Iwona Wybrańska, Małgorzata Malczewska-Malec, Aldona Dembińska-Kieć

Department of Clinical Biochemistry,
The Jagiellonian University Medical College
Kopernika 15a
31 501 Krak�w
Poland

tel: + 48 12 421 40 06
fax: + 48 12 421 40 73

Abstract

The paper reviews recent problems in understanding of the genetic basis and gene/gene, as well as gene/environment interaction in the development of obesity and its complications.

Introduction

In recent years, obesity has become a major public health problem owing to its prevalence, which stands at more than 25% in certain countries, with its alarming increase in children. The molecular mechanisms responsible for fat mass accumulation and maintenance are still to be elucidated. Obesity results from the interaction of environmental factors ( high calorie density diet and reduction in physical activity) and hereditary factors. This has been shown by numerous epidemiological studies carried out in large and different populations which vary ethnically (twins brought up together or separately, adopted children, nuclear families, etc.) [1-5].
Obesity has a very heterogeneous phenotypic expression and the molecular mechanisms involved in its development are diverse. According to several studies, 30 to 80% of weight variation might be attributed to genetic factors [1-5]. Today, the participation of genetic factors in the development of obesity can be summarized as follows:
� single mutations contribute to the development of obesity (monogenic obesity). These forms of obesity are rare, but very severe and generally start in childhood [6];
� several genetic variants interact with an �at-risk� environment what results in the development of common obesity (polygenic obesity).
Genetic variation of each susceptibility gene, taken individually, has a minimal effect on weight gain. However, the cumulative contribution of these genes becomes significant when there is an interaction with environmental factors predisposing to their phenotypic expression (overeating, reduction in physical activity, hormonal changes, or socioeconomic factors). The common disease/common variant hypothesis for obesity has been proposed [7]. It suggests that the genetic risk for obesity is due to the coexistence of disease-promoting frequent alleles in the organism. As a consequence, the percentage of developed obesity attributed to them is high (attributable risk). This is a current hypothesis in understanding of �multifactorial diseases�. However, even strong believers of this hypothesis agree, that in certain circumstances, the role of rare variant alleles may intervene (8,9) . The risk for common obesity could be also attributed to the presence of the certain number of loci, each with the multifactorial disease - obesity predisposing, low frequency alleles [8]. At present time, these two hypotheses are valid, because the genetic approach has not allowed one to be confirmed more than the other [9].
Numerous studies using both candidate gene and genome-wide approaches have been used to identify genes predisposing to obesity. As a result, the Human Obesity Gene Map currently updated was published in March 2005. It was the 11th from the series of reviews collecting and reporting the publications about genes related to obesity [10]. In the current work we present the problems with linkage of gene polymorphism and phenotype expressed by metabolic traits.

Monogenic forms of obesity

Obesity is seldom caused by a single gene defect in general population [11].Table 1 shows the gene variants identified so far in families with obesity, or the variants which were only found in obese individuals [10].

Table 1 Genes causing monogenic forms of obesity in humans.

The cloning of the ob gene in the mouse and its human homologue, Leptin [27] provided the first example of a causal relationship between a mutation and obesity. Two different mutations disrupting the structure of the Leptin gene have so far been identified in 6 morbidly obese children [12,13,14]. Treatment of these children with recombinant leptin protein, dramatically normalised weight, puberty as well as reduced the most of metabolic syndrome symptoms [28]. It is however not the case of the common obesity, since obese individuals demonstrate the elevated serum leptin levels and leptin resistance [29,30]. For the rare forms of obesity, mutations of the melanocortin receptor (MC4R) were identified, and their prevalence has been estimated to be as high as 2-4% among obese children [10, 34,35,]. The degree of obesity in individuals carrying the MC4R mutation varies and these individuals are usually also taller. A recent meta-analysis suggests that the common allele of the Val103Ile variant in the coding region of the MC4R is associated with obesity, whereas the rare allele (Ile103) (with a frequency of 4%) is more common in lean individuals [36]. The Ile103 allele has been found to be also associated with lower BMI [37].

