scholarly journals Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

2022 ◽  
Vol 12 ◽  
Author(s):  
Daniel Ho ◽  
William Schierding ◽  
Sophie L. Farrow ◽  
Antony A. Cooper ◽  
Andreas W. Kempa-Liehr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Variants ◽  
Disease Risk ◽  
Meta Analysis ◽  
Atrial Appendage ◽  
Specific Expression ◽  
Tissue Specific ◽  
Transcriptional Changes ◽  
The Uk ◽  
Eqtl Data

Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

scholarly journals Machine learning identifies six genetic variants and alterations in the Heart Atrial Appendage as key contributors to PD risk predictivity.

2021 ◽  
Author(s):  
Daniel Ho ◽  
William Schierding ◽  
Sophie L Farrow ◽  
Antony Cooper ◽  
Justin M. O'Sullivan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Variants ◽  
Disease Risk ◽  
Meta Analysis ◽  
Atrial Appendage ◽  
Specific Expression ◽  
Tissue Specific ◽  
Transcriptional Changes ◽  
The Uk ◽  
Eqtl Data

Parkinson disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium, the UK Biobank, and NeuroX. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the brain cerebellum were also recognized to confer major PD risk. These findings warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

scholarly journals Case-control association mapping without cases

2016 ◽  
Author(s):  
Jimmy Z Liu ◽  
Yaniv Erlich ◽  
Joseph K Pickrell
Keyword(s):  
Association Mapping ◽  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Large Population ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
The Uk ◽  
Control Association

AbstractThe case-control association study is a powerful method for identifying genetic variants that influence disease risk. However, the collection of cases can be time-consuming and expensive; if a disease occurs late in life or is rapidly lethal, it may be more practical to identify family members of cases. Here, we show that replacing cases with their first-degree relatives enables genome-wide association studies by proxy (GWAX). In randomly-ascertained cohorts, this approach enables previously infeasible studies of diseases that are absent (or nearly absent) in the cohort. As an illustration, we performed GWAX of 12 common diseases in 116,196 individuals from the UK Biobank. By combining these results with published GWAS summary statistics in a meta-analysis, we replicated established risk loci and identified 17 newly associated risk loci: four in Alzheimer’s disease, eight in coronary artery disease, and five in type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping using family history of disease as a phenotype to be mapped. We anticipate that this approach will prove useful in future genetic studies of complex traits in large population cohorts.

scholarly journals Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Zhenyao Ye ◽  
Chen Mo ◽  
Hongjie Ke ◽  
Qi Yan ◽  
Chixiang Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Tissue Expression ◽  
Nicotine Addiction ◽  
Reference Panel ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Eqtl Data ◽  
Brain Tissues

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

BioLearner: A Machine Learning-Powered Smart Heart Disease Risk Prediction System Utilizing Biomedical Markers

2021 ◽  
Author(s):  
Syed Saad Amer ◽  
Gurleen Wander ◽  
Manmeet Singh ◽  
Rami Bahsoon ◽  
Nicholas R. Jennings ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Risk ◽  
Support Vector ◽  
Prediction System ◽  
Developing Heart ◽  
Nearest Neighbours ◽  
Different Types ◽  
History Of ◽  
The Uk

Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include Age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as [Formula: see text] Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods.

Plasma adiponectin and risk of asthma: observational analysis, genetic Mendelian randomisation and meta-analysis

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2021-217675
Author(s):  
Maria Booth Nielsen ◽  
Børge G Nordestgaard ◽  
Marianne Benn ◽  
Yunus Çolak
Keyword(s):  
General Population ◽  
Population Study ◽  
Genetic Variants ◽  
Meta Analysis ◽  
High Plasma ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
General Population Study ◽  
Plasma Adiponectin ◽  
The Uk

BackgroundAdiponectin, an adipocyte-secreted protein-hormone with inflammatory properties, has a potentially important role in the development and progression of asthma. Unravelling whether adiponectin is a causal risk factor for asthma is an important issue to clarify as adiponectin could be a potential novel drug target for the treatment of asthma.ObjectiveWe tested the hypothesis that plasma adiponectin is associated observationally and causally (using genetic variants as instrumental variables) with risk of asthma.MethodsIn the Copenhagen General Population Study, we did an observational analysis in 28 845 individuals (2278 asthma cases) with plasma adiponectin measurements, and a genetic one-sample Mendelian randomisation analysis in 94 868 individuals (7128 asthma cases) with 4 genetic variants. Furthermore, in the UK Biobank, we did a genetic two-sample Mendelian randomisation analysis in 462 933 individuals (53 598 asthma cases) with 12 genetic variants. Lastly, we meta-analysed the genetic findings.ResultsWhile a 1 unit log-transformed higher plasma adiponectin in the Copenhagen General Population Study was associated with an observational OR of 1.65 (95% CI 1.29 to 2.08) for asthma, the corresponding genetic causal OR was 1.03 (95% CI 0.75 to 1.42). The genetic causal OR for asthma in the UK Biobank was 1.00 (95% CI 0.99 to 1.00). Lastly, genetic meta-analysis confirmed lack of association between genetically high plasma adiponectin and causal OR for asthma.ConclusionObservationally, high plasma adiponectin is associated with increased risk of asthma; however, genetic evidence could not support a causal association between plasma adiponectin and asthma.

