Who’s afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data

Epigenetics & Chromatin

Methodology

Amy M. Inkster¹  Wendy P. Robinson¹  Allison M. Matthews²  Carolyn J. Brown³  Martin T. Wong³ 

[1] BC Children’s Hospital Research Institute, 950 W 28th Ave, V6H 3N1, Vancouver, BC, Canada;Department of Medical Genetics, University of British Columbia, 4500 Oak St, V6H 3N1, Vancouver, Canada;BC Children’s Hospital Research Institute, 950 W 28th Ave, V6H 3N1, Vancouver, BC, Canada;Department of Pathology & Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, V6T 1Z7, Vancouver, Canada;Department of Medical Genetics, University of British Columbia, 4500 Oak St, V6H 3N1, Vancouver, Canada;

关键词: X chromosome;    Y chromosome;    Sex differences;    X-chromosome inactivation;    Sex chromosomes;    DNA methylation;    Array;    Illumina DNA methylation;    Batch-correction;   

DOI  :  10.1186/s13072-022-00477-0

 received in 2022-09-02, accepted in 2022-12-27,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundMany human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data.ResultsWith Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters.ConclusionWhile there may be no single “best” approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.

【 授权许可】

CC BY   
© The Author(s) 2023

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