Hassan S Dashti1,2,3, Jordi Merino1,2,4, Jacqueline M Lane1,2,3, Yanwei Song1,2, Caren E Smith5, Toshiko Tanaka6, Nicola M McKeown7, Chandler Tucker1, Dianjianyi Sun8, Traci M Bartz9, Ruifang Li-Gao10, Hoirun Nisa8, Sirimon Reutrakul11, Rozenn N Lemaitre12, Tahani M Alshehri10, Renée de Mutsert10, Lydia Bazzano8, Lu Qi8,13,14, Kristen L Knutson15, Bruce M Psaty16,17, Dennis O Mook-Kanamori10,18, Vesna Boraska Perica19, Marian L Neuhouser20, Frank A J L Scheer2,21,22, Martin K Rutter23,24, Marta Garaulet25,26, Richa Saxena1,2,3. 1. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA. 2. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA. 3. Department of Anesthesia, Critical Care, and Pain Medicine. 4. Diabetes Unit, Massachusetts General Hospital, and Department of Medicine, Harvard Medical School, Boston, MA. 5. Nutrition and Genomics. 6. Translational Gerontology Branch, National Institute on Aging, Baltimore, MD. 7. Nutritional Epidemiology Program, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. 8. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA. 9. Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, Seattle, WA. 10. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands. 11. Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Illinois at Chicago, Chicago, IL. 12. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA. 13. Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA. 14. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 15. Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL. 16. Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine, and Health Services, University of Washington, Seattle, WA. 17. Kaiser Permanente Washington Health Research Institute, Seattle, WA. 18. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands. 19. Department for Medical Biology, University of Split School of Medicine, Split, Croatia. 20. Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA. 21. Division of Sleep Medicine, Harvard Medical School, Boston, MA. 22. Medical Chronobiology Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA. 23. Division of Endocrinology, Diabetes, and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, United Kingdom. 24. Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom. 25. Department of Physiology, University of Murcia, Murcia, Spain. 26. IMIB-Arrixaca, Murcia, Spain.
Abstract
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.
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.
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