Skip to main content

Advertising Disclaimer »

Main menu

  • Journals
    • Pediatrics
    • Hospital Pediatrics
    • Pediatrics in Review
    • NeoReviews
    • AAP Grand Rounds
    • AAP News
  • Authors/Reviewers
    • Submit Manuscript
    • Author Guidelines
    • Reviewer Guidelines
    • Open Access
    • Editorial Policies
  • Content
    • Current Issue
    • Online First
    • Archive
    • Blogs
    • Topic/Program Collections
    • AAP Meeting Abstracts
  • Pediatric Collections
    • COVID-19
    • Racism and Its Effects on Pediatric Health
    • More Collections...
  • AAP Policy
  • Supplements
  • Multimedia
    • Video Abstracts
    • Pediatrics On Call Podcast
  • Subscribe
  • Alerts
  • Careers
  • Other Publications
    • American Academy of Pediatrics

User menu

  • Log in
  • Log out

Search

  • Advanced search
American Academy of Pediatrics

AAP Gateway

Advanced Search

AAP Logo

  • Log in
  • Log out
  • Journals
    • Pediatrics
    • Hospital Pediatrics
    • Pediatrics in Review
    • NeoReviews
    • AAP Grand Rounds
    • AAP News
  • Authors/Reviewers
    • Submit Manuscript
    • Author Guidelines
    • Reviewer Guidelines
    • Open Access
    • Editorial Policies
  • Content
    • Current Issue
    • Online First
    • Archive
    • Blogs
    • Topic/Program Collections
    • AAP Meeting Abstracts
  • Pediatric Collections
    • COVID-19
    • Racism and Its Effects on Pediatric Health
    • More Collections...
  • AAP Policy
  • Supplements
  • Multimedia
    • Video Abstracts
    • Pediatrics On Call Podcast
  • Subscribe
  • Alerts
  • Careers

Discover Pediatric Collections on COVID-19 and Racism and Its Effects on Pediatric Health

American Academy of Pediatrics
Article

Genome Scan for Childhood and Adolescent Obesity in German Families

Kathrin Saar, Frank Geller, Franz Rüschendorf, André Reis, Susann Friedel, Nadine Schäuble, Peter Nürnberg, Wolfgang Siegfried, Hans-Peter Goldschmidt, Helmut Schäfer, Andreas Ziegler, Helmut Remschmidt, Anke Hinney and Johannes Hebebrand
Pediatrics February 2003, 111 (2) 321-327; DOI: https://doi.org/10.1542/peds.111.2.321
Kathrin Saar
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frank Geller
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Franz Rüschendorf
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
André Reis
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Susann Friedel
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nadine Schäuble
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Nürnberg
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wolfgang Siegfried
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hans-Peter Goldschmidt
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Helmut Schäfer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andreas Ziegler
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Helmut Remschmidt
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anke Hinney
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johannes Hebebrand
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • Comments
Loading
Download PDF

Abstract

Objective. Several genome scans have been performed for adult obesity. Because single formal genetic studies suggest a higher heritability of body weight in adolescence and because genes that influence body weight in adulthood might not be the same as those that are relevant in childhood and adolescence, we performed a whole genome scan.

Methods. The genome scan was based on 89 families with 2 or more obese children (sample 1). The mean age of the index patients was 13.63 ± 2.75 years. A total of 369 individuals were initially genotyped for 437 microsatellite markers. A second sample of 76 families was genotyped using microsatellite markers that localize to regions for which maximum likelihood binomial logarithm of the odd (MLB LOD) scores on use of the concordant sibling pair approach exceeded 0.7 in sample 1.

Results. The regions with MLB LOD scores >0.7 were on chromosomes 1p32.3-p33, 2q37.1-q37.3, 4q21, 8p22, 9p21.3, 10p11.23, 11q11-q13.1, 14q24-ter, and 19p13-q12 in sample 1; MLB LOD scores on chromosomes 8p and 19q exceeded 1.5. In sample 2, MLB LOD scores of 0.68 and 0.71 were observed for chromosomes 10p11.23 and 11q13, respectively.

Conclusion. We consider that several of the peaks identified in other scans also gave a signal in this scan as promising for ongoing pursuits to identify relevant genes. The genetic basis of childhood and adolescent obesity might not differ that much from adult obesity.

