期刊
CLINICAL INFECTIOUS DISEASES
卷 72, 期 10, 页码 E515-E525出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cid/ciaa1230
关键词
Leishmania; GWAS; post-GWAS integrated analysis; interferon-gamma; IFNG-ASI
资金
- British Medical Research Council (MRC) [MR/N017390/1]
- Brazilian Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)
- MRC/Conselho Nacional das Fundacoes Estaduais de Amparo a Pesquisa (CONFAP) [CBB-APQ-00883-16]
- National Institute of Science and Technology in Tropical Diseases, Brazil [573839/2008-5]
- CNPq
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
- National Institutes of Health [AI 30639]
- Wellcome Trust [102858/Z/13/Z]
- Wellcome Trust [102858/Z/13/Z] Funding Source: Wellcome Trust
- MRC [MR/N017390/1] Funding Source: UKRI
This study identified 32 genomic loci associated with cutaneous leishmaniasis, with 6 most plausible genetic risk loci being SERPINB10, CRLF3, STX7, KRT80, LAMP3, and IENG-AS1. Several of these genes showed differential expression in CL biopsies compared to normal skin, with LAMP3, STX7, and CRLF3 being upregulated and KRT80 being downregulated. Cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of different cell types, while those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells.
Background. Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. Methods. Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay. Results. Positional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 x 10(-8)). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (P-imputed_1000G = 2.67 x 10(-6)), CRLF3 (P-imputed_1000G = 5.12 x 10(-6)), STX7 (P-imputed_1000G = 6.06 x 10(-6)), KRT80 (P-imputed_1000G = 6.58 x 10(-6)), LAMP 3 (P-imputed_1000G = 6.54 x 10(-6)), and IENG-AS1 (P-imputed_1000G = 1.32 x 10(-5)). LAMP3 (P-adjusted = 9.25 x 10(-12); +6-fold), STX7 (P-adjusted = 7.62 x 10(-3); +1.3-fold), and CRLF3 (P-adjusted = 9.19 x 10(-9); +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (P-adjusted = 3.07 x 10(-8) ; -3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3(+) T cells making antigen-specific interferon-nu differed significantly by IFNG-AS1 genotype. Conclusions. This first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-gamma by IFNG antisense RNA 1.
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