scholarly journals Genome-wide environmental interaction analysis identifies dietary habit related risk loci for depression and intelligence in UK Biobank

2020 ◽  
Author(s):  
Bolun Cheng ◽  
Xiaomeng Chu ◽  
Yan Wen ◽  
Yumeng Jia ◽  
Chujun Liang ◽  
...  
Keyword(s):  
Dietary Habits ◽  
Interaction Analysis ◽  
Significant Snps ◽  
Dietary Habit ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Genome Wide ◽  
A Genome ◽  
Study Results ◽  
The Individual

Abstract Background Dietary habits have considerable impact on brain development and mental health. Our aim is to explore the possible association of dietary habits with depression and intelligence. Methods A total of 814 independent loci from a genome-wide association study (GWAS) of dietary habits were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habits related traits. The individual genotype data were obtained from UK Biobank cohort. Regression analyses were then conducted to evaluate the possible association of dietary habits with depression (including 153,549 subjects) and intelligence (including 160,121 subjects), respectively. Using dietary habits related PRSs as covariates, PLINK 2.0 was utilized to detect the SNP × dietary habit interaction effect on the risks of depression and intelligence, respectively. Results We detected 32 and 41 candidate dietary habits related traits for depression and intelligence, respectively, such as never eat sugar vs. no sugar restrictions (P = 1.09 × 10− 2) for depression, and coffee type: decaffeinated vs. any other (P = 8.77 × 10− 3) for intelligence. We also detected 22 common dietary habits related traits shared by depression and intelligence, such as red wine glasses per month (Pdepression = 8.75 × 10− 3, Pintelligence = 3.35 × 10− 19), and overall alcohol intake (Pdepression = 3.60 × 10− 2, Pintelligence = 8.31 × 10− 8). Interaction analysis of depression detected OLFM1 with 9 significant SNPs interacted with champagne/white wine glasses per month. Interaction analysis of intelligence detected SYNPO2 with 3 significant SNPs interacted with coffee type: decaffeinated vs. any other. Conclusions Our study results provide novel useful information for understanding how eating habits affecting the intelligence and the risk of depression.

scholarly journals Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1150
Author(s):  
Bolun Cheng ◽  
Xiaomeng Chu ◽  
Xuena Yang ◽  
Yan Wen ◽  
Yumeng Jia ◽  
...  
Keyword(s):  
Mental Health ◽  
Fluid Intelligence ◽  
Dietary Habits ◽  
Interaction Analysis ◽  
Dietary Habit ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Study Results ◽  
The Individual ◽  
The Uk

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.

Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases

2021 ◽  
pp. ASN.2020111599
Author(s):  
Zhi Yu ◽  
Jin Jin ◽  
Adrienne Tin ◽  
Anna Köttgen ◽  
Bing Yu ◽  
...  
Keyword(s):  
Kidney Function ◽  
Kidney Diseases ◽  
Association Studies ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Plasma Proteome ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Genome Wide ◽  
A Genome

Background: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (estimated glomerular filtration rate, eGFR). The relationship of polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. Methods: We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS (N=765,348) and UK Biobank GWAS (90% of the cohort; N=451,508), followed by best parameter selection using the remaining 10% of UK Biobank (N=45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study (N=8,866) with incident chronic kidney disease, kidney failure, and acute kidney injury. We also examined associations between the PRS and 4,877 plasma proteins measured at at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. Results: The developed PRS showed significant associations with all outcomes with hazard ratios (95% CI) per 1 SD lower PRS ranged from 1.06 (1.01, 1.11) to 1.33 (1.28, 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin-C, collagen alpha-1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for 5 proteins including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. Conclusions: A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.

scholarly journals Large-Scale, Genome-Wide Gene-Diet Interaction Testing for HbA1c Using Derived Dietary Patterns in the UK Biobank

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1280-1280
Author(s):  
Kenneth Westerman ◽  
Ye Chen ◽  
Han Chen ◽  
Jose Florez ◽  
Joanne Cole ◽  
...  
Keyword(s):  
Principal Components ◽  
Dietary Patterns ◽  
Genetic Variants ◽  
Interaction Analysis ◽  
European Ancestry ◽  
Uk Biobank ◽  
Genome Wide ◽  
A Genome ◽  
Gene Level ◽  
The Uk