  • linkage analysis
  • BMI
  • body weight

The number of whole genome scans for obesity and obesity-related phenotypes has rapidly increased after publication of the initial scan pertaining to a search for genes that influence percentage body fat in Pima Indians in 1997.1 The ethnically diverse populations include Pima Indians1–4; Mexican Americans5–7; European and African Americans8–11; French Canadians12,13; Old Order Amish14; and Europeans from France,15 Finland,16–18 the Netherlands,19 and Sweden.17 Several different chromosomal regions have been identified in these whole genome scans, some of which have been confirmed in independent whole genome or regional studies, including linkage to chromosomes 2p,7,15 7q,11,14,20–25 10p and q,9,15,26,27 and 20q.9,28

One of the highest heritability estimates for body mass index (BMI; kg/m2) has been determined in a twin study based on adolescents.29 These findings suggest that heritability might even be higher at this age than in adulthood, for which estimates derived from twin studies typically range in the magnitude of 0.6 to 0.8.30,31

Human obesity as a result of rare single gene mutations such as in the leptin32,33 and leptin receptor genes34 typically manifests early in life. Nonsense, frameshift, and functionally relevant missense mutations in the melanocortin-4 receptor gene, which occur with a frequency of 2% to 4% among extremely obese adolescents and adults, are often associated with (extreme) obesity during childhood.35–41 It has been estimated that only 40% of the genes that influence BMI at age 20 continue to do so at ages 40 and 60.42 Nevertheless, obesity, in particular extreme obesity in adolescence, commonly persists in adulthood,43 the risk being even higher when at least 1 parent is also obese.44

Currently, no published whole genome scan for obesity has been based on children or adolescents as index patients. In light of the potentially stronger genetic determination of childhood and adolescent BMI and the possibility of age-dependent genetic influences on body weight, genome-wide scans based on children and adolescents are of obvious interest. Furthermore, scans based on young probands entail the advantage that the parents can be readily ascertained, thus enabling more accurate determination of the identity by descent status. Because the typical complications of obesity, including non-insulin-dependent diabetes and hypertension, have not fully become manifest at adolescence, these complications cannot be co-assessed in a scan based on young siblings. Particularly, a scan based on adolescents is complicated by the fact that several obesity-related traits are influenced by pubertal status; serum leptin levels represent a good and well-characterized example45 of this phenomenon.

The standard definitions for obesity based on absolute BMI46 cannot be applied to children and adolescents. In our molecular genetic studies, we have used BMI centiles based on the representative German National Nutrition Survey47 to define the degree of obesity of our index patients in the age range 5 to 22 years and their siblings (eg,26,38,39,48). Different centiles have been used to define overweight and obesity in children and adolescents, including the 85th and 95th49 and the 90th and 97th50 centiles, respectively. On the basis of the extremely concordant sibling pair approach (ECSP51) we have ascertained obese index patients and their siblings via a BMI ≥95th for 1 sibling and ≥90th centile for the other(s). Under consideration of the parameters estimated in previous segregation analyses for obesity, we have shown that the ECSP is better suited than the extreme discordant sibling pair approach to detect linkage.52 On use of the ECSP approach, we have confirmed linkage of obesity to chromosome 10p based on a regional scan encompassing 93 families with 2 or more obese offspring.26

In this study, we present for the first time results of a whole genome scan based on 89 young obese affected sibling pairs. The 452 microsatellite markers were spaced at an average distance of 8.4 cM and included markers for fine mapping; maximum likelihood binomial logarithm of the odd (MLB LOD) scores53 were calculated to determine linkage on the basis of the ECSP approach. We were subsequently able to genotype an additional 76 families with 2 young obese offspring in chromosomal regions of interest identified in the first group.

METHODS

Ascertainment of obese index patients was performed at 3 German hospitals (Klinik Hochried, Murnau; Adipositas Rehabilitationszentrum Insula, Berchtesgaden; and Spessart Klinik, Bad Orb) that specialize in the inpatient treatment of extremely obese children and adolescents. Families that were willing to participate were included when 1) at least 1 offspring had an age- and gender-specific BMI centile ≥95, 2) at least 1 sibling had an age- and gender-specific BMI centile ≥90, and 3) the DNA of both biological parents was available. There were 77, 11, and 1 families with 2, 3, and 4 obese children, respectively. Accordingly, the total number of individuals genotyped for the whole genome scan was n = 369 (sample 1). During genotyping of sample 1, an additional 76 families (sample 2) were recruited as part of an ongoing ascertainment to enable a future genome scan based on 300 families. Sample 2 also fulfilled the aforementioned 3 criteria. Again, the majority of these families (n = 68) had 2 obese offspring; 6, 1, and 1 had 3, 4, or 5 obese siblings, respectively. These families (sample 2) were genotyped for those markers that contributed to peak regions identified in sample 1 as defined by a MLB LOD >0.70. Finally, families of samples 1 and 2 were genotyped for 15 additional markers that localize within the identified peak regions. These markers were chosen by applying informativity criteria. Descriptive statistics for both samples are presented in Tables 1 and 2. In single families in samples 1 and 2, an obese offspring was aged ≥18 years: the oldest index patient (22 years) had an 18-year-old sibling; 18 siblings of index patients who were younger than 17 years were older than 22 years. Age- and gender-adjusted BMI centiles were calculated from the large and representative German National Nutrition Survey.47 Written informed consent was given by all participants; in the case of minors, consent was given by their parents. This study was approved by the Ethics Committee of the University of Marburg.

