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      Combinatorial Approach for Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders

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      Genetics

      Genetics Society of America

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          Abstract

          <p class="first" id="d285905e146">Early prediction of complex disorders ( <i>e.g.</i>, autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can improve the prognosis, increase the effectiveness of interventions and treatments, and enhance the life quality of affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a combinatorial framework for addressing this problem, denoted as Odin (Oracle for DIsorder predictioN), to make a prediction for a small, yet significant, subset of affected cases while having very low false positive rate (FPR) prediction for unaffected samples. Odin also takes advantage of the available functional information ( <i>e.g.</i>, pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without any severe variant in known recurrent mutated genes with a &lt;1% FPR. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Approaches such as the one presented in this paper are needed to translate the biomedical discoveries into actionable items by clinicians. Odin is publicly available at <a data-untrusted="" href="https://github.com/HormozdiariLab/Odin" id="d285905e154" target="xrefwindow">https://github.com/HormozdiariLab/Odin</a>. </p>

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          Most cited references 19

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          Network-based prediction of protein function

          Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
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            Whole-genome sequencing in autism identifies hot spots for de novo germline mutation.

            De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network.

              Genes disrupted in schizophrenia may be revealed by de novo mutations in affected persons from otherwise healthy families. Furthermore, during normal brain development, genes are expressed in patterns specific to developmental stage and neuroanatomical structure. We identified de novo mutations in persons with schizophrenia and then mapped the responsible genes onto transcriptome profiles of normal human brain tissues from age 13 weeks gestation to adulthood. In the dorsolateral and ventrolateral prefrontal cortex during fetal development, genes harboring damaging de novo mutations in schizophrenia formed a network significantly enriched for transcriptional coexpression and protein interaction. The 50 genes in the network function in neuronal migration, synaptic transmission, signaling, transcriptional regulation, and transport. These results suggest that disruptions of fetal prefrontal cortical neurogenesis are critical to the pathophysiology of schizophrenia. These results also support the feasibility of integrating genomic and transcriptome analyses to map critical neurodevelopmental processes in time and space in the brain. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                December 06 2018
                December 2018
                December 2018
                October 08 2018
                : 210
                : 4
                : 1483-1495
                Article
                10.1534/genetics.118.301280
                6283174
                30297454
                © 2018

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