| Literature DB >> 33513338 |
Joery den Hoed1, Elke de Boer2, Norine Voisin3, Alexander J M Dingemans2, Nicolas Guex4, Laurens Wiel5, Christoffer Nellaker6, Shivarajan M Amudhavalli7, Siddharth Banka8, Frederique S Bena9, Bruria Ben-Zeev10, Vincent R Bonagura11, Ange-Line Bruel12, Theresa Brunet13, Han G Brunner14, Hui B Chew15, Jacqueline Chrast3, Loreta Cimbalistienė16, Hilary Coon17, Emmanuèlle C Délot18, Florence Démurger19, Anne-Sophie Denommé-Pichon12, Christel Depienne20, Dian Donnai8, David A Dyment21, Orly Elpeleg22, Laurence Faivre23, Christian Gilissen24, Leslie Granger25, Benjamin Haber26, Yasuo Hachiya27, Yasmin Hamzavi Abedi28, Jennifer Hanebeck26, Jayne Y Hehir-Kwa29, Brooke Horist30, Toshiyuki Itai31, Adam Jackson32, Rosalyn Jewell33, Kelly L Jones34, Shelagh Joss35, Hirofumi Kashii27, Mitsuhiro Kato36, Anja A Kattentidt-Mouravieva37, Fernando Kok38, Urania Kotzaeridou26, Vidya Krishnamurthy30, Vaidutis Kučinskas16, Alma Kuechler20, Alinoë Lavillaureix39, Pengfei Liu40, Linda Manwaring41, Naomichi Matsumoto31, Benoît Mazel42, Kirsty McWalter43, Vardiella Meiner22, Mohamad A Mikati44, Satoko Miyatake31, Takeshi Mizuguchi31, Lip H Moey45, Shehla Mohammed46, Hagar Mor-Shaked22, Hayley Mountford47, Ruth Newbury-Ecob48, Sylvie Odent39, Laura Orec26, Matthew Osmond21, Timothy B Palculict43, Michael Parker49, Andrea K Petersen25, Rolph Pfundt50, Eglė Preikšaitienė16, Kelly Radtke51, Emmanuelle Ranza52, Jill A Rosenfeld53, Teresa Santiago-Sim43, Caitlin Schwager7, Margje Sinnema54, Lot Snijders Blok55, Rebecca C Spillmann56, Alexander P A Stegmann57, Isabelle Thiffault58, Linh Tran44, Adi Vaknin-Dembinsky59, Juliana H Vedovato-Dos-Santos60, Samantha A Schrier Vergano61, Eric Vilain18, Antonio Vitobello12, Matias Wagner62, Androu Waheeb63, Marcia Willing41, Britton Zuccarelli64, Usha Kini65, Dianne F Newbury47, Tjitske Kleefstra2, Alexandre Reymond3, Simon E Fisher66, Lisenka E L M Vissers2.
Abstract
Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation of 42 individuals carrying SATB1 variants identified overt genotype-phenotype relationships, associated with different pathophysiological mechanisms, established by functional assays. Missense variants in the CUT1 and CUT2 DNA-binding domains result in stronger chromatin binding, increased transcriptional repression, and a severe phenotype. In contrast, variants predicted to result in haploinsufficiency are associated with a milder clinical presentation. A similarly mild phenotype is observed for individuals with premature protein truncating variants that escape nonsense-mediated decay, which are transcriptionally active but mislocalized in the cell. Our results suggest that in-depth mutation-specific genotype-phenotype studies are essential to capture full disease complexity and to explain phenotypic variability.Entities:
Keywords: HPO-based analysis; SATB1; cell-based functional assays; de novo variants; intellectual disability; neurodevelopmental disorders; seizures; teeth abnormalities
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Year: 2021 PMID: 33513338 PMCID: PMC7895900 DOI: 10.1016/j.ajhg.2021.01.007
Source DB: PubMed Journal: Am J Hum Genet ISSN: 0002-9297 Impact factor: 11.025