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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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Phenotypic concordances amongst patients whose copy number variant genes contribute to the same functional associations and molecular pathways.(A) Overall, patients with genes that contribute to the same functional association are phenotypically similar (p = 1 x 10–4). The Y-axis gives the significance of the overall phenotypic similarity amongst patients within a patient-phenotype group whose variant genes contribute to a functional association (Intra) as compared to those patients in the same phenotype group who do not contribute (Inter), with higher values indicating increasing relative similarity amongst association-contributing patients. Each point represents a single significant patient-phenotype group association, while the methods used to identify the association are shown on the X-axis (KEGG, MGI mouse KO phenotypes, GO, BS BrainSpan gene co-expression). Combinations of methods (e.g. GO-KEGG) illustrate the relative phenotypic similarity amongst patients possessing copy variant genes that individually contribute to multiple functional associations (see Results). “PPI” values are those among patients contributing the interacting molecular networks identified in Fig. 2 (see Results). Dots coloured blue or red indicate nominally significantly phenotypic similarity or dissimilarity, respectively. The black line connects all enrichments associated with the intellectual disability (ID) patient-phenotype group. (B) BrainSpan (BS) was the only pathway-resource to consistently identify phenotypically similar subgroups through a shared molecular association. Detail on the phenotypic similarities shown in Panel A. Solid line: p = 0.5, dashed line: p = 0.05, dotted line: p = 0.007 conferring significance after a Bonferroni correction. (C) The significant phenotypic similarities amongst patients who contribute to the same functional association are not derived from these patients presenting more specific subphenotypes of the original phenotype. Y-axis as in panel A. For all nominally significant enrichments in panel A (top, solid points) we recalculated the patient phenotypic similarities considering only child terms of the original HPO phenotype (open points connected to their respective solid point by an arrow). Points are grouped horizontally by HPO and coloured by enrichment-type. Solid line: p = 0.5, dashed line: p = 0.05. (D) In general, the fewer patient-phenotype groups that a functional enrichment term was associated with, the more phenotypically similar the patients associated with that functional term were. Patient-phenotype groups associated with the same KEGG pathway or GO term were combined and for each association the phenotypic similarity amongst those patients whose variant genes contributed to the given association was compared to those who did not contribute. Y-axis as in panel A. The number of patient-phenotype groups each functional association is associated with is given on the X-axis.
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pgen.1005012.g004: Phenotypic concordances amongst patients whose copy number variant genes contribute to the same functional associations and molecular pathways.(A) Overall, patients with genes that contribute to the same functional association are phenotypically similar (p = 1 x 10–4). The Y-axis gives the significance of the overall phenotypic similarity amongst patients within a patient-phenotype group whose variant genes contribute to a functional association (Intra) as compared to those patients in the same phenotype group who do not contribute (Inter), with higher values indicating increasing relative similarity amongst association-contributing patients. Each point represents a single significant patient-phenotype group association, while the methods used to identify the association are shown on the X-axis (KEGG, MGI mouse KO phenotypes, GO, BS BrainSpan gene co-expression). Combinations of methods (e.g. GO-KEGG) illustrate the relative phenotypic similarity amongst patients possessing copy variant genes that individually contribute to multiple functional associations (see Results). “PPI” values are those among patients contributing the interacting molecular networks identified in Fig. 2 (see Results). Dots coloured blue or red indicate nominally significantly phenotypic similarity or dissimilarity, respectively. The black line connects all enrichments associated with the intellectual disability (ID) patient-phenotype group. (B) BrainSpan (BS) was the only pathway-resource to consistently identify phenotypically similar subgroups through a shared molecular association. Detail on the phenotypic similarities shown in Panel A. Solid line: p = 0.5, dashed line: p = 0.05, dotted line: p = 0.007 conferring significance after a Bonferroni correction. (C) The significant phenotypic similarities amongst patients who contribute to the same functional association are not derived from these patients presenting more specific subphenotypes of the original phenotype. Y-axis as in panel A. For all nominally significant enrichments in panel A (top, solid points) we recalculated the patient phenotypic similarities considering only child terms of the original HPO phenotype (open points connected to their respective solid point by an arrow). Points are grouped horizontally by HPO and coloured by enrichment-type. Solid line: p = 0.5, dashed line: p = 0.05. (D) In general, the fewer patient-phenotype groups that a functional enrichment term was associated with, the more phenotypically similar the patients associated with that functional term were. Patient-phenotype groups associated with the same KEGG pathway or GO term were combined and for each association the phenotypic similarity amongst those patients whose variant genes contributed to the given association was compared to those who did not contribute. Y-axis as in panel A. The number of patient-phenotype groups each functional association is associated with is given on the X-axis.

