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Journal of Clinical Endocrinology & Metabolism, doi:10.1210/jc.2004-1752
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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 6 3197-3201
Copyright © 2005 by The Endocrine Society

Genomewide Linkage Analysis of Weight Change in the Framingham Heart Study

Caroline S. Fox, Nancy L. Heard-Costa, Ramachandran S. Vasan, Joanne M. Murabito, Ralph B. D’Agostino, Sr. and Larry D. Atwood

National Heart, Lung, and Blood Institute’s Framingham Heart Study (C.S.F., N.L.H.-C., R.S.V., J.M.M., R.B.D., L.D.A.), Framingham, Massachusetts 01702; National Heart, Lung, and Blood Institute (C.S.F.), Bethesda, Maryland 20892; Department of Endocrinology, Hypertension, and Metabolism, Brigham and Women’s Hospital and Harvard Medical School (C.S.F.), Boston, Massachusetts 02115; and Department of Neurology (N.L.H.-C., L.D.A.), Division of General Internal Medicine (J.M.M.), Division of Cardiology (R.S.V.), and Department of Mathematics and Statistics (R.B.D.), Boston University School of Medicine, Boston, Massachusetts 02118

Address all correspondence and requests for reprints to: Dr. Caroline S. Fox, M.D., MPH, 73 Mount Wayte Avenue, Suite 2, Framingham, Massachusetts 01702. E-mail: foxca{at}nhlbi.nih.gov.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Background: Weight gain adversely affects blood pressure, lipids, and glycemia. The genetic contribution to weight change is unknown.

Methods: Variance components linkage analysis using microsatellites was performed on 336 families from the Framingham Heart Study offspring cohort, using a 10-cM genome-wide linkage analysis. We evaluated linkage to two traits: short-term (8-yr) weight change and long-term (up to 24-yr) weight change. Models were adjusted for age, age squared, baseline weight, smoking status, and menopausal status.

Results: Mean short-term weight change ranged from 1.4–3.8 kg, and mean long-term weight change was 7.7 kg. The heritability of long-term weight change was 0.24; weight change was minimally heritable among younger individuals and over shorter follow-up intervals. We found significant evidence for linkage for long-term weight change, with a peak LOD score of 3.10 on chromosome 20 at 63.7 cM (nearest marker, D20S481). We also found suggestive evidence for linkage on chromosome 1 at 239.7 cM (LOD score, 2.28; nearest marker, D1S1644).

Conclusion: Long-term weight change is heritable, and evidence for linkage exists on chromosomes 1 and 20. Potential candidate genes include MC3R, ASIP, AGT, and HSD11B1. Additional research is necessary to uncover the genetic underpinnings of weight change that might contribute to associated adverse metabolic profiles.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
OBESITY INCREASES THE risk of all-cause mortality (1), vascular disease (2), and nonvascular causes of death, including certain cancers (3). Weight gain adversely affects cardiovascular disease risk factors, including changes in lipids, fasting insulin, and blood pressure (4, 5, 6), and is one of the leading risk factors for type 2 diabetes (7, 8).

Risk factor profiles can be modified by minimal gains and losses in weight (8, 9), suggesting that overall measures of adiposity may not accurately reflect the dynamic interaction of weight change on metabolic profiles. Reductions in weight of 5–10% can result in significant improvements in lipoprotein profiles (10), insulin sensitivity (11), and blood pressure (12), whereas weight gain of more than 10 kg is associated with an increased risk for hypertension, elevated low density lipoprotein cholesterol, and higher triglycerides (13). Although we have previously reported quantitative trait loci (QTLs) for body mass index (BMI) and waist circumference to 6q23–24 and 11q14 in the Framingham Heart Study (14, 15), it is possible that different loci underlie the genetics involved with weight change over time.

