Abstract
Discovery of most autosomal recessive disease-associated genes has involved analysis of large, often consanguineous multiplex families or small cohorts of unrelated individuals with a well-defined clinical condition. Discovery of new dominant causes of rare, genetically heterogeneous developmental disorders has been revolutionized by exome analysis of large cohorts of phenotypically diverse parent-offspring trios1,2. Here we analyzed 4,125 families with diverse, rare and genetically heterogeneous developmental disorders and identified four new autosomal recessive disorders. These four disorders were identified by integrating Mendelian filtering (selecting probands with rare, biallelic and putatively damaging variants in the same gene) with statistical assessments of (i) the likelihood of sampling the observed genotypes from the general population and (ii) the phenotypic similarity of patients with recessive variants in the same candidate gene. This new paradigm promises to catalyze the discovery of novel recessive disorders, especially those with less consistent or nonspecific clinical presentations and those caused predominantly by compound heterozygous genotypes.
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Acknowledgements
We thank the families for their participation and patience. We are grateful to the Exome Aggregation Consortium for making their data available. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the UK Department of Health, and the Wellcome Trust Sanger Institute (grant WT098051). The views expressed in this publication are those of the author(s) and not necessarily those of the Wellcome Trust or the UK Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South Research Ethics Committee and GEN/284/12, granted by the Republic of Ireland Research Ethics Committee). The research team acknowledges the support of the National Institutes for Health Research, through the Comprehensive Clinical Research Network. The authors wish to thank the Sanger Mouse Genetics Project for generating and providing mouse phenotyping information, N. Karp for statistical input on the mouse data and V. Narasimhan for making the bcftools roh algorithm available. D.R.F. is funded through an MRC Human Genetics Unit program grant to the University of Edinburgh. Work on the Mmp21-mutant mouse models was supported by US National Institutes of Health grant U01-HL098180 to C.W.L. V.P. was funded by a fellowship from the DFG German Research Foundation.
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N.A., J.M., S.C., T.W.F., W.D.J., D.K., J.L., A. Sifrim, J.C.B., D.R.F. and M.E.H. developed analytical methods and/or analyzed human genotype and phenotype data. M. Blyth, A.F.B., M. Balasubramanian, T.C., C.D., N.F., J.G., E.H., S.J., A.K., M.L., M.O'R., D.O., E.R., A. Smith, P.T. and J.W. phenotyped patients. R.F., G.G., S.S.G., N.K., C.L., V.P. and C.W.L. generated and analyzed model organism data. M.A., D.M., E.P. and D.R. performed validation experiments. G.J.S. performed protein structure analysis. C.F.W., H.V.F., J.C.B., D.R.F. and M.E.H. supervised the experimental and analytical work. M.E.H., D.R.F., N.A., J.M. and C.W.L. wrote the manuscript. D.R.F. and M.E.H. jointly supervised the project.
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M.E.H. is a consultant for and shareholder in Congenica, Ltd, which provides genetic diagnostic services.
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A full list of members appears in the Supplementary Note.
Integrated supplementary information
Supplementary Figure 1 Singleton ratio for last G in exon compared to LOF and missense variants.
Comparing the proportion of variants belonging to different functional classes that are singletons among the DDD parents.
Supplementary Figure 2 Comparison of cumulative allele frequencies for loss-of-function and functional variants in DDD versus ExAC.
The cumulative allele frequencies per gene obtained from unaffected DDD parents of European ancestry are on the x axis (log scaled), and the cumulative allele frequencies from the NFE subset of the ExAC data set are on the y axis (log scaled). The dashed line identifies equivalence for DDD and ExAC derived cumulative allele frequencies.
Supplementary Figure 3 Rates of autozygosity per gene within the probands
The distribution of the proportion of probands autozygous across a given gene is shown. The grey vertical lines show the autozygosity proportions for the genes with P < 5 × 10-4, which fall in the bulk of distribution. The X-axis uses a logarithmic scale.
Supplementary Figure 4 QQ plot from testing genes for enrichment of families with rare biallelic synonymous variants.
Comparison of QQ plots from four different tests of genotype enrichment for rare, biallelicly inherited synonymous genotypes. Black dots show the QQ plot from testing for enrichment using cumulative allele frequencies from the NFE subset of ExAC (shaded black), without correcting for population structure or autozygosity. Red dots show the QQ plot after correcting for population structure within the DDD probands by considering all possible combinations of counts among the four ancestral continental populations defined in ExAC. Blue dots show a QQ plot after correcting for gene-specific autozygosity in the probands. Gray dots show a QQ plot after correcting for both population structure and autozygosity; this plot is the closest match to the null expectation (shown as a dashed red line).
