A new method for identifying causal genes of schizophrenia and anti-tuberculosis drug-induced hepatotoxicity

Huang, Tao; Liu, Cheng Lin; Li, Lin Lin; Cai, Mei Hong; Chen, Wen Zhong; Xu, Yi; O’Reilly, Paul F.

doi:10.1038/srep32571

Cited by 17 publications

(17 citation statements)

References 48 publications

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“…It includes almost all proteins in various species and had been widely used as a reference protein interaction network. The GO enrichment score of g on GO j is defined based on the hypergeometric test P ‐value of G GO and G ( g ), computed by

S_{GO} (g, G O_{j}) = - \log_{10} (\sum_{k = m}^{n} \frac{(\begin{matrix} M \\ k \end{matrix}) (\begin{matrix} N - M \\ n - k \end{matrix})}{true(\begin{matrix} N \\ n \end{matrix} true)})

where n and m are the number of genes in G ( g ) and in both G ( g ) and G GO , respectively; and N and M are the number of human genes and the number of genes in G GO , respectively. The R function phyper was used to calculate the hypergeometric test P ‐value of gene g on GO j .…”

Section: Methodsmentioning

confidence: 99%

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Identification of synthetic lethality based on a functional network by using machine learning algorithms

Lin

Zhang

et al. 2018

J of Cellular Biochemistry

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Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.

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S_{GO} (g, G O_{j}) = - \log_{10} (\sum_{k = m}^{n} \frac{(\begin{matrix} M \\ k \end{matrix}) (\begin{matrix} N - M \\ n - k \end{matrix})}{true(\begin{matrix} N \\ n \end{matrix} true)})

Section: Methodsmentioning

confidence: 99%

Section: Go Enrichment Scorementioning

confidence: 99%

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Lin

Zhang

et al. 2018

J of Cellular Biochemistry

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“…A Chi-square test and Cochran-Armitage trend test were used to detect the difference of the genotypic and allelic distribution between PCOS patients and healthy women, and one way analysis of variance (ANOVA) and Kruskal-Wallis tests were performed to detect the differences among three groups (Cai et al 2016b;Huang et al 2016). The Power and Sample Size Program was used to calculate the power (Dupont and Plummer 1990).…”

Section: Discussionmentioning

confidence: 99%

A Missense Variant rs4645843 in <i>TNF-</i><i>α</i> Gene Is a Risk Factor of Polycystic Ovary Syndrome in the Uygur Population

Zhao

Wan

2017

Tohoku J. Exp. Med.

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Polycystic ovary syndrome (PCOS) is a complex endocrine syndrome, resulting from the interaction of gene variants and environmental factors. PCOS is viewed as a proinflammatory state and is characterized by hyperandrogenism, hyperinsulinemia, and over-weight. In China, the incidence of PCOS is higher in the Uygur population than that in the Chinese Han population. The association of the tumor necrosis factor α (TNF-α ) gene with PCOS remains to be clarified. Here, we investigated the association of TNF-α polymorphisms with PCOS in the Uygur population (393 patients with PCOS and 381 healthy subjects). Two single-nucleotide polymorphisms in TNF-α were analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method: rs1800629 (-308G/A polymorphism), a commonly tested variant and rs4645843 (6213C/T polymorphism) that causes a Pro-to-Leu substitution at position 84, the most damaging variant of TNF-α based on in silico analysis. We thus found that both the genotypic and allelic distributions of rs4645843 were significantly different between PCOS and control groups (p = 0.03 and 0.024, respectively), whereas those of rs1800629 were similar between the groups. Furthermore, rs4645843 was significantly associated with serum testosterone levels and the score of Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), but no such association was found with rs1800629. Importantly, both rs4645843 and rs1800629 were significantly associated with higher body mass index (p < 0.05). This is the first study that shows the association of TNF-α gene with PCOS in the Uygur population. The TNF-α gene may influence the pathogenesis of PCOS through regulating testosterone level, obesity and HOMA-IR.

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“…In other words, the neighbors of genes related to one disease have strong associations with the disease. Besides, many other methods used some classic network algorithms, such as Random Walk with Restart (RWR) [ 17 ], Shortest Path (SP) Algorithm [ 18 ], as the basic searching engine to discover novel disease genes [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Based on the known disease genes, the network algorithms were adopted to diffuse them, thereby discovering novel disease genes.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

Lei

Zhang

et al. 2017

Genes

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The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein–protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity–disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity–disease co-occurrence and provide useful insights for disease prevention and intervention.

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A new method for identifying causal genes of schizophrenia and anti-tuberculosis drug-induced hepatotoxicity

Cited by 17 publications

References 48 publications

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Identification of synthetic lethality based on a functional network by using machine learning algorithms

A Missense Variant rs4645843 in <i>TNF-</i><i>α</i> Gene Is a Risk Factor of Polycystic Ovary Syndrome in the Uygur Population

Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

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