Abstract Objectives Gene-diet interaction analysis can inform the development of precision nutrition for diabetes by uncovering genetic variants whose effects on glycemic traits vary across dietary behaviors. However, due to noise in dietary datasets and the low statistical power inherent in interaction analysis, there is a lack of confident, well-replicated gene-diet interactions for glycemic traits. Emerging computationally-efficient software tools have made it feasible to conduct well-powered, genome-wide interaction analysis in hundreds of thousands of individuals. Here, our objective was to conduct a genome-wide gene-diet interaction analysis for glycated hemoglobin (HbA1c; a measure of hyperglycemia), leveraging the large sample size of the UK Biobank cohort and data-driven dietary patterns to discover genetic variants whose effect is modulated by diet. Methods Food frequency questionnaires were previously used to derive empirical dietary patterns using principal components analysis (FFQ-PCs) in the UK Biobank. FFQ-PCs were used in genome-wide interaction analysis for HbA1c levels in unrelated, non-diabetic individuals of European ancestry (N = 331,610), adjusting for age, sex, and 10 genetic principal components. P-values were calculated for both the interaction (P-int) and a joint test (significance of the variant-HbA1c association combining the main and interaction effects) and the MAGMA tool was used to calculate gene-level enrichment statistics. Results Preliminary results from the first two FFQ-PCs confirmed known genetic loci for HbA1c using the joint test, such as at G6PC2 and GCK. Though no interaction tests reached genome-wide significance, suggestive signals (P-int < 1e-5) emerged at the variant level (including one near TPSD1, which codes for a tryptase and has been linked to red blood cell traits) and the gene level (such as for GTF3C2, which has previously been shown to interact with sleep in impacting lipid traits). Conclusions We have conducted the largest genome-wide study of gene-diet interactions for glycemic traits to-date and identified regions in the genome whose effect on HbA1c may be modulated by dietary intake, suggesting that this approach has the potential to reveal new insights into the genetics of glycemic traits and inform individualized dietary guidelines for diabetes prevention and management. Funding Sources NHLBI.

scholarly journals Clinical and Genetic Associations of Deep Learning-Derived Cardiac Magnetic Resonance-Based Left Ventricular Mass

2022 ◽  
Author(s):  
Shaan Khurshid ◽  
Julieta Lazarte ◽  
James Pirruccello ◽  
Lu-Chen Weng ◽  
Seung Hoan Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Genome Wide Association Study ◽  
Learning Algorithm ◽  
Left Ventricular ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Genome Wide ◽  
A Genome

Increased left ventricular (LV) mass (LVM) and LV hypertrophy (LVH) are risk markers for adverse cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance (CMR) is the gold standard for LVM estimation, but is challenging to obtain at scale, which has limited the power of prior genetic analyses. In the current study, we performed a genome-wide association study (GWAS) of CMR-derived LVM indexed to body surface area (LVMI) estimated using a deep learning algorithm within nearly 50,000 participants from the UK Biobank. We identified 12 independent associations (1 known at TTN and 11 novel) meeting genome-wide significance, implicating several candidate genes previously associated with cardiac contractility and cardiomyopathy. Greater CMR-derived LVMI was associated with higher risk of incident dilated (hazard ratio [HR] 2.58 per 1-SD increase, 95% CI 2.10-3.17) and hypertrophic (HR 2.62, 95% CI 2.09-3.30) cardiomyopathies. A polygenic risk score (PRS) for LVMI was also associated with incident hypertrophic cardiomyopathy within a separate set of UK Biobank participants (HR] 1.12, 95% CI 1.01-1.12) and among individuals in an external Mass General Brigham dataset (HR 1.18, 95% CI 1.01-1.37). In summary, using CMR-derived LVM available at scale, we have identified 12 common variants associated with LVMI (11 novel) and demonstrated that both CMR-derived and genetically determined LVMI are associated with risk of incident cardiomyopathy.

