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determine rare copy number variants that effect patients with SZC [87]. Their

results indicated that combining technologies enables the detection of variants

that are too small (<100KB) to be accurately observed in only array data.

Palmer and colleagues performed analysis on curated whole exome se-

quencing data from patients with BD [88]. They discovered that AKAP-11

engages with GSK3B, which is assumed to be the target of lithium, the pri-

mary therapy for BD. Their findings show that uncommon coding variation

is a major risk contributor in the etiology of BD, supporting the polygenicity

of BD. In order to study the exonic variation in BD, Jia and colleagues did

whole genome and whole exome sequencing in a variety of cohorts [89]. In their

work, which was the broadest investigation of exonic variation in BD, they

discovered that there is no consistent enrichment of rare pathogenic/likely

pathogenic (P-LP) changes in the exome or in any of the numerous gene

families with biological significance in BD patients. Furthermore, despite a

significant shared vulnerability between BD and SCZ due to similar genetic

variation, a connection among BD risk and infrequent P-LP coding mutations

in genes known to affect SCZ risk was not discovered. In an interesting study

Kathuria et al generated 3D organoid model from human induced pluripotent

stem cells (IPSCs) and analyzed them by designing an RNA-seq experiment

with BD and healthy individuals [90]. Their findings demonstrated that neuro-

can plays a critical role in the biology of BD. They offered evidence of aberrant

neurotransmission, and they revealed dysregulation in genes associated to cell

adhesion, immunological signaling, and endoplasmic reticulum biology.

A growing resource for the autism research community, SFARI gene is

focused on curated genes thought to play a role in autism susceptibility

(https://gene.sfari.org). Other ASD scoring approaches and even data regard-

ing other neurodevelopmental diseases are influenced by the SFARI gene scor-

ing system’s impact. Arpi and colleagues carried out research to determine the

relationship between ASD-specific transcriptomic data and SFARI genes using

RNA-seq data [91]. Their findings suggested that in order to effectively analyze

the link between SFARI genes and ASD-specific RNAseq transcriptomic data,

information drawn from the entire gene co-expression network is necessary.

Furthermore, they have demonstrated that SFARI genes are not significantly

connected with either differential expression results or co-expression modules

with a high connection to diagnostic state. Rather, for the novel candidate gene

prediction strategy described in the research, thorough systems-level network

analysis and the use of machine learning models to combine different data

sources in disease scenarios may out to be extremely beneficial. In another

study, RNA sequencing was carried out by Tomaiuolo and colleagues on the

peripheral blood of children with ASD and their unaffected siblings [92]. In

addition to providing more proof that neurodevelopment, innate immunity,

and transcriptional control are important factors in the etiology of ASD, their

findings showed that transcriptome signatures can help increase the sensitivity

of an intra-familial multimarker screening for the disorder.