Obesity linked to the large-scale chromosomal mutations

In addition to genetic defects mostly affecting body weight, numerous syndromes featuring obesity as one of the symptoms have been mapped to certain chromosomal loci, and for some of these cases the underlying gene has been identified [10]. Prader-Willi is the most common syndrome affecting every 16,000-25,000 newborns a year [31,32]. The Prader-Willi syndrome is an imprinting disorder that is usually caused by a deletion of  paternally inherited chromosome 15q region.
The origin of obesity is more complex in Bardet-Biedl syndrome (BBS), which is characterized by six main features such as: rod-cone-dystrophy (the most frequent phenotype), polydactyly, learning disabilities, hypogonadism in males, renal abnormalities and obesity. In BBS patients, obesity occurs with an early onset, usually arising within the first few years of life. The genetic basis of BBS is typically autosomal recessive, however, the occurrence of triallelic inheritance has been suggested in some families [77].

The genetic background of the common forms of obesity
 The first evidence that genetics is important in common, non-syndromic obesity came from a study that was published nearly 30 years ago. In 1977, the National Heart, Lung and Blood Institute (NHLBI) Twin Study first indicated the possibility that the observed familial aggregation for obesity was due to genetic factors rather  than  environment [78].
Subsequently, in 1986, Stunkard used 1,974 monozygotic and 2,097 dizygotic twin pairs, and estimated a heritability value for weight of 0.78, which increased to 0.81 after the 25-year follow-up [2]. These values were similar to the heritability value of 0.80 for height that was estimated in the same study.
A children adoption study showed at the same time similar results in support of a genetic influence on body weight, with adopted children having body sizes more similar to those of their biological parents than their adopted parents across the whole range of body size [3]. These studies were effectively combined in a seminal paper in 1990 that examined identical and fraternal twins that were reared together and apart [4]. Similarly to the previous studies the intra-pair correlation coefficients for obesity phenotypes of 0.70 for men and 0.66 for women were reported. Shared environment seemed to have no measurable effect and non-shared personal environment contributed about 30% of the variance ( 2,4).
 Multiple genome-wide scans have been performed for obesity and traits related to body composition [10]. Typically in complex disorders,  first identification of a region linked to the disease does not automatically lead to a replication in a follow-up study performed in another study sample . However, evidence of linkage (lod score > 3.0 or p < 0.001) for obesity and traits related to body composition has been identified in several studies for certain chromosome regions (10).
More than three hundred gene polymorphisms have been found to be associated with common forms of obesity, although much smaller number of them has been confirmed by the studies carried out in different ethnic populations[10]. This can be explained by the several causes including the small risk that the disease associated variant presents, small study sample size , and the linkage disequilibrium between the actual causative variants and the variants tested in the study. The gene-gene or gene environment interactions and phenotypic heterogeneity may also complicate the analysis, if the study populations have the different haplotype backgrounds or different environmental exposures. Despite these difficulties, the initial associations of some genes with obesity or related phenotypes have been replicated (reviewed in 10). The results of many genetic studies, that concern a large number of genes and chromosomal regions, are reported each year in the international journal: Obesity Research [10]. We will not give details on all studies, but will provide illustrative examples. Table 2 illustrates the pathways in which genetic polymorphisms may affect the physiological pathways involved in the regulation of energy balance, hereby increasing the susceptibility to developing obesity in a given environmental setting. Examples of putative candidate genes are given for each pathway.

