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Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders.

Andrews T, Meader S, Vulto-van Silfhout A, Taylor A, Steinberg J, Hehir-Kwa J, Pfundt R, de Leeuw N, de Vries BB, Webber C - PLoS Genet. (2015)

Bottom Line: Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups.We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype.Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

View Article: PubMed Central - PubMed

Affiliation: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

ABSTRACT
Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

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Functional genomics enrichments significantly enriched in genes affected by de novo CNVs in 33 patients presenting with seizures.(A) Significant functional genomics enrichments. Many of these functions have links to seizures or associated phenomena (synaptic deficits, receptor signaling, gustatory aura[73]) but also to regions prone to copy number variation[74]. (B) Genes disrupted by short CNVs in patients were also observed to cluster significantly in a brain-specific gene co-expression network. Here we display the strongest clusters (r > 0.92 for all co-expression similarities) of genes from seizure patients from this network. (C) Overall, the functional enrichments identified known (HPO-defined) seizure genes for 11 of the 33 patients, and proposed causal genes for 21 of the remaining 22 patients.
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pgen.1005012.g001: Functional genomics enrichments significantly enriched in genes affected by de novo CNVs in 33 patients presenting with seizures.(A) Significant functional genomics enrichments. Many of these functions have links to seizures or associated phenomena (synaptic deficits, receptor signaling, gustatory aura[73]) but also to regions prone to copy number variation[74]. (B) Genes disrupted by short CNVs in patients were also observed to cluster significantly in a brain-specific gene co-expression network. Here we display the strongest clusters (r > 0.92 for all co-expression similarities) of genes from seizure patients from this network. (C) Overall, the functional enrichments identified known (HPO-defined) seizure genes for 11 of the 33 patients, and proposed causal genes for 21 of the remaining 22 patients.

Mentions: Each of these functional association approaches was applied to each of 408 sets of genes disrupted by CNVs in patients presenting a particular phenotype (patient-phenotype groups). Genes variant in only two patient-phenotype groups were found to possess significant functional associations using all four of the methods applied, namely HP:0001250 (Seizures) (Fig. 1) and HP:0010864 (Intellectual disability, Severe). Significant enrichments using three of the methods were observed in a further 64 patient-phenotype groups, enrichments for two methods for 120 patient-phenotype groups, and for just one method in a further 143 groups. Of the four methods employed, enrichments of phenotypes from mouse-orthologue knockouts (MGI) gave the least number of significant results, identifying functional association among affected genes in only 12 phenotype groups including HP:0001250 (Seizures) and HP:0000717 (Autism) (S1 Table). While the MGI method identified fewest associations, the enriched terms were the most relevant to the particular HPO phenotypes. For example, for patients with Seizures we saw an enrichment of genes whose mouse orthologue knockouts present with, amongst others (Fig. 1), Absence seizures (MP:0003216; 6.2-fold enrichment; p = 3 x 10–4). Similarly, in patients with HP:0010864 (Intellectual Disability, Severe) we see an enrichment of mouse synaptic phenotypes such as Abnormal synaptic transmission (MP:0003635; 3.3-fold enrichment; p = 2.0x10–5). This was in contrast with the results for Intellectual Disability, mild (HP:0001256) which was significantly enriched in learning phenotypes in mouse, such as abnormal associative learning (MP:0002062; p = 3.6x10–6). When these two subsets of patients were combined under the more general term of Intellectual Disability (HP:0001249) no mouse phenotypes were significantly associated but many signalling pathways described in KEGG were significantly enriched among the patients, including the MAPK signalling pathway, and Neurotrophin signalling pathway. Other methods provided a larger number of significant results, with enrichments of GO terms found for each of 121 human phenotypes; however these enrichments were generally smaller, and involve less specific categories than those observed using mouse phenotypes (S1 and S2Tables, S1 Fig.). Additionally, affected genes within each of 189 phenotype groups were associated with particular KEGG pathways (S3 Table), while genes affected within each of 262 phenotype groups showed similarities in their brain spatiotemporal expression patterns, clustering significantly in the BrainSpan expression network (S4 Table). For 186 of 408 patient-phenotype groups, we found functional associations using multiple methods, and of these 177 (95%) phenotype groups identified the same genes using multiple methods, with the number of genes repeatedly identified for these groups ranging from 1 to 355 (Fig. 2, S5 Table).


Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders.

