Affiliations: [a] Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| [b] John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| [c] Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| [d] Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| [e] Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Correspondence:
[*]
Correspondence to: William S. Bush, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA. Tel.: +1 216 368 0957; E-mail: wsb36@case.edu.
Note: [1] These authors contributed equally to this work.
Abstract: Background:The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer’s disease (AD). Objective:To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods:Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer’s Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results:Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions:We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
Keywords: Alzheimer’s disease, bioinformatics, elastic net regression, gene
expression, gene prediction, sex differences, transcriptome, X chromosome
