Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson’s Disease Risk

Rodrigo, Linduni M.; Nyholt, Dale R.

doi:10.3390/genes12050689

Cited by 10 publications

(5 citation statements)

References 38 publications

(46 reference statements)

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“…These findings together with the haplotype results are acquired after the gene-wide Bonferroni correction and should be valid in general since such correction contributes to overcorrection of the inflated false-positive rate ( Nyholt, 2004 ). In addition, rs9305012 appears to marginally associate with PD in a recent study of imputation and reanalysis using genome-wide association data ( Rodrigo and Nyholt, 2021 ). Local environmental exposures may also contribute to the susceptibility since epigenetic regulations are prone to such factors ( Ammal Kaidery et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Gene-wide significant association analyses of DNMT1 genetic variants with Parkinson’s disease

et al. 2023

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Background: DNA methylation plays an important role in Parkinson’s disease (PD) pathogenesis. DNA methyltransferase 1 (DNMT1) is critical for maintaining DNA methylation in mammals. The link between DNMT1 polymorphisms and PD remains elusive.Methods: The DNMT1 gene contained a total of 28 single nucleotide polymorphisms (SNPs). Four representing tag-SNPs (rs16999593, rs2162560, rs11880553, and rs9305012) were identified and genotyped in a Han Chinese population comprising 712 PD patients and 696 controls. Association analyses were performed at gene-wide significance (p < 1.8 × 10−3).Results: Rs9305012, but not the other 3 tag-SNPs, was gene-wide significantly associated with PD risk (p = 0.8 × 10−3). The rs9305012/C was a protective allele against PD (p = 1.5 × 10−3, OR 0.786, 95% CI 0.677–0.912). No significant association was observed in individual genders or PD subtypes. Haplotypes of the 4 tag-SNPs showed a significant overall distribution difference between PD patients and controls (p < 1 × 10−4). The 3-allele ACC module in the order of rs2162560, rs11880553, and rs9305012 was the highest-risk haplotype associated with PD (p < 1 × 10−4, OR 2.439, 95% CI 1.563–3.704). Rs9305012 displayed certain probability to affect transcription factor binding and target gene expression based on functional annotation analyses.Conclusion: The DNMT1 variant rs9305012 together with its haplotypes may gene-wide significantly modulate PD susceptibility. Our results support a role of DNMT1 in PD pathogenesis and provide novel insights into the genetic connection in between.

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Section: Discussionmentioning

confidence: 99%

Gene-wide significant association analyses of DNMT1 genetic variants with Parkinson’s disease

et al. 2023

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“…The UpSet plot enables users to observe the intersection of important SNPs selected from each method. Additionally, PGSbuilder incorporates our original GenEpi software 36 , which provides a unique method to uncover the genetic epistasis associated with phenotypes, as demonstrated in other recent studies 51,52 . Finally, as clinical factors are provided, PGSbuilder will rank the weights of them and PRS to highlight the most predictive feature, which helps users investigate the risk factor precisely.…”

Section: Discussionmentioning

confidence: 99%

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Lee¹,

Lee²,

Hsieh³

et al. 2023

Preprint

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Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible at http://pgsb.tw23.org.

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“…It is also implicated in multiple neurological disorders 44 including Alzheimer's disease 45,46 , schizophrenia [47][48][49][50][51][52] , and alcohol-dependence 53 . ITGAE canonically mediates the adhesion of intraepithelial T-lymphocytes to epithelial cells and is involved in intra-tumoral immunity [54][55][56] , but it has also been implicated in Parkinson's disease 57 and Attention-deficit/hyperactivity disorder (ADHD) [58][59][60] .…”

Section: Discussionmentioning

confidence: 99%

Disentangling the roles of trauma and genetics in psychiatric disorders using an Electronic Health Records-based approach

Marchese

Cuddleston

Seah

et al. 2022

Preprint

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Posttraumatic stress disorder (PTSD) requires an exposure to trauma for diagnosis by the DSM-V. Despite this, there is no documented linear relationship between degree of trauma and severity/chronicity of PTSD.To determine whether traumatic and stressful life events (TSLEs) collected from Electronic Health Records (EHR) interact with PTSD genetics to better define individual risk of developing PTSD. We collected trauma information from patient records in the Mount Sinai BioMe™ biobank population-based cohort and tested for associations with PTSD. We generated a TSLE risk score (TRS), tested its association with PTSD, and for interactions with a polygenic risk score (PRS) of PTSD and a GWAS of PTSD using our biobank population.We used the Mount Sinai BioMe™ biobank patient population of 31,704 individuals with matched genotype and EHR data, which has been enrolling patients since 2006. Patient enrollment in BioMe™ is unrestricted, resulting in high diversity. Our study includes 1,990 individuals with PTSD and 28,478 individuals without PTSD from the Mount Sinai BioMe™ biobank.A total of 1,990 individuals with PTSD and 28,478 controls were analyzed (average age 42-78 years, 58.7% women). We identified a total of 22 EHR-derived TSLEs that were associated with PTSD (β> 0.029, p<1.61×10−3). TSLEs interacted with each other to increase the risk of developing PTSD, with the most significant interaction between being female (as a proxy for experiencing sexism) and being HIV+ (β=0.013, p=8.9×10−11). PRS of PTSD and lead SNPS from GWAS interacted with TSLEs and our TRS to increase PTSD risk. In addition to TRS interactions, we found significant interactions between PTSD PRS and domestic violence, and sexual assault in European Americans (β>207.74, p<1.80×10−3). rs113282988 and rs189796944 variants reached genome-wide significance in interactions with TRS (β>0.056, p<9.04×10−9), and rs189796944 in an interaction with Physical Survival TSLEs (β>0.127, p<4×10−8).In conclusion, quantification of trauma type, severity, and magnitude—alone and in concert with genetics—provides better prediction of PTSD risk than PRS alone. Understanding who is at risk of developing PTSD allows for timely clinical intervention and possible prevention.

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Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson’s Disease Risk

Cited by 10 publications

References 38 publications

Gene-wide significant association analyses of DNMT1 genetic variants with Parkinson’s disease

Gene-wide significant association analyses of DNMT1 genetic variants with Parkinson’s disease

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Disentangling the roles of trauma and genetics in psychiatric disorders using an Electronic Health Records-based approach

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