Genetically determined risk of keratinocyte carcinoma and risk of other cancers

2020 ◽  
Author(s):  
Jean Claude Dusingize ◽  
Catherine M Olsen ◽  
Jiyuan An ◽  
Nirmala Pandeya ◽  
Upekha E Liyanage ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Epidemiological Studies ◽  
Risk Scores ◽  
Cancer Site ◽  
Uk Biobank ◽  
Individual Level ◽  
Increased Risk ◽  
The Uk

Abstract Background Epidemiological studies have consistently documented an increased risk of developing primary non-cutaneous malignancies among people with a history of keratinocyte carcinoma (KC). However, the mechanisms underlying this association remain unclear. We conducted two separate analyses to test whether genetically predicted KC is related to the risk of developing cancers at other sites. Methods In the first approach (one-sample), we calculated the polygenic risk scores (PRS) for KC using individual-level data in the UK Biobank (n = 394 306) and QSkin cohort (n = 16 896). The association between the KC PRS and each cancer site was assessed using logistic regression. In the secondary (two-sample) approach, we used genome-wide association study (GWAS) summary statistics identified from the most recent GWAS meta-analysis of KC and obtained GWAS data for each cancer site from the UK-Biobank participants only. We used inverse-variance-weighted methods to estimate risks across all genetic variants. Results Using the one-sample approach, we found that the risks of cancer at other sites increased monotonically with KC PRS quartiles, with an odds ratio (OR) of 1.16, 95% confidence interval (CI): 1.13–1.19 for those in KC PRS quartile 4 compared with those in quartile 1. In the two-sample approach, the pooled risk of developing other cancers was statistically significantly elevated, with an OR of 1.05, 95% CI: 1.03–1.07 per doubling in the odds of KC. We observed similar trends of increasing cancer risk with increasing KC PRS in the QSkin cohort. Conclusion Two different genetic approaches provide compelling evidence that an instrumental variable for KC constructed from genetic variants predicts the risk of cancers at other sites.

scholarly journals Sugar-sweetened Beverage Consumption May Modify Associations between Genetic Variants in the CHREBP Locus and HDL-C and TG Concentrations

2021 ◽  
Author(s):  
Danielle E. Haslam ◽  
Gina M. Peloso ◽  
Melanie Guirette ◽  
Fumiaki Imamura ◽  
Traci M. Bartz ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Meta Analysis ◽  
Density Lipoprotein ◽  
Nucleotide Polymorphisms ◽  
Linear Regression Models ◽  
Aging Research ◽  
Sugar Sweetened Beverage ◽  
Genomic Epidemiology ◽  
Sweetened Beverage ◽  
The Uk

Background - Carbohydrate responsive element binding protein (ChREBP) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the CHREBP locus have separately been linked to high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) concentrations. We hypothesized SSB consumption would modify the association between genetic variants in the CHREBP locus and dyslipidemia. Methods - Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (N=63,599) and the UK Biobank (UKB) (N=59,220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and TG concentrations using linear regression models. A total of 1,606 single-nucleotide polymorphisms (SNPs) within or near CHREBP were considered. SSB consumption was estimated from validated questionnaires and participants were grouped by their estimated intake. Results - In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers [β (95% CI) = 2.12 (1.16, 3.07) mg/dl; p <0.0002], but not significantly among the lowest SSB consumers ( p =0.81; p Diff <0.0001). Similar results were observed for two additional variants (rs35709627 and rs71556736). For TG, rs55673514 was positively associated with TG concentrations only among the highest SSB consumers [β (95% CI): 0.06 (0.02, 0.09) per allele count for log(mg/dl), p =0.001], but not the lowest SSB consumers ( p =0.84; p Diff =0.0005). Conclusions - Our results identified genetic variants in the CHREBP locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in TG concentrations.

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