A Pooling Genome-Wide Association Study Combining a Pathway Analysis for Typical Sporadic Parkinson’s Disease in the Han Population of Chinese Mainland

Hu, Yakun; Deng, Libing; Zhang, Jie; Fang, Xin; Mei, Puming; Cao, Xin; Lin, Jiari; Yi, Wei; Zhang, Xiong; Xu, Renshi

doi:10.1007/s12035-015-9331-y

Cited by 32 publications

(28 citation statements)

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“…We compared PEGASUS to four existing methods for generating gene-based scores from GWA SNP-level P -values: (i) minSNP ( Torkamani et al 2008 ; Fehringer et al 2012 ; Gelernter et al 2015 ; Hu et al 2015 ), (ii) permSNP ( Wang et al 2007 ; Eleftherohorinou et al 2009 ; Ballard et al 2010 ; Christoforou et al 2014 ; Evangelou et al 2014 ; Backes et al 2016 ), (iii) SKAT ( Wu et al 2010 , 2011 ), and (iv) VEGAS ( Liu et al 2010 ). For n markers in a given gene, these methods use different strategies, each detailed below and summarized in Figure 1 , to combine P -values

within the gene and calculate a gene-level P -value.…”

Section: Methodsmentioning

confidence: 99%

“…The minSNP method ( Torkamani et al 2008 ; Fehringer et al 2012 ; Gelernter et al 2015 ; Hu et al 2015 ) for generating gene scores assigns the smallest GWA SNP-level P -value in a given gene to be the gene score ( Equation 1 ):…”

Section: Methodsmentioning

confidence: 99%

“…An informative gene score is a necessary ingredient for accurate gene-set analyses, but all commonly used methods for generating gene scores have substantial drawbacks. Commonly used methods include choosing the best SNP P -value within a gene to be the gene score, which is sometimes referred to as “minSNP” ( Torkamani et al 2008 ; Fehringer et al 2012 ; Gelernter et al 2015 ; Hu et al 2015 ); permutation-based methods such as permSNP ( Wang et al 2007 ; Eleftherohorinou et al 2009 ; Ballard et al 2010 ; Christoforou et al 2014 ; Evangelou et al 2014 ; Backes et al 2016 ); regression-based methods such as the sequence kernel association test (SKAT) family of tests ( Wu et al 2010 , 2011 ) and stratified LD score regression ( Finucane et al 2015 ); and VEGAS ( Liu et al 2010 ) and RAREMETALS ( Liu et al 2013 ), which correct for linkage disequilibrium (LD) between SNPs, using simulations from a multivariate normal distribution whose variance is the empirical LD observed among SNPs within each gene being analyzed. Multiple methods exist that use the same null distribution as VEGAS ( Tzeng and Zhang 2007 ; Pan 2009 ).…”

mentioning

confidence: 99%

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Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

2016

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Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist–hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

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within the gene and calculate a gene-level P -value.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

2016

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“…For example, inflammation occurred in month 4, such as leukocyte extravasation signaling. Tight junction signaling and endocytosis are two of the most likely pathways associated with sporadic PD [38]. Remodeling of epithelial adherens junctions and agrin interactions at the neuromuscular junction are two of the most affected pathways in PD [39].…”

Section: Functional Enrichment Analysis Of Differential Proteinsmentioning

confidence: 99%

Urine proteome changes in an α-synuclein transgenic mouse model of Parkinson’s disease

Pan

Wang

et al. 2020

Preprint

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Urine accommodates more changes than other fluids, and it is a good source in the search for early sensitive biomarkers. The present study collected urine samples from 2-, 4-, 6-, 8-and 10-month-old αsynuclein transgenic mice. Based on data-independent acquisition (DIA) technology, liquid chromatographytandem mass spectrometry (LC-MS/MS) was used for quantitative analysis. Seventeen human homologous differential proteins were screened and compared with those in the urine of 2-month-old mice, and 9 proteins were related to Parkinson's disease (PD). Formin-2, Splicing factor 3A subunit 1, and Isopentenyl-diphosphate Deltaisomerase 1 changed continuously in months 6, 8 and 10. These experiments and analyses demonstrated that the urine proteome reflected the development of α-synuclein transgenic mice and provided clues for the early clinical diagnosis of PD. Parkinson's disease (PD) is a neurodegenerative disease characterized by progressive motor and non-motor disorders [1]. Its main pathological features are the progressive loss of dopaminergic neurons in the dense substantia nigra and the presence of α-synuclein as the main component of eosinophilic inclusion bodies, i.e., Lewy bodies, in the remaining dopaminergic neurons. PD is the second most common neurodegenerative disease, and it affects approximately 1% of people over 65 years of age [2]. With the aging of the population and the prolongation of life expectancy, the incidence rate of PD is expected to double in the next 20 years [3]. PD seriously affects the quality of life of patients, and it results in a huge social and economic burden [4, 5]. The early diagnosis of PD is of great significance to improve the quality of life of patients and delay the development of the disease. There is no effective way to cure PD. Drug therapy and surgical treatment only delay the progress of the disease. Drug therapy is the primary method, but the long-term use of drugs often weakens the curative effects and produces some side effects. The clinical diagnosis of PD is currently based on motor characteristics. When the motor characteristics of PD manifest, approximately 60-70% of the neurons are lost [6]. Because the early symptoms of PD are mild and generally considered the result of aging, the early diagnosis of PD is very difficult, and there is a certain rate of misdiagnosis. At the time of diagnosis, most patients exhibit obvious behavior disorders and accumulated pathological changes. Therefore, to provide better clinical treatment for PD patients and help researchers find new treatment methods, it is urgent to identify sensitive early biomarkers. Biomarkers are indicators that objectively reflect normal physiological and pathological processes [7]. The most studied biomarkers are present in blood and cerebrospinal fluid. Because there is no barrier between the cerebrospinal fluid and the brain, cerebrospinal fluid is considered an ideal choice for the discovery of biomarkers of neurodegenerative diseases. Cerebrospinal fluidis more stable than blood due to the blood-...