Table 2 Different mechanisms by which genetics is expected to play a role in the development of obesity; examples of putative candidate genes are given for each category. The genes are annotated with the approved gene symbol (Human Genome Nomenclature Database). Adapted from reference 9 and10

Abbreviations:

ADRB1- beta-1 adrenergic receptor; ADRB2-beta-2 adrenergic receptor; ADRA2A -alpha-2A-, receptor ADRA2B -alpha-2B-, receptor; ADRB3-beta-3 adrenergic receptor; adrenergic,; AGRP- agouti-related protein; AMP1- carcinoembryonic antigen-related cell adhesion molecule pseudogene 1; APOA-IV- apolipoprotein A-IV; CCK- cholecystokinin; CCKAR- cholecystokinin A receptor; Dopamin 2R- dopaminergic D2 receptor; FABP: - Fatty acid binding protein;FOXC2- forkhead box protein C2; GHRL- ghrelin; GLP- glucagon-like peptide; 5HT2C-5-hydroxytryptamine 2C receptor;IL6- interleukin 6 (interferon, beta 2); INSR- insulin receptor; LEP-leptin; LEPR- leptin receptor; LIPE- lipase, hormone-sensitive; LPL- lipoprotein lipase; MC4R- melanocortin 4 receptor; NPPA- natriuretic peptide precursor A; NPY- neuropeptide Y; NPYR- neuropeptide Y receptor; POMC- proopiomelanocortin; PPARA- peroxisome proliferative activated receptor, alpha; PPARD- peroxisome proliferative activated receptor, delta; PPARG- peroxisome proliferative activated receptor, gamma; PPY- pancreatic polypeptide; RXRA- retinoic acid receptor RXR-alpha; RXRB- retinoic acid receptor RXR-beta; SCD- stearoyl-CoA desaturase (delta-9-desaturase); SPARC- secreted protein, acidic, cysteine-rich (osteonectin); TAS1R- taste receptor, type 1; TAS2R- taste receptor, type 2; TNF- tumor necrosis factor; UCP1- uncoupling protein 1 (mitochondrial, proton carrier); UCP2- uncoupling protein 2 (mitochondrial, proton carrier); UCP3- uncoupling protein 3 (mitochondrial, proton carrier); VLDLR- very low-density lipoprotein receptor;

The genes for which at least five different studies found association with common obesity or obesity related phenotypes include Adiponectin; Adrenergic: beta-2- and beta-3- receptors (ADRB2 and ADRB3); Guanine nucleotide binding protein (G protein), beta polypeptide 3 (GNB3); Interleukin 6 (interferon, beta 2) (IL6); Insulin; Leptin (LEP), Leptin receptor (LEPR); Lamin A/C (LIPE); Nuclear receptor subfamily 3, group C member 1 (NR3C1); PPARG; Tumor necrosis factor TNF superfamily, member 2 (TNF); as well as Uncoupling  proteins 1, 2 and 3 (mitochondrial, proton carrier) (UCP1, UCP2 and UCP3) [reviewed in 10].
 Recently marked progress has been made in the identification of obesity predisposing genes using genome-wide linkage and subsequent fine mapping studies [11,38, 39,]. The benefit of the positional cloning strategy is that it does not rely on any pre-existing knowledge of the genes that underlie the investigated trait. Particularly, for conditions such as obesity this may be useful since there is as yet limited information available, for instance regarding the appetite regulation. The first candidate gene for obesity identified through the genome wide approach was Glutamate decarboxylase 1 (GAD2) on chromosome 10p12 [38]. It encodes the glutamic acid decarboxylase enzyme GAD65 which is suggested to be connected with obesity by the hypothalamic regulation of food intake by the formation of the -aminobutyric acid (GABA) from the glutamic acid. GABA functions together with neuropeptide Y in the paraventricular nucleus increasing food intake. GAD2 polymorphisms were demonstrated to be associated with childhood obesity, high birth weight and binge eating [40]. However, the recent replication study on German, American and Canadian populations did not confirm the significant association of this gene with obesity [41].
Another interesting example is the Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) gene in chromosome 6q22-23. This 6q22-23 region has been previously demonstrated to be linked to obesity [42, 43], BMI [42], insulin secretion [44, 45] and T2DM [46, 47, 48, 49]. Recently, variants in ENPP1 were identified to be connected with obesity and T2DM [40]. ENPP1 encodes a prohormone convertase-1, the enzyme which inhibits the insulin receptor kinase activity and subsequent cellular signaling of insulin [50, 51]. ENPP1 is also involved in the post-translational processing of the propeptide that is encoded by the POMC gene. Splicing of the propeptide results in the generation of adrenocorticotropin, β-lipotropin, α-, β- and γ-melanocyte-stimulating hormones
Beside the mutations of the nuclear genome, several mutations in the mitochondrial DNA have been proved to be associated with obesity and related complications [33].