Mentions: Overall, we found a significant excess of instances where contributing patients are more similar to each other than they are to non-contributing patients; p = 4 x 10–4, one-sided binomial test (Fig. 4A). However, this is highly variable between the different functional genomics resources used to identify the functional enrichment, as well as between phenotypes examined. Only patients whose copy number variant genes showed significantly co-ordinated brain expression patterns within the BrainSpan data were consistently found to be more similar in their overall phenotypes as compared to pairs of patients whose genes were not similarly co-ordinately expressed in the brain (Fig. 4A and B). Considering the remaining 3,871 patients without de novo CNVs, patients whose CNVs affected genes co-expressed within BrainSpan continued to show the most significant phenotypic similarity when the analysis was repeated considering the phenotypic similarity amongst patients whose inherited CNVs affected the previously-identified candidate pathway genes. This was also true for phenotypic comparisons involving patients whose CNVs affected genes related to the candidate pathways (have the same annotation or are co-expressed with or have a PPI with candidate pathway genes (See Methods)), but here patients whose CNVs affected novel genes with the same GO annotation also demonstrated phenotypic convergence (S6 Fig.). Furthermore, restricting the patient phenotype comparisons to those patients possessing copy number variant genes that contributed to enrichments identified by two different functional resources did not increase the proportion of cases where contributing patients were more similar to each other than to non-contributing patients (14% vs 13%, p >0.9, Fig. 4A).


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)

Phenotypic concordances amongst patients whose copy number variant genes contribute to the same functional associations and molecular pathways.(A) Overall, patients with genes that contribute to the same functional association are phenotypically similar (p = 1 x 10–4). The Y-axis gives the significance of the overall phenotypic similarity amongst patients within a patient-phenotype group whose variant genes contribute to a functional association (Intra) as compared to those patients in the same phenotype group who do not contribute (Inter), with higher values indicating increasing relative similarity amongst association-contributing patients. Each point represents a single significant patient-phenotype group association, while the methods used to identify the association are shown on the X-axis (KEGG, MGI mouse KO phenotypes, GO, BS BrainSpan gene co-expression). Combinations of methods (e.g. GO-KEGG) illustrate the relative phenotypic similarity amongst patients possessing copy variant genes that individually contribute to multiple functional associations (see Results). “PPI” values are those among patients contributing the interacting molecular networks identified in Fig. 2 (see Results). Dots coloured blue or red indicate nominally significantly phenotypic similarity or dissimilarity, respectively. The black line connects all enrichments associated with the intellectual disability (ID) patient-phenotype group. (B) BrainSpan (BS) was the only pathway-resource to consistently identify phenotypically similar subgroups through a shared molecular association. Detail on the phenotypic similarities shown in Panel A. Solid line: p = 0.5, dashed line: p = 0.05, dotted line: p = 0.007 conferring significance after a Bonferroni correction. (C) The significant phenotypic similarities amongst patients who contribute to the same functional association are not derived from these patients presenting more specific subphenotypes of the original phenotype. Y-axis as in panel A. For all nominally significant enrichments in panel A (top, solid points) we recalculated the patient phenotypic similarities considering only child terms of the original HPO phenotype (open points connected to their respective solid point by an arrow). Points are grouped horizontally by HPO and coloured by enrichment-type. Solid line: p = 0.5, dashed line: p = 0.05. (D) In general, the fewer patient-phenotype groups that a functional enrichment term was associated with, the more phenotypically similar the patients associated with that functional term were. Patient-phenotype groups associated with the same KEGG pathway or GO term were combined and for each association the phenotypic similarity amongst those patients whose variant genes contributed to the given association was compared to those who did not contribute. Y-axis as in panel A. The number of patient-phenotype groups each functional association is associated with is given on the X-axis.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4362763&req=5