In fact, changes in body mass and adiposity have been shown to be heritable, with estimates ranging from 0.23–0.86 for changes in BMI (16, 17), 0.70 for weight gain (18), and 0.45 for change in waist circumference (16). However, limited data exist looking at linkage to weight change. Thus, we determined heritability and linkage to weight change in the Framingham Heart Study offspring study.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
The Framingham Heart Study began in 1948 with the enrollment of 5209 men and women, 28–62 yr of age at study entry, with subjects undergoing repeat exams every 2 yr (19). In 1971, 5124 men and women were enrolled in the offspring cohort of the Framingham Heart Study, which included the children or spouses of children of the original cohort. Offspring subjects underwent examinations approximately every 4 yr; the design and methodology of the offspring cohort has been previously described (20). The Boston Medical Center institutional review board approved the study, and all participants provided written informed consent.

Details regarding the methods of risk factor measurement and laboratory analysis have been described previously (21). Each examination included anthropometry, an extensive cardiovascular disease assessment, 12-lead electrocardiogram, and blood testing. Weight was measured to the nearest pound with the participant wearing only a gown without shoes or slippers and standing in the middle of the scale with weight equally distributed on both feet. For this analysis, we only used information regarding age, sex, weight, smoking status (defined as smoker at baseline or follow-up), and menopausal status (pre- or postmenopausal). BMI was calculated by dividing weight (in kilograms) by height (in meters squared). Adjustments were made for age and age squared to be consistent with our previously published work (14). We also adjusted for menopausal status, because the menopausal transition is associated with increases in body weight (22), increased visceral (23) and central fat depots (24), and higher BMI (24) compared with premenopausal women. We adjusted for smoking status, because smoking cessation has been associated with body weight gain (25). We adjusted for baseline weight to investigate genetic influences on weight change independent of baseline weight, because the magnitude of absolute weight change depends on baseline weight.

Leukocyte DNA was extracted from 5–10 ml whole blood or buffy coat specimens using a standard protocol (26). Aliquots of DNA from members of the largest Framingham Heart Study families were sent in four batches to the Mammalian Genotyping Service Laboratory at Marshfield Clinic (Marshfield, WI), consisting of 336 pedigrees. A 10-cM density genome-wide scan was performed (average heterozygosity, 0.77). Genotype data cleaning, including verification of family relationships and Mendelian inconsistencies, have been previously described (27).

For the purpose of this analysis, we created three datasets using information from participant examinations that occurred at roughly 8-yr intervals (1971–1975 to 1979–1982, 1979–1982 to 1987–1992, and 1987–1992 to 1995–1998), enabling the creation of a weight change variable over 8 yr (short-term weight change), and a fourth dataset that examined long-term weight change (1971–1975 to 1995–1998) over approximately 24 yr. Participants were excluded from these analyses if they were under age 20 yr or had a BMI less than 18 kg/m2 at any of the baseline examinations. Severe weight cyclers, defined as participants who repeatedly lost and gained 9 kg in at least three examination cycles, were also excluded from analyses. The rationale for this particular exclusion is derived from data from prior observational studies that suggest that individuals with weight fluctuation are at increased risk for all-cause mortality (28), indicating that it may be a different phenotype from more typical weight change over time. Because linkage signals are often the strongest when phenotypes are parsed into their most basic components, we removed the weight cyclers from our analysis. We also performed a secondary analysis in which we limited our study sample to participants under the age of 65 yr. Although obesity increases with age in young and middle-aged adults, declines are seen after age 60 yr (29), which may confound the assessment of the genetic component.

