AJCN Cancer Health Disparities Conference
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Google Scholar
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.
Agricola
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.
American Journal of Clinical Nutrition, Vol. 88, No. 4, 1142-1148, October 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Evidence of a quantitative trait locus for energy and macronutrient intakes on chromosome 3q27.3: the Québec Family Study1,2,3

Anne C Choquette, Simone Lemieux, Angelo Tremblay, Yvon C Chagnon, Claude Bouchard, Marie-Claude Vohl and Louis Pérusse

1 From the Division of Kinesiology, Department of Social and Preventive Medicine, Faculty of Medicine (ACC, AT, and LP), the Lipid Research Centre (ACC, M-CV, and LP), the Department of Food Science and Nutrition (SL and M-CV), the Nutraceuticals and Functional Foods Institute (SL, AT, and M-CV), and the Psychiatric Genetic Unit, Robert Giffard Research Center (YCC), Laval University, Québec, Canada; and the Pennington Biomedical Research Center, Baton Rouge, LA (CB)

2 Supported by grants no. MOP-77652 and OHN-63276 from the Canadian Institutes of Health Research, by the Canada Research Chair in Physical Activity, Nutrition and Energy Balance (to AT), and by the George A Bray Chair in Nutrition (to CB).

3 Reprints not available. Address correspondence to L Pérusse, Division of Kinesiology, Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Québec (QC), Canada, G1V 0A6. E-mail: louis.perusse{at}kin.msp.ulaval.ca.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
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 ≤ 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.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Obesity, which represents a major risk factor for chronic diseases such as type 2 diabetes, hypertension, and cardiovascular disease, has reached epidemic proportions worldwide. This marked increase in the prevalence of obesity is generally attributable to an environment favoring a positive energy balance, which may lead to weight gain in persons with a genetic susceptibility. So far, the search for these susceptibility genes has mainly been based on phenotypes related to body mass and various body fatness measures (1). Relatively few studies have been undertaken in humans to identify genes associated with energy intake or eating behaviors (or both), despite the fact that both are known to be associated with obesity (2) and to be influenced by genetic factors (3-5).

A few candidate gene studies have shown evidence of an association between eating behavior and genes that code for neurotransmitters (6-10). Similarly, some genes that play a role in the control of food intake were shown to be associated with intake of energy or macronutrient (or both) (4, 11, 12). Besides candidate gene studies, a few genome scans of energy and macronutrient intakes and of eating behaviors have been undertaken. Among them, a genome-wide linkage scan study for dietary energy and nutrient intakes was performed in black and white subjects who were part of the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study (HFS). Nutrient intake was reported to be linked to the 1p21 and 20q regions in whites and to the 12q14 region in blacks (13). That study was the first to report quantitative trait linkages (QTLs) for dietary energy and macronutrient intake. Another genome-wide linkage scan study. in Hispanic children, showed a QTL on chromosome 18q for physical activity and dietary intake (14). The melanocortin 4 receptor gene (MC4R) is a strong positional candidate gene for this QTL because of its known role in regulating food intake and energy expenditure (15, 16). Finally, a genome scan in Mexican Americans reported a QTL for macronutrient intake on chromosome 2p22 (17).

Two genome scans of eating behavior traits were performed in the Old Order Amish (18) and the Québec Family Study (QFS) (19) populations. First, linkage analyses in the Old Order Amish study showed QTLs for cognitive dietary restraint on 3p26.1 and 6p22.2, those for disinhibition on 7q21.3 and 16q22, and those for susceptibility to hunger on 3q13.31 and 15q24-q25. Furthermore, a genome-wide scan of the QFS population reported that chromosomes 15q24-q25 and 17q23-q24 were both linked to disinhibition and susceptibility to hunger, whereas 15q21 and 19p13 were linked to susceptibility to hunger and disinhibition, respectively. The purpose of the present study was to identify, through a genome-wide linkage scan, the QTLs that are associated with energy and macronutrient intakes measured by using a 3-d dietary record in subjects from the QFS.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
The Québec Family Study
The QFS is composed of French-Canadian families, and it focuses on the genetics of obesity and its co-morbidities (20). This cohort represents an admixture of random sampling and ascertainment through obese probands [body mass index (BMI; in kg/m2) > 32]. In the current study, dietary energy and nutrient intake information was available for 836 members of 217 nuclear families ranging in size from 2 to 8 members. The maximum number of sibling pairs available was 454. The characteristics of subjects in each of the sex and generation groups (fathers, mothers, sons, and daughters) are reported in Table 1Go. Body weight and height were measured according to standardized procedures from the Airlie Conference (21), and BMI was calculated.