View this table:
  • View inline
  • View popup
TABLE 1.

Descriptive Statistics Based on 89 Families With at Least 1 ECSP Used to Perform a Whole Genome Scan (Sample 1)

View this table:
  • View inline
  • View popup
TABLE 2.

Descriptive Statistics Based on 76 Families With at Least 1 ECSP (Sample 2) Used in an Attempt to Confirm Linkage to Regions With an MLB LOD score >0.70 as Detected in Sample 1

Genotyping

DNA was isolated from peripheral white blood cells using standard protocols.48 The Gene Mapping Centre panel of 372 highly polymorphic microsatellite markers with an average distance of 9.9 cM and an average heterozygosity of 0.78 was selected from the final Généthon linkage map as previously described.54 In brief, markers were amplified on microtiter plates in single reactions on Tetrad polymerase chain reaction (PCR) machines (MJ Research Biozym, Hessisch Oldendorf, Germany). All pre- and post-PCR pipetting steps were performed using robotic devices. PCR product pools were separated on ABI377XL (Applied Biosystems [ABI], Darmstadt, Germany) sequencers and on MegaBace sequencers (Pharmacia Amersham, Freiburg, Germany), respectively. Semiautomated genotyping was performed using the Genescan and Genotyper (ABI) software and the genetic profiler in the case of MegaBace data. Instrument allele calling was checked manually. All genotypes were subject to an automatic Mendelian check using the Linkrun routine (T. F. Wienker, unpublished), which in turn calls the program Unknown v5.20 from the Linkage Package.55 All allele sizes were standardized to known Centre d’Etude Polymorphisme Humain control individuals. Sixty-five additional markers were typed where the total information content56 was below 0.6 in our samples. Statistical analyses included Crimap to minimize false double recombinants. Peak regions from sample 1 with MLB LOD scores >0.70 were then typed with 2 additional flanking markers on each side of the peak, encompassing 10 cM on either side of the peak loci on chromosomes 1, 2, 4, 8, 9, 10, 11, 14, and 19.

Statistical Analysis

We conducted model-free linkage analysis because the mode of inheritance is unknown for obesity. Multipoint LOD score analysis was performed using the MLB statistics as implemented in MLBGH, Version 1.0.53 This test statistic is based on the binomial distribution of parental alleles among extremely concordant offspring and accounts for multiple sibships in a natural way.

Gender-averaged map distances (cM) from the Généthon map were transformed to recombination fractions and vice versa using Haldane’s map function. X chromosomal calculations were conducted with Genehunter, version 1.3.56

RESULTS

The whole genome scan performed with sample 1 based on all 452 markers did not reveal a MLB LOD score ≥2. Two peaks on chromosomes 8p and 19q surpassed a MLB LOD score of 1.5. MLB LOD scores of 2 additional peaks on chromosomes 2q and 11q were ≥1.0. Figure 1 demonstrates that 2 peaks on chromosomes 10p and 11q out of the total of 9 peak regions, for which sample 2 was also genotyped, revealed a MLB LOD score ≥0.5. In addition, Table 3 gives an overview of all 21 markers with 2-point LOD scores ≥0.70 in the first sample. These markers primarily contribute to the reported multipoint LOD scores.

  • Download figure
  • Open in new tab
  • Download powerpoint
Fig 1.

MLB LOD scores for chromosomes 1 to 12 (A) and 13 to 22 and X (B) based on a genome scan of 89 families with 2 or more obese children (black lines) and MLB LOD scores obtained in an additional sample of 76 families in regions of interest (gray lines).

View this table:
  • View inline
  • View popup
TABLE 3.