Statistical analysis

Using SAS (30), sex-specific residuals were computed using weight change, as defined by follow-up weight minus baseline weight. Sex-specific residuals were used to account for gender differences in adiposity. Residuals included adjustment for age, age squared, baseline weight, smoking status, and menopausal status (in women). Residuals were obtained from a regression-based linear model and were used to predict values of weight change after correction for variation attributable to covariates. The residuals were computed by calculating the difference between the actual and predicted values obtained by linear regression. In this way the correction for covariates was fixed across the dataset. The use of residuals was necessary to standardize values between different examination cycles and genders as well as to account for the effects of covariates. A residual value greater than 4 SD from the mean was reduced to the 4 SD value (i.e. Winsorized) to reduce the skewness and kurtosis of the data to an acceptable level (31). Specifically, the sex-specific residuals were derived for each of the four datasets separately (resulting in eight regression equations). Residuals were derived separately in men and women, then merged. The regression equations in women for weight change residual were: exams 1 to 6, 35.1 + (0.19 x age) – (0.01 x age2) – (0.03 x wt) + (1.94 x smok_baseline) – (7.15 x smok_follow) + (0.06 x menopausal status); exams 1 to 2, 0.23 + (age x 0.53) – (0.01 x age2) – (0.01 x wt) + (0.54 x smok_baseline) – (2.89 x smok_follow) + (0.33 x menopausal); exams 2 to 4, –9.6 + (age x 0.91) – (0.01 x age 2) + (0.02 x wt) + (4.84 x smok_baseline) – (5.48 x smok_follow) – (2.8 x menopausal); exams 4 to 6, 40.5 – (age x 0.53) – (0.0003 x age2) – (0.05 x wt) + (4.28 x smok_baseline) – (6.06 x smok_follow) + (2.01 x menopausal), where wt stands for weight, smok_baseline is smoking status at baseline, smok_follow is smoking status at follow-up, and menopausal is menopausal status. The regression equations among men for weight change residual were: exams 1 to 6, 45.6 – (0.52 x age) – (0.004 x age2) – (0.06 x wt) + (4.70 x smok_baseline) – (4.84 x smok_follow); exams 1 to 2, 25.5 – (0.37 x age) + (0.002 x age2) – (0.08 x wt) + (1.50 x smok_baseline) – (1.34 x smok_follow). exams 2 to 4, 23.9-(0.21 x age) – (0.001 x age2) – (0.05 x wt) + (5.03 x smok_baseline) – (4.22 x smok_follow); and exams 4 to 6, 43.5 – (1.23 x age) + (0.01 x age2) + (0.001 x wt) + (8.22 x smok_baseline) – (9.44 x smok_follow). R2 values ranged from 0.02–0.13. For women, we included menopausal status as a covariate. Otherwise, models were identical for all datasets. Residuals were computed and then merged together for use in Genehunter to compute the heritability and linkage estimates.

Variance components linkage analysis (32, 33), as implemented in Genehunter (34), was used to perform the genomewide linkage analysis using residuals for weight change as the phenotype of interest. This approach uses the genotype information at a locus to decompose the phenotypic variance into a component attributable to the locus [known as a quantitative trait locus (QTL)], a polygenic component and an environmental component. Genotype information at a locus is characterized by the probability that two related individuals share zero, one, or two alleles identical by descent (IBD). IBD computation grows exponentially with pedigree size in Genehunter. To make the IBD computation feasible, 19 of the largest families were split into smaller pedigrees. The resulting small loss of genetic information should be conservative with respect to linkage results.

All variance components were estimated by maximum likelihood. Linkage was tested by a likelihood ratio test in which the hypothesis that the QTL variance component is equal to zero was compared with it being greater than zero. The resulting {chi}2 statistic was converted to a traditional LOD score by dividing by 2 x ln (10). It is possible to obtain estimates of the proportion of the total phenotypic variation due to the QTL. However, two recent studies (35, 36) have shown that the estimate of this effect is strongly correlated with the LOD score estimate and is poorly correlated with the true proportion. Therefore, we will not present effect estimates here. The Framingham Heart Study is population based and is not selected for any particular trait; therefore, no ascertainment correction is necessary.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
The characteristics of the four datasets are presented in Table 1Go. As expected, the sample aged with each progressive dataset; mean weight increased as well. The mean long-term weight change was 7.7 kg. The short-term (8-yr) weight change ranged from 1.4–3.8 kg. The most heritable trait was long-term weight change (0.24). Short-term weight change was minimally heritable, with estimates ranging from 0–0.12. Short-term weight change heritability was higher among younger individuals than among older individuals.


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  		TABLE 1. Baseline characteristics of the study sample

 
LOD scores greater than 1.0 are presented in Table 2Go for all datasets. We found significant evidence for linkage for long-term weight change, with a peak LOD score of 3.10 on chromosome 20 at 63.7 cM (nearest marker, D20S481; Fig. 1Go). We also found suggestive evidence for linkage on chromosome 1 at 239.7 cM (LOD score, 2.28; nearest marker, D1S1644; Fig. 2Go). When the sample was limited to participants less than 65 yr of age, the peak LOD score on chromosome 20 was attenuated (LOD score, 2.32 at 63.7 cM).