The gene polymorphism promoting the obesity-related metabolic complications

 In addition to genes - regulating restrictions of caloric intake (apetite), the numerous gene variants have been identified which appearance contribute to variation in lipid metabolism, termogenesis, adipose tissue differentiation, immuno-inflammatory process, insulin resistance/ predispose to diabetes, thromboembolism etc. Examples of such genes are given in Table 3.

Table 3. Examples of genes with common variations associated with obesity-related metabolic disturbances. Adapted from references  54, 55, 9 and10

* Abbreviations: ABCA1- ATP-binding cassette, sub-family A (ABC1), member 1; ACE- angiotensin-converting enzyme; AGT angiotensinogen; apo(a)- apolipoprotein little a;APOA-I- apolipoprotein A-I; APOA-II- apo A-II; APOB- apo B; APOC-II- apo C-II; APOC-III- apo C-III; APOC-IV- apo C-IV; APOD- apo D; APOE- apo E; APOH- apo H; APOJ- apo J; CAPN10- calpain 10; CD14- monocyte differentiation antigen CD14; CETP- cholesteryl ester transfer protein; CYP11B2- cytochrome P450, family 11, subfamily B, polypeptide 2; EnaC- enactin; ENOS- endothelial nitric oxide synthase; FABP2- fatty acid binding protein 2; FATPI- Fatty acid transporter-1; GNAS1- Gs protein alpha subunit; HL- hepatic lipase; HNF1A- hepatocyte nuclear factor 1-alpha; HNF4A- hepatocyte nuclear factor 4-alpha; IL1- interleukin 1; IL1ra- interleukin-1 receptor antagonist protein; INOS- inducible nitric oxide synthetase; IRS1- insulin receptor substrate 1; KCNJ11- potassium inwardly-rectifying channel, subfamily J, member 11; LCAT- lecithin-cholesterol acyltransferase; LDLR- low density lipoprotein receptor; LRP- low density lipoprotein receptor-related protein; MTP- microsomal triglyceride transfer protein (large polypeptide, 88kDa); PCAm-1- platelet/endothelial cell adhesion molecule (CD31 antigen; PLTP- phospholipid transfer protein; PON1- paraoxonase 1; PON2- paraoxonase 2; SR-BI- scavenger receptor class B; TCF1- transcription factor 1, hepatic; TNFa- tumor necrosis factor alpha; TNFb- tumor necrosis factor beta; TNFb1- tumor necrosis factor beta-1; TNFb21- tumor necrosis factor beta-2; TPA- tissue plasminogen activator;

Gene-gene interaction in polygenic model of obesity

The interpretation of genetic and environmental variances for multivariate and function valued phenotypes remain the main problem for estimation and interpretation. Deviation from health attributable to common complex disorders such as all components of obesity typically aggregate in families, but they do not segregate as Mendelian single genes. The new applications of known multidimensional statistical methods: such as principal component analysis (PC) and cluster analysis to evaluate the genetic variation consequences is presently used to solve this problem. PCs are implemented because they are statistically independent (orthogonal), describe the maximum amount of variation with the minimum numbers of parameters, and they are easy to be presented graphically. Even when the phenotype of interest has a large number of characteristics (dimensions in Euclides space), most of variations are typically associated with a small number of principal components and the principal component analysis may be used to visualize pattern of genetic variation. In this mode, the genetic principal components are calculated from an estimate of the full genetic covariance structure [74,75]. Direct estimation of the principal components reduces the number of parameters to be measured [76].
The another method- cluster analysis, similarly to principal component analysis, presents not only main pools of genes but also the hierarchy with different physiological significance [76,77,78].


