Gene‐based evaluation of low‐frequency variation and genetically‐predicted gene expression impacting risk of keloid formation. (27th February 2018)

• Record Type: Journal Article
• Authors: Hellwege, Jacklyn N.; Russell, Shirley B.; Williams, Scott M.; Edwards, Todd L.; Velez Edwards, Digna R.
• Is Part Of: Annals of human genetics. Volume 82:Number 4(2018:Jul.)
• Page Start: 206
• Page End: 215
• Publication Date: 2018-02-27
• Subjects: African Americans -- gene expression -- genetic predisposition -- keloid -- skin; Human genetics -- Periodicals; 599.935
• DOI: 10.1111/ahg.12245
• Languages: English
• ISSNs: 0003-4800

Abstract

Abstract: Keloids are benign dermal tumors occurring approximately 20 times more often in individuals of African descent as compared to individuals of European descent. While most keloids occur sporadically, a genetic predisposition is supported by both familial aggregation of some keloids and large differences in risk among populations. Despite Africans and African Americans being at increased risk over lighter‐skinned individuals, little genetic research exists into this phenotype. Using a combination of admixture mapping and exome analysis, we reported multiple common variants within chr15q21.2‐22.3 associated with risk of keloid formation in African Americans. Here we describe a gene‐based association analysis using 478 African American samples with exome genotyping data to identify genes containing low‐frequency variants associated with keloids, with evaluation of genetically‐predicted gene expression in skin tissues using association summary statistics. The strongest signal from gene‐based association was located in C15orf63 ( P ‐value = 6.6 × 10 −6 ) located at 15q15.3. The top result from gene expression was increased predicted DCAF4 expression ( P ‐value = 5.5 × 10 −4 ) in non–sun‐exposed skin, followed by increased predicted OR10A3 expression in sun‐exposed skin ( P ‐value = 6.9 × 10 −4 ). Our findings identify variation with putative roles in keloid formation, enhanced by the use of predicted gene expression to support the biological roles of variation identified only though genetic association studies.