Genetic determinants of liking and intake of coffee and other bitter foods and beverages

AbstractCoffee is a widely consumed beverage that is naturally bitter and contains caffeine. Genome-wide association studies (GWAS) of coffee drinking have identified genetic variants involved in caffeine-related pathways but not in taste perception. The taste of coffee can be altered by addition of milk/sweetener, which has not been accounted for in GWAS. Using UK and US cohorts, we test the hypotheses that genetic variants related to taste are more strongly associated with consumption of black coffee than with consumption of coffee with milk or sweetener and that genetic variants related to caffeine pathways are not differentially associated with the type of coffee consumed independent of caffeine content. Contrary to our hypotheses, genetically inferred caffeine sensitivity was more strongly associated with coffee taste preferences than with genetically inferred bitter taste perception. These findings extended to tea and dark chocolate. Taste preferences and physiological caffeine effects intertwine in a way that is difficult to distinguish for individuals which may represent conditioned taste preferences.

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Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome Medicine ◽

10.1186/s13073-021-00857-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shuquan Rao ◽

Yao Yao ◽

Daniel E. Bauer

Keyword(s):

Genome Editing ◽

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Functional Studies ◽

Functional Genetics ◽

Genome Wide ◽

Causal Variants ◽

Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

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CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkz1026 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianhua Wang ◽

Dandan Huang ◽

Yao Zhou ◽

Hongcheng Yao ◽

Huanhuan Liu ◽

...

Keyword(s):

Fine Mapping ◽

Genetic Variants ◽

Association Studies ◽

Complex Trait ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Credible Sets ◽

Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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GWASdb: a database for human genetic variants identified by genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkr1182 ◽

2011 ◽

Vol 40 (D1) ◽

pp. D1047-D1054 ◽

Cited By ~ 125

Author(s):

Mulin Jun Li ◽

Panwen Wang ◽

Xiaorong Liu ◽

Ee Lyn Lim ◽

Zhangyong Wang ◽

...

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: pathophysiological implications

Pharmacogenomics ◽

10.2217/pgs-2019-0054 ◽

2019 ◽

Vol 20 (10) ◽

pp. 765-780 ◽

Cited By ~ 1

Author(s):

Diana Cruz ◽

Ricardo Pinto ◽

Margarida Freitas-Silva ◽

José Pedro Nunes ◽

Rui Medeiros

Keyword(s):

Atrial Fibrillation ◽

Genetic Variants ◽

Complex Traits ◽

Association Studies ◽

Cardioembolic Stroke ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Genome Wide ◽

Genome Wide Significance

Atrial fibrillation (AF) and stroke are included in a group of complex traits that have been approached regarding of their study by susceptibility genetic determinants. Since 2007, several genome-wide association studies (GWAS) aiming to identify genetic variants modulating AF risk have been conducted. Thus, 11 GWAS have identified 26 SNPs (p < 5 × 10-2), of which 19 reached genome-wide significance (p < 5 × 10-8). From those variants, seven were also associated with cardioembolic stroke and three reached genome-wide significance in stroke GWAS. These associations may shed a light on putative shared etiologic mechanisms between AF and cardioembolic stroke. Additionally, some of these identified variants have been incorporated in genetic risk scores in order to elucidate new approaches of stroke prediction, prevention and treatment.

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GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkv1317 ◽

2015 ◽

Vol 44 (D1) ◽

pp. D869-D876 ◽

Cited By ~ 93

Author(s):

Mulin Jun Li ◽

Zipeng Liu ◽

Panwen Wang ◽

Maria P. Wong ◽

Matthew R. Nelson ◽

...

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2014-0073 ◽

2015 ◽

Vol 14 (3) ◽

Author(s):

Yun Li ◽

George T. O’Connor ◽

Josée Dupuis ◽

Eric Kolaczyk

Keyword(s):

Genetic Variants ◽

Immunoglobulin E ◽

Group Structure ◽

Association Studies ◽

Genome Wide Association ◽

Estimation Methods ◽

Biological Knowledge ◽

Genome Wide Association Studies ◽

Sparse Regression ◽

Genome Wide

AbstractIn genome-wide association studies (GWAS), it is of interest to identify genetic variants associated with phenotypes. For a given phenotype, the associated genetic variants are usually a sparse subset of all possible variants. Traditional Lasso-type estimation methods can therefore be used to detect important genes. But the relationship between genotypes at one variant and a phenotype may be influenced by other variables, such as sex and life style. Hence it is important to be able to incorporate gene-covariate interactions into the sparse regression model. In addition, because there is biological knowledge on the manner in which genes work together in structured groups, it is desirable to incorporate this information as well. In this paper, we present a novel sparse regression methodology for gene-covariate models in association studies that not only allows such interactions but also considers biological group structure. Simulation results show that our method substantially outperforms another method, in which interaction is considered, but group structure is ignored. Application to data on total plasma immunoglobulin E (IgE) concentrations in the Framingham Heart Study (FHS), using sex and smoking status as covariates, yields several potentially interesting gene-covariate interactions.