View this table:
[in this window]
[in a new window]

  		TABLE 1 Characteristics of participants by sex and generation group1

 
All subjects gave written informed consent. The Medical Ethics Committee of Laval University approved all procedures.

Dietary intake measurements
Total energy (kcal) and carbohydrate, fat, and protein intakes constitute the dietary phenotypes. Macronutrient intakes were analyzed in absolute values (in g) and as percentages of total energy intake. Dietary intake was assessed by using a 3-d dietary record, which was completed during 2 weekdays and 1 weekend day. Subjects were asked to record all foods and beverages ingested (except water) with the use of a balance and measuring cups and spoons. All subjects received instructions from a nutritionist on the procedures needed to complete the dietary record and to measure food portions (22). After completion, the record was verified by a nutritionist who was well trained in analysis of a dietary journal. Macronutrient and micronutrient intakes were estimated with the use of a computerized version of the Canadian Nutrient File (23). Our group (22) previously showed that this 3-d dietary record provides reliable estimates of energy and macronutrient intakes.

Genomic DNA studies
A total of 443 markers spanning the 22 autosomal chromosomes with an average intermarker distance of 6.24 megabases (Mb) were genotyped as described previously (24). These markers included 337 microsatellite markers (dinucleotide, trinucleotide, and tetranucleotide repeats) and 106 polymorphisms in 65 candidate genes. The results were stored in a local dBase IV database (GENEMARK; Laval University, Quebec, Canada). Mendelian inheritance incompatibilities within nuclear families and extended pedigrees were tested by using PedCheck software (version 1.1; University of Pittsburgh, Pittsburgh, PA).

Statistical analysis
Phenotypes were adjusted for the effects of age, including squared and cubic terms to allow for nonlinearity, and for the effects of sex. The adjustments were performed by using a stepwise multiple regression procedure retaining only significant terms (P < 0.05). Separate regression models were used for each of the age-by-sex groups. Regression parameters were estimated after the exclusion of outliers (±3 SD), but residuals were computed subsequently for all subjects. The residuals of dietary phenotypes from the stepwise regression procedures were standardized to a mean of zero and a unit variance (0, 1) within each subgroup; they constituted the variables for linkage analysis. All of the statistical procedures described above were performed with the use of SAS statistical software (version 8.02; SAS Institute Inc, Cary, NC).

We performed QTL analyses by using 2 methods. First, we used the modified Haseman-Elston regression-based method (25), which models the trait covariance between sibpairs. That method regresses the mean corrected sibpair product on the number of alleles shared identical by descent (IBD). Single-point and multipoint estimates of alleles with shared IBD were generated and linkage was tested by using the GENIBD program and the SIBPAL2 program, respectively, both in SAGE statistical software [version 5.1.1 (26)]. Empirical P values were computed by using a Monte Carlo permutation procedure with 10 000 permutations for genomic regions with suggestive evidence of linkage (P < 0.0023).

Second, we conducted multi-point linkage analyses for all traits by using the variance-components (VC) model implemented in MERLIN software (27). VC methods model the phenotypic variance explained by estimated IBD sharing at a chromosomal position. MERLIN software calculates exact IBD-sharing probabilities by using the Lander-Green algorithm with sparse gene-flow trees, and it can handle pedigrees for {approx}20 persons for multipoint analysis (27).