Two-Point MLB LOD Scores >0.70 in Sample 1 and Corresponding LOD Scores in Sample 2 (if Available)

DISCUSSION

To our knowledge, this study represents the first genome scan for adolescent obesity; the mean ages of the index patients and their siblings range between 13 and 15 years (Tables 1 and 2). Only single offspring were older than 18 years; in all sibships, 1 sibling was aged ≤18 years. Because the majority of the index patients had BMIs above the maximal BMI observed in the age- and gender-matched population-based reference group,47 it seems reasonable to assume that the onset of obesity dated before age 10 in most of the index patients and their siblings. We had hypothesized that a genome scan based on childhood- and adolescent-onset obesity has a greater potential to detect relevant chromosomal regions than a scan based on obese adult sibling pairs. This hypothesis stemmed from findings indicating a potentially higher genetic load in childhood and adolescent obesity.29 Furthermore, such young sibling pairs are more homogeneous with respect to age at onset, thus potentially limiting a major source of heterogeneity. Finally, and in contrast to most genome scans based on adult probands, we ascertained both parents of all of our young sibling pairs so that the parental phase was primarily used as source of information instead of allele frequencies.

Recently, Altmüller et al57 reviewed 101 published genome scans for complex disorders. They pointed out that most of the analyzed studies were not able to detect “significant” linkage according to the Lander and Kruglyak criteria.58 The results of our genome scan based on only 89 families fall within this category. Despite our failure to detect suggestive evidence for linkage according to the strict Lander and Kruglyak criteria,58 the following aspects need to be considered:

First, for 77 of our 89 families, only 2 obese offspring were ascertained. The advantage of a genome scan based on mostly single and independent sibling pairs is that the respective results can be considered more representative of families with obese offspring in a given population than a scan that includes a mixture of both small and large or only large sibships. However, because heterogeneity of obesity is evident, the reliance on single sibling pairs entails the disadvantage that a hypothetical major gene operative in a limited number of families cannot lead to a high LOD score in a small sample. For identifying such major genes, large pedigrees with several affected family members should be sampled.

Second, despite the low MLB LOD scores, it is of interest to observe that some of the peaks localize to the same or close to chromosomal regions that have previously been detected in other scans based on adult populations of European origin (Table 4). Thus, previously identified peaks on chromosomes 1p,19 2q,15 8q,9 10p,15 11q,9 and 14q2 also gave a peak signal in the current scan. The moderate size of our peaks is in line with considerations that substantially larger sample sizes are needed to replicate previous findings with LOD scores that fulfill the Lander and Kruglyak criteria.58 In addition, it is worthwhile to point out that with the exception of our peaks on chromosome 14q and 19p, all of the other peaks with a MLB ≥0.70 have previously been identified in 2 of the scans based on extremely obese probands of European origin.9,15 Because in both of these studies extremely obese adult index patients were ascertained, it is possible that several of these also had a childhood onset of their obesity.

View this table:
  • View inline
  • View popup
TABLE 4.

Chromosomal Regions Identified Both in the Current and in Previous Genome Scans

Finally, despite the finding that study groups several times the size of the original sample need to be analyzed to confirm reliably the linkage regions,59 2 of our peaks (chromosome 10p and 11q) also showed up—albeit weakly—in our second sample. The chromosome 10p linkage currently can be considered one of the most consistent findings in obesity scans.15,26,27 Promising candidate genes localized within the chromosome 10 and 11 regions of interest include glutamic acid decarboxylase 2 (chromosome 10) and angiotensin receptor-like 1, ciliary neurotrophic factor, galanin, and uncoupling proteins 2 and 3 (www.ensembl.org/). Previously, we had not found evidence for association of a null allele of the ciliary neurotrophic factor gene with obesity.60 Furthermore, despite a positive association study pertaining to an uncoupling protein 2 promoter polymorphism,61 we were not able to replicate this finding; we also did not find evidence for linkage based on the families investigated in the current study.62

CONCLUSION

Whereas our scan did not reveal MLB LOD scores ≥2, we nevertheless consider that several of the previously identified peaks also gave a signal in this scan as promising for ongoing pursuits to identify genes within the respective chromosomal regions.

Acknowledgments

This work was supported by the Deutsche Forschungsgemeinschaft and by the German Bundesministerium für Forschung und Technologie (FKZ 01KW0006, 01GS0118, and 01KW9967).

We thank all probands for participation.