scholarly journals Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

2019 ◽  
Author(s):  
Renato Polimanti ◽  
Raymond K. Walters ◽  
Emma C. Johnson ◽  
Jeanette N. McClintick ◽  
Amy E. Adkins ◽  
...  
Keyword(s):  
Opioid Dependence ◽  
Risk Tolerance ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Opioid Use ◽  
Genome Wide ◽  
A Genome ◽  
Significant Difference ◽  
The Difference ◽  
The Uk

AbstractTo provide novel insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing up to 4,503 OD cases, 4,173 opioid-exposed controls, and 32,500 opioid-unexposed controls. Among the variants identified, rs9291211 was associated with OE (a comparison of exposed vs. unexposed controls; z=-5.39, p=7.2×10−8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N>360,000) found association of this variant with propensity to use dietary supplements (p=1.68×10−8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (z=4.69, p=10−6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (z=5.55, p=2.9×10−8) and a significant association with musculoskeletal disorders in the UK Biobank (p=4.88×10−7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (N=466,571) was positively associated with OD (OD cases vs. unexposed controls, p=8.1×10−5; OD cases vs. exposed controls, p=0.054) and OE (exposed controls vs. unexposed controls, p=3.6×10−5). A PRS based on a GWAS of neuroticism (N=390,278) was positively associated with OD (OD cases vs. unexposed controls, p=3.2×10−5; OD cases vs. exposed controls, p=0.002) but not with OE (p=0.671). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls (exposed vs. unexposed) in studies of addiction.

scholarly journals Vitamin D and the Risks of Depression and Anxiety: An Observational Analysis and Genome-Wide Environment Interaction Study

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3343
Author(s):  
Zhen Zhang ◽  
Xuena Yang ◽  
Yumeng Jia ◽  
Yan Wen ◽  
Shiqiang Cheng ◽  
...  
Keyword(s):  
Vitamin D ◽  
Genome Wide Association Study ◽  
Polygenic Risk Score ◽  
Environment Interaction ◽  
Depression And Anxiety ◽  
Uk Biobank ◽  
Genome Wide ◽  
Study Results ◽  
Functional Relevance ◽  
The Uk

Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10−8, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10−8, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

scholarly journals Evaluating the genetic effects of sex hormone on the development of mental traits: a polygenic score analysis and genome-wide genetic interaction analysis in the UK Biobank cohort

2020 ◽  
Author(s):  
Xiao Liang ◽  
ShiQiang Cheng ◽  
Jing Ye ◽  
XiaoMeng Chu ◽  
Yan Wen ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Fluid Intelligence ◽  
Genetic Interaction ◽  
Interaction Analysis ◽  
Genetic Effects ◽  
Sex Hormone ◽  
Uk Biobank ◽  
Genome Wide ◽  
Study Results ◽  
The Uk

Abstract Objective: To evaluate the genetic effects of sex hormone on the development of mental traits.Methods: The SNPs significantly associated with sex hormone traits were driven from a two-stage genome-wide association study (GWAS). Four sex hormone were selected in this study, including sex hormone-binding globulin (SHBG), testosterone, bioavailable testosterone and estradiol. The polygenic risk scores (PRS) of sex hormone traits were calculated from individual-level genotype data of the UK Biobank cohort. We then used logistic and linear regression models to assess the associations between individual PRS of sex hormone traits and the frequency of alcohol consumption, anxiety, intelligence and so on. Finally, genome-wide genetic interaction study (GWGIS) was performed to detect novel candidate genes interacting with the sex hormone on the development of fluid intelligence and the frequency of smoking and alcohol consumption by PLINK2.0.Results: We observed positive associations between SHBG and the frequency of alcohol consumption (b=0.01, p=3.84×10–11) in males and females. In addition, estradiol was positively associated with the frequency of alcohol consumption (b=0.01, p=1,96×10–8), fluid intelligence (b=0.01, p=1.90×10–2) and the frequency of smoking (b=0.01, p=1.77×10–2) in males. Moreover, SHBG was associated with the frequency of alcohol consumption (b=0.01, p=2.60×10–3), fluid intelligence (b=0.01, p=4.25×10–2) and anxiety (b=-0.01, p=3.79×10–2) in females. Finally, GWGIS identified one significant loci, Tenascin R (TNR) (rs34633780, p=3.45×10–8) interacting with total testosterone for fluid intelligence.Conclusion: Our study results support the genetic effects of sex hormone on the development of intelligence and the frequency of alcohol consumption.