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  		TABLE 2. LOD scores greater than 1.0 for all datasets

 


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  		FIG. 1. Multipoint linkage analysis for weight change between exams 1 to 2, 2 to 4, 4 to 6, and 1 to 6: chromosome 20.

 


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  		FIG. 2. Multipoint linkage analysis for weight change between exams 1 to 2, 2 to 4, 4 to 6, and 1 to 6: chromosome 1.

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Long-term weight change is heritable, whereas short-term weight change is less heritable. We have found evidence for significant linkage to long-term weight change on chromosome 20 and evidence for suggestive linkage on chromosome 1.

Previous heritability studies have demonstrated that changes in measures of adiposity over time, including BMI, weight, and waist circumference, are heritable (16, 17, 18). These previous estimates were higher than ours, with estimates of heritability as high as 0.70 for weight gain.

Limited data exist on linkage to weight gain in the published literature. As part of the Genetic Analysis Workshop proceedings, linkage analysis was performed on the slope of weight change in the Framingham Heart Study, which demonstrated a peak multipoint LOD score of 1.6 on chromosome 8 at 152 cM for body weight change and 1.9 on chromosome 8 at 102 cM for body weight gain and body weight change up to 50 yr of age (37). Different methodologies, including use of the regression slope instead of actual weight change, may contribute to differences from our findings.

The QTL that we identified on chromosome 20q13 has been replicated in several other linkage analyses that have examined BMI, percent body fat, and eating behavior, with LOD scores ranging from 2.2–3.2 (38). This region harbors several interesting candidate genes, including the melanocortin 3 receptor, MC3R, which has been shown to be associated with BMI (39) and severe obesity (40); the agouti-signaling protein, ASIP; GHRH, which has been associated with central adiposity (41); and the adenosine deaminase gene, ADA, which has been associated with BMI (42).

We also found suggestive linkage on chromosome 1 to weight change, with a LOD score of 2.28. Interestingly, this QTL is the same location where long-term fasting glucose values were mapped from the Framingham Heart Study (43), raising the possibility that weight change may be an important genetic component in the development of diabetes. Furthermore, the angiotensinogen (AGT) gene lies in this region, and polymorphisms in this gene have been associated with weight gain among treated hypertensive patients (44). This region also contains the 11ß-hydroxysteroid dehydrogenase 1 gene (HSD11B1), which has been shown to be associated with obesity in expression studies (45).

Candidate gene analyses have been performed looking at weight change. Polymorphisms in the leptin receptor and the ß2-adrenergic receptor were shown to be associated with weight gain in a case-control study comparing participants who gained an average of 12.8 kg over 7 yr with those who remained weight stable (46). Polymorphisms in the serotonin 5-HT2c receptor gene have been associated with clozapine-induced weight gain among patients with schizophrenia (47). Candidate gene studies of overfeeding also shed light on genetic susceptibility to weight gain. In 12 pairs of identical male twins, polymorphisms in the UCP gene were involved in recovery from overfeeding (48), and polymorphisms in the ß2-adrenergic receptor (49) and the resistin gene (50) were shown to be associated with depot-specific fat accumulation after overfeeding. These data suggest that weight gain is an important phenotype that warrants additional study in the general population.

Strengths of our study include repeated measures of weight collected over a 27-yr period using standardized methods and well documented covariate data. Some limitations do exist. The predominantly Caucasian sample that comprises the majority of the Framingham offspring cohort may limit the generality of our findings. However, cardiovascular disease risk factor relationships from Framingham have been validated in six ethnically and geographically diverse cohorts and were found to be applicable in other populations, reinforcing the generalizability of our data (51). Our genetic data were not collected until 1995; therefore, there may have been a survival bias, because subjects with a greater genetic predisposition to more severe weight change may have died prematurely. Our peak LOD score of 3.1 is significant for genome-wide significance, but there is a possibility that our results may represent a false positive finding. Our sample size and the marker spacing of approximately 10 cM in the genome-wide linkage analysis may limit our power to localize disease-influencing QTL in linkage to weight change. Although there may be additional QTLs not detected in our study, simulation studies using finer marker maps have demonstrated that the location error is low in maps using a marker spacing of 10 cM compared with 0.5 cM (52), suggesting that this should not have affected our results significantly. Lastly, we were unable to adjust for physical activity and diet in our multivariable regressions.