We used a logarithm of odds (LOD) score of ≥3.0 to indicate significant (P ≤ 0.0001) evidence of linkage, whereas a LOD threshold of ≥1.75 was considered to be suggestive (P ≤ 0.0023) (24). Finally, the Human Genome National Center for Biotechnology Information genetic map (version 36.2) was used to identify potential candidate genes.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Results of the linkage analyses are presented in Table 2Go. All chromosomal regions with a VC LOD score of ≥1.75 or a suggestive allele sharing (P ≤ 0.0023) are reported. Linkages were observed on chromosomes 1, 3, 5, 8, 10, 18, 19, 20, and 22 in the QFS subjects.


View this table:
[in this window]
[in a new window]

  		TABLE 2 Summary of empirical P ≤ 0.0023 from the SIBPAL program or logarithm of odds (LOD) ≥ 1.75 from the MERLIN program

 
For energy intake, a significant linkage was observed on chromosome 3q27.3 with marker D3S1262. The same marker was also found to be suggestively linked with lipid (LOD = 1.65) and carbohydrate (LOD = 2.00) intakes, as shown in Figure 1Go.


Figure 1
View larger version (13K):
[in this window]
[in a new window]

  		FIGURE 1. Logarithm of odds (LOD) scores for energy, lipid, carbohydrate, and protein intakes on chromosome 3. Analysis was conducted with the use of MERLIN software's variance component.

 
The peak linkage for lipid intake was found on region 1p35.3-p35 with marker D1S1622 (LOD = 2.37) and on the FABP3 gene (LOD = 2.41), whereas the peak linkage for carbohydrate intake was found on 1p32.2 with suggestive linkage with marker D1S476 (LOD = 2.39). Another marker located on chromosome 5q15 (D5S1462; LOD = 2.19) provided evidence of linkage with lipid intake, whereas marker D3S1259, located on chromosome 3p25.2, was found to be linked with carbohydrate intake (LOD = 2.34).

A significant linkage signal for protein intake was observed on region 10p15.3-p14 with markers D10S1435 (LOD = 2.72) and D10S189 (LOD = 2.13). Other linkages for protein intake were found on chromosomes 3p25.2 (D3S1259; LOD = 2.36) and 4q32.3 (D4S2368; LOD = 2.10). Evidence of linkage with protein was also observed on chromosome 18q21.1 with markers D18S851 (LOD = 1.45) and D18S38 (LOD = 1.86). The evidence of linkage was generally less strong when macronutrient intakes were expressed as a percentage of total energy intake than when they were expressed in grams. For the percentage of total energy as carbohydrate, the peak linkage was observed on chromosome 20q11.21 with marker D20S425 (LOD = 1.86).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The present study is, to our knowledge, 1 of 3 human genome-wide linkage analyses for energy and macronutrient intakes and the first based on dietary intake derived from 3-d dietary records. Results show that 6 QTLs contributed to variations in dietary intake in the QFS. Chromosomal regions 1p35.3-p35.2, 1p32.2, 3p25.2, 3q27.3, 5q15, and 10p15.3-p14 were those providing the strongest evidence of linkage with dietary intake phenotypes. The linkage results were generally consistent across the regression-based and VC methods. Moreover, the QTLs identified in the current study remain significant (for one or both linkage methods) after adjustment for BMI, which suggest that the genes underlying these QTLs influence energy and macronutrient intakes independent of body size.

The best evidence of linkage was on region 3q27.3, where marker D3S1262 was linked with energy, lipid, and carbohydrate intakes. The adiponectin gene (ADIPOQ), a key hormone in energy balance regulation that has been linked to obesity and adiposity traits in many studies (28, 29), is located 0.3 Mb from D3S1262. A study in mice (30) showed that peripheral injection of adiponectin increased AMP-activated protein kinase (AMPK) activity in the arcuate nucleus, which resulted in a stimulation of food intake, a reduction in energy expenditure, and a weight gain. Moreover, hypothalamic AMPK activation was limited in adiponectin-deficient mice and was related to a reduction in food intake, an increase in energy expenditure, and a lean phenotype. The study in mice (30) also reported that the adiponectin concentration in cerebrospinal fluid increases after fasting and decreases after refeeding. Together, these data suggest that adiponectin may act as a starvation signal (30) and may constitute a good candidate gene linking feeding behaviors and adiposity.

