| Literature DB >> 32031521 |
Huan Liu1,2,3, Kaylia Duncan4, Annika Helverson2, Priyanka Kumari2, Camille Mumm2, Yao Xiao1, Jenna Colavincenzo Carlson5, Fabrice Darbellay6, Axel Visel6,7,8, Elizabeth Leslie9, Patrick Breheny10, Albert J Erives11, Robert A Cornell2,4.
Abstract
Genome-wide association studies for non-syndromic orofacial clefting (OFC) have identified single nucleotide polymorphisms (SNPs) at loci where the presumed risk-relevant gene is expressed in oral periderm. The functional subsets of such SNPs are difficult to predict because the sequence underpinnings of periderm enhancers are unknown. We applied ATAC-seq to models of human palate periderm, including zebrafish periderm, mouse embryonic palate epithelia, and a human oral epithelium cell line, and to complementary mesenchymal cell types. We identified sets of enhancers specific to the epithelial cells and trained gapped-kmer support-vector-machine classifiers on these sets. We used the classifiers to predict the effects of 14 OFC-associated SNPs at 12q13 near KRT18. All the classifiers picked the same SNP as having the strongest effect, but the significance was highest with the classifier trained on zebrafish periderm. Reporter and deletion analyses support this SNP as lying within a periderm enhancer regulating KRT18/KRT8 expression.Entities:
Keywords: computational biology; developmental biology; enhancers; human; machine learning; mouse; orofacial cleft; periderm; systems biology; zebrafish
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Year: 2020 PMID: 32031521 PMCID: PMC7039683 DOI: 10.7554/eLife.51325
Source DB: PubMed Journal: Elife ISSN: 2050-084X Impact factor: 8.140