Loss of function, missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts. Issue 2 (4th December 2018)

• Record Type: Journal Article
• Authors: Helle, Emmi; Córdova‐Palomera, Aldo; Ojala, Tiina; Saha, Priyanka; Potiny, Praneetha; Gustafsson, Stefan; Ingelsson, Erik; Bamshad, Michael; Nickerson, Deborah; Chong, Jessica X.; Ashley, Euan; Priest, James R.
• Is Part Of: Genetic epidemiology. Volume 43:Issue 2(2019)
• Page Start: 215
• Page End: 226
• Publication Date: 2018-12-04
• Subjects: congenital heart defects -- left ventricular outflow tract obstruction (LVOTO) -- NOTCH1 -- UK Biobank; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22176
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low‐frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1, 085 cases and 332, 788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European‐ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68, 762 controls of non‐European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population‐based data sets in quantifying the specific risk of individual variants for disease‐related phenotypes.