Background Little is known about the contribution of genetic variation to food timing, and breakfast has been determined to exhibit the most heritable meal timing. As breakfast timing and skipping are not routinely measured in large cohort studies, alternative approaches include analyses of correlated traits. Objectives The aim of this study was to elucidate breakfast skipping genetic variants through a proxy-phenotype genome-wide association study (GWAS) for breakfast cereal skipping, a commonly assessed correlated trait. Methods We leveraged the statistical power of the UK Biobank (n = 193,860) to identify genetic variants related to breakfast cereal skipping as a proxy-phenotype for breakfast skipping and applied several in silico approaches to investigate mechanistic functions and links to traits/diseases. Next, we attempted validation of our approach in smaller breakfast skipping GWAS from the TwinUK (n = 2,006) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (n = 11,963). Results In the UK Biobank, we identified 6 independent GWAS variants, including those implicated for caffeine (ARID3B/CYP1A1), carbohydrate metabolism (FGF21), schizophrenia (ZNF804A), and encoding enzymes important for N6-methyladenosine RNA transmethylation (METTL4, YWHAB, and YTHDF3), which regulates the pace of the circadian clock. Expression of identified genes was enriched in the cerebellum. Genome-wide correlation analyses indicated positive correlations with anthropometric traits. Through Mendelian randomization (MR), we observed causal links between genetically determined breakfast skipping and higher body mass index, more depressive symptoms, and smoking. In bidirectional MR, we demonstrated a causal link between being an evening person and skipping breakfast, but not vice versa. We observed association of our signals in an independent breakfast skipping GWAS in another British cohort (P = 0.032), TwinUK, but not in a meta-analysis of non-British cohorts from the CHARGE consortium (P = 0.095). Conclusions Our proxy-phenotype GWAS identified 6 genetic variants for breakfast skipping, linking clock regulation with food timing and suggesting a possible beneficial role of regular breakfast intake as part of a healthy lifestyle.
Dietary intake, a major contributor to the global obesity epidemic, is a complex phenotype partially affected by innate physiological processes. However, previous genome-wide association studies have only implicated a few loci in variability of dietary intake. Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium identifying 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in 1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants based on clinical and physiological similarities yields three main genetic clusters with distinct associations with obesity and type 2 diabetes. Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.
Dietary intake, a major contributor to the global obesity epidemic, is a complex phenotype partially affected by innate physiological processes. However, previous genome-wide association studies have only implicated a few loci in variability of dietary intake. Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium identifying 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in 1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants based on clinical and physiological similarities yields three main genetic clusters with distinct associations with obesity and type 2 diabetes. Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.
Background There currently are no standard, low-cost, and validated methods to assess the timing of food intake. Methods The concordance between recall based survey questions and food times estimated from multiple daily food records in 249 generally healthy, free-living adults from the SHIFT Study (ClinicalTrials.gov #NCT02997319) was assessed. At baseline, participants were asked: “At what time do you first start and stop eating on weekdays/workdays and weekends/non-workdays?” and “At what time do you have your main meal on weekdays/workdays and weekends/non-workdays?” Participants were then asked to complete up to 14 days of food records noting the start time of each eating occasion. The timing of the first, last, and main (largest % calories) eating occasions, and the midpoint of energy intake were determined from food records. Wilcoxon matched pairs signed rank and Kendall's coefficient of concordance were used to compare differences and determine agreements between the methods for 4 food timing parameters. Results Eating occasions on work and free days showed significant agreements between the two methods, except for the main eating occasion on free days. Significant agreements were generally modest and ranged from 0.16 (work days main eating occasion) to 0.45 (work days first eating occasion). Generally, times based on recall were later than those estimated from food records and the differences in estimated times were smaller on work days compared to free days and smaller for the first compared to the last eating occasion. Main eating occasions from food records alternated between lunch and dinner times, contributing to low concordance with recalled times. Conclusions Modest agreements were found between food times derived from simple, recall based survey questions and food times estimated from multiple daily food records. Single administration of these questions can effectively characterize the overall timing of eating occasions within a population for chrononutrition research purposes. Summary There currently are no standard and low-cost methods to assess the timing of food intake. This study validates simple, recall-based questions that can effectively characterize food timing in free-living populations. Trial Registration: Shift Work, Heredity, Insulin, and Food Timing (SHIFT) Study (ClinicalTrials.gov: # NCT02997319).
Dietary intake, a major contributor to the global obesity epidemic 1-5 , is a complex phenotype partially affected by innate physiological processes. [6][7][8][9][10][11] However, previous genome-wide association studies (GWAS) have only implicated a few loci in variability of dietary composition. 12-14 Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium, and identify 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in b1tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brainspecific epigenomic annotations identify 15 additional loci. Clustering of genomewide significant variants yields three main genetic clusters with distinct associations with obesity and type 2 diabetes (T2D). Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.As dietary components are strongly correlated, we conducted a multi-trait genomewide association meta-analysis of overall variation in dietary intake among 283,119European-ancestry participants from the UK Biobank 15 and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium 14 (Methods; Supplementary Table 1). First, we conducted single-trait GWAS for the proportion of total energy intake from carbohydrate, fat, and protein in UK Biobank (n=192,005).Next, single-trait GWAS from the UK Biobank and CHARGE Consortium (n=91,114) were meta-analyzed and combined into a multi-trait genome-wide association metaanalysis (Methods). An analysis overview is presented in Supplementary Fig. 1.We evaluated dietary intake using 24-hour web-based diet recall in the UK Biobank 16,17 and validated food frequency questionnaires, diet history and diet records in the CHARGE Consortium. 14 We observed strong genome-wide genetic correlations for nutrient estimates between the UK Biobank and CHARGE datasets (r g >0.6 for all; P <0.001; Supplementary Table 2). The quantile-quantile plots of single-trait and multi-trait meta-analyses showed moderate inflation (l GC ranging from 1.12 to 1.17) with a linkage disequilibrium (LD) score intercept 18 of ~1 (standard error (s.e.) = 0.01), indicating that most inflation could be explained by polygenic signal ( Supplementary Fig. 2, Supplementary Table 3). In single-trait meta-analyses, genome-wide SNP-based heritability 19 was estimated at 3.9% (s.e.=0.01), 2.8% (s.e.=0.01), and 3.0% (s.e.=0.01) for carbohydrate, fat, and protein, respectively ( Supplementary Table 3), in line with previous GWAS findings 12,14 and other behavioral phenotypes such as tobacco or alcohol use. 20
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