BMI, body mass index • ECSP, extremely concordant sibling pair • MLB LOD, maximum likelihood binomial logarithm of the odd • PCR, polymerase chain reaction

REFERENCES

  1. ↵
    Norman RA, Thompson DB, Foroud T, et al. Genomewide search for genes influencing percent body fat in Pima Indians: suggestive linkage at chromosome 11q21–q22. Am J Hum Genet.1997;60 :166– 173
    OpenUrlPubMed
  2. ↵
    Norman RA, Tataranni PA, Pratley R, et al. Autosomal genomic scan for loci linked to obesity and energy metabolism in Pima Indians. Am J Hum Genet.1998;62 :659– 668
    OpenUrlCrossRefPubMed
  3. Hanson RL, Ehm MG, Pettitt DJ, et al. An autosomal genomic scan for loci linked to type II diabetes mellitus and body mass index in Pima Indians. Am J Hum Genet.1998;63 :1130– 1138
    OpenUrlCrossRefPubMed
  4. ↵
    Walder K, Hanson RL, Kobes S, Ravussin E. An autosomal genomic scan for loci linked to plasma leptin concentration in Pima Indians. Int J Obes.2000;24 :559– 565
    OpenUrlCrossRefPubMed
  5. ↵
    Duggirala R, Blangero J, Almasy L, et al. A major susceptibility locus influencing plasma triglyceride concentrations is located on chromosome 15q in Mexican Americans. Am J Hum Genet.2000;66 :1237– 1245
    OpenUrlCrossRefPubMed
  6. Duggirala R, Blangero J, Almasy L, et al. A major locus for fasting insulin concentrations and insulin resistance on chromosome 6q with strong pleiotropic effects on obesity-related phenotypes in nondiabetic Mexican Americans. Am J Hum Genet.2001;68 :1149– 1164
    OpenUrlCrossRefPubMed
  7. ↵
    Comuzzie AG, Hixson JE, Almasy L, et al. A major quantitative trait locus determining serum leptin levels and fat mass is located on human chromosome 2. Nat Genet.1997;15 :273– 276
    OpenUrlCrossRefPubMed
  8. ↵
    Kissebah AH, Sonnenberg GE, Myklebust J, et al. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci U S A.2000;97 :14478– 14483
    OpenUrlAbstract/FREE Full Text
  9. ↵
    Lee JH, Reed DR, Li WD, et al. Genome scan for human obesity and linkage to markers in 20q13. Am J Hum Genet.1999;64 :196– 209 (published erratum appears in Am J Hum Genet. 2000;66:1472)
    OpenUrlCrossRefPubMed
  10. Zhu X, Cooper RS, Luke A, et al. A genome-wide scan for obesity in African-Americans. Diabetes.2002;51 :541– 544
    OpenUrlAbstract/FREE Full Text
  11. ↵
    Feitosa MF, Borecki IB, Rich SS, et al. Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study. Am J Hum Genet.2002;70 :72– 82
    OpenUrlCrossRefPubMed
  12. ↵
    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– 207
    OpenUrlCrossRefPubMed
  13. ↵
    Perusse L, Rice T, Chagnon YC, et al. A genome-wide scan for abdominal fat assessed by computed tomography in the Quebec Family Study. Diabetes.2001;50 :614– 621
    OpenUrlAbstract/FREE Full Text
  14. ↵
    Hsueh WC, Mitchell BD, Schneider JL, et al. Genome-wide scan of obesity in the Old Order Amish. J Clin Endocrinol Metab.2001;86 :1199– 1205
    OpenUrlCrossRefPubMed
  15. ↵
    Hager J, Dina C, Francke S, et al. A genome-wide scan for human obesity genes reveals a major susceptibility locus on chromosome 10. Nat Genet.1998;20 :304– 308
    OpenUrlCrossRefPubMed
  16. ↵
    Öhmann M, Oksanen L, Kaprio J, et al. Genome-wide scan of obesity in Finnish sibpairs reveals linkage to chromosome Xq24. J Clin Endocrinol Metab.2000;85 :3183– 3190
    OpenUrlCrossRefPubMed
  17. ↵
    Parker A, Meyer J, Lewitzky S, et al. A gene conferring susceptibility to type 2 diabetes in conjunction with obesity is located on chromosome 18p11. Diabetes.2001;50 :675– 680 (published erratum appears in Diabetes. 2001;50:1512)
    OpenUrlAbstract/FREE Full Text
  18. ↵
    Perola M, Öhmann M, Hiekkalinna T, et al. Quantitative-trait-locus analysis of body-mass index and of stature, by combined analysis of genome scans of five Finnish study groups. Am J Hum Genet.2001;69 :117– 123
    OpenUrlCrossRefPubMed
  19. ↵
    van der Kallen CJH, Lindgren CM, Daly MJ, et al. Genome scan for adiposity in Dutch dyslipidemic families reveals novel quantitative trait loci for leptin, body mass index and soluble tumor necrosis factor receptor superfamily 1A. Int J Obes.2000;24 :1381– 1391
