scholarly journals Heritability and genetic variance of dementia with Lewy bodies

2018 ◽  
Author(s):  
Rita Guerreiro ◽  
Valentina Escott-Price ◽  
Dena G. Hernandez ◽  
Celia Kun-Rodrigues ◽  
Owen A. Ross ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Complex Traits ◽  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Lewy Bodies ◽  
Significant Snps ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Appreciable Effect ◽  
Risk Variants

AbstractRecent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinson’s disease (PD) or Alzheimer’s disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

scholarly journals Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications

2018 ◽  
Author(s):  
Simon Haworth ◽  
Ruth Mitchell ◽  
Laura Corbin ◽  
Kaitlin H Wade ◽  
Tom Dudding ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Data ◽  
Population Based ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Common Genetic Variants ◽  
The Uk

Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.We found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

scholarly journals Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure

2019 ◽  
Author(s):  
Vincent Laville ◽  
Timothy Majarian ◽  
Yun J Sung ◽  
Karen Schwander ◽  
Mary F Feitosa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Biological Significance ◽  
Genetic Effect ◽  
Cell Types ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genetic Associations ◽  
Genetic Heritability ◽  
Meta Analyses

AbstractTheCHARGE Gene-Lifestyle Interactions Working Groupis a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

scholarly journals Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies

2019 ◽  
Vol 40 (15) ◽  
pp. 4537-4550 ◽  
Author(s):  
Arianna Sala ◽  
Silvia Paola Caminiti ◽  
Leonardo Iaccarino ◽  
Luca Beretta ◽  
Sandro Iannaccone ◽  
...  
Keyword(s):  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Brain Networks ◽  
Lewy Bodies

scholarly journals Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci

2016 ◽  
Vol 47 (6) ◽  
pp. 1116-1125 ◽  
Author(s):  
M. Liu ◽  
S. M. Malone ◽  
U. Vaidyanathan ◽  
M. C. Keller ◽  
G. Abecasis ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Electrodermal Activity ◽  
Association Studies ◽  
Causal Chain ◽  
Event Related Potential ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disorder Risk

BackgroundEndophenotypes are laboratory-based measures hypothesized to lie in the causal chain between genes and clinical disorder, and to serve as a more powerful way to identify genes associated with the disorder. One promise of endophenotypes is that they may assist in elucidating the neurobehavioral mechanisms by which an associated genetic polymorphism affects disorder risk in complex traits. We evaluated this promise by testing the extent to which variants discovered to be associated with schizophrenia through large-scale meta-analysis show associations with psychophysiological endophenotypes.MethodWe genome-wide genotyped and imputed 4905 individuals. Of these, 1837 were whole-genome-sequenced at 11× depth. In a community-based sample, we conducted targeted tests of variants within schizophrenia-associated loci, as well as genome-wide polygenic tests of association, with 17 psychophysiological endophenotypes including acoustic startle response and affective startle modulation, antisaccade, multiple frequencies of resting electroencephalogram (EEG), electrodermal activity and P300 event-related potential.ResultsUsing single variant tests and gene-based tests we found suggestive evidence for an association between contactin 4 (CNTN4) and antisaccade and P300. We were unable to find any other variant or gene within the 108 schizophrenia loci significantly associated with any of our 17 endophenotypes. Polygenic risk scores indexing genetic vulnerability to schizophrenia were not related to any of the psychophysiological endophenotypes after correction for multiple testing.ConclusionsThe results indicate significant difficulty in using psychophysiological endophenotypes to characterize the genetically influenced neurobehavioral mechanisms by which risk loci identified in genome-wide association studies affect disorder risk.

scholarly journals Genetics Contributes to Concomitant Pathology and Clinical Presentation in Dementia with Lewy Bodies

2021 ◽  
pp. 1-11
Author(s):  
Sven J. van der Lee ◽  
Inger van Steenoven ◽  
Marleen van de Beek ◽  
Niccolo Tési ◽  
Iris E. Jansen ◽  
...  
Keyword(s):  
Dementia With Lewy Bodies ◽  
Mini Mental State Examination ◽  
Disease Duration ◽  
Age At Onset ◽  
Lewy Bodies ◽  
Risk Scores ◽  
Clinical Measures ◽  
Core Symptoms ◽  
State Examination

Background: Dementia with Lewy bodies (DLB) is a complex, progressive neurodegenerative disease with considerable phenotypic, pathological, and genetic heterogeneity. Objective: We tested if genetic variants in part explain the heterogeneity in DLB. Methods: We tested the effects of variants previously associated with DLB (near APOE, GBA, and SNCA) and polygenic risk scores for Alzheimer’s disease (AD-PRS) and Parkinson’s disease (PD-PRS). We studied 190 probable DLB patients from the Alzheimer’s dementia Cohort and compared them to 2,552 control subjects. The p-tau/Aβ 1–42 ratio in cerebrospinal fluid was used as in vivo proxy to separate DLB cases into DLB with concomitant AD pathology (DLB-AD) or DLB without AD (DLB-pure). We studied the clinical measures age, Mini-Mental State Examination (MMSE), and the presence of core symptoms at diagnosis and disease duration. Results: We found that all studied genetic factors significantly associated with DLB risk (all-DLB). Second, we stratified the DLB patients by the presence of concomitant AD pathology and found that APOE ɛ4 and the AD-PRS associated specifically with DLB-AD, but less with DLB-pure. In addition, the GBA p.E365K variant showed strong associated with DLB-pure and less with DLB-AD. Last, we studied the clinical measures and found that APOE ɛ4 associated with reduced MMSE, higher odds to have fluctuations and a shorter disease duration. In addition, the GBA p.E365K variant reduced the age at onset by 5.7 years, but the other variants and the PRS did not associate with clinical features. Conclusion: These finding increase our understanding of the pathological and clinical heterogeneity in DLB.