Andrews T, Meader S, Vulto-van Silfhout A, Taylor A, Steinberg J, Hehir-Kwa J, Pfundt R, de Leeuw N, de Vries BB, Webber C - PLoS Genet. (2015)

Functional genomics enrichments significantly enriched in genes affected by de novo CNVs in 33 patients presenting with seizures.(A) Significant functional genomics enrichments. Many of these functions have links to seizures or associated phenomena (synaptic deficits, receptor signaling, gustatory aura[73]) but also to regions prone to copy number variation[74]. (B) Genes disrupted by short CNVs in patients were also observed to cluster significantly in a brain-specific gene co-expression network. Here we display the strongest clusters (r > 0.92 for all co-expression similarities) of genes from seizure patients from this network. (C) Overall, the functional enrichments identified known (HPO-defined) seizure genes for 11 of the 33 patients, and proposed causal genes for 21 of the remaining 22 patients.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4362763&req=5

pgen.1005012.g001: Functional genomics enrichments significantly enriched in genes affected by de novo CNVs in 33 patients presenting with seizures.(A) Significant functional genomics enrichments. Many of these functions have links to seizures or associated phenomena (synaptic deficits, receptor signaling, gustatory aura[73]) but also to regions prone to copy number variation[74]. (B) Genes disrupted by short CNVs in patients were also observed to cluster significantly in a brain-specific gene co-expression network. Here we display the strongest clusters (r > 0.92 for all co-expression similarities) of genes from seizure patients from this network. (C) Overall, the functional enrichments identified known (HPO-defined) seizure genes for 11 of the 33 patients, and proposed causal genes for 21 of the remaining 22 patients.
Mentions: Each of these functional association approaches was applied to each of 408 sets of genes disrupted by CNVs in patients presenting a particular phenotype (patient-phenotype groups). Genes variant in only two patient-phenotype groups were found to possess significant functional associations using all four of the methods applied, namely HP:0001250 (Seizures) (Fig. 1) and HP:0010864 (Intellectual disability, Severe). Significant enrichments using three of the methods were observed in a further 64 patient-phenotype groups, enrichments for two methods for 120 patient-phenotype groups, and for just one method in a further 143 groups. Of the four methods employed, enrichments of phenotypes from mouse-orthologue knockouts (MGI) gave the least number of significant results, identifying functional association among affected genes in only 12 phenotype groups including HP:0001250 (Seizures) and HP:0000717 (Autism) (S1 Table). While the MGI method identified fewest associations, the enriched terms were the most relevant to the particular HPO phenotypes. For example, for patients with Seizures we saw an enrichment of genes whose mouse orthologue knockouts present with, amongst others (Fig. 1), Absence seizures (MP:0003216; 6.2-fold enrichment; p = 3 x 10–4). Similarly, in patients with HP:0010864 (Intellectual Disability, Severe) we see an enrichment of mouse synaptic phenotypes such as Abnormal synaptic transmission (MP:0003635; 3.3-fold enrichment; p = 2.0x10–5). This was in contrast with the results for Intellectual Disability, mild (HP:0001256) which was significantly enriched in learning phenotypes in mouse, such as abnormal associative learning (MP:0002062; p = 3.6x10–6). When these two subsets of patients were combined under the more general term of Intellectual Disability (HP:0001249) no mouse phenotypes were significantly associated but many signalling pathways described in KEGG were significantly enriched among the patients, including the MAPK signalling pathway, and Neurotrophin signalling pathway. Other methods provided a larger number of significant results, with enrichments of GO terms found for each of 121 human phenotypes; however these enrichments were generally smaller, and involve less specific categories than those observed using mouse phenotypes (S1 and S2Tables, S1 Fig.). Additionally, affected genes within each of 189 phenotype groups were associated with particular KEGG pathways (S3 Table), while genes affected within each of 262 phenotype groups showed similarities in their brain spatiotemporal expression patterns, clustering significantly in the BrainSpan expression network (S4 Table). For 186 of 408 patient-phenotype groups, we found functional associations using multiple methods, and of these 177 (95%) phenotype groups identified the same genes using multiple methods, with the number of genes repeatedly identified for these groups ranging from 1 to 355 (Fig. 2, S5 Table).

Bottom Line: Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups.We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype.Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

View Article: PubMed Central - PubMed

Affiliation: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

ABSTRACT
Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.

Show MeSH
Related in: MedlinePlus