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“…Intersecting (Chang et al, 2017;Hamza et al, 2010;Hu et al, 2016;Liu et al, 2011;Simon-Sanchez et al, 2009) [2] (Chung et al, 2018;Cummings et al, 2012;Furney et al, 2011;Herold et al, 2016;Jun et al, 2016;Jun et al, 2017;Kunkle et al, 2019;Lee et al, 2017;Logue et al, 2011;Marioni et al, 2018;Mez et al, 2017;Nazarian et al, 2019;Zhu et al, 2019) [3] (Moss et al, 2017) [4] (Xie et al, 2014) [5] (Aberg et al, 2013;Amare et al, 2018;Athanasiu et al, 2015 (Fig. 12A).…”

Section: Disease/disordermentioning

confidence: 99%

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

Frederiksen

Wicki-Stordeur

Swayne

2019

Preprint

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The Pannexin 1 (Panx1) channel-forming protein is enriched in the central nervous system, and has been associated with several critical neurodevelopmental and plasticity functions; these include dendritic spine formation, neuronal network development, synaptic plasticity, and pathological brain states such as ischemia, epilepsy, and neurodegeneration. Despite major advances in understanding the properties and activation modes of Panx1, the Panx1 interactome remains largely uncharacterized. Considering that Panx1 has been implicated in critical neurodevelopmental and neurodegenerative processes and diseases, we investigated the Panx1 interactome (482 Panx1interacting proteins) identified from mouse N2a cells. These proteins were cross-analyzed with the postsynaptic proteome of the adult mouse brain previously identified by mass spectrometry (LC-MS/MS), and neurodegenerative disease and neurodevelopmental disorder susceptibility genes previously identified by genome-wide association studies (GWAS); and then further investigated using various bioinformatics tools (PAN-THER, GO, KEGG and STRING databases). A total of 104 of the Panx1-interacting proteins were located at the postsynapse, and 99 of these formed a 16-cluster protein-protein interaction (PPI) network (hub proteins: Eef2, Rab6A, Ddx39b, Mapk1, Fh1, Ndufv1 et cetera). The cross-analysis led to the discovery of proteins and candidate genes involved in synaptic function and homeostasis. Of particular note, our analyses also revealed that certain Panx1-interacting proteins are implicated in Parkinson disease, Alzheimer disease, Huntington disease, amyotrophic lateral sclerosis, schizophrenia, autism spectrum disorder and epilepsy. Altogether, our work revealed important clues to the role of Panx1 in neuronal function in health and disease by expanding our knowledge of the PPI network of Panx1, and unveiling previously unidentified Panx1-interacting proteins and networks involved in biological processes and disease. Panx1 | PPIs | pathway analysis | neurodegenerative diseases | neurodevelopmental disorders | Parkinson disease | Alzheimer disease | chaperones | vesicle-mediated transportFunctional annotation, categorization and overrepresentation analyses of the Panx1 interactome using the PANTHER database. Bioinformatics analysis of all proteins in our Panx1-interacting protein list (using UniProt accession numbers) was carried out using the PANTHER database v14.1 (Mi et al., 2013;Thomas et al., 2003), which is a curated database that includes several bioinformatics tools. The proteins (or their corresponding genes, depending on the nature of the analysis) were annotated to (i) PAN-THER protein classes from the PANTHER protein class ontology, (ii) gene ontology (GO) terms within the biological process, molecular function and cellular component domains from the GO knowledge base and (iii) PANTHER pathways created using the CellDesigner tool, a modelling tool for biochemical networks (Funahashi et al., 2008;Mi et al., 2019a;Mi et al., 2013). Note that PANTHER pa...

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A Pooling Genome-Wide Association Study Combining a Pathway Analysis for Typical Sporadic Parkinson’s Disease in the Han Population of Chinese Mainland

Cited by 32 publications

References 65 publications

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Urine proteome changes in an α-synuclein transgenic mouse model of Parkinson’s disease

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

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