Logo image
Back
Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology
Preprint   Open access

Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology

L A Machado-Paula, J Romanowska, R T Lie, L Hovey, B Doolittle, W Awotoye, L Dunlay, X J Xie, E Zeng, A Butali, …
medRxiv
Cold Spring Harbor Laboratory Press, 1.1
02/12/2025
DOI: 10.1101/2025.02.09.25321494
PMCID: PMC11844571
PMID: 39990564
url
https://doi.org/10.1101/2025.02.09.25321494View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Nonsyndromic orofacial clefts (OFCs) etiology involves multiple genetic and environmental factors with over 60 identified risk loci; however, they account for only a minority of the estimated risk. Epigenetic factors such as differential DNA methylation (DNAm) are also associated with OFCs risk and can alter risk for different cleft types and modify OFCs penetrance. DNAm is a covalent addition of a methyl (CH3) group to the nucleotide cytosine that can lead to changes in expression of the targeted gene. DNAm can be affected by environmental influences and genetic variation via methylation quantitative loci (meQTLs). We hypothesize that aberrant DNAm and the resulting alterations in gene expression play a key role in the etiology of OFCs, and that certain common genetic variants that affect OFCs risk do so by influencing DNAm. We used genotype from 10 cleft-associated SNPs and genome-wide DNA methylation data (Illumina 450K array) for 409 cases with OFCs and 456 controls and identified 23 cleft-associated meQTLs. We then used an independent cohort of 362 cleft-discordant sib pairs for replication. We used methylation-specific qPCR to measure methylation levels of each CpG site and combined genotypic and methylation data for an interaction analysis of each SNP-CpG pair using the R package MatrixeQTL in a linear model. We also performed a Paired T-test to analyze differences in DNA methylation between each member of the sibling pairs. We replicated 9 meQTLs, showing interactions between rs13041247 (MAFB) - cg18347630 (PLCG1) (P=0.04); rs227731 (NOG) - cg08592707 (PPM1E) (P=0.01); rs227731 (NOG) - cg10303698 (CUEDC1) (P=0.001); rs3758249 (FOXE1) - cg20308679 (FRZB) (P=0.04); rs8001641 (SPRY2) - cg19191560 (LGR4) (P=0.04); rs987525(8q24) - cg16561172(MYC) (P=0.00000963); rs7590268(THADA) - cg06873343 (TTYH3) (P=0.04); rs7078160 (VAX1) - cg09487139 (P=0.05); rs560426 (ABCA4/ARHGAP29) - cg25196715 (ABCA4/ARHGAP29) (P=0,03). Paired T-test showed significant differences for cg06873343 (TTYH3) (P=0.04); cg17103269 (LPIN3) (P=0.002), and cg19191560 (LGR4) (P=0.05). Our results confirm previous evidence that some of the common non-coding variants detected through GWAS studies can influence the risk of OFCs via epigenetic mechanisms, such as DNAm, which can ultimately affect and regulate gene expression. Given the large prevalence of non-coding SNPs in most OFCs genome wide association studies, our findings can potentially address major knowledge gaps, like missing heritability, reduced penetrance, and variable expressivity associated with OFCs phenotypes.
 Genetic and Genomic Medicine

Details

Metrics

62 Record Views
Logo image