scholarly journals A GWAS-Derived Polygenic Score for Interleukin-1β is Associated with Hippocampal Volume in Two Samples

2019 ◽  
Author(s):  
Reut Avinun ◽  
Adam Nevo ◽  
Annchen R. Knodt ◽  
Maxwell L. Elliott ◽  
Ahmad R. Hariri
Keyword(s):  
Genome Wide Association Study ◽  
Positive Association ◽  
Hippocampal Volume ◽  
Imaging Genetics ◽  
Interleukin 1Β ◽  
Polygenic Score ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Two Samples ◽  
The Uk

AbstractAccumulating research suggests that the pro-inflammatory cytokine interleukin-1β (IL-1β) has a modulatory effect on the hippocampus, a brain structure important for learning and memory as well as linked with both psychiatric and neurodegenerative disorders. Here, we use an imaging genetics strategy to test an association between an IL-1β polygenic score, derived from summary statistics of a recent genome-wide association study (GWAS) of circulating cytokines, and hippocampal volume, in two independent samples. In the first sample of 512 non-Hispanic Caucasian university students (274 women, mean age 19.78 ± 1.24 years) from the Duke Neurogenetics Study, we identified a significant positive correlation between higher polygenic scores, which presumably reflect higher circulating IL-1β levels, and average hippocampal volume. This positive association was successfully replicated in a second sample of 7,960 white British volunteers (4,158 women, mean age 62.63±7.45 years) from the UK Biobank. Collectively, our results suggest that a functional GWAS-derived score of IL-1β blood circulating levels affects hippocampal volume, and lend further support in humans, to the link between IL-1β and the structure of the hippocampus.

scholarly journals A tool for translating polygenic scores onto the absolute scale using summary statistics

2022 ◽  
Author(s):  
Oliver Pain ◽  
Alexandra C. Gillett ◽  
Jehannine C. Austin ◽  
Lasse Folkersen ◽  
Cathryn M. Lewis
Keyword(s):  
Genome Wide Association Study ◽  
Absolute Risk ◽  
Summary Statistics ◽  
Clinical Implementation ◽  
Polygenic Score ◽  
Absolute Scale ◽  
Polygenic Scores ◽  
The Absolute ◽  
The Uk ◽  
Normally Distributed

AbstractThere is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. We also evaluate methods for estimating polygenic score AUC/R2 from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute (https://opain.github.io/GenoPred/PRS_to_Abs_tool.html). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.

scholarly journals Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease

2017 ◽  
Author(s):  
Amit V. Khera ◽  
Mark Chaffin ◽  
Krishna G. Aragam ◽  
Connor A. Emdin ◽  
Derek Klarin ◽  
...  
Keyword(s):  
Coronary Disease ◽  
Fold Increase ◽  
Predictive Capacity ◽  
Cumulative Impact ◽  
Polygenic Score ◽  
Genome Wide ◽  
A Genome ◽  
Increased Risk ◽  
Polygenic Scores ◽  
Artery Disease

AbstractIdentification of individuals at increased genetic risk for a complex disorder such as coronary disease can facilitate treatments or enhanced screening strategies. A rare monogenic mutation associated with increased cholesterol is present in ~1:250 carriers and confers an up to 4-fold increase in coronary risk when compared with non-carriers. Although individual common polymorphisms have modest predictive capacity, their cumulative impact can be aggregated into a polygenic score. Here, we develop a new, genome-wide polygenic score that aggregates information from 6.6 million common polymorphisms and show that this score can similarly identify individuals with a 4-fold increased risk for coronary disease. In >400,000 participants from UK Biobank, the score conforms to a normal distribution and those in the top 2.5% of the distribution are at 4-fold increased risk compared to the remaining 97.5%. Similar patterns are observed with genome-wide polygenic scores for two additional diseases – breast cancer and severe obesity.One Sentence SummaryA genome-wide polygenic score identifies 2.5% of the population born with a 4-fold increased risk for coronary artery disease.

scholarly journals Genome-Wide Association Meta-Analysis Supports Genes Involved in Valve and Cardiac Development to Associate With Mitral Valve Prolapse

2021 ◽  
Author(s):  
Mengyao Yu ◽  
Sergiy Kyryachenko ◽  
Stephanie Debette ◽  
Philippe Amouyel ◽  
Jean-Jacques Schott ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Cardiac Development ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Additional Risk ◽  
Genome Wide ◽  
The Uk