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P708Identification of 26 novel loci that confer susceptibility to early-onset coronary artery disease in a Japanese population

European Heart Journal ◽

10.1093/eurheartj/ehz747.0313 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

M Oguri ◽

K Kato ◽

H Horibe ◽

T Fujimaki ◽

J Sakuma ◽

...

Keyword(s):

Coronary Artery Disease ◽

Genetic Variants ◽

Early Onset ◽

Association Studies ◽

Allele Frequencies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Exact Test ◽

Genome Wide ◽

Artery Disease

Abstract Background Early-onset coronary artery disease (CAD) has a strong genetic component. Although genome-wide association studies have identified various genes and loci significantly associated with CAD mainly in European ancestry populations, genetic variants that contribute to susceptibility to this condition in Japanese individuals remain to be identified definitively. Purpose The purpose of the study was to identify genetic variants that confer susceptibility to early-onset CAD in Japanese. We have now performed exome-wide association studies (EWASs) in subjects with early-onset CAD and controls. Methods A total of 7256 individuals aged ≤65 years was enrolled in the study. The EWAS was conducted with 1482 subjects with CAD and 5774 controls. Genotyping of single nucleotide polymorphisms (SNPs) was performed with Illumina Human Exome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. The relation of allele frequencies for 31,465 SNPs that passed quality control to CAD was examined with Fisher's exact test. To compensate for multiple comparisons of allele frequencies with CAD, we applied a false discovery rate (FDR) of <0.05 for statistical significance of association. Results The relation of allele frequencies for 31,465 SNPs to CAD with the use of Fisher's exact test showed that 170 SNPs were significantly (FDR <0.05) associated with CAD. Multivariable logistic regression analysis with adjustment for age, sex, and the prevalence of hypertension, diabetes mellitus, and dyslipidemia revealed that 162 SNPs were significantly (P<0.05) related to CAD. A stepwise forward selection procedure was performed to examine the effects of genotypes for the 162 SNPs on CAD. The 54 SNPs were significant (P<0.05) and independent [coefficient of determination (R2), 0.0008 to 0.0297] determinants of CAD. These SNPs together accounted for 15.5% of the cause of CAD. After examination of results from previous genome-wide association studies and linkage disequilibrium of the identified SNPs, we newly identified 21 genes (RNF2, YEATS2, USP45, ITGB8, TNS3, FAM170B-AS1, PRKG1, BTRC, MKI67, STIM1, OR52E4, KIAA1551, MON2, PLUT, LINC00354, TRPM1, ADAT1, KRT27, LIPE, GFY, EIF3L) and five chromosomal regions (2p13, 4q31.2, 5q12, 13q34, 20q13.2) that were significantly associated with CAD. Gene ontology analysis showed that various biological functions were predicted in the 18 genes identified in the present study. The network analysis revealed that the 18 genes had potential direct or indirect interactions with the 30 genes previously shown to be associated with CAD or with the 228 genes identified in previous genome-wide association studies of CAD. Conclusion We have newly identified 26 loci that confer susceptibility to CAD. Determination of genotypes for the SNPs at these loci may prove informative for assessment of the genetic risk for CAD in Japanese.

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Polymorphisms of ACMSD-TMEM163, MCCC1, and BCKDK-STX1B Are Not Associated with Parkinson’s Disease in Taiwan

Parkinson s Disease ◽

10.1155/2019/3489638 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Kuo-Hsuan Chang ◽

Chiung-Mei Chen ◽

Yi-Chun Chen ◽

Hon-Chung Fung ◽

Yih-Ru Wu

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Amino Acid ◽

Genetic Variants ◽

Transmembrane Protein ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Amino Acid Catabolism ◽

Genome Wide

Previous genome-wide association studies in Caucasian populations suggest that genetic loci in amino acid catabolism may be associated with Parkinson’s disease (PD). However, these genetic disease associations were limitedly reported in Asian populations. Herein, we investigated the effect of top three PD-associated genetic variants related to amino acid catabolism in Caucasians listed on the top risk loci identified by meta-analysis of genome-wide association studies in PDGene database, including aminocarboxymuconate-semialdehyde decarboxylase- (ACMSD-) transmembrane protein 163 (TMEM163) rs6430538, methylcrotonyl-CoA carboxylase 1 (MCCC1) rs12637471, and branched-chain ketoacid dehydrogenase kinase- (BCKDK-) syntaxin 1B (STX1B) rs14235, by genotyping 599 Taiwanese patients with PD and 598 age-matched control subjects. PD patients demonstrate similar allelic and genotypic frequencies in all tested genetic variants. These ethnic discrepancies of genetic variants suggest a distinct genetic background of amino acid catabolism between Taiwanese and Caucasian PD patients.

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