In summary, we have found evidence for linkage to long-term weight gain to two genomic regions. Both of these regions have been implicated in independent studies of obesity-related phenotypes. Additional studies are warranted to understand the genetic underpinnings to weight change, which affects blood pressure, lipids, and glycemia.


   Footnotes
 
This work was supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study [Contract N01-HC-25195 and Grant 2K24-HL-04334 (to R.S.V.)].

First Published Online March 15, 2005

Abbreviations: BMI, Body mass index; IBD, identical by descent; QTL, quantitative trait locus.

Received September 7, 2004.

Accepted March 7, 2005.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath Jr CW 1999 Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 341:1097–1105[Abstract/Free Full Text]
  2. Hubert HB, Feinleib M, McNamara PM, Castelli WP 1983 Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation 67:968–977[Abstract/Free Full Text]
  3. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ 2003 Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med 348:1625–1638[Abstract/Free Full Text]
  4. Norman JE, Bild D, Lewis CE, Liu K, West DS 2003 The impact of weight change on cardiovascular disease risk factors in young black and white adults: the CARDIA study. Int J Obes Relat Metab Disord 27:369–376[CrossRef][Medline]
  5. Yarnell JW, Patterson CC, Thomas HF, Sweetnam PM 2000 Comparison of weight in middle age, weight at 18 years, and weight change between, in predicting subsequent 14 year mortality and coronary events: Caerphilly Prospective Study. J Epidemiol Community Health 54:344–348[Abstract/Free Full Text]
  6. Bakx JC, van den Hoogen HJ, Deurenberg P, van Doremalen J, van den Bosch WJ 2000 Changes in serum total cholesterol levels over 18 years in a cohort of men and women: the Nijmegen Cohort Study. Prev Med 30:138–145[CrossRef][Medline]
  7. Chan JM, Rimm EB, Colditz GA, Stampfer MJ, Willett WC 1994 Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 17:961–969[Abstract]
  8. Colditz GA, Willett WC, Rotnitzky A, Manson JE 1995 Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med 122:481–486[Abstract/Free Full Text]
  9. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanneimi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M, Finnish Diabetes Prevention Study Group 2001 Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 344:1343–1350[Abstract/Free Full Text]
  10. Poston WS, Reeves RS, Haddock CK, Stormer S, Balasubramanyan A, Satterwhite O, Taylor JE, Foreyt JP 2003 Weight loss in obese Mexican Americans treated for 1-year with orlistat and lifestyle modification. Int J Obes Relat Metab Disord 27:1486–1493[CrossRef][Medline]
  11. Tong PC, Lee ZS, Sea MM, Chow CC, Ko GT, Chan WB, So WY, Ma RC, Ozaki R, Woo J, Cockram CS, Chan JC 2002 The effect of orlistat-induced weight loss, without concomitant hypocaloric diet, on cardiovascular risk factors and insulin sensitivity in young obese Chinese subjects with or without type 2 diabetes. Arch Intern Med 162:2428–2435[Abstract/Free Full Text]
  12. Czernichow S, Mennen L, Bertrais S, Preziosi P, Hercberg S, Oppert JM 2002 Relationships between changes in weight and changes in cardiovascular risk factors in middle-aged French subjects: effect of dieting. Int J Obes Relat Metab Disord 26:1138–1143[CrossRef][Medline]
  13. Pihl E, Jurimae T 2001 Relationships between body weight change and cardiovascular disease risk factors in male former athletes. Int J Obes Relat Metab Disord 25:1057–1062[CrossRef][Medline]