pgen.1005012.g004: Phenotypic concordances amongst patients whose copy number variant genes contribute to the same functional associations and molecular pathways.(A) Overall, patients with genes that contribute to the same functional association are phenotypically similar (p = 1 x 10–4). The Y-axis gives the significance of the overall phenotypic similarity amongst patients within a patient-phenotype group whose variant genes contribute to a functional association (Intra) as compared to those patients in the same phenotype group who do not contribute (Inter), with higher values indicating increasing relative similarity amongst association-contributing patients. Each point represents a single significant patient-phenotype group association, while the methods used to identify the association are shown on the X-axis (KEGG, MGI mouse KO phenotypes, GO, BS BrainSpan gene co-expression). Combinations of methods (e.g. GO-KEGG) illustrate the relative phenotypic similarity amongst patients possessing copy variant genes that individually contribute to multiple functional associations (see Results). “PPI” values are those among patients contributing the interacting molecular networks identified in Fig. 2 (see Results). Dots coloured blue or red indicate nominally significantly phenotypic similarity or dissimilarity, respectively. The black line connects all enrichments associated with the intellectual disability (ID) patient-phenotype group. (B) BrainSpan (BS) was the only pathway-resource to consistently identify phenotypically similar subgroups through a shared molecular association. Detail on the phenotypic similarities shown in Panel A. Solid line: p = 0.5, dashed line: p = 0.05, dotted line: p = 0.007 conferring significance after a Bonferroni correction. (C) The significant phenotypic similarities amongst patients who contribute to the same functional association are not derived from these patients presenting more specific subphenotypes of the original phenotype. Y-axis as in panel A. For all nominally significant enrichments in panel A (top, solid points) we recalculated the patient phenotypic similarities considering only child terms of the original HPO phenotype (open points connected to their respective solid point by an arrow). Points are grouped horizontally by HPO and coloured by enrichment-type. Solid line: p = 0.5, dashed line: p = 0.05. (D) In general, the fewer patient-phenotype groups that a functional enrichment term was associated with, the more phenotypically similar the patients associated with that functional term were. Patient-phenotype groups associated with the same KEGG pathway or GO term were combined and for each association the phenotypic similarity amongst those patients whose variant genes contributed to the given association was compared to those who did not contribute. Y-axis as in panel A. The number of patient-phenotype groups each functional association is associated with is given on the X-axis.
Mentions: Overall, we found a significant excess of instances where contributing patients are more similar to each other than they are to non-contributing patients; p = 4 x 10–4, one-sided binomial test (Fig. 4A). However, this is highly variable between the different functional genomics resources used to identify the functional enrichment, as well as between phenotypes examined. Only patients whose copy number variant genes showed significantly co-ordinated brain expression patterns within the BrainSpan data were consistently found to be more similar in their overall phenotypes as compared to pairs of patients whose genes were not similarly co-ordinately expressed in the brain (Fig. 4A and B). Considering the remaining 3,871 patients without de novo CNVs, patients whose CNVs affected genes co-expressed within BrainSpan continued to show the most significant phenotypic similarity when the analysis was repeated considering the phenotypic similarity amongst patients whose inherited CNVs affected the previously-identified candidate pathway genes. This was also true for phenotypic comparisons involving patients whose CNVs affected genes related to the candidate pathways (have the same annotation or are co-expressed with or have a PPI with candidate pathway genes (See Methods)), but here patients whose CNVs affected novel genes with the same GO annotation also demonstrated phenotypic convergence (S6 Fig.). Furthermore, restricting the patient phenotype comparisons to those patients possessing copy number variant genes that contributed to enrichments identified by two different functional resources did not increase the proportion of cases where contributing patients were more similar to each other than to non-contributing patients (14% vs 13%, p >0.9, Fig. 4A).

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