Background
Monozygotic twins are valuable in assessing the genetic vs environmental contribution to diseases. In the era of complete genome sequences, they allow identification of mutational mechanisms and specific genes and pathways that offer predisposition to the development of complex diseases including schizophrenia.
Methods
We sequenced the complete genomes of two pairs of monozygotic twins discordant for schizophrenia (MZD), including one representing a family tetrad. The family specific complete sequences have allowed identification of post zygotic mutations between MZD genomes. It allows identification of affected genes including relevant network and pathways that may account for the diseased state in pair specific patient.
Results
We found multiple twin specific sequence differences between co-twins that included small nucleotides [single nucleotide variants (SNV), small indels and block substitutions], copy number variations (CNVs) and structural variations. The genes affected by these changes belonged to a number of canonical pathways, the most prominent ones are implicated in schizophrenia and related disorders. Although these changes were found in both twins, they were more frequent in the affected twin in both pairs. Two specific pathway defects, glutamate receptor signaling and dopamine feedback in cAMP signaling pathways, were uniquely affected in the two patients representing two unrelated families.
Conclusions
We have identified genome-wide post zygotic mutations in two MZD pairs affected with schizophrenia. It has allowed us to use the threshold model and propose the most likely cause of this disease in the two patients studied. The results support the proposition that each schizophrenia patient may be unique and heterogeneous somatic de novo events may contribute to schizophrenia threshold and discordance of the disease in monozygotic twins.
Electronic supplementary material
The online version of this article (10.1186/s40169-017-0174-1) contains supplementary material, which is available to authorized users.
The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list. Gene lists are readily generated from microarray, RNA sequencing, or other molecular screens that typically aim to prioritize genes based on the differences in their expression. Gene lists, however, are often unsatisfying because the information they provide is simply tabular and does not explain how genes interact with each other, or how genetic interactions change in real time under social or environmental circumstances. Here, we promote a view that is relatively common to molecular systems biology, where gene lists are converted into gene networks that better describe the functional connections that regulate behavioral traits. We present a narrative related to honeybee worker sterility to show how network analysis can be used to reprioritize candidate genes based on connectivity rather than their freestanding expression values. Networks can also reveal multigene modules, motifs, clusters or other system‐wide properties that might not be apparent from an ab initio list. We argue that because network analyses are not restricted to “genes” as nodes, their implementation can potentially connect multiple levels of biological organization into a single, progressively complex study system.
ON THE COVER: The cover image is based on the Perspective and Hypothesis From gene list to gene network: Recognizing functional connections that regulate behavioral traits by Kyrillos M. Faragalla et al., DOI: https://doi.org/10.1002/jez.b.22829. Photo Credit: Emma Mullen, Honey Bee Extension Associate, Cornell University.
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