  14. Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PW, D’Agostino RB 2002 Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study. Am J Hum Genet 71:1044–1050[CrossRef][Medline]
  15. Fox CS, Heard-Costa NL, Wilson PW, Levy D, D’Agostino RB, Sr., Atwood LD 2004 Genome-wide linkage to chromosome 6 for waist circumference in the Framingham Heart Study. Diabetes 53:1399–1402[Abstract/Free Full Text]
  16. Hunt MS, Katzmarzyk PT, Perusse L, Rice T, Rao DC, Bouchard C 2002 Familial resemblance of 7-year changes in body mass and adiposity. Obes Res 10:507–517[Medline]
  17. Austin MA, Friedlander Y, Newman B, Edwards K, Mayer-Davis EJ, King MC 1997 Genetic influences on changes in body mass index: a longitudinal analysis of women twins. Obes Res 5:326–331[Medline]
  18. Fabsitz RR, Sholinsky P, Carmelli D 1994 Genetic influences on adult weight gain and maximum body mass index in male twins. Am J Epidemiol 140:711–720[Abstract/Free Full Text]
  19. Dawber TR, Meadors GF, Moore FE 1951 Epidemiologic approaches to heart disease: the Framingham study. Am J Public Health 41:279–286
  20. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP 1975 The Framingham Offspring Study. Design and preliminary data. Prev Med 4:518–525[CrossRef][Medline]
  21. Cupples LA, D’Agostino RB 1987 Some risk factors related to the annual incidence of cardiovascular disease and death using pooled repeated biennial measurements: Framingham Study, 30-year follow-up. In: Kannel WB, Polf PA, Garrison RJ, eds. The Framingham Heart Study: an epidemiological investigation of cardiovascular disease. Washington DC: U.S. Government Printing Office; NIH Publication 87-203, sect 34
  22. Blumel JE, Castelo-Branco C, Rocangliolo ME, Bifa L, Tacla X, Mamani L 2001 Changes in body mass index around menopause: a population study of Chilean woman. Menopause 8:239–244[CrossRef][Medline]
  23. Ozbey N, Sencer E, Molvalilar S, Orhan Y 2002 Body fat distribution and cardiovascular disease risk factors in pre- and postmenopausal obese women with similar BMI. Endocr J 49:503–509[CrossRef][Medline]
  24. Chang CJ, Wu CH, Yao WJ, Yang YC, Wu JS, Lu FH 2000 Relationships of age, menopause and central obesity on cardiovascular disease risk factors in Chinese women. Int J Obes Relat Metab Disord 24:1699–1704[CrossRef][Medline]
  25. Janzon E, Hedblad B, Berglund G, Engstrom G 2004 Changes in blood pressure and body weight following smoking cessation in women. J Intern Med 255:266–272[CrossRef][Medline]
  26. Miller SA, Dykes DD, Polesky HF 1988 A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16:1215[Free Full Text]
  27. Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH 2000 Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension 36:477–483[Abstract/Free Full Text]
  28. Wannamethee SG, Shaper AG, Walker M 2002 Weight change, weight fluctuation, and mortality. Arch Intern Med 162:2575–2580[Abstract/Free Full Text]
  29. Elia M 2001 Obesity in the elderly. Obes Res 9(Suppl 4):244S–248S
  30. SAS Institute 2000 SAS/STAT user’s guide, version 8. Cary: SAS Institute
  31. Fernandez JR, Etzel C, Beasley TM, Shete S, Amos CI, Allison DB 2002 Improving the power of sib pair quantitative trait loci detection by phenotype winsorization. Hum Hered 53:59–67[Medline]
  32. Almasy L, Blangero J 1998 Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211[CrossRef][Medline]
  33. Amos CI 1994 Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54:535–543[Medline]
  34. Pratt SC, Daly MJ, Kruglyak L 2000 Exact multipoint quantitative-trait linkage analysis in pedigrees by variance components. Am J Hum Genet 66:1153–1157[CrossRef][Medline]
