scholarly journals De novo mutations in regulatory elements in neurodevelopmental disorders

Nature ◽  
2018 ◽  
Vol 555 (7698) ◽  
pp. 611-616 ◽  
Author(s):  
Patrick J. Short ◽  
Jeremy F. McRae ◽  
Giuseppe Gallone ◽  
Alejandro Sifrim ◽  
Hyejung Won ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
De Novo ◽  
Regulatory Elements ◽  
De Novo Mutations

scholarly journals De novo mutations in regulatory elements cause neurodevelopmental disorders

2017 ◽  
Author(s):  
Patrick J. Short ◽  
Jeremy F. McRae ◽  
Giuseppe Gallone ◽  
Alejandro Sifrim ◽  
Hyejung Won ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Developmental Disorders ◽  
De Novo ◽  
Genetic Disorders ◽  
Fetal Brain ◽  
Regulatory Elements ◽  
De Novo Mutations ◽  
Active Elements ◽  
Diagnostic Coding ◽  
Genome Wide

SummaryDe novo mutations in hundreds of different genes collectively cause 25-42% of severe developmental disorders (DD). The cause in the remaining cases is largely unknown. The role of de novo mutations in regulatory elements affecting known DD associated genes or other genes is essentially unexplored. We identified de novo mutations in three classes of putative regulatory elements in almost 8,000 DD patients. Here we show that de novo mutations in highly conserved fetal-brain active elements are significantly and specifically enriched in neurodevelopmental disorders. We identified a significant two-fold enrichment of recurrently mutated elements. We estimate that, genome-wide, de novo mutations in fetaLbrain active elements are likely to be causal for 1-3% of patients without a diagnostic coding variant and that only a small fraction (<2%) of de novo mutations in these elements are pathogenic. Our findings represent a robust estimate of the contribution of de novo mutations in regulatory elements to this genetically heterogeneous set of disorders, and emphasise the importance of combining functional and evolutionary evidence to delineate regulatory causes of genetic disorders.

scholarly journals Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tianyun Wang ◽  
◽  
Kendra Hoekzema ◽  
Davide Vecchio ◽  
Huidan Wu ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Large Scale ◽  
De Novo ◽  
Targeted Sequencing ◽  
Published Data ◽  
De Novo Mutations ◽  
Risk Genes ◽  
Mutation Burden ◽  
Number Of Patients ◽  
Significant Burden

Abstract Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E−07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.

scholarly journals Noncoding de novo mutations contribute to autism spectrum disorder via chromatin interactions

2019 ◽  
Author(s):  
Il Bin Kim ◽  
Taeyeop Lee ◽  
Junehawk Lee ◽  
Jonghun Kim ◽  
Hyunseong Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Stem Cells ◽  
Target Genes ◽  
De Novo ◽  
Regulatory Elements ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
De Novo Mutations ◽  
Chromatin Interactions

Three-dimensional chromatin structures regulate gene expression across genome. The significance of de novo mutations (DNMs) affecting chromatin interactions in autism spectrum disorder (ASD) remains poorly understood. We generated 931 whole-genome sequences for Korean simplex families to detect DNMs and identified target genes dysregulated by noncoding DNMs via long-range chromatin interactions between regulatory elements. Notably, noncoding DNMs that affect chromatin interactions exhibited transcriptional dysregulation implicated in ASD risks. Correspondingly, target genes were significantly involved in histone modification, prenatal brain development, and pregnancy. Both noncoding and coding DNMs collectively contributed to low IQ in ASD. Indeed, noncoding DNMs resulted in alterations, via chromatin interactions, in target gene expression in primitive neural stem cells derived from human induced pluripotent stem cells from an ASD subject. The emerging neurodevelopmental genes, not previously implicated in ASD, include CTNNA2, GRB10, IKZF1, PDE3B, and BACE1. Our results were reproducible in 517 probands from MSSNG cohort. This work demonstrates that noncoding DNMs contribute to ASD via chromatin interactions.

scholarly journals Love the one you’re with: replicate viral adaptations converge on the same phenotypic change

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2227 ◽  
Author(s):  
Craig R. Miller ◽  
Anna C. Nagel ◽  
LuAnn Scott ◽  
Matt Settles ◽  
Paul Joyce ◽  
...  
Keyword(s):  
De Novo ◽  
Regulatory Elements ◽  
Host Cells ◽  
Phenotypic Change ◽  
De Novo Mutations ◽  
Promoter Mutation ◽  
Genetic Changes ◽  
Evolutionary Forces ◽  
Parallel Change ◽  
The One

Parallelism is important because it reveals how inherently stochastic adaptation is. Even as we come to better understand evolutionary forces, stochasticity limits how well we can predict evolutionary outcomes. Here we sought to quantify parallelism and some of its underlying causes by adapting a bacteriophage (ID11) with nine different first-step mutations, each with eight-fold replication, for 100 passages. This was followed by whole-genome sequencing five isolates from each endpoint. A large amount of variation arose—281 mutational events occurred representing 112 unique mutations. At least 41% of the mutations and 77% of the events were adaptive. Within wells, populations generally experienced complex interference dynamics. The genome locations and counts of mutations were highly uneven: mutations were concentrated in two regulatory elements and three genes and, while 103 of the 112 (92%) of the mutations were observed in ≤4 wells, a few mutations arose many times. 91% of the wells and 81% of the isolates had a mutation in the D-promoter. Parallelism was moderate compared to previous experiments with this system. On average, wells shared 27% of their mutations at the DNA level and 38% when the definition of parallel change is expanded to include the same regulatory feature or residue. About half of the parallelism came from D-promoter mutations. Background had a small but significant effect on parallelism. Similarly, an analyses of epistasis between mutations and their ancestral background was significant, but the result was mostly driven by four individual mutations. A second analysis of epistasis focused on de novo mutations revealed that no isolate ever had more than one D-promoter mutation and that 56 of the 65 isolates lacking a D-promoter mutation had a mutation in genes D and/or E. We assayed time to lysis in four of these mutually exclusive mutations (the two most frequent D-promoter and two in gene D) across four genetic backgrounds. In all cases lysis was delayed. We postulate that because host cells were generally rare (i.e., high multiplicity of infection conditions developed), selection favored phage that delayed lysis to better exploit their current host (i.e., ‘love the one you’re with’). Thus, the vast majority of wells (at least 64 of 68, or 94%) arrived at the same phenotypic solution, but through a variety of genetic changes. We conclude that answering questions about the range of possible adaptive trajectories, parallelism, and the predictability of evolution requires attention to the many biological levels where the process of adaptation plays out.

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