Genetics of Parkinson’s Disease: The Role of Copy Number Variations

Cognata, Valentina La; D’Agata, Velia; Cavalcanti, Francesca; Cavallaro, Sebastiano

doi:10.5772/62881

Cited by 4 publications

(2 citation statements)

References 146 publications

(188 reference statements)

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“…Many evidences reveal the causal relationship between CNVs and many human disease phenotypes, including scores of known genomic diseases and hundreds of complex disease traits [2][3][4]. One of the essential issues in CNV research is to understand how CNVs affect the occurrence of diseases [5,6].…”

Section: Integrated Analysis Of Cnv Gene Expression and Disease Statmentioning

confidence: 99%

Integrated Analysis of CNV, Gene Expression and Disease State Data in Prostate Cancer

Lin¹,

Sun²,

Zhao³

et al. 2021

Preprint

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Background: Copy number variation (CNV) may contribute to development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene and disease label data provide us with an opportunity to design a new machine learning framework to predict potential disease related CNVs.Results: In this paper, we developed a novel machine learning approach, namely IHI BMLLR (Integrating Heterogeneous Information sources with Biweight Mid correlation and L1 regularized Logistic Regression under stability selection), to predict the CNV disease path associations by using a data set containing CNV, disease state labels and gene data. CNVs, genes, and diseases are connected through edges, and then constitute a biological association network. To construct a biological network, we first used a self adaptive biweight mid correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find top D path associations and important CNVs.Conclusions: Compared with state of the art methods, IHI BMLLR discovers CNVs disease path associations by integrating analysis of CNV, gene expression and disease label data combined with stability selection strategy and weighted path search algorithm, thereby mining more information in the data sets, and improving the accuracy of obtained CNVs. The experimental results on both simulation and prostate cancer data show that IHI BMLLR is significantly better than two state of the art CNV detection methods (i.e., CCRET and DPtest) under false positive control. Furthermore, we applied IHI BMLLR to prostate cancer data and found significant path associations. Three new cancer related genes were discovered in the paths and these genes need to be verified by biological research in the future.

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Section: Integrated Analysis Of Cnv Gene Expression and Disease Statmentioning

confidence: 99%

Integrated Analysis of CNV, Gene Expression and Disease State Data in Prostate Cancer

Lin¹,

Sun²,

Zhao³

et al. 2021

Preprint

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“…are an important source for genetic diversity and significantly contribute to pathogenic variants. It has been demonstrated that CNVs present in the germline DNA contribute to human diseases such as, but not limited to, neurodevelopmental disorders (Wilfert et al 2017), Parkinson's disease (La Cognata et al 2016), ALzheimer's disease (Cuccaro et al 2017) and cancer susceptibility (Krepischi et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Validation of an ultra-fast CNV calling tool for Next Generation Sequencing data using MLPA-verified copy number alterations

Tolhuis¹,

Karten²

2018

Preprint

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DNA Copy Number Variations (CNVs) are an important source for genetic diversity and pathogenic variants. Next Generation Sequencing (NGS) methods have become increasingly more popular for CNV detection, but its data analysis is a growing bottleneck. Genalice CNV is a novel tool for detection of CNVs. It takes care of turnaround time, scalability and cost issues associated with NGS computational analysis. Here, we validate Genalice CNV with MLPA-verified exon CNVs and genes with normal copy numbers. Genalice CNV detects 61 out of 62 exon CNVs and its false positive rate is less than 1%. It analyzes 96 samples from a targeted NGS assay in less than 45 minutes, including read alignment and CNV detection, using a single node. Furthermore, we describe data quality measures to minimize false discoveries. In conclusion, Genalice CNV is highly sensitive and specific, as well as extremely fast, which will be beneficial for clinical detection of CNVs.

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