scholarly journals Altered structural connectivity networks in dementia with lewy bodies

2021 ◽  
Author(s):  
Nicolas Nicastro ◽  
Elijah Mak ◽  
Ajenthan Surendranathan ◽  
Timothy Rittman ◽  
James B. Rowe ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Lewy Bodies ◽  
Occipital Lobe ◽  
Mri Imaging ◽  
Structural Networks ◽  
Graph Parameters

AbstractThe impairment of large-scale brain networks has been observed in dementia with Lewy bodies (DLB) using functional connectivity, but the potential for an analogous effect on structural covariance patterns has not been determined. Twenty-four probable DLB subjects (mean age 74.3 ± 6.7 years, 16.7% female) and 23 similarly aged Controls were included. All participants underwent 3T MRI imaging with high-resolution T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence. Graph theoretical analyses were performed using variation in regional cortical thickness to construct a structural association matrix with pairwise Pearson correlations. Global and nodal graph parameters were computed to assess between-group differences and community structure was studied in order to quantify large-scale brain networks in both groups. In comparison to Controls, DLB subjects had decreased global efficiency, clustering, modularity and small-worldness of structural networks (all p < 0.05). Nodal measures showed that DLB subjects also had decreased clustering in bilateral temporal regions and decreased closeness centrality in extensive areas including right middle frontal, left cingulate and bilateral occipital lobe (all false-discovery rate (FDR)-corrected q < 0.05). Whereas four distinct modules could be clearly identified in Controls, DLB showed extensively disorganized modules, including default-mode network and dorsal attentional network. Our results suggest a marked impairment in large-scale brain structural networks in DLB, mirroring functional connectivity networks disruption.

scholarly journals Large-scale analyses provide no evidence for gene-gene interactions influencing type 2 diabetes risk

2020 ◽  
Author(s):  
Ada Admin ◽  
Abhishek Nag ◽  
Mark I McCarthy ◽  
Anubha Mahajan
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Gene Interaction ◽  
Risk Scores ◽  
Biological Processes ◽  
Common Variants ◽  
Risk Variants ◽  
Significant Gene ◽  
Linear Interactions

A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D). Despite longstanding interest in understanding whether non-linear interactions between these risk-variants additionally influence T2D-risk, the ability to detect significant gene-gene interaction (GGI) effects has to date been limited. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D-risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 T2D cases). In addition to conventional single variant-based analysis, we employed a complementary polygenic score-based approach which included partitioned T2D-risk scores that capture biological processes relevant to T2D pathophysiology. Nevertheless, we found no evidence in support of GGI effects influencing T2D-risk. The present study was powered to detect interactions between common variants with odds ratios >1.2, so these findings place limits on the contribution of GGIs to the overall heritability of T2D.<b> </b>

scholarly journals Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bingxin Zhao ◽  
Yue Shan ◽  
Yue Yang ◽  
Zhaolong Yu ◽  
Tengfei Li ◽  
...  
Keyword(s):  
Association Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Brain Structures ◽  
Imaging Genetics ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Wide Range

AbstractStructural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10−8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10−31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.

scholarly journals Large-scale analyses provide no evidence for gene-gene interactions influencing type 2 diabetes risk

2020 ◽  
Author(s):  
Ada Admin ◽  
Abhishek Nag ◽  
Mark I McCarthy ◽  
Anubha Mahajan
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Gene Interaction ◽  
Risk Scores ◽  
Biological Processes ◽  
Common Variants ◽  
Risk Variants ◽  
Significant Gene ◽  
Linear Interactions

A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D). Despite longstanding interest in understanding whether non-linear interactions between these risk-variants additionally influence T2D-risk, the ability to detect significant gene-gene interaction (GGI) effects has to date been limited. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D-risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 T2D cases). In addition to conventional single variant-based analysis, we employed a complementary polygenic score-based approach which included partitioned T2D-risk scores that capture biological processes relevant to T2D pathophysiology. Nevertheless, we found no evidence in support of GGI effects influencing T2D-risk. The present study was powered to detect interactions between common variants with odds ratios >1.2, so these findings place limits on the contribution of GGIs to the overall heritability of T2D.<b> </b>

scholarly journals Incidence and Comorbidity of Dementia with Lewy Bodies: A Population-Based Cohort Study

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Sheng-Kung Yang ◽  
Weishan Chen ◽  
Chun-Hsien Su ◽  
Chung-Hsiang Liu
Keyword(s):  
Cohort Study ◽  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Retrospective Studies ◽  
Lewy Bodies ◽  
Epidemiological Studies ◽  
Population Based ◽  
Research Database ◽  
The Third ◽  
Taiwan National Health Insurance

Background and Aims. Dementia with Lewy bodies (DLB) is the third most common form of dementia. Epidemiological studies of DLB in Taiwan are scarce. In this study, we estimated the incidence of DLB and comorbidity in the population of Taiwan. Methods. Data were obtained from the Taiwan National Health Insurance Research Database (NHIRD). DLB patients between 2000 and 2013 were enrolled in assessments of incidence and comorbidity. Results. The incidence of DLB was shown to be 7.10 per 100,000 person-years (95% CI = 6.63–7.59), which increased with age. The average age at diagnosis was 76.3, and this was higher for males than for females. The comorbidity rates of hypertension and hyperlipidemia in DLB patients were higher in females than in males. Conclusions. Epidemiologic data from large-scale retrospective studies is crucial to the prevention of DLB.

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