  35. Allison DB, Neale MC, Zannolli R, Schork NJ, Amos CI, Blangero J 1999 Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure. Am J Hum Genet 65:531–544[CrossRef][Medline]
  36. Goring HH, Terwilliger JD, Blangero J 2001 Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69:1357–1369[CrossRef][Medline]
  37. Golla A, Strauch K, Dietter J, Baur MP 2003 Quantitative trait linkage analysis of longitudinal change in body weight. BMC Genet 4(Suppl 1):S7
  38. Snyder EE, Walts B, Perusse L, Chagnon YC, Weisnagel SJ, Rankinen T, Bouchard C 2004 The human obesity gene map: the 2003 update. Obes Res 12:369–439[Medline]
  39. Boucher N, Lanouette CM, Larose M, Perusse L, Bouchard C, Chagnon YC 2002 A +2138InsCAGACC polymorphism of the melanocortin receptor 3 gene is associated in human with fat level and partitioning in interaction with body corpulence. Mol Med 8:158–165[Medline]
  40. Lee YS, Poh LK, Loke KY 2002 A novel melanocortin 3 receptor gene (MC3R) mutation associated with severe obesity. J Clin Endocrinol Metab 87:1423–1426[Abstract/Free Full Text]
  41. Barreto-Filho JA, Alcantara MR, Salvatori R, Baretto MA, Sousa AC, Bastos V, Souza AS, Pereira RM, Clayton PE, Gill MS, Aguiar-Oliveira MH 2002 Familial isolated growth hormone deficiency is associated with increased systolic blood pressure, central obesity, and dyslipidemia. J Clin Endocrinol Metab 87:2018–2023[Abstract/Free Full Text]
  42. Bottini E, Gloria-Bottini F 1999 Adenosine deaminase and body mass index in non-insulin-dependent diabetes mellitus. Metabolism 48:949–951[CrossRef][Medline]
  43. Meigs JB, Panhuysen CI, Myers RH, Wilson PW, Cupples LA 2002 A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study. Diabetes 51:833–840[Abstract/Free Full Text]
  44. Chaves FJ, Giner V, Corella D, Pascual J, Marin P, Armengod ME, Redon J 2002 Body weight changes and the A-6G polymorphism of the angiotensinogen gene. Int J Obes Relat Metab Disord 26:1173–1178[CrossRef][Medline]
  45. Lindsay RS, Wake DJ, Nair S, Bunt J, Livingstone DE, Permana PA, Tataranni PA, Walker RB 2003 Subcutaneous adipose 11ß-hydroxysteroid dehydrogenase type 1 activity and messenger ribonucleic acid levels are associated with adiposity and insulinemia in Pima Indians and Caucasians. J Clin Endocrinol Metab 88:2738–2744[Abstract/Free Full Text]
  46. van Rossum CT, Hoebee B, Seidell JC, Bouchard C, van Baak MA, de Groot CP, Chagnon M, de Graaf C, Saris WH 2002 Genetic factors as predictors of weight gain in young adult Dutch men and women. Int J Obes Relat Metab Disord 26:517–528[CrossRef][Medline]
  47. Reynolds GP, Zhang Z, Zhang X 2003 Polymorphism of the promoter region of the serotonin 5-HT(2C) receptor gene and clozapine-induced weight gain. Am J Psychiatry 160:677–679[Abstract/Free Full Text]
  48. Ukkola O, Tremblay A, Sun G, Chagnon YC, Bouchard C 2001 Genetic variation at the uncoupling protein 1, 2 and 3 loci and the response to long-term overfeeding. Eur J Clin Nutr 55:1008–1015[CrossRef][Medline]
  49. Ukkola O, Tremblay A, Bouchard C 2001 ß-2 adrenergic receptor variants are associated with subcutaneous fat accumulation in response to long-term overfeeding. Int J Obes Relat Metab Disord 25:1604–1608[CrossRef][Medline]
  50. Ukkola O, Kesaniemi YA, Tremblay A, Bouchard C 2004 Two variants in the resistin gene and the response to long-term overfeeding. Eur J Clin Nutr 58:654–659[CrossRef][Medline]
  51. D’Agostino RB, Sr., Grundy S, Sullivan LM, Wilson P 2001 Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 286:180–187[Abstract/Free Full Text]
  52. Atwood LD, Heard-Costa NL 2003 Limits of fine-mapping a quantitative trait. Genet Epidemiol 24:99–106[CrossRef][Medline]




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