Background: Mitral valve prolapse (MVP) is a common cardiac valve disease, which affects 1 in 40 in the general population. Previous genome-wide association study have identified 6 risk loci for MVP. But these loci explained only partially the genetic risk for MVP. We aim to identify additional risk loci for MVP by adding data set from the UK Biobank. Methods: We reanalyzed 1007/479 cases from the MVP-France study, 1469/862 controls from the MVP-Nantes study for reimputation genotypes using HRC and TOPMed panels. We also incorporated 434 MVP cases and 4527 controls from the UK Biobank for discovery analyses. Genetic association was conducted using SNPTEST and meta-analyses using METAL. We used FUMA for post-genome-wide association study annotations and MAGMA for gene-based and gene-set analyses. Results: We found TOPMed imputation to perform better in terms of accuracy in the lower ranges of minor allele frequency below 0.1. Our updated meta-analysis included UK Biobank study for ≈8 million common single-nucleotide polymorphisms (minor allele frequency >0.01) and replicated the association on Chr2 as the top association signal near TNS1 . We identified an additional risk locus on Chr1 ( SYT2 ) and 2 suggestive risk loci on chr8 ( MSRA ) and chr19 ( FBXO46 ), all driven by common variants. Gene-based association using MAGMA revealed 6 risk genes for MVP with pronounced expression levels in cardiovascular tissues, especially the heart and globally part of enriched GO terms related to cardiac development. Conclusions: We report an updated meta-analysis genome-wide association study for MVP using dense imputation coverage and an improved case-control sample. We describe several loci and genes with MVP spanning biological mechanisms highly relevant to MVP, especially during valve and heart development.

scholarly journals Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

2018 ◽  
Vol 115 (22) ◽  
pp. E4970-E4979 ◽  
Author(s):  
Thomas A. DiPrete ◽  
Casper A. P. Burik ◽  
Philipp D. Koellinger
Keyword(s):  
Instrumental Variable ◽  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Body Height ◽  
Outcome Variable ◽  
Endogeneity Bias ◽  
Multiple Indicators ◽  
Instrumental Variable Regression ◽  
Genome Wide ◽  
Polygenic Scores

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

2021 ◽  
Author(s):  
Wan-Yu Lin
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Lifestyle Factors ◽  
Biological Age ◽  
Biological Aging ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
A Genome ◽  
The Uk

Abstract Background Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians. Methods I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS). Results A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively. Conclusions A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

scholarly journals Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emrin Horgusluoglu-Moloch ◽  
◽  
Shannon L. Risacher ◽  
Paul K. Crane ◽  
Derrek Hibar ◽  
...  
Keyword(s):  
Association Analysis ◽  
Adult Neurogenesis ◽  
Enrichment Analysis ◽  
Hippocampal Volume ◽  
Imaging Genetics ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Genome Wide

Abstract Adult neurogenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neurogenesis-related pathways are associated with hippocampal volume, we performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer’s Disease Neuroimaging Initiative Cohort (ADNI). Gene-set enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neurogenesis-related processes including neurogenesis, generation of new neurons, neuronal development, and neuronal migration and differentiation. For genes highly represented in the significantly enriched neurogenesis-related pathways, gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neurogenesis related pathways. Our results suggest that neurogenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neurogenesis.

scholarly journals Genetic heterogeneity in depressive symptoms following the death of a spouse: Polygenic score analysis of the US Health and Retirement Study

2016 ◽  
Author(s):  
Benjamin W. Domingue ◽  
Hexuan Liu ◽  
Aysu Okbay ◽  
Daniel W. Belsky
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Life Stress ◽  
Genome Wide Association Study ◽  
The United States ◽  
Health And Retirement Study ◽  
Polygenic Score ◽  
Polygenic Scores ◽  
Retirement Study

AbstractExperience of stressful life events is associated with risk of depression. Yet many exposed individuals do not become depressed. A controversial hypothesis is that genetic factors influence vulnerability to depression following stress. This hypothesis is most commonly tested with a “diathesis-stress” model, in which genes confer excess vulnerability. We tested an alternative model, in which genes may buffer against the depressogenic effects of life stress. We measured the hypothesized genetic buffer using a polygenic score derived from a published genome-wide association study (GWAS) of subjective wellbeing. We tested if married older adults who had higher polygenic scores were less vulnerable to depressive symptoms following the death of their spouse as compared to age-peers who had also lost their spouse and who had lower polygenic scores. We analyzed data from N=9,453 non-Hispanic white adults in the Health and Retirement Study (HRS), a population-representative longitudinal study of older adults in the United States. HRS adults with higher wellbeing polygenic scores experienced fewer depressive symptoms during follow-up. Those who survived death of their spouses during follow-up (n=1,829) experienced a sharp increase in depressive symptoms following the death and returned toward baseline over the following two years. Having a higher polygenic score buffered against increased depressive symptoms following a spouse's death. Effects were small and clinical relevance is uncertain, although polygenic score analyses may provide clues to behavioral pathways that can serve as therapeutic targets. Future studies of gene-environment interplay in depression may benefit from focus on genetics discovered for putative protective factors.

scholarly journals Genetic Instrumental Variable (GIV) regression: Explaining socioeconomic and health outcomes in non-experimental data

2017 ◽  
Author(s):  
Thomas A. DiPrete ◽  
Casper A.P. Burik ◽  
Philipp D. Koellinger
Keyword(s):  
Experimental Data ◽  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Body Height ◽  
Outcome Variable ◽  
Endogeneity Bias ◽  
Multiple Indicators ◽  
Genome Wide ◽  
Alternative Approach ◽  
Polygenic Scores

Identifying causal effects in non-experimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables (i.e. Mendelian Randomization - MR). However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in non-experimental data that would also undermine the ability of MR to correct for endogeneity bias from non-genetic sources. Here, we propose an alternative approach, GIV regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGS) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into non-overlapping subsamples, we obtain multiple indicators of the outcome PGS that can be used as instruments for each other, and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.

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