Gene-based association analysis identifies 190 genes affecting neuroticism

AbstractNeuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called “polygene pruning”. As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.

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Gene-based association analysis identified 190 genes with polymorphisms affecting neuroticism

10.1101/2020.08.06.240721 ◽

2020 ◽

Author(s):

Nadezhda M. Belonogova ◽

Irina V. Zorkoltseva ◽

Yakov A. Tsepilov ◽

Tatiana I. Axenovich

Keyword(s):

Gene Expression ◽

Detailed Analysis ◽

Association Analysis ◽

Genetic Variants ◽

Bioinformatics Analysis ◽

Summary Statistics ◽

Uk Biobank ◽

Protein Coding ◽

Pathogenic Variants ◽

Genome Wide

AbstractRecent genome-wide studies have reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes whose polymorphisms can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of genetic variants within a gene differing in their protein coding properties. To guard against the influence of strong GWAS signals outside the gene, we used the specially designed procedure. As a result, we identified 190 genes associated with neuroticism due to their polymorphisms. Thirty eight of these genes were novel. Within all genes identified, we distinguished two slightly overlapping groups comprising genes that demonstrated association when using protein-coding and non-coding SNPs. Many genes from the first group included potentially pathogenic variants. For some genes from the second group, we found evidence of pleiotropy with gene expression. We demonstrated that the association of almost two hundred known genes could be inflated by the GWAS signals outside the gene. Using bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes influencing the trait by their polymorphisms are the most appropriate candidate genes.

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Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

10.1101/2021.12.24.21268381 ◽

2021 ◽

Author(s):

Abhishek Nag ◽

Lawrence Middleton ◽

Ryan S Dhindsa ◽

Dimitrios Vitsios ◽

Eleanor M Wigmore ◽

...

Keyword(s):

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Protein Coding ◽

The Uk ◽

Metabolic Biomarkers ◽

Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

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0014 Genome-wide Association Analysis Of Excessive Daytime Sleepiness In The Uk Biobank Identifies 42 Novel Loci

SLEEP ◽

10.1093/sleep/zsy061.013 ◽

2018 ◽

Vol 41 (suppl_1) ◽

pp. A6-A6

Author(s):

H Wang ◽

J M Lane ◽

H S Dashti ◽

S Jones ◽

B E Cade ◽

...

Keyword(s):

Association Analysis ◽

Excessive Daytime Sleepiness ◽

Daytime Sleepiness ◽

Genome Wide Association ◽

Uk Biobank ◽

Genome Wide Association Analysis ◽

Genome Wide ◽

The Uk

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Cancer PRSweb – an Online Repository with Polygenic Risk Scores (PRS) for Major Cancer Traits and Their Phenome-wide Exploration in Two Independent Biobanks

10.1101/2020.01.22.915751 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lars G. Fritsche ◽

Snehal Patil ◽

Lauren J. Beesley ◽

Peter VandeHaar ◽

Maxwell Salvatore ◽

...

Keyword(s):

Association Studies ◽

Predictive Performance ◽

Risk Scores ◽

P Value ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Polygenic Risk ◽

Genome Wide ◽

Study Results

AbstractTo facilitate scientific collaboration on polygenic risk scores (PRS) research, we created an extensive PRS online repository for 49 common cancer traits integrating freely available genome-wide association studies (GWAS) summary statistics from three sources: published GWAS, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWAS. Our framework condenses these summary statistics into PRS using various approaches such as linkage disequilibrium pruning / p-value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRS in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance, calibration, and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRS. We expect this integrated platform to accelerate PRS-related cancer research.

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Genome-wide heritability analysis of severe malaria susceptibility and resistance reveals evidence of polygenic inheritance

10.1101/649095 ◽

2019 ◽

Author(s):

Delesa Damena ◽

Emile R. Chimusa

Keyword(s):

