BACKGROUND: Little is known about the genes influencing dietary energy and nutrient intakes, despite evidence that these intakes are influenced by genetic factors. OBJECTIVE: We aimed to identify, by using a genome-wide linkage analysis, chromosomal regions harboring genes that affect energy and macronutrient intakes. DESIGN: Energy, carbohydrate, lipid, and protein intakes were assessed in 836 subjects from 217 families by using a 3-d dietary record. A total of 443 markers were genotyped and tested for linkage; age- and sex-adjusted energy and macronutrient intakes were expressed in grams and as percentages of total energy intake. Regression-based (Haseman-Elston) and variance-component (MERLIN) methods were applied to test for linkage with dietary data. A maximum of 454 sibpairs from 217 nuclear families were available for analysis. RESULTS: The genome scan provided suggestive evidence (P < or = 0.0023) for the presence of 6 quantitative trait linkages influencing total caloric and macronutrient intakes in the Québec Family Study. Of these, multiple linkages were found on chromosome 3q27.3, in a region harboring the adiponectin gene, at marker D3S1262 for energy [logarithm of odds (LOD): 2.24], carbohydrate (LOD: 2.00), and lipid (LOD: 1.65) intakes. The peak linkages for carbohydrate, lipid, and protein intakes were found on chromosomes 1p32.2 (LOD: 2.39), 1p35.2 (LOD: 2.41), and 10p15.3 (LOD: 2.72), respectively. The linkage results remained significant after adjustment for body mass index, which suggested that the genes underlying these quantitative trait linkages influence dietary intake independent of body size. CONCLUSION: The linkage on chromosome 3q27.3 with energy, lipid, and carbohydrate intakes suggests that this region of the genome may harbor genes that influence energy and macronutrient intakes in humans.
BACKGROUND: Little is known about the genes influencing dietary energy and nutrient intakes, despite evidence that these intakes are influenced by genetic factors. OBJECTIVE: We aimed to identify, by using a genome-wide linkage analysis, chromosomal regions harboring genes that affect energy and macronutrient intakes. DESIGN: Energy, carbohydrate, lipid, and protein intakes were assessed in 836 subjects from 217 families by using a 3-d dietary record. A total of 443 markers were genotyped and tested for linkage; age- and sex-adjusted energy and macronutrient intakes were expressed in grams and as percentages of total energy intake. Regression-based (Haseman-Elston) and variance-component (MERLIN) methods were applied to test for linkage with dietary data. A maximum of 454 sibpairs from 217 nuclear families were available for analysis. RESULTS: The genome scan provided suggestive evidence (P < or = 0.0023) for the presence of 6 quantitative trait linkages influencing total caloric and macronutrient intakes in the Québec Family Study. Of these, multiple linkages were found on chromosome 3q27.3, in a region harboring the adiponectin gene, at marker D3S1262 for energy [logarithm of odds (LOD): 2.24], carbohydrate (LOD: 2.00), and lipid (LOD: 1.65) intakes. The peak linkages for carbohydrate, lipid, and protein intakes were found on chromosomes 1p32.2 (LOD: 2.39), 1p35.2 (LOD: 2.41), and 10p15.3 (LOD: 2.72), respectively. The linkage results remained significant after adjustment for body mass index, which suggested that the genes underlying these quantitative trait linkages influence dietary intake independent of body size. CONCLUSION: The linkage on chromosome 3q27.3 with energy, lipid, and carbohydrate intakes suggests that this region of the genome may harbor genes that influence energy and macronutrient intakes in humans.
Authors: Irene Ramos-Álvarez; Paola Moreno; Samuel A Mantey; Taichi Nakamura; Bernardo Nuche-Berenguer; Terry W Moody; David H Coy; Robert T Jensen Journal: Peptides Date: 2015-05-11 Impact factor: 3.750
Authors: Pedro M Quirós; Andrew J Ramsay; David Sala; Erika Fernández-Vizarra; Francisco Rodríguez; Juan R Peinado; Maria Soledad Fernández-García; José A Vega; José A Enríquez; Antonio Zorzano; Carlos López-Otín Journal: EMBO J Date: 2012-03-20 Impact factor: 11.598
Authors: P Kochunov; D C Glahn; J L Lancaster; A M Winkler; S Smith; P M Thompson; L Almasy; R Duggirala; P T Fox; J Blangero Journal: Neuroimage Date: 2010-01-29 Impact factor: 6.556
Authors: A Haghighi; M G Melka; M Bernard; M Abrahamowicz; G T Leonard; L Richer; M Perron; S Veillette; C J Xu; C M T Greenwood; A Dias; A El-Sohemy; D Gaudet; T Paus; Z Pausova Journal: Mol Psychiatry Date: 2013-01-22 Impact factor: 15.992
Authors: K Ganesh Kumar; Lisa M DiCarlo; Julia Volaufova; Aamir R Zuberi; Brenda K Smith Richards Journal: Mamm Genome Date: 2009-12-24 Impact factor: 2.957
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Authors: Toshiko Tanaka; Julius S Ngwa; Frank J A van Rooij; M Carola Zillikens; Mary K Wojczynski; Alexis C Frazier-Wood; Denise K Houston; Stavroula Kanoni; Rozenn N Lemaitre; Jian'an Luan; Vera Mikkilä; Frida Renstrom; Emily Sonestedt; Jing Hua Zhao; Audrey Y Chu; Lu Qi; Daniel I Chasman; Marcia C de Oliveira Otto; Emily J Dhurandhar; Mary F Feitosa; Ingegerd Johansson; Kay-Tee Khaw; Kurt K Lohman; Ani Manichaikul; Nicola M McKeown; Dariush Mozaffarian; Andrew Singleton; Kathleen Stirrups; Jorma Viikari; Zheng Ye; Stefania Bandinelli; Inês Barroso; Panos Deloukas; Nita G Forouhi; Albert Hofman; Yongmei Liu; Leo-Pekka Lyytikäinen; Kari E North; Maria Dimitriou; Goran Hallmans; Mika Kähönen; Claudia Langenberg; Jose M Ordovas; André G Uitterlinden; Frank B Hu; Ioanna-Panagiota Kalafati; Olli Raitakari; Oscar H Franco; Andrew Johnson; Valur Emilsson; Jennifer A Schrack; Richard D Semba; David S Siscovick; Donna K Arnett; Ingrid B Borecki; Paul W Franks; Stephen B Kritchevsky; Terho Lehtimäki; Ruth J F Loos; Marju Orho-Melander; Jerome I Rotter; Nicholas J Wareham; Jacqueline C M Witteman; Luigi Ferrucci; George Dedoussis; L Adrienne Cupples; Jennifer A Nettleton Journal: Am J Clin Nutr Date: 2013-05-01 Impact factor: 7.045