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. 2015 Mar 12;519(7542):223-8.
doi: 10.1038/nature14135. Epub 2014 Dec 24.

Large-scale discovery of novel genetic causes of developmental disorders

Deciphering Developmental Disorders Study
Collaborators

Large-scale discovery of novel genetic causes of developmental disorders

Deciphering Developmental Disorders Study. Nature. .

Abstract

Despite three decades of successful, predominantly phenotype-driven discovery of the genetic causes of monogenic disorders, up to half of children with severe developmental disorders of probable genetic origin remain without a genetic diagnosis. Particularly challenging are those disorders rare enough to have eluded recognition as a discrete clinical entity, those with highly variable clinical manifestations, and those that are difficult to distinguish from other, very similar, disorders. Here we demonstrate the power of using an unbiased genotype-driven approach to identify subsets of patients with similar disorders. By studying 1,133 children with severe, undiagnosed developmental disorders, and their parents, using a combination of exome sequencing and array-based detection of chromosomal rearrangements, we discovered 12 novel genes associated with developmental disorders. These newly implicated genes increase by 10% (from 28% to 31%) the proportion of children that could be diagnosed. Clustering of missense mutations in six of these newly implicated genes suggests that normal development is being perturbed by an activating or dominant-negative mechanism. Our findings demonstrate the value of adopting a comprehensive strategy, both genome-wide and nationwide, to elucidate the underlying causes of rare genetic disorders.

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Figures

EDF1
EDF1. Characteristics of the families
A. Gestation Adjusted Decimal Age at Last Clinical Assessment. Histogram showing the distribution of the gestation adjusted decimal age at last clinical assessment across the 1133 probands. The dashed red line shows the median age. B. Frequency of HPO Term Usage. Bar plot showing, for each used HPO term, the number of times it was observed across the 1133 proband patient records. C. Projection PCA plot of the 1133 probands. PCA plot of 1133 DDD probands projected onto a PCA analysis using 4 different HapMap populations from the 1000 genomes project. Black: African, Red: European, Green: East Asian, Blue: South Asian and the 1133 DDD probands are represented by orange triangles. D. Self Declared and Genetically Defined Consanguinity. Overlaid histogram showing the distribution of kinship coefficients from KING comparing parental samples for each trio. Green: Trios where consanguinity was not entered in the patient record on DECIPHER. Red: Trios consanguinity was declared in the patient record on DECIPHER.
EDF2
EDF2. Number of Validated de novo SNVs and indels per Proband
Bar plot showing the distribution of the observed number of validated SNVs and indels per proband sample, and the expected distribution assuming a Poisson distribution with the same mean as the observed distribution.
EDF3
EDF3. Number of Diagnoses per Gene
Histogram showing the number of diagnoses per gene for genes with at least two diagnoses from different proband samples.
EDF4
EDF4. Burden of Large CNVs in 1133 DDD Proband Samples
Plot comparing the frequency of rare CNVs in three sample groups against CNV size. Y-axis is the on a log scale. Red: DDD probands who have not had previous microarray based genetic testing, Purple: DDD probands who have had negative previous microarray based genetic testing Green: DDD controls.
EDF5
EDF5. Expected and observed numbers of de novo mutations
The expected and observed numbers of mutations of different functional consequences in three mutually exclusive sets of genes are shown, along with the p value from an assessment of a statistical excess of observed mutations. The three classes of genes are described in the main text.
EDF6
EDF6. Haploinsufficiency analyses
A. Saturation analysis for detecting haploinsufficient DD genes. A boxplot showing the distribution of statistical power to detect a significant enrichment of LoF mutations across 18,272 genes in the genome, for different numbers of trios studied, from 1,000 trios to 12,000 trios. B. Distribution of haplinsufficiency scores in selected sets of de novo mutations. Violin plot of haploinsufficiency scores in five sets of de novo mutations: Silent - all synonymous mutations, Diagnostic - mutations in known DD genes in diagnosed individuals, Undiagnosed_Func - all functional mutations in undiagnosed individuals, Undiagnosed_LoF - All LoF mutations in undiagnosed individuals, Undiagnosed_recur - mutations in genes with recurrent functional mutations in undiagnosed individuals. P values for a Mann-Whitney test comparing each of the latter four distributions to that observed for the silent (synonymous) variants are plotted at the top of each violin.
Figure 1
Figure 1. Excess of recurrently mutated genes
Each panel shows the observed number of recurrently mutated genes (diamond) and the distribution of the number of recurrently mutated genes in 10,000 simulations (box indicates interquartile range, whiskers indicates 95% confidence interval) under a model of no gene-specific enrichment of mutations: a. all protein-altering mutations in all DDD children and undiagnosed DDD children, b. all LoF mutations in all DDD children and undiagnosed DDD children. Each diamond is annotated with the median excess of recurrently mutated genes, with 95% confidence intervals in brackets. P value of observed excess is <0.0001 for all four tests.
Figure 2
Figure 2. Gene-specific significance of enrichment for DNMs
The –log10(p) value of testing for mutation enrichment is plotted only for each gene with at least one mutation in DDD children. On the X-axis is the p value of the most significant test in the DDD dataset, and on the Y-axis is the minimal p value from the significance testing in the meta-analysis dataset. Red indicates genes already known to be associated with DDs (in DDG2P). Only genes with a p value of less than 0.05/18,272 (red lines) are labeled.
Figure 3
Figure 3. Five novel genes with clustered mutations
The domains (blue), post-translational modifications, and mutation locations (red stars) are shown for five proteins with highly clustered de novo mutations in unrelated children with severe, undiagnosed DDs. For two proteins (COL4A3BP and PCGF2) where all observed mutations are identical, photos are shown to highlight the facial similarities of patients carrying the same mutation.
Figure 4
Figure 4. Candidate gene Loss of Function modeling in zebrafish reveals enrichment for developmentally important proteins
a, Examples of developmental phenotypes: Knockdown of pkn2a results in reduced cartilaginous jaw structures (black arrows), knockdown of fryl results in cardiac and craniofacial defects (white arrowheads and arrows, respectively), while knockdown of psmd3 results in smaller ear primordia (red arrows), and mis-patterned CNS neurons (compare red double arrows and brackets). b, Knockdown outcomes of 7 genes with variants present in microcephaly patients: Interocular measurements of brightfield images from control and LoF embryos reveal significant decreases in head size. A neuronal antibody stain (anti-HuC/D, green channel) labels the brains of control and morphant zebrafish. Measurements taken across the widest extent of the midbrain identify significant reductions in brain size, likely underlying the concomitant head size reductions seen in brightfield. In b, tables show average percentage reduction in head and brain width, and p-values of a t-test.
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