Results also showed a linkage of carbohydrate intake with marker D1S476 on 1p32.2, whereas marker D3S1259 on chromosome 3p25.2 was linked with carbohydrate and protein intakes. This region of chromosome 3 includes the peroxisome proliferator–activated receptor-{gamma} gene (PPAR{gamma}), a key regulator of adiposity and energy balance (31). The QTLs observed on chromosome 3 (3p25.2 and 3q27.3; see Figure 1Go) were found to influence energy and macronutrient intakes. Because energy and macronutrient intakes are highly correlated (correlations ranging from 0.75 to 0.86; data not shown), this overlap in peak linkages suggests that these phenotypes probably are influenced by common genes.

Markers on chromosomal regions 10p15.3-p14 (D10S189 and D10S1435) and 18q21-q22 (D18S8851 and D18S38) have been linked to protein intake. The MC4R gene, a strong regulator of food intake and energy expenditure (15, 16, 32, 33) that has been associated with obesity in population studies (34) and in a recent genome-wide association study (35), is located on chromosome 18q22, and it represents a good candidate gene for the linkage on chromosome 18q21-q22.

An overview of the QTLs for dietary intake identified in the QFS, the HFS, and the San Antonio Family Heart Study with potential candidate genes is presented in Table 3Go. The present study did not replicate the findings of the San Antonio study (17) or HFS (13). Genome scan replication is difficult because of the complex nature of the phenotype. Genes and cultural and environmental exposures constitute important factors in the acquisition and expression of food preferences (3, 36). Sample size and the use of different data-collection methods also may explain differences among studies. Collection of the dietary intake data used for this report was monitored by professional nutritionists who used standardized procedures. However, self-reported values assessed by 3-d dietary journals tended to underestimate food intake (37, 38). A search of the human-mouse homology database (Internet: http://www.ncbi.nlm.nih.gov/Omim/Homology/) showed that 3 QTLs, identified from a genome-wide scan study on dietary intake in mice (Mnic2 for carbohydrate intake, Kcal1 for kilocalorie intake, and Mnif2 for fat intake) (39), are located in mouse genome regions corresponding to human chromosomes 3 and 5, where we found QTLs for energy and macronutrient intakes.


View this table:
[in this window]
[in a new window]

  		TABLE 3 Summary of the evidence for the presence of linkage with dietary intakes from 3 family studies1

 
Even if genome-wide association studies were proven to be more successful than genome-wide linkage studies in identifying genes for complex traits, we believe that a genome-wide linkage analysis of energy and macronutrient intakes remains highly relevant in the field of obesity-related traits because of the small number of linkage studies performed to date for traits related to macronutrient intake or eating behaviors. In addition, with the performance of a genome-wide linkage analysis, comparison with several other linkage studies that have reported QTLs for energy intake or obesity-related phenotypes (or both) is possible. Moreover, as mentioned above, we previously identified QTLs for eating behaviors in the QFS (19). Because eating behaviors are usually correlated to irregularities in diet, we wanted to determine whether any of these QTLs overlap with those for energy and macronutrient intakes found in the present study. However, no overlaps in peaks were observed between the studies.

