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. 2017 Apr;49(4):515-526.
doi: 10.1038/ng.3792. Epub 2017 Feb 13.

Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases

Holly A F Stessman  1 , Bo Xiong  1   2 , Bradley P Coe  1 , Tianyun Wang  3 , Kendra Hoekzema  1 , Michaela Fenckova  4   5 , Malin Kvarnung  6   7 , Jennifer Gerdts  8 , Sandy Trinh  8 , Nele Cosemans  9 , Laura Vives  1 , Janice Lin  1 , Tychele N Turner  1 , Gijs Santen  10 , Claudia Ruivenkamp  10 , Marjolein Kriek  10 , Arie van Haeringen  10 , Emmelien Aten  10 , Kathryn Friend  11   12 , Jan Liebelt  13 , Christopher Barnett  13 , Eric Haan  11   13 , Marie Shaw  11 , Jozef Gecz  11   12   14 , Britt-Marie Anderlid  6   7 , Ann Nordgren  6   7 , Anna Lindstrand  6   7 , Charles Schwartz  15 , R Frank Kooy  16 , Geert Vandeweyer  16 , Celine Helsmoortel  16 , Corrado Romano  17 , Antonino Alberti  17 , Mirella Vinci  18 , Emanuela Avola  17 , Stefania Giusto  19 , Eric Courchesne  20 , Tiziano Pramparo  20 , Karen Pierce  20 , Srinivasa Nalabolu  20 , David G Amaral  21 , Ingrid E Scheffer  22   23   24 , Martin B Delatycki  22   25   26 , Paul J Lockhart  22   26 , Fereydoun Hormozdiari  27 , Benjamin Harich  4   5 , Anna Castells-Nobau  4   5 , Kun Xia  3 , Hilde Peeters  9 , Magnus Nordenskjöld  6   7 , Annette Schenck  4   5 , Raphael A Bernier  8 , Evan E Eichler  1   28
Affiliations

Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases

Holly A F Stessman et al. Nat Genet. 2017 Apr.

Abstract

Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most of the related pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 cases and >2,867 controls. We identified 91 genes, including 38 new NDD genes, with an excess of de novo mutations or private disruptive mutations in 5.7% of cases. Drosophila functional assays revealed a subset with increased involvement in NDDs. We identified 25 genes showing a bias for autism versus intellectual disability and highlighted a network associated with high-functioning autism (full-scale IQ >100). Clinical follow-up for NAA15, KMT5B, and ASH1L highlighted new syndromic and nonsyndromic forms of disease.

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Conflict of interest statement

CONFLICT OF INTERESTS

E.E.E. is on the scientific advisory board (SAB) of DNAnexus, Inc. and was an SAB member of Pacific Biosciences, Inc. (2009–2013) and SynapDx Corp. (2011–2013); E.E.E. is a consultant for Kunming University of Science and Technology (KUST) as part of the 1000 China Talent Program.

Figures

Figure 1
Figure 1. ASID patient network
13,475 probands with a primary diagnosis of ASD, ID, or DD collected from 15 international groups were screened using smMIPs. Circle size corresponds to the number of samples screened for each cohort. Cohort numbers (1–15) correspond to Supplementary Table 8.
Figure 2
Figure 2. Targeted sequencing highlights genes that reach significance for DN mutations and private disruptive variant burden
(a–c) Quantile-quantile plots comparing the probability (FDR-corrected, inverse log transformed) of recurrent DN mutation for individual genes among proband samples compared to a uniform distribution given the number of genes tested (dashed gray line = significance threshold). Black dashed box (panels (a) and (b)) are zoomed in (panels (b) and (c), respectively). *Genes that reached significance for mutation burden. (d–e) Scatterplots depict the odds ratio (OR) for private variants compared to unaffected controls from ExAC (y-axis) versus the FDR-corrected DN p-value (x-axis; values have been inverse log transformed for plotting) by gene. Gray lines indicate the significance threshold for the DN p-value (horizontal) and an OR of two (vertical). Genes are classified as DN significant and OR > 2 (red dots), OR > 2 only (orange), and those that show a significant DN p-value only (blue). Gene name labels indicate a significant burden (FDR q < 0.1, simulation test) of either private LGD (d) or MIS30 (e) mutations in probands (Table 2; Methods). *Genes in which no control counts were observed where the 95% lower confidence bound was used as the most conservative OR estimate. See Supplementary Table 14 for underlying data.
Figure 3
Figure 3. Protein location of private disruptive variants in new NDD candidate risk genes
(a–c) Protein diagrams of (a) NAA15, (b) KMT5B, and (c) ASH1L with novel private LGD and MIS30 mutations identified in this study and published DN variants indicated in HGVS format. Annotated protein domains are shown (colored blocks) for the largest protein isoforms. Previously published DN variants (below protein structure, Supplementary Table 2) are compared to new variants in this study (above). Variants above the dashed line are of unknown inheritance; variants below the line have been validated for inheritance. Domain abbreviations: NARP1, NMDA receptor-regulated protein 1; CC, coiled coil; TRP, tetratrico peptide repeat region; PHD, plant homeodomain.
Figure 4
Figure 4. ASD versus ID genes
(a) Probands were categorized based on primary ascertainment either ASD or ID (including DD) and the combined number of LGD and MIS30 events per gene (published and this study) shown. Genes were tested for a bias to one phenotype (ASD or ID) by two one-tailed binomial tests (p < 0.025 for either bias). The solid line indicates equal proportions of mutations corrected for the screened population size. Significantly biased genes (red) are indicated with respect to the threshold (dashed line) and insignificant genes (blue). Darker shades of red or blue indicate multiple genes. (b) Scatterplot shows a negative correlation (Pearson’s correlation) between ASD and ID diagnosis by gene (Table 3). (c) Bar graph compares phenotypic features of patients where genes are associated primarily with ASD diagnosis (>95%, black bars) compared to all other genes (gray bars) in Table 3. Significance was calculated by Fisher’s two-tailed exact test, and p-values were FDR corrected. Exact p values: seizures (p = 1.20x10−4), congenital abnormalities (p = 1.88x10−2), microcephaly (p = 1.79x10−7), macrocephaly (p = 5.25x10−3), males (p = 1.65x10−4). *p < 0.05, **p < 0.001, ***p < 0.0001. (d) SSC probands with ASD and an FSIQ > 100 were selected for pathway enrichment. Node size indicates the mutation score (calculated by MAGI based upon the number of DN mutations), and the color of the node indicates the number of DN LGD (red) and DN missense (no CADD cut-off; blue) mutations have been observed in affected probands, respectively. For SPEN, 2 LGD and 1 missense mutation have been observed and for RANBP2, 1 LGD and 1 missense mutation. White nodes indicate no DN mutations have been observed. Gray lines connect genes with both protein-protein interactions and brain co-expression (Pearson’s correlation coefficient r2 > 0.37, Methods). Thicker lines correspond to more highly co-expressed gene pairs.
Figure 5
Figure 5. Habituation deficits in Drosophila knockdown models
(a–b) Representative jump response curves for (a) hmt4-20 (ortholog of KMT5B) and (b) bchs (ortholog of WDFY3) panneuronal knockdown flies. The ratios of flies that responded to light-off stimuli are plotted over 100 trials (64 individual flies were tested for each genotype). Controls are plotted in blue and knockdowns are plotted in red. (c) Distribution of trials to no-jump criterion (TTC, Methods) of knockdowns versus corresponding control flies are plotted (cross, mean; middle line, median; box boundaries, upper and lower quartile; end of whiskers, maximum and minimum; dots, outliers). * p < 0.05, ** p < 0.01, *** p < 0.001 (linear regression model; 64 flies tested for each genotype; exact p values in Supplementary Table 23).
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