scholarly journals The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yu-Fang Pei ◽  
Yao-Zhong Liu ◽  
Xiao-Lin Yang ◽  
Hong Zhang ◽  
Gui-Juan Feng ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Lean Mass ◽  
Genetic Architecture ◽  
Polygenic Risk Score ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Appendicular Lean Mass ◽  
Genome Wide ◽  
A Genome

Abstract Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown. Here we conducted a genome-wide association study (GWAS) with 450,243 UK Biobank participants to uncover its genetic architecture. A total of 1059 conditionally independent variants from 799 loci were identified at the genome-wide significance level (p < 5 × 10−9), all of which were also significant at p < 5 × 10–5 in both sexes. These variants explained ~15.5% of the phenotypic variance, accounting for more than one quarter of the total ~50% GWAS-attributable heritability. There was no difference in genetic effect between sexes or among different age strata. Heritability was enriched in certain functional categories, such as conserved and coding regions, and in tissues related to the musculoskeletal system. Polygenic risk score prediction well distinguished participants with high and low ALM. The findings are important not only for lean mass but also for other complex diseases, such as type 2 diabetes, as ALM is shown to be a protective factor for type 2 diabetes.

scholarly journals Variants in urate transporters, ADH1B, GCKR and MEPE genes associate with transition from asymptomatic hyperuricaemia to gout: results of the first gout versus asymptomatic hyperuricaemia GWAS in Caucasians using data from the UK Biobank

2021 ◽  
pp. annrheumdis-2020-219796
Author(s):  
Gabriela Sandoval-Plata ◽  
Kevin Morgan ◽  
Abhishek Abhishek
Keyword(s):  
Genome Wide Association Study ◽  
Predictive Ability ◽  
Serum Urate ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Uk Biobank ◽  
Genome Wide ◽  
A Genome ◽  
Using Data ◽  
The Uk

ObjectivesTo perform a genome-wide association study (GWAS) of gout cases versus asymptomatic hyperuricaemia (AH) controls, and gout cases versus normouricaemia controls, and to generate a polygenic risk score (PRS) to determine gout-case versus AH-control status.MethodsGout cases and AH controls (serum urate (SU) ≥6.0 mg/dL) from the UK Biobank were divided into discovery (4934 cases, 56 948 controls) and replication (2115 cases, 24 406 controls) cohorts. GWAS was conducted and PRS generated using summary statistics in discovery cohort as the base dataset and the replication cohort as the target dataset. The predictive ability of the model was evaluated. GWAS were performed to identify variants associated with gout compared with normouricaemic controls using SU <6.0 mg/dL and <7.0 mg/dL thresholds, respectively.ResultsThirteen independent single nucleotide polymorphisms (SNPs) in ABCG2, SLC2A9, SLC22A11, GCKR, MEPE, PPM1K-DT, LOC105377323 and ADH1B reached genome-wide significance and replicated as predictors of AH to gout transition. Twelve of 13 associations were novel for this transition, and rs1229984 (ADH1B) was identified as GWAS locus for gout for the first time. The best PRS model was generated from association data of 17 SNPs; and had predictive ability of 58.5% that increased to 69.2% on including demographic factors. Two novel SNPs rs760077(MTX1) and rs3800307(PRSS16) achieved GWAS significance for association with gout compared with normouricaemic controls using both SU thresholds.ConclusionThe association of urate transporters with gout supports the central role of hyperuricaemia in its pathogenesis. Larger GWAS are required to identify if variants in inflammatory pathways contribute to progression from AH to gout.

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