Many of the QTLs identified in the present study were previously linked to adiposity-related phenotypes. First, marker D3S1262 (3q27.3) has been linked to the percentage body fat in non-Hispanic white men and African American men from the Hypertension Genetic Epidemiology Network Study (40) and to the percentage body fat in the population of American Samoa (41). Chromosome 3q27.3 has also been linked to BMI in the African American population (42). Marker D3S1259, which is linked to energy, lipid, and carbohydrate intakes in the present study, has also been linked to BMI (43). Moreover, the chromosomal region 3p25.2 harboring marker D3S1259 has been found to contribute to the eating behavior trait of cognitive dietary restraint in an Old Order Amish cohort (18). The marker D1S476 on chromosome 1p32.2, which was found to be linked with carbohydrate intake, was previously found also to be linked to insulin concentration and to adiposity-related traits in the QFS (44). The linkage with both carbohydrate intake and insulin concentration is noteworthy, considering that low-carbohydrate diets have consistently shown benefits in improving insulin sensitivity (45-47). Finally, markers D10S1435 and D10S189 on chromosomal regions 10p15.3-p14 have been found to influence BMI and fat mass in whites from the HFS (48) and BMI in Pima Indians (49).

In summary, the present study showed linkages at several chromosomal regions for energy and dietary intakes in the QFS cohort. Six QTLs influencing energy and macronutrient intakes were identified: 1p35.3-p35.2 (lipid and protein intakes), 1p32.2 (carbohydrate intake), 3p25.2 (energy, protein, and carbohydrate intakes), 3q27.3 (energy, lipid, and carbohydrate intakes), 5q15 (lipid intake), and 10p15.3-p14 (protein intake). Although fine mapping and candidate gene studies are needed to identify the genes underlying these QTLs, these results suggest that food intake in humans is influenced by several genes. Further genetic and functional studies will be needed to improve our understanding of the mechanisms underlying caloric intake and macronutrient preferences.


   ACKNOWLEDGMENTS
 
We thank Claude Leblanc for his assistance with the computer database and Guy Fournier and Lucie Allard for their dedicated work in the Québec Family Study.