    OpenUrlCrossRefPubMed
  20. ↵
    Clement K, Garner C, Hager J, et al. Indication for linkage of the human OB gene region with extreme obesity. Diabetes.1996;45 :687– 690
    OpenUrlAbstract/FREE Full Text
  21. Reed DR, Ding Y, Xu W, Cather C, Green ED, Price RA. Extreme obesity may be linked to markers flanking the human OB gene. Diabetes.1996;45 :691– 694
    OpenUrlAbstract/FREE Full Text
  22. 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– 1256
    OpenUrlCrossRefPubMed
  23. Duggirala R, Stern MP, Mitchell BD, et al. Quantitative variation in obesity-related traits and insulin precursors linked to the OB gene region on human chromosome 7. Am J Hum Genet.1996;59 :694– 703
    OpenUrlPubMed
  24. Lapsys NM, Furler SM, Moore KR, et al. Relationship of a novel polymorphic marker near the human obese (OB) gene to fat mass in healthy women. Obes Res.1997;5 :430– 433
    OpenUrlPubMed
  25. ↵
    Roth H, Hinney A, Ziegler A, et al. Further support for linkage of extreme obesity to the obese gene in a study group of obese children and adolescents. Exp Clin Endocrinol Diabetes.1997;105 :341– 344
    OpenUrlPubMed
  26. ↵
    Hinney A, Ziegler A, Oeffner F, et al. Independent confirmation of a major locus for obesity on chromosome 10. J Clin Endocrinol Metab.2000;85 :2962– 2965
    OpenUrlCrossRefPubMed
  27. ↵
    Price RA, Li WD, Bernstein A, et al. A locus affecting obesity in human chromosome region 10p12. Diabetologia.2001;44 :363– 366
    OpenUrlCrossRefPubMed
  28. ↵
    Hunt SC, Abkevich V, Hensel CH, et al. Linkage of body mass index to chromosome 20 in Utah pedigrees. Hum Genet.2001;109 :279– 285
    OpenUrlCrossRefPubMed
  29. ↵
    Pietilainen KH, Kaprio J, Rissanen A, et al. Distribution and heritability of BMI in Finnish adolescents aged 16 y and 17 y: a study of 4884 twins and 2509 singletons. Int J Obes Relat Metab Disord.1999;23 :107– 115
    OpenUrlCrossRefPubMed
  30. ↵
    Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet.1997;27 :325– 351
    OpenUrlCrossRefPubMed
  31. ↵
    Bouchard C, Perusse L. Genetic aspects of obesity. Ann N Y Acad Sci.1993;699 :26– 35
    OpenUrlPubMed
  32. ↵
    Montague CT, Farooqi IS, Whitehead JP, et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature.1997;387 :903– 908
    OpenUrlCrossRefPubMed
  33. ↵
    Strobel A, Issad T, Camoin L, Ozata M, Strosberg AD. A leptin missense mutation associated with hypogonadism and morbid obesity. Nat Genet.1998;18 :213– 215
    OpenUrlCrossRefPubMed
  34. ↵
    Clement K, Vaisse C, Lahlou N, et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature.1998;392 :398– 401
    OpenUrlCrossRefPubMed
  35. ↵
    Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, O’Rahilly S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Genet.1998;20 :111– 112
    OpenUrlCrossRefPubMed
  36. Vaisse C, Clément K, Guy-Grand B, Froguel P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet.1998;20 :113– 114
    OpenUrlCrossRefPubMed
  37. Vaisse C, Clement K, Durand E, Hercberg S, Guy-Grand B, Froguel P. Melanocortin-4-receptor mutations are a frequent and heterogeneous cause of morbid obesity. J Clin Invest.2000;106 :253– 262
    OpenUrlCrossRefPubMed
  38. ↵
    Hinney A, Schmidt A, Nottebom K, et al. Several mutations in the melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J Clin Endocrinol Metab.1999;84 :1483– 1486
    OpenUrlCrossRefPubMed
  39. ↵
    Sina M, Hinney A, Ziegler A, et al. Phenotypes in three pedigrees with autosomal dominant obesity due to haplo-insufficiency mutations in the melanocortin-4 receptor gene. Am J Hum Genet.1999;65 :1501– 1507
    OpenUrlCrossRefPubMed
  40. Farooqi IS, Yeo GS, Keogh JM, et al. Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. J Clin Invest.2000;106 :271– 279
    OpenUrlCrossRefPubMed
  41. ↵
    Mergen M, Mergen H, Ozata M, Oner R, Oner C. A novel melanocortin 4 receptor (MC4R) gene mutation associated with morbid obesity. J Clin Endocrinol Metab.2001;86 :3448
