medRxivGenetic & Genomic MedicinePreprint
Genome-wide meta-analysis of pilonidal sinus disease and its genetic overlap with hidradenitis suppurativa, hair patterning and adiposity
João V. F. CavalcanteiD, Julia A. de AmorimiD, Rodrigo J. S. DalmoliniD
Abstract
Pilonidal sinus disease (PSD) is a common, recurrent inflammatory disease of the natal cleft that clusters in families, but few genetic risk loci are known and its relationship with hidradenitis suppurativa (HS), acne and obesity is unclear. We meta-analysed public genome-wide summary statistics from the Million Veteran Program, UK Biobank, FinnGen and Genes & Health (7,868 cases and 1.6 million controls from four ancestry groups). Of 25 genome-wide significant loci outside the MHC, 19 were novel and 21 remained significant after correction for test-statistic inflation. Liability-scale SNP heritability was 13-15%. Fine-mapping resolved the SOX2, PDE9A and KLF5 loci to single likely causal variants and identified a WNT10A missense variant, p.(Phe228Ile), associated with reduced risk. PSD was strongly genetically correlated with HS (rg = 0.76). In genomic structural equation models, the genetic correlation between PSD and body mass index (BMI, rg = 0.33) disappeared once HS was modelled ({beta} = 0.01), whereas an independent component shared with male-pattern balding remained. Local genetic correlation and colocalisation located this overlap: six loci, including the KLF5 and SOX9 regions, shared a causal variant with HS and five with balding, whereas loci shared with BMI harboured distinct causal variants. Expression quantitative trait locus colocalisation implicated SEMA4B in skin and WNT16 in fibroblasts. PSD is therefore a heritable disorder of the hair follicle that shares most of its common-variant liability with HS, with a separate hair-patterning component and a probable indirect association with obesity.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: fine-mapping
- research focus: eQTL / molecular QTL mapping
- approach: statistical model
- approach: matrix factorization
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