The authors’ responsibilities were as follows—ACC, SL, AT, and LP: designed the experiment; ACC, SL, AT, YCC, CB, and LP: collected the data; ACC and LP: performed the analyses; SL, AT, YCC, M-CV, and CB: provided significant advice regarding the analyses and interpretation of the data; ACC: wrote the manuscript; and all authors: reviewed the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Rankinen T, Zuberi A, Chagnon YC, et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006;14:529–644.[Medline]
  2. Jeffery RW, Harnack LJ. Evidence implicating eating as a primary driver for the obesity epidemic. Diabetes 2007;56:2673–6.[Medline]
  3. Perusse L, Tremblay A, Leblanc C, et al. Familial resemblance in energy intake: contribution of genetic and environmental factors. Am J Clin Nutr 1988;47:629–35.[Abstract/Free Full Text]
  4. Rankinen T, Bouchard C. Genetics of food intake and eating behavior phenotypes in humans. Annu Rev Nutr 2006;26:413–34.[Medline]
  5. Faith MS, Rha SS, Neale MC, Allison DB. Evidence for genetic influences on human energy intake: results from a twin study using measured observations. Behav Genet 1999;29:145–54.[Medline]
  6. Boutin P, Dina C, Vasseur F, et al. GAD2 on chromosome 10p12 is a candidate gene for human obesity. PLoS Biol 2003;1:E68.[Medline]
  7. Boutin P, Froguel P. GAD2: a polygenic contribution to genetic susceptibility for common obesity? Pathol Biol (Paris) 2005;53:305–7.[Medline]
  8. Durand E, Boutin P, Meyre D, et al. Polymorphisms in the amino acid transporter solute carrier family 6 (neurotransmitter transporter) member 14 gene contribute to polygenic obesity in French Caucasians. Diabetes 2004;53:2483–6.[Medline]
  9. Dziedzic B, Szemraj J, Bartkowiak J, Walczewska A. Various dietary fats differentially change the gene expression of neuropeptides involved in body weight regulation in rats. J Neuroendocrinol 2007;19:364–73.[Medline]
  10. Suviolahti E, Oksanen LJ, Ohman M, et al. The SLC6A14 gene shows evidence of association with obesity. J Clin Invest 2003;112:1762–72.[Medline]
  11. Adan RA, Tiesjema B, Hillebrand JJ, la Fleur SE, Kas MJ, de Krom M. The MC4 receptor and control of appetite. Br J Pharmacol 2006;149:815–27.[Medline]
  12. Schwartz MW, Woods SC, Porte D Jr, Seeley RJ, Baskin DG. Central nervous system control of food intake. Nature 2000;404:661–71.[Medline]
  13. Collaku A, Rankinen T, Rice T, et al. A genome-wide linkage scan for dietary energy and nutrient intakes: the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study. Am J Clin Nutr 2004;79:881–6.[Abstract/Free Full Text]
  14. Cai G, Cole SA, Butte N, et al. A quantitative trait locus on chromosome 18q for physical activity and dietary intake in Hispanic children. Obesity (Silver Spring) 2006;14:1596–604.[Medline]
  15. Samama P, Rumennik L, Grippo JF. The melanocortin receptor MCR4 controls fat consumption. Regul Pept 2003;113:85–8.[Medline]
  16. Loos RJ, Rankinen T, Tremblay A, Perusse L, Chagnon Y, Bouchard C. Melanocortin-4 receptor gene and physical activity in the Quebec Family Study. Int J Obes (Lond) 2005;29:420–8.[Medline]
  17. Cai G, Cole SA, Bastarrachea RA, Maccluer JW, Blangero J, Comuzzie AG. Quantitative trait locus determining dietary macronutrient intakes is located on human chromosome 2p22. Am J Clin Nutr 2004;80:1410–4.[Abstract/Free Full Text]
  18. Steinle NI, Hsueh WC, Snitker S, et al. Eating behavior in the Old Order Amish: heritability analysis and a genome-wide linkage analysis. Am J Clin Nutr 2002;75:1098–106.[Abstract/Free Full Text]
  19. Bouchard L, Drapeau V, Provencher V, et al. Neuromedin beta: a strong candidate gene linking eating behaviors and susceptibility to obesity. Am J Clin Nutr 2004;80:1478–86.[Abstract/Free Full Text]
  20. Bouchard C. Genetic epidemiology, association, and sib-pair linkage: results from the Québec Family Study. In: Bray G, Ryan D, eds. Molecular and genetic aspects of obesity. Baton Rouge, LA: Louisiana State University Press, 1996:470–81.
  21. Lohman TRA, Martorel R. The Airlie consensus conference: standardization of anthropometric measurements. Champaign, IL: Human Kinetics Publishers, 1988.
  22. Tremblay AJS, C Leblanc, Bouchard C. The reproducibility of a three-day dietary record. Nutr Res 1983;3:819–30.
  23. Verdier P, Beare-Rogers JL. The Canadian Nutrient File. J Can Diet Assoc 1984;45:52–5.[Medline]
  24. Chagnon YC, Borecki IB, Perusse L, et al. Genome-wide search for genes related to the fat-free body mass in the Quebec family study. Metabolism 2000;49:203–7.[Medline]