    OpenUrlCrossRefPubMed
  42. ↵
    Fabsitz RR, Carmelli D, Hewitt JK. Evidence for independent genetic influences on obesity in middle age. Int J Obes Relat Metab Disord.1992;16 :657– 666
    OpenUrlPubMed
  43. ↵
    Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med.1993;22 :167– 177
    OpenUrlCrossRefPubMed
  44. ↵
    Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med.1997;337 :869– 873
    OpenUrlCrossRefPubMed
  45. ↵
    Blum WF, Englaro P, Hanitsch S, et al. Plasma leptin levels in healthy children and adolescents: dependence on body mass index, body fat mass, gender, pubertal stage, and testosterone. J Clin Endocrinol Metab.1997;82 :2904– 2910
    OpenUrlCrossRefPubMed
  46. ↵
    World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Geneva, Switzerland: WHO; 1998
  47. ↵
    Hebebrand J, Himmelmann GW, Heseker H, Schäfer H, Remschmidt H. Use of percentiles for the body mass index in anorexia nervosa: diagnostic, epidemiological, and therapeutic considerations. Int J Eat Disord.1996;19 :359– 369
    OpenUrlCrossRefPubMed
  48. ↵
    Hinney A, Lentes KU, Rosenkranz K, et al. Beta 3-adrenergic-receptor allele distributions in children, adolescents and young adults with obesity, underweight or anorexia nervosa. Int J Obes Relat Metab Disord.1997;21 :224– 230
    OpenUrlCrossRefPubMed
  49. ↵
    Himes JH, Dietz WH. Guidelines for overweight in adolescent preventive services: recommendations from an expert committee. Am J Clin Nutr.1994;59 :839– 846
    OpenUrl
  50. ↵
    Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ.2000;320 :1– 6
    OpenUrlFREE Full Text
  51. ↵
    Risch N, Zhang H. Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science.1995;268 :1584– 1589
    OpenUrlAbstract/FREE Full Text
  52. ↵
    Ziegler A, Hebebrand J. Sample size calculations for linkage analysis using extreme sib pairs based on segregation analysis with the quantitative phenotype body weight as an example. Genet Epidemiol.1998;15 :577– 593
    OpenUrlCrossRefPubMed
  53. ↵
    Abel L, Müller-Myhsok B. Robustness and power of the maximum-likelihood-binomial and maximum-likelihood-score methods, in multipoint linkage analysis of affected-sibship data. Am J Hum Genet.1998;63 :638– 647
    OpenUrlCrossRefPubMed
  54. ↵
    Saar K, Al-Gazali L, Sztriha L, et al. Homozygosity mapping in families with Joubert syndrome identifies a locus on chromosome 9q34.3 and evidence for genetic heterogeneity. Am J Hum Genet.1999;65 :1666– 1671
    OpenUrlCrossRefPubMed
  55. ↵
    Lathrop GM, Lalouel JM, Julier C, Ott J. Strategies for multilocus linkage analysis in humans. Proc Natl Acad Sci U S A.1984;81 :3443– 3446
    OpenUrlAbstract/FREE Full Text
  56. ↵
    Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet.1996;58 :1347– 1363
    OpenUrlPubMed
  57. ↵
    Altmüller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomwide scans of complex diseases: true linkage is hard to find. Am J Hum Genet.2001;69 :936– 950
    OpenUrlCrossRefPubMed
  58. ↵
    Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet.1995;11 :241– 247
    OpenUrlCrossRefPubMed
  59. ↵
    Lernmark A, Ott J. Sometimes it’s hot, sometimes it’s not. Nat Genet.1998;19 :213– 214
    OpenUrlCrossRefPubMed
  60. ↵
    Münzberg H, Tafel J, Büsing B, et al. Screening for variability in the ciliary neurotrophic factor (CNTF) gene: no evidence for association with human obesity. Exp Clin Endocrinol Diabetes.1998;106 :108– 112
    OpenUrlPubMed
  61. ↵
    Esterbauer H, Schneitler C, Oberkofler H, et al. A common polymorphism in the promoter of UCP2 is associated with decreased risk of obesity in middle-aged humans. Nat Genet.2001;28 :178– 183
    OpenUrlCrossRefPubMed
  62. ↵
    Schäuble N, Geller F, Siegfried W, et al. No evidence for involvement of the promoter polymorphism −866 G/A of the UCP2 gene in body weight regulation. Exp Clin Endocrinol Diabetes. In press
  • Copyright © 2003 by the American Academy of Pediatrics
PreviousNext
Back to top

