on Liability

5 Mathematical Framework for Sex-Based Liability Differences in Genetic Models

6 Estimating the Number of X-Linked Risk Loci (n)

7 Results

About this Honors Thesis

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School	Emory College
Department	Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Biology, Genetics
Keyword	quantitative genetics
Committee Chair / Thesis Advisor	David Cutler, Emory University
Committee Members	Hojin Kim, Emory University Michal Arbilly, Emory University

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