  25. Elston RC, Buxbaum S, Jacobs KB, Olson JM. Haseman and Elston revisited. Genet Epidemiol 2000;19:1–17.[Medline]
  26. SAGE: Statistical Analysis for Genetic Epidemiology software, version 5.1.1. Cork, Ireland, Statistical Solutions Ltd, 2005.
  27. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002;30:97–101.[Medline]
  28. Sutton BS, Weinert S, Langefeld CD, et al. Genetic analysis of adiponectin and obesity in Hispanic families: the IRAS Family Study. Hum Genet 2005;117:107–18.[Medline]
  29. Gale SM, Castracane VD, Mantzoros CS. Energy homeostasis, obesity and eating disorders: recent advances in endocrinology. J Nutr 2004;134:295–8.[Abstract/Free Full Text]
  30. Kubota N, Yano W, Kubota T, et al. Adiponectin stimulates AMP-activated protein kinase in the hypothalamus and increases food intake. Cell Metab 2007;6:55–68.[Medline]
  31. Cecil JE, Watt P, Palmer CN, Hetherington M. Energy balance and food intake: the role of PPARgamma gene polymorphisms. Physiol Behav 2006;88:227–33.[Medline]
  32. Kishi T, Aschkenasi CJ, Lee CE, Mountjoy KG, Saper CB, Elmquist JK. Expression of melanocortin 4 receptor mRNA in the central nervous system of the rat. J Comp Neurol 2003;457:213–35.[Medline]
  33. Mountjoy KG, Mortrud MT, Low MJ, Simerly RB, Cone RD. Localization of the melanocortin-4 receptor (MC4-R) in neuroendocrine and autonomic control circuits in the brain. Mol Endocrinol 1994;8:1298–308.[Abstract]
  34. Chagnon YC, Chen WJ, Perusse L, et al. Linkage and association studies between the melanocortin receptors 4 and 5 genes and obesity-related phenotypes in the Quebec Family Study. Mol Med 1997;3:663–73.[Medline]
  35. Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008;40:768–75.[Medline]
  36. Reed DR, Bachmanov AA, Beauchamp GK, Tordoff MG, Price RA. Heritable variation in food preferences and their contribution to obesity. Behav Genet 1997;27:373–87.[Medline]
  37. Hill RJ, Davies PS. The validity of self-reported energy intake as determined using the doubly labelled water technique. Br J Nutr 2001;85:415–30.[Medline]
  38. Trabulsi J, Schoeller DA. Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy intake. Am J Physiol Endocrinol Metab 2001;281:E891–9.[Abstract/Free Full Text]
  39. Smith Richards BK, Belton BN, Poole AC, et al. QTL analysis of self-selected macronutrient diet intake: fat, carbohydrate, and total kilocalories. Physiol Genomics 2002;11:205–17.[Abstract/Free Full Text]
  40. Lewis CE, North KE, Arnett D, et al. Sex-specific findings from a genome-wide linkage analysis of human fatness in non-Hispanic whites and African Americans: the HyperGEN study. Int J Obes (Lond) 2005;29:639–49.[Medline]
  41. Dai F, Keighley ED, Sun G, et al. Genome-wide scan for adiposity-related phenotypes in adults from American Samoa. Int J Obes (Lond) 2007;31:1832–42.[Medline]
  42. Luke A, Wu X, Zhu X, Kan D, Su Y, Cooper R. Linkage for BMI at 3q27 region confirmed in an African-American population. Diabetes 2003;52:1284–7.[Medline]
  43. Wu X, Cooper RS, Borecki I, et al. A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Am J Hum Genet 2002;70:1247–56.[Medline]
  44. Chagnon YC, Perusse L, Lamothe M, et al. Suggestive linkages between markers on human 1p32-p22 and body fat and insulin levels in the Quebec Family Study. Obes Res 1997;5:115–21.[Medline]
  45. Bloch AS. Low carbohydrate diets, pro: time to rethink our current strategies. Nutr Clin Pract 2005;20:3–12.[Abstract/Free Full Text]
  46. Crowe TC. Safety of low-carbohydrate diets. Obes Rev 2005;6:235–45.[Medline]
  47. Meckling KA, O'Sullivan C, Saari D. Comparison of a low-fat diet to a low-carbohydrate diet on weight loss, body composition, and risk factors for diabetes and cardiovascular disease in free-living, overweight men and women. J Clin Endocrinol Metab 2004;89:2717–23.[Abstract/Free Full Text]
  48. Chagnon YC, Rice T, Perusse L, et al. Genomic scan for genes affecting body composition before and after training in Caucasians from HERITAGE. J Appl Physiol 2001;90:1777–87.[Abstract/Free Full Text]
  49. Lindsay RS, Kobes S, Knowler WC, Bennett PH, Hanson RL. Genome-wide linkage analysis assessing parent-of-origin effects in the inheritance of type 2 diabetes and BMI in Pima Indians. Diabetes 2001;50:2850–7.[Medline]
Received for publication May 5, 2008. Accepted for publication July 6, 2008.





This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Google Scholar
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.
Agricola
Right arrow Articles by Choquette, A. C
Right arrow Articles by Pérusse, L.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS