Analysis of 17 genes detects mutations in 81% of 811 patients with lissencephaly. (November 2018)

• Record Type: Journal Article
• Authors: Di Donato, Nataliya; Timms, Andrew; Aldinger, Kimberly; Mirzaa, Ghayda; Bennett, James; Collins, Sarah; Olds, Carissa; Mei, Davide; Chiari, Sara; Carvill, Gemma; Myers, Candace; Rivière, Jean-Baptiste; Zaki, Maha; Gleeson, Joseph; Rump, Andreas; Conti, Valerio; Parrini, Elena; Ross, M; Ledbetter, David; Guerrini, Renzo; Dobyns, William
• Is Part Of: Genetics in medicine. Volume 20:Number 11(2018)
• Page Start: 1354
• Page End: 1364
• Publication Date: 2018-11
• Subjects: actinopathy -- lissencephaly -- reelinopathy -- subcortical band heterotopia -- tubulinopathy; Medical genetics -- Periodicals; Genetic disorders -- Periodicals; 616.04205
• DOI: 10.1038/gim.2018.8
• Languages: English
• ISSNs: 1098-3600

Abstract

Abstract Purpose To estimate diagnostic yield and genotype-phenotype correlations in a cohort of 811 patients with lissencephaly or subcortical band heterotopia. Methods We collected DNA from 756 children with lissencephaly over 30 years. Many were tested for deletion 17p13.3 and mutations ofLIS1, DCX, andARX, but few other genes. Among those tested, 216 remained unsolved and were tested by a targeted panel of 17 genes (ACTB, ACTG1, ARX, CRADD, DCX, LIS1, TUBA1A, TUBA8, TUBB2B, TUBB, TUBB3, TUBG1, KIF2A, KIF5C, DYNC1H1, RELN, andVLDLR ) or by whole-exome sequencing. Fifty-five patients studied at another institution were added as a validation cohort. Results The overall mutation frequency in the entire cohort was 81%.LIS1 accounted for 40% of patients, followed byDCX (23%), TUBA1A (5%), andDYNC1H1 (3%). Other genes accounted for 1% or less of patients. Nineteen percent remained unsolved, which suggests that several additional genes remain to be discovered. The majority of unsolved patients had posterior pachygyria, subcortical band heterotopia, or mild frontal pachygyria. Conclusion The brain-imaging pattern correlates with mutations in single lissencephaly-associated genes, as well as in biological pathways. We propose the first LIS classification system based on the underlying molecular mechanisms.