BACKGROUND: Genotype-phenotype correlation measures the correlation between the presence of a physical trait with a group of similar mutations but is dependent on reliable phenotyping. It can provide information on disease pathogenesis, future disease progression, severity or activity. Such indicators would be valuable in hidradenitis suppurativa (HS). OBJECTIVES: To assess inter-rater reliability (IRR) of HS clinical phenotypes and perform exploratory genotype-phenotype correlation in cases of HS with identified sequence variants. METHODS: Linkage disequilibrium between variants was assessed. Genotype-phenotype correlations were explored using Spearman correlation coefficients. IRR was calculated using Cohen's κ. Correlation between phenotype classifications was assessed using the χ2 statistic. RESULTS: Forty-three sequence variants with clinical information were identified. Clinical phenotypes were classified as LC2 (n = 29; 67%), scarring folliculitis (n = 18; 42%), atypical (n = 38; 88%) and nodular (n = 26; 60%). LC1 phenotype was associated with regular (χ2 = 41·289, P < 0·001) and typical (χ2 = 29·013, P < 0·001) phenotypes. Cohen's κ was highest for van der Zee and Jemec (0·815), followed by Martorell-Calatayud et al. (0·813), Naasan and Affleck (0·774) and Canoui-Poitrine et al. (0·435) classifications. High linkage disequilibrium was seen between variants of Han Chinese pedigrees. No significant genotype-phenotype correlations were identified. CONCLUSIONS: These findings may be influenced by selection, publication bias and the assumption that HS is a monogenic disorder. The poor IRR of existing phenotype measures suggests limited utility of existing measures. Further investigations into the correlation of clinical phenotypes with inflammatory biomarkers may aid in prognostic efforts for this disease. What's already known about this topic? Genotype-phenotype correlation can provide information regarding disease pathogenesis and predictions for future disease progression, severity or activity. The identification of such indicators in hidradenitis suppurativa (HS) would be valuable for patients and clinicians alike, given the lack of biomarkers or clinical predictors of disease. What does this study add? Sixty-five sequence variants across 20 separate genes were identified. There was no significant correlation between phenotype classification in four separate classification schema and gene, mutation type or impact on Notch signalling. Utility of current phenotype measurements are limited. The lack of genotype-phenotype correlation in HS is suggestive that the underlying assumption of inherited HS as a monogenic disorder may need revision.
BACKGROUND: Genotype-phenotype correlation measures the correlation between the presence of a physical trait with a group of similar mutations but is dependent on reliable phenotyping. It can provide information on disease pathogenesis, future disease progression, severity or activity. Such indicators would be valuable in hidradenitis suppurativa (HS). OBJECTIVES: To assess inter-rater reliability (IRR) of HS clinical phenotypes and perform exploratory genotype-phenotype correlation in cases of HS with identified sequence variants. METHODS: Linkage disequilibrium between variants was assessed. Genotype-phenotype correlations were explored using Spearman correlation coefficients. IRR was calculated using Cohen's κ. Correlation between phenotype classifications was assessed using the χ2 statistic. RESULTS: Forty-three sequence variants with clinical information were identified. Clinical phenotypes were classified as LC2 (n = 29; 67%), scarring folliculitis (n = 18; 42%), atypical (n = 38; 88%) and nodular (n = 26; 60%). LC1 phenotype was associated with regular (χ2 = 41·289, P < 0·001) and typical (χ2 = 29·013, P < 0·001) phenotypes. Cohen's κ was highest for van der Zee and Jemec (0·815), followed by Martorell-Calatayud et al. (0·813), Naasan and Affleck (0·774) and Canoui-Poitrine et al. (0·435) classifications. High linkage disequilibrium was seen between variants of Han Chinese pedigrees. No significant genotype-phenotype correlations were identified. CONCLUSIONS: These findings may be influenced by selection, publication bias and the assumption that HS is a monogenic disorder. The poor IRR of existing phenotype measures suggests limited utility of existing measures. Further investigations into the correlation of clinical phenotypes with inflammatory biomarkers may aid in prognostic efforts for this disease. What's already known about this topic? Genotype-phenotype correlation can provide information regarding disease pathogenesis and predictions for future disease progression, severity or activity. The identification of such indicators in hidradenitis suppurativa (HS) would be valuable for patients and clinicians alike, given the lack of biomarkers or clinical predictors of disease. What does this study add? Sixty-five sequence variants across 20 separate genes were identified. There was no significant correlation between phenotype classification in four separate classification schema and gene, mutation type or impact on Notch signalling. Utility of current phenotype measurements are limited. The lack of genotype-phenotype correlation in HS is suggestive that the underlying assumption of inherited HS as a monogenic disorder may need revision.
Authors: Sue Richards; Nazneen Aziz; Sherri Bale; David Bick; Soma Das; Julie Gastier-Foster; Wayne W Grody; Madhuri Hegde; Elaine Lyon; Elaine Spector; Karl Voelkerding; Heidi L Rehm Journal: Genet Med Date: 2015-03-05 Impact factor: 8.822
Authors: Ruwan A Weerakkody; Jana Vandrovcova; Christina Kanonidou; Michael Mueller; Piyush Gampawar; Yousef Ibrahim; Penny Norsworthy; Jennifer Biggs; Abdulshakur Abdullah; David Ross; Holly A Black; David Ferguson; Nicholas J Cheshire; Hanadi Kazkaz; Rodney Grahame; Neeti Ghali; Anthony Vandersteen; F Michael Pope; Timothy J Aitman Journal: Genet Med Date: 2016-03-24 Impact factor: 8.822
Authors: Gautham Vellaichamy; Peter Dimitrion; Li Zhou; David Ozog; Henry W Lim; Wilson Liao; Iltefat H Hamzavi; Qing-Sheng Mi Journal: J Invest Dermatol Date: 2021-04-07 Impact factor: 7.590
Authors: Gautham Vellaichamy; Anya T Amin; Peter Dimitrion; Zaakir Hamzavi; Li Zhou; Indra Adrianto; Qing-Sheng Mi Journal: Front Genet Date: 2022-08-26 Impact factor: 4.772