Severe Malaria ◽

Genome Wide Association Study ◽

Summary Statistics ◽

Cell Type ◽

Protein Coding ◽

Polygenic Inheritance ◽

Genome Wide ◽

Cell Type Specific ◽

Malaria Susceptibility ◽

Susceptibility And Resistance

ABSTRACTObjectiveEstimating SNP-heritability (h2g) of severe malaria/resistance and its distribution across the genome might shed new light in to the underlying biology.MethodWe investigated h2g of severe malaria susceptibility and resistance from genome-wide association study (GWAS) dataset (sample size =11, 657). We partitioned the h2g in to chromosomes, allele frequencies and annotations. We further examined none-cell type specific and cell type specific enrichments from GWAS-summary statistics.ResultsWe estimated the h2g of severe malaria at 0.21 (se=0.05, p=2.7×10−5), 0.20 (se =0.05, p=7.5×10−5) and 0.17 (se =0.05, p= 7.2×10−4) in Gambian, Kenyan and Malawi populations, respectively. The h2g attributed to the GWAS significant SNPs and the well-known sickle cell (HbS) variant was approximately 0.07 and 0.03, respectively. We prepared African population reference panel and obtained comparable h2g estimate (0.21 (se = 0.02, p< 1×10−5)) from GWAS-summary statistics meta-analysed across the three populations. Partitioning analysis from raw genotype data showed significant enrichment of h2g in protein coding genic SNPs while summary statistics analysis suggests pattern of enrichment in multiple categories.ConclusionWe report for the first time that the heritability of malaria susceptibility and resistance is largely ascribed by common SNPs and the causal variants are overrepresented in protein coding regions of the genome. Overall, our results suggest that malaria susceptibility and resistance is a polygenic trait. Further studies with larger sample sizes are needed to better understand the underpinning genetics of resistance and susceptibility to severe malaria.

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The Frequency, Penetrance and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants

10.1101/2021.11.22.21266651 ◽

2021 ◽

Author(s):

Ravi Shah ◽

Babken Asatryan ◽

Ghaith Sharaf Dabbagh ◽

Nay Aung ◽

Mohammed Y Khanji ◽

...

Keyword(s):

Dilated Cardiomyopathy ◽

Adult Population ◽

Variant Calling ◽

Gene Variants ◽

Uk Biobank ◽

Ventricular Dilation ◽

Negative Results ◽

Pathogenic Variants ◽

The Uk ◽

Pathogenic Gene

Background: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general, adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional and arrhythmic disease features. Methods: UK Biobank participants who had undergone whole exome sequencing (WES), ECG and cardiovascular magnetic resonance (CMR) imaging were selected for study. Three different variant calling strategies (one primary and two secondary) were used to identify subjects with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded to either 1) DCM (clinical or CMR diagnosis); 2) early DCM features, including arrhythmia and/or conduction disease, isolated ventricular dilation, and hypokinetic non-dilated cardiomyopathy; or 3) phenotype-negative. Results: Among 18,665 individuals included in the study, 1,463 (7.8%) subjects possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and CMR analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in 15.9% of individuals with putative pathogenic variants. Arrhythmias and/or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic non-dilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all three variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes, as compared to those with variants in moderate/limited evidence genes. Conclusions: In the UK Biobank, approximately 1/6 of adults with putative pathogenic variants in DCM genes exhibited a subclinical phenotype based on ECG and/or CMR, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation/dysfunction.

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RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants

Nucleic Acids Research ◽

10.1093/nar/gkz327 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W106-W113 ◽

Cited By ~ 2

Author(s):

Jana Marie Schwarz ◽

Daniela Hombach ◽

Sebastian Köhler ◽

David N Cooper ◽

Markus Schuelke ◽

...

Keyword(s):

Disease Genes ◽

Phenotypic Data ◽

Protein Coding ◽

Web Based ◽

Genome Wide ◽

Dna Variants ◽

Regulatory Effects ◽

Key Aspects ◽

User Friendly ◽

Coding Variants

Abstract RegulationSpotter is a web-based tool for the user-friendly annotation and interpretation of DNA variants located outside of protein-coding transcripts (extratranscriptic variants). It is designed for clinicians and researchers who wish to assess the potential impact of the considerable number of non-coding variants found in Whole Genome Sequencing runs. It annotates individual variants with underlying regulatory features in an intuitive way by assessing over 100 genome-wide annotations. Additionally, it calculates a score, which reflects the regulatory potential of the variant region. Its dichotomous classifications, ‘functional’ or ‘non-functional’, and a human-readable presentation of the underlying evidence allow a biologically meaningful interpretation of the score. The output shows key aspects of every variant and allows rapid access to more detailed information about its possible role in gene regulation. RegulationSpotter can either analyse single variants or complete VCF files. Variants located within protein-coding transcripts are automatically assessed by MutationTaster as well as by RegulationSpotter to account for possible intragenic regulatory effects. RegulationSpotter offers the possibility of using phenotypic data to focus on known disease genes or genomic elements interacting with them. RegulationSpotter is freely available at https://www.regulationspotter.org.

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