Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking

Leveraging genetic data for predicting consumer choices of alcoholic products

Purpose

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

Design/methodology/approach

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

Findings

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

Originality/value

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

Complex relationship between TAS2 receptor variations, bitterness perception, and alcohol consumption observed in a population of wine consumers

Our ability to taste bitterness affects our food choices and alcohol consumption. Alleles in the taste 2 receptor member TAS2R38 have been linked to the ability to perceive bitterness in bitter-tasting compounds and in many foods, and people with these bitterness sensitivity alleles have been shown to be less likely to consume alcohol, presumably because of alcohol's bitter taste. In a survey of 519 participants, almost all of whom regularly consumed alcohol, we observed that genetic variants in TAS2R38 were significantly associated with both increased alcohol consumption and the ability to perceive bitterness in several foods and a bitter chemical. In total, we assayed 39 variants in 25 genes that have been implicated in the genetics of taste perception, and no other variants predicted alcohol consumption. Perception of bitterness in broccoli and a preference for black coffee were also positively associated with alcohol consumption. As the consumption of alcohol is a social activity there may be incentive to appreciate its bitter aspects, and increased perception of bitterness could therefore be associated with consumption of some bitter beverages. As this study's respondents were predominantly frequent consumers of alcohol, these findings may be consistent with previous studies that have seen that increased experience in the consumption of wine is associated with an increased perception of PROP bitterness. Further work elucidating the complex relationship between the genetics of bitter perception and alcohol consumption will better describe these connections.

A Brief Review of Genetic Approaches to the Study of Food Preferences: Current Knowledge and Future Directions

Genetic variation plays a crucial role in individual differences in food preferences which ultimately influence food selection and health. Our current understanding of this pathway has been informed through twin studies (to assess the heritability of food preferences), candidate gene studies, and genome-wide association studies (GWAS). However, most of this literature is mainly focused on genes previously identified as having taste or smell functions. New data suggests that genes not associated with taste or smell perception may be involved in food preferences and contribute to health outcomes. This review highlights these emerging findings and suggests a polygenic risk assessment approach to explore new relationships between food preferences and health risks.

Genetic determinants of beverage consumption: Implications for nutrition and health

Beverages make important contributions to nutritional intake and their role in health has received much attention. This review focuses on the genetic determinants of common beverage consumption and how research in this field is contributing insight to what and how much we consume and why this genetic knowledge matters from a research and public health perspective. The earliest efforts in gene-beverage behavior mapping involved genetic linkage and candidate gene analysis but these approaches have been largely replaced by genome-wide association studies (GWAS). GWAS have identified biologically plausible loci underlying alcohol and coffee drinking behavior. No GWAS has identified variants specifically associated with consumption of tea, juice, soda, wine, beer, milk or any other common beverage. Thus far, GWAS highlight an important behavior-reward component (as opposed to taste) to beverage consumption which may serve as a potential barrier to dietary interventions. Loci identified have been used in Mendelian randomization and gene×beverage interaction analysis of disease but results have been mixed. This research is necessary as it informs the clinical relevance of SNP-beverage associations and thus genotype-based personalized nutrition, which is gaining interest in the commercial and public health sectors.

Influence of yeasts of the genus Saccharomyces and not Saccharomyces in elaboration of white wines

Traditionally the Serra Gaúcha region, in the state of Rio Grande do Sul, is known as a barn productor of excellent quality wines. The aromatic complexity of wine in general, and white wine in particular, is what is essential to satisfy an increasingly demanding consumer. Among the most used techniques to achieve this purpose is the addition of yeasts of different genres, thus providing a range of aromatic characteristics that are accentuated in it. In this sense, the objective of this work was to evaluate the use of different strains of yeasts in white wines of Riesling Italic variety, made from grapes grown in the Serra Gaúcha region, in the state of Rio Grande do Sul. Based on the results, it was possible to observe that there were no significant differences between the treatments in relation to the variables pH, total acidity and alcoholic degree. However, with respect to the fermentation yield, T3 was the treatment that obtained the best performance, reaching the ideal density (below 1000 g.cm3) in the course of 6 to 7 days, followed by treatments T1 (Saccharomyces cerevisae) and T5 (Levulia pulcherrima) (7 to 8 days), with treatments T2 (Saccharomyces cerevisaecerevisae) and T4 (Torulaspora delbrueckii), which had the lowest performance (9 to 10 days). The T4 treatment was also the one that presented a higher amount of residual sugars, which proves the less activity of this yeast in more alcoholic means. All the yeasts used have a low production of volatile acidity, but the lowest concentration was Saccharomyces cerevisae cerevisae, used in treatment T2 (0.1 gL−1), and the other treatments presented higher concentrations (0, 4 to 0.5 gL−1), although it is still within the parameters considered ideal for obtaining quality white wines. T2 was also the treatment with lower concentrations of glycerol (5.1 g.L−1). This compound is mainly formed by glyceropyruvic fermentation through the metabolism of yeasts at the beginning of alcoholic fermentation, usually being produced by the first 50 grams of fermented sugars, which may indicate a greater activity of this yeast in this fermentation period. In general, we can say that all the yeasts used have the potential to produce quality white wines, since they had good fermentation yields, satisfactory production of alcohol and glycerol, and low production of volatile acidity.

What has GWAS done for HLA and disease associations?

The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.

The CHRM3 gene is implicated in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia

BACKGROUND

The genetic influences in human brain structure and function and impaired functional connectivities are the hallmarks of the schizophrenic brain. To explore how common genetic variants affect the connectivities in schizophrenia, we applied genome-wide association studies assaying the abnormal neural connectivities in schizophrenia as quantitative traits.

METHOD

We recruited 161 first-onset and treatment-naive patients with schizophrenia and 150 healthy controls. All the participants underwent scanning with a 3 T-magnetic resonance imaging scanner to acquire structural and functional imaging data and genotyping using the HumanOmniZhongHua-8 BeadChip. The brain-wide association study approach was employed to account for the inherent modular nature of brain connectivities.

RESULTS

We found differences in four abnormal functional connectivities [left rectus to left thalamus (REC.L-THA.L), left rectus to right thalamus (REC.L-THA.R), left superior orbital cortex to left thalamus (ORBsup.L-THA.L) and left superior orbital cortex to right thalamus (ORBsup.L-THA.R)] between the two groups. Univariate single nucleotide polymorphism (SNP)-based association revealed that the SNP rs6800381, located nearest to the CHRM3 (cholinergic receptor, muscarinic 3) gene, reached genomic significance (p = 1.768 × 10-8) using REC.L-THA.R as the phenotype. Multivariate gene-based association revealed that the FAM12A (family with sequence similarity 12, member A) gene nearly reached genomic significance (nominal p = 2.22 × 10-6, corrected p = 0.05).

CONCLUSIONS

Overall, we identified the first evidence that the CHRM3 gene plays a role in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia. Identification of these genetic variants using neuroimaging genetics provides insights into the causes of variability in human brain development, and may help us determine the mechanisms of dysfunction in schizophrenia.