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. 2018 Nov;20(11):1354-1364.
doi: 10.1038/gim.2018.8. Epub 2018 Apr 19.

Analysis of 17 genes detects mutations in 81% of 811 patients with lissencephaly

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Analysis of 17 genes detects mutations in 81% of 811 patients with lissencephaly

Nataliya Di Donato et al. Genet Med. 2018 Nov.

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 of LIS1, DCX, and ARX, 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, and VLDLR) 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 by DCX (23%), TUBA1A (5%), and DYNC1H1 (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.

Keywords: actinopathy; lissencephaly; reelinopathy; subcortical band heterotopia; tubulinopathy.

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Conflict of interest statement

Conflict of interest notification page

None of the authors have any competing interests. The content is solely the responsibility of the authors, and does not necessarily represent the official views of the National Institutes of Health. The funding sources had no role in the design and conduct of the study, collection, management, analysis and interpretation of the data, preparation, review or approval of the manuscript, or decision to submit the manuscript for publication.

Figures

Figure 1
Figure 1
Flow chart describing subject selection for the primary study cohort (blue-green-orange panels), and lissencephaly genes tested and not tested (purple panels).
Figure 2
Figure 2
Number and frequency of mutations detected in lissencephaly (LIS) cohorts. The upper panel (A) shows the number and relative proportion (on a log base 2 scale) of mutations in our LIS cohorts: 5-year Dobyns cohort represents the subset of subjects with LIS recruited in Seattle between 2010 and 2015, 5-year Guerrini cohort represents an independent cohort ascertained at A. Meyer Children’s Hospital in Florence; 30-year Dobyns cohort includes all patients with LIS ascertained in the Ledbetter or Dobyns labs since 1982; the Combined cohort sums the 30-year Dobyns and 5-year Guerrini cohorts. The table below the graph shows the exact number of patients carrying mutations in each gene. The lower panel (B) is a pie chart showing the diagnostic yield per gene in the Combined cohort. *Mutations in TUBB, TUBB3 and VLDLR each accounted for less than one percent of subjects.
Figure 3
Figure 3
Diagnostic algorithm. The upper panel shows a standard algorithm for genetic testing in patients with lissencephaly (LIS). Following initial clinical assessment (red box), a genome-wide chromosome microarray should be ordered to detect CNV (orange box), preferably an array with exon level coverage of most LIS genes. The next step is a targeted sequencing panel (yellow box). We recommend an exome slice approach, as this allows re-analysis for additional genes as new genes are reported or the phenotype of the child evolves to suggest tests for other disorders. The coverage of a standard exome is currently ~50x, sufficient to detect mosaicism with alternate allele fractions down to 20% with high reliability, and down to 10% for some variants. The alternative approach of targeted individual gene sequencing at ~100x or greater will detect lower levels of mosaicism, although levels below 20% are rare with LIS-associated phenotypes. If not done in step 1, duplication-deletion analysis with exon level coverage of LIS genes should be done to detect small intragenic deletions and duplications missed by exome or individual gene sequencing (based on data to date, this is not needed for alpha and beta tubulin genes). With these results in hand, phenotype re-review (green box) is useful to confirm that the phenotype matches any reported mutations, or if negative determine which type of LIS the phenotype best matches. When the child’s condition and/or family’s concerns support further testing, the next step is more complicated and involves either genome-wide testing such as whole exome sequencing, or deep targeted sequencing for low level mosaicism (blue box). A final phenotype review with all test results available is indicated for genotype-phenotype analysis and counseling (purple box). If the disorder appears to be rare or remains unsolved, referral to a research group for other approaches may be useful. The lower panel shows several alternative approaches for testing that experts in LIS may choose to pursue. For example, sequencing may be performed before testing for CNV for tubulinopathies and disorders with autosomal recessive inheritance. For novel phenotypes, CNV testing could be followed directly by whole exome sequencing. Testing for X-linked LIS with abnormal genitalia (XLAG) could begin with single gene sequencing, while mild variants of LIS or subcortical band heterotopia might begin with deep targeted sequencing to be sure to detect mosaicism. Abbreviations: CNV, copy number variants; D, deep targeted sequencing; LIS, lissencephaly; MCD, malformation of cortical development; MOS, mosaicism; R, refer to research program; SEQ, sequencing.

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