Supplementary Figure 5 QQ plot from testing the similarity of Human Phenotype Ontology terms among probands.
Comparison of QQ plots from testing the similarity of HPO terms within a test data set. The figure includes QQ plots from testing probands in genes with recurrent de novo variants (standard) and a distribution from testing the same genes but with randomly sampled individuals (permuted). The permuted genes matched proband numbers to the number of recurrent de novo variants in each gene. The minimum P value for these tests is limited to 1 × 10−5, which is determined by the number of iterations (100,000) used to test the similarity of HPO terms.
Supplementary Figure 6 Protein modeling of missense variants in PRMT7.
(a) The overall structure of the human PRMT7 model was based on mouse Prmt7 (PDB ID 4C4A). The protein contains two catalytic modules (shown by a horizontal line), each containing an AdoMet-binding domain (orange), a C-terminal β barrel (red) and a dimerization domain (blue). Residues that are mutated in DDD patients are shown in magenta. The donor homolog S-adenosyl homocysteine (SAH) was bound to the mouse structure and is also shown here. (b) The predicted consequences of the missense mutations detected in this study on PRMT7 protein structure and function. Arg32Thr. Arg32 sits at the entrance of the N-terminal catalytic module (orange and red) but mainly interacts with residues from the C-terminal catalytic module (blue, raspberry and olive). R32T mutation results in the loss of a hydrogen bond and changes the nature of the donor-binding pocket. Arg387Gly. E478Q-mutated proteins have less than 0.1% the activity of the native protein (Acta Crystallogr. D Biol. Crystallogr. 70, 2401–2412, 2014). This is attributed to loss of the critical hydrogen bonds that this residue makes with Arg387 and Arg378. Mutation R387G will result in the loss of these hydrogen bonds, which will likely affect the activity of the protein. Trp494Arg. Trp494 sits on an α helix that forms part of the predicted donor-binding site of the C-terminal catalytic module surrounded by hydrophobic residues. The W494R mutation is predicted to cause conformational change around this region and lead to changes in the donor-binding pocket.
Supplementary Figure 7 Skeletal phenotyping of 10-d-old PRMT7 loss-of-function mice: reduced body size, bone defects and reduced digit length.
Prmt7tm1a/tm1a mice have severely reduced body size (a,b,h) and weight (g) compared to control littermates. Prmt7tm1a/wt mice are not affected and resemble wild-type littermates (data not shown). Skeletal staining of Prmt7wt/wt and Prmt7tm1a/tm1a mice was examined at P10 for full skeleton (b), hind paws (c), forelimbs (d), skull (e) and rib cage (f). Mice display growth retardation (b–f) and brachydactyly of the fifth metatarsal bone (c,i). The null mice present a duplication of the first rib (f; 100% of Prmt7tm1a/tm1a mice at P10, n = 5). Although all digit lengths were reduced in the mutant mice, when normalized to overall body size reduction (using radius length as a proxy), only the fifth metatarsal shows statistically significant disproportional reduction in length compared to controls (i). Metacarpal bones normalized to radius were not significantly reduced in length in Prmt7tm1a/tm1a mice at this stage (P10; data not shown). Scale bars in b–f indicate 5 mm.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–7, Supplementary Tables 1, 2 and 4, and Supplementary Note. (PDF 1748 kb)
Supplementary Table 3
Clinical data for four novel recessive genes. (XLSX 65 kb)
Ciliary motility in the Mmp21 Miri mutant.
Videomicroscopy of the embryonic node from an Mmp21 Miri mutant shows robust ciliary motility and leftward fluid flow similar to that seen in the embryonic node of a wild-type littermate control. Flow videos are shown at 200% the speed of real time to facilitate the visualization of bead movement, while cilia motion videos are shown at 15% the speed of real time to allow for better visualization of nodal ciliary motion. (MOV 2845 kb)
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Akawi, N., McRae, J., Ansari, M. et al. Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families. Nat Genet 47, 1363–1369 (2015). https://doi.org/10.1038/ng.3410
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DOI: https://doi.org/10.1038/ng.3410
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