Journal of Alzheimer s DiseaseBioinformaticsPeer-reviewed journal article
An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease
Xueyi ZhangiD, Lissette Gomez, Jennifer Elizabeth BelowiD, Adam Christian NajiD, Eden R. MartiniD, Brian W. KunkleiD, William S. BushiD
Abstract
BackgroundThe X chromosome is often omitted in disease association studies despite containing thousands of genes which may provide insight into well-known sex differences in the risk of Alzheimers Disease. ObjectiveTo model the expression of X chromosome genes and evaluate their impact on Alzheimers Disease risk in a sex-stratified manner. MethodsUsing 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 Alzheimers disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. ResultsAcross 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 ( = 0.002). ConclusionsWe 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.
- organism: human
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- research focus: differential expression
- approach: statistical model
- Citations
- 3
- FWCI
- 0.17
- Field percentile
- 24th percentile
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