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.
Original language | English (US) |
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Pages (from-to) | 473-484 |
Number of pages | 12 |
Journal | American Journal of Clinical Nutrition |
Volume | 110 |
Issue number | 2 |
DOIs | |
State | Published - Aug 1 2019 |
Funding
HSD was supported by NIH grant R01DK107859. JM was supported by a postdoctoral fellowship funded by the European Commission Horizon 2020 program: Marie Skłodowska-Curie Actions (H2020-MSCA-IF-2015-703787). FAJLS was supported in part by NIH grants R01HL094806, R01HL118601, R01DK099512, R01DK102696, and R01DK105072. NMM is supported in part by USDA agreement #58-1950-4-003 and funding from the General Mills Bell Institute of Health and Nutrition. MG was supported by the Spanish Government of Investigation, Development, and Innovation (SAF2017-84135-R) including FEDER co-funding, and NIDDK R01DK105072. RS was supported by NIH grants R01DK107859, R01HL113338, and R01DK105072, and the Phyllis and Jerome Lyle Rappaport Massachusetts General Hospital Research Scholar Award. The Infrastructure for the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium is supported in part by National Heart, Lung, and Blood Institute (NHLBI) grant HL105756. Funding sources for individual CHARGE cohort studies appear in Supplemental Table 1. General Mills Bell Institute of Health and Nutrition was not involved in the design, implementation, analysis, or interpretation of the data. The authors’ contributions were as follows—HSD, JM, FAJLS, MG, and RS: designed the study; HSD, JM, JMM, YS, DS, TMB, RL-G, VBP, and HN: conducted research and contributed to statistical analyses; HSD, JM, CES, TT, NMM, MLN, FAJLS, MKR, MG, and RS: interpreted data; HSD, JM, JML, CES, TT, NMM, CT, and RS wrote the manuscript; and all authors: read and approved the final version of the manuscript. HSD and RS are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. BMP serves on the Data and Safety Monitoring Board of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. FAJLS received speaker fees from Bayer Healthcare, Sentara Healthcare, Philips, Kellogg Company, Vanda Pharmaceuticals, and Pfizer Pharmaceuticals. NMM is a scientific advisor on the Whole Grains Council. HSD, JM, JML, YS, CES, TT, CT, DS, TMB, RL-G, HN, SR, RNL, TMA, RdM, LB, LQ, KLK, DOM-K, VBP, MLN, MKR, MG, and RS have no conflicts of interest. HSD was supported by NIH grant R01DK107859. JM was supported by a postdoctoral fellowship funded by the European Commission Horizon 2020 program: Marie Sk'odowska-Curie Actions (H2020-MSCA-IF- 2015-703787). FAJLS was supported in part by NIH grants R01HL094806, R01HL118601, R01DK099512, R01DK102696, and R01DK105072. NMM is supported in part by USDA agreement #58-1950-4-003 and funding from the General Mills Bell Institute of Health and Nutrition. MG was supported by the Spanish Government of Investigation, Development, and Innovation (SAF2017-84135-R) including FEDER co-funding, and NIDDK R01DK105072. RS was supported by NIH grants R01DK107859, R01HL113338, and R01DK105072, and the Phyllis and Jerome Lyle Rappaport Massachusetts General Hospital Research Scholar Award. The Infrastructure for the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium is supported in part by National Heart, Lung, and Blood Institute (NHLBI) grant HL105756. Funding sources for individual CHARGE cohort studies appear in Supplemental Table 1. General Mills Bell Institute of Health and Nutrition was not involved in the design, implementation, analysis, or interpretation of the data. 1Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA; 2Program in Medical and Population Genetics, Broad Institute, Cambridge, MA; 3Department of Anesthesia, Critical Care, and Pain Medicine and 4Diabetes Unit, Massachusetts General Hospital, and Department of Medicine, Harvard Medical School, Boston, MA; 5Nutrition and Genomics and 6Nutritional Epidemiology Program, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA; 7Translational Gerontology Branch, National Institute on Aging, Baltimore, MD; 8Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA; 9Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, Seattle, WA; 10Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands; 11Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Illinois at Chicago, Chicago, IL; 12Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA; 13Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA; 14Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA; 15Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL; 16Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine, and Health Services, University of Washington, Seattle, WA; 17Kaiser Permanente Washington Health Research Institute, Seattle, WA; 18Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands; 19Department for Medical Biology, University of Split School of Medicine, Split, Croatia; 20Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA; 21Division of Sleep Medicine, Harvard Medical School, Boston, MA; 22Medical Chronobiology Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA; 23Division of Endocrinology, Diabetes, and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, United Kingdom; 24Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; 25Department of Physiology, University of Murcia, Murcia, Spain; and 26IMIB-Arrixaca, Murcia, Spain
Keywords
- GWAS
- breakfast
- chronobiology
- circadian clock
- food timing
ASJC Scopus subject areas
- Medicine (miscellaneous)
- Nutrition and Dietetics