Advertising Disclaimer »

In this issue

Pediatrics
Vol. 111, Issue 2
1 Feb 2003
  • Table of Contents
  • Index by author
View this article with LENS
PreviousNext
Email Article

Thank you for your interest in spreading the word on American Academy of Pediatrics.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Genome Scan for Childhood and Adolescent Obesity in German Families
(Your Name) has sent you a message from American Academy of Pediatrics
(Your Name) thought you would like to see the American Academy of Pediatrics web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Request Permissions
Article Alerts
Log in
You will be redirected to aap.org to login or to create your account.
Or Sign In to Email Alerts with your Email Address
Citation Tools
Genome Scan for Childhood and Adolescent Obesity in German Families
Kathrin Saar, Frank Geller, Franz Rüschendorf, André Reis, Susann Friedel, Nadine Schäuble, Peter Nürnberg, Wolfgang Siegfried, Hans-Peter Goldschmidt, Helmut Schäfer, Andreas Ziegler, Helmut Remschmidt, Anke Hinney, Johannes Hebebrand
Pediatrics Feb 2003, 111 (2) 321-327; DOI: 10.1542/peds.111.2.321

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Genome Scan for Childhood and Adolescent Obesity in German Families
Kathrin Saar, Frank Geller, Franz Rüschendorf, André Reis, Susann Friedel, Nadine Schäuble, Peter Nürnberg, Wolfgang Siegfried, Hans-Peter Goldschmidt, Helmut Schäfer, Andreas Ziegler, Helmut Remschmidt, Anke Hinney, Johannes Hebebrand
Pediatrics Feb 2003, 111 (2) 321-327; DOI: 10.1542/peds.111.2.321
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Print
Download PDF
Insight Alerts
  • Table of Contents

Jump to section

  • Article
    • Abstract
    • METHODS
    • RESULTS
    • DISCUSSION
    • CONCLUSION
    • Acknowledgments
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • Comments

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Loss of mitochondrial protease OMA1 alters processing of the GTPase OPA1 and causes obesity and defective thermogenesis in mice
  • Thematic Review Series: Glycerolipids. DGAT enzymes and triacylglycerol biosynthesis
  • Quantitative Trait Loci for Fasting Glucose in Young Europeans Replicate Previous Findings for Type 2 Diabetes in 2q23-24 and Other Locations
  • Genetics of obesity in Hispanic children.
  • Human Galanin (GAL) and Galanin 1 Receptor (GALR1) Variations Are Not Involved in Fat Intake and Early Onset Obesity
  • Identification of Two Novel Human Acyl-CoA Wax Alcohol Acyltransferases: MEMBERS OF THE DIACYLGLYCEROL ACYLTRANSFERASE 2 (DGAT2) GENE SUPERFAMILY
  • A Genome-Wide Scan for Childhood Obesity-Associated Traits in French Families Shows Significant Linkage on Chromosome 6q22.31-q23.2
  • APOE and TGF-{beta}1 genes are associated with obesity phenotypes
  • Google Scholar

More in this TOC Section

  • Predictive Models of Neurodevelopmental Outcomes After Neonatal Hypoxic-Ischemic Encephalopathy
  • A Technology-Assisted Language Intervention for Children Who Are Deaf or Hard of Hearing: A Randomized Clinical Trial
  • Standard Versus Long Peripheral Catheters for Multiday IV Therapy: A Randomized Controlled Trial
Show more Articles

Similar Articles

Subjects

  • Genetics
    • Genetics
  • Journal Info
  • Editorial Board
  • Editorial Policies
  • Overview
  • Licensing Information
  • Authors/Reviewers
  • Author Guidelines
  • Submit My Manuscript
  • Open Access
  • Reviewer Guidelines
  • Librarians
  • Institutional Subscriptions
  • Usage Stats
  • Support
  • Contact Us
  • Subscribe
  • Resources
  • Media Kit
  • About
  • International Access
  • Terms of Use
  • Privacy Statement
  • FAQ
  • AAP.org
  • shopAAP
  • Follow American Academy of Pediatrics on Instagram
  • Visit American Academy of Pediatrics on Facebook
  • Follow American Academy of Pediatrics on Twitter
  • Follow American Academy of Pediatrics on Youtube
  • RSS
American Academy of Pediatrics

© 2021 American Academy of Pediatrics