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Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M - Mol. Syst. Biol. (2014)

Bottom Line: Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center.RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells.Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA.

No MeSH data available.


Related in: MedlinePlus

Candidate genes from sequencing screensAn overview of the identified loci from whole-genome and exome sequencing. Evolutionary conservation is quantified by GERP++ score, where the higher scores indicate greater selective pressure on the genomic loci. For genes with multiple significant loci, the most conserved residue is considered. Variants absent in the 1,000 Genome dataset are considered rare variants. The genes were colorized based on the fraction of deleterious mutations predicted by MutationTaster among all the identified mutations in the gene (MRI image of the corpus callosum: Allen Institute of Brain Science).Validation using another larger patient cohort. In this dataset, variants with allele frequencies with increased absolute differences between cases and controls are more likely to affect genes that were also detected in our study (red line). The allele frequency difference is the absolute difference between cases and controls. This trend cannot be observed by 10,000 simulations (blue line for one randomized dataset).
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fig02: Candidate genes from sequencing screensAn overview of the identified loci from whole-genome and exome sequencing. Evolutionary conservation is quantified by GERP++ score, where the higher scores indicate greater selective pressure on the genomic loci. For genes with multiple significant loci, the most conserved residue is considered. Variants absent in the 1,000 Genome dataset are considered rare variants. The genes were colorized based on the fraction of deleterious mutations predicted by MutationTaster among all the identified mutations in the gene (MRI image of the corpus callosum: Allen Institute of Brain Science).Validation using another larger patient cohort. In this dataset, variants with allele frequencies with increased absolute differences between cases and controls are more likely to affect genes that were also detected in our study (red line). The allele frequency difference is the absolute difference between cases and controls. This trend cannot be observed by 10,000 simulations (blue line for one randomized dataset).

Mentions: Excluding the variants also identified in the control subjects that were sequenced on the same platform, we considered 113 non-synonymous sites in this module collectively identified from WGS or WES. We compared their allele frequencies to those in the 1,000 Genomes dataset, both the entire global populations and the European populations, and from the 25 patients, we identified a total of 38 genes affected by significant non-synonymous variants in this module with an expected false-positive rate at 0.1 (determined by Fisher's exact test followed by Benjamini–Hochberg correction). The high gene overlap between WGS and WES was not expected by chance (P = 0.03 by random permutation test). Furthermore, the identification of genes in our module was not affected by the CDS length of the identified genes relative to the average CDS length in the module (P = 0.16, Wilcoxon rank-sum test). The identified genes and a summary of the variant information are shown in Fig2A. For example, LRP2 harbored seven distinct non-synonymous mutations (z-axis, Fig2A), four of which were predicted to be deleterious by MutationTaster (Schwarz et al, 2010). LRP2 has recently been identified as an ASD candidate gene (Ionita-Laza et al, 2012), whose clinical mutations cause the Donnai–Barrow syndrome (Kantarci et al, 2007) with the underdeveloped or absent corpus callosum. This syndrome exhibits many autistic-like symptoms. Figure2A further underlines its tissue specificity in the corpus callosum using Brain Explorer (http://www.brain-map.org). Other well-characterized ASD-associated genes included SHANK2, SCN1A, NLGN4X and NLGN3 as well as several LRP2 interacting proteins (LRP2BP, ANKS1B). Overall, the affected loci in these genes were more likely to be both rare in the population (y-axis) and evolutionarily conserved (x-axis), suggesting their functional importance (Fig2A). We also noted that 28 genes of the 38 ASD candidates have not been described previously (see Supplementary Dataset S3). To better support their association with this disease, we further examined their mouse mutant phenotypes in Mouse Genome Informatics (http://www.informatics.jax.org) and observed that 10 of the 28 new candidate genes displayed abnormal behavioral traits or a defective nervous system in their respective mouse mutants (see Supplementary Dataset S3). For example, mouse mutants of (i) ANKS1B and KCNJ12 exhibited hyperactivity, (ii) ERBB2IP hyporesponsive behavior to stimuli, (iii) GRID2IP abnormal reflex and (iv) SCN5A seizure.


Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M - Mol. Syst. Biol. (2014)

Candidate genes from sequencing screensAn overview of the identified loci from whole-genome and exome sequencing. Evolutionary conservation is quantified by GERP++ score, where the higher scores indicate greater selective pressure on the genomic loci. For genes with multiple significant loci, the most conserved residue is considered. Variants absent in the 1,000 Genome dataset are considered rare variants. The genes were colorized based on the fraction of deleterious mutations predicted by MutationTaster among all the identified mutations in the gene (MRI image of the corpus callosum: Allen Institute of Brain Science).Validation using another larger patient cohort. In this dataset, variants with allele frequencies with increased absolute differences between cases and controls are more likely to affect genes that were also detected in our study (red line). The allele frequency difference is the absolute difference between cases and controls. This trend cannot be observed by 10,000 simulations (blue line for one randomized dataset).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4300495&req=5

fig02: Candidate genes from sequencing screensAn overview of the identified loci from whole-genome and exome sequencing. Evolutionary conservation is quantified by GERP++ score, where the higher scores indicate greater selective pressure on the genomic loci. For genes with multiple significant loci, the most conserved residue is considered. Variants absent in the 1,000 Genome dataset are considered rare variants. The genes were colorized based on the fraction of deleterious mutations predicted by MutationTaster among all the identified mutations in the gene (MRI image of the corpus callosum: Allen Institute of Brain Science).Validation using another larger patient cohort. In this dataset, variants with allele frequencies with increased absolute differences between cases and controls are more likely to affect genes that were also detected in our study (red line). The allele frequency difference is the absolute difference between cases and controls. This trend cannot be observed by 10,000 simulations (blue line for one randomized dataset).
Mentions: Excluding the variants also identified in the control subjects that were sequenced on the same platform, we considered 113 non-synonymous sites in this module collectively identified from WGS or WES. We compared their allele frequencies to those in the 1,000 Genomes dataset, both the entire global populations and the European populations, and from the 25 patients, we identified a total of 38 genes affected by significant non-synonymous variants in this module with an expected false-positive rate at 0.1 (determined by Fisher's exact test followed by Benjamini–Hochberg correction). The high gene overlap between WGS and WES was not expected by chance (P = 0.03 by random permutation test). Furthermore, the identification of genes in our module was not affected by the CDS length of the identified genes relative to the average CDS length in the module (P = 0.16, Wilcoxon rank-sum test). The identified genes and a summary of the variant information are shown in Fig2A. For example, LRP2 harbored seven distinct non-synonymous mutations (z-axis, Fig2A), four of which were predicted to be deleterious by MutationTaster (Schwarz et al, 2010). LRP2 has recently been identified as an ASD candidate gene (Ionita-Laza et al, 2012), whose clinical mutations cause the Donnai–Barrow syndrome (Kantarci et al, 2007) with the underdeveloped or absent corpus callosum. This syndrome exhibits many autistic-like symptoms. Figure2A further underlines its tissue specificity in the corpus callosum using Brain Explorer (http://www.brain-map.org). Other well-characterized ASD-associated genes included SHANK2, SCN1A, NLGN4X and NLGN3 as well as several LRP2 interacting proteins (LRP2BP, ANKS1B). Overall, the affected loci in these genes were more likely to be both rare in the population (y-axis) and evolutionarily conserved (x-axis), suggesting their functional importance (Fig2A). We also noted that 28 genes of the 38 ASD candidates have not been described previously (see Supplementary Dataset S3). To better support their association with this disease, we further examined their mouse mutant phenotypes in Mouse Genome Informatics (http://www.informatics.jax.org) and observed that 10 of the 28 new candidate genes displayed abnormal behavioral traits or a defective nervous system in their respective mouse mutants (see Supplementary Dataset S3). For example, mouse mutants of (i) ANKS1B and KCNJ12 exhibited hyperactivity, (ii) ERBB2IP hyporesponsive behavior to stimuli, (iii) GRID2IP abnormal reflex and (iv) SCN5A seizure.

Bottom Line: Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center.RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells.Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA.

No MeSH data available.


Related in: MedlinePlus