Figure 1:The principal component analysis segregate the genetic traits according to its similarities in formation of phenotype

Figure 1 and Figure 2 show the examples of usage of both: PC-analysis as well as cluster analysis in our study. An application to the analysis of 14 most popular genetic traits and its phenotypic characteristics recorded during the familiar obesity study in South Poland is given. (our results, Journal of Clin Chem Lab Med 2007 in press).
Due to the implemented analysis it has been demonstrated that transcription factors (FOXC2 and PPAR-2) polymorphisms closely interact with each other and with the variability of genes regulating the carbohydrate metabolism. It is an argument for search of methods describing the interaction between the polymorphism of nutrient-sensitive transcription factors in regulating the metabolism of the body.
The risk of developing diet-dependent obesity is likely to involve interactions between many common but weakly penetrant genetic variants and numerous environmental and/or personal history factors. The construction of the diet - dependent disease risk model with both genetic and environmental/personal history measures to predict a specific risk of development of diseases in the early stage of life and at the stage of the absence of syndromes is very ambitious but unrealistic goal at this time. Thus the specific tests to validate genetic markers in aspects of nutrition are necessary.
The insulin output in response to the high carbohydrate or high fat diet seems to be the very promising parameter. We have demonstrated the difference in insulin output between the opposite allele of obesity risk that gene carries in reaction to standard high fat or high glucose overload (postprandial tests). The genotype dependent, nutrient induced insulin output ratio (NIOR), as a result of the different dietary load is presented in Fig 3. The introduction of NIOR allows for possible segregation of the patients with gene polymorphisms at risk to develop  insulin resistance in relation to diet. (Fig 4) (our results, Journal of Clin Chem Lab Med 2007 in press)


Figure 3. The ranking of the �candidates-genes� according to AUCinsins during OGTT. Such ranking is different for OLTT. The relative differences expressed as individual percent of AUCinsins determined by certain alleles according to formula:The relative difference = [{(AUCuc iIns (BB)-AUCuciIns (AA)}/AUCuc iIns (AA)] * 100




Figure 4:The ranking of the genotypic sub-groups according to NIOR differences between opposite allele carriers express as percent of change. The relative difference= [{(NIOR (BB)-NIOR (AA)}/NIOR (AA)] * 100

Discussion

In the present paper we  tried to present some of the examples of recent understanding of genetic background of obesity, gene-gene and gene-diet/environmental interactions. Development of complex markers, based on the integration of the functional tests and genomic technologies represent an exciting, technically challenging, approach for application of a new biomarkers at early stage of the disease.
 The new scientific branch based on the recent, high throughput high-tech biotechnology and bioinformatics named "system biology" will be helpful in future to predict the gene-nutrient-environmental interaction and allows to introduce proper preventive therapy.

Acknowledgements

Granted by Polish Ministry of Education and Science: No 3PO5D08424 and No 501/NKL/49/L
and supported by NuGO (The European Nutrigenomics Organisation: linking genomics, nutrition and health research (NuGO, CT-2004-505944).

References:

1. Sørensen, T.: The genetics of obesity. Metabolism. 1995 Sep;44(9 Suppl 3):4-6.
2. Stunkard AJ., Sorensen TI., Hanis C. et al.: An adoption study of human obesity. New England Journal of Medicine 1986, 314, 193-198.
3. Stunkard AJ.: Genetic contributions to human obesity. Res Publ Assoc Res Nerv Ment Dis 1991;69:205-18
4. Bouchard C, Tremblay A, Despres JP, Nadeau A, Lupien PJ, Theriault G, Dussault J, Moorjani S, Pinault S, Fournier G. The response to long-term overfeeding in identical twins. N Engl J Med 1990 May 24;322(21):1477-82
5. Sorensen TI., Price RA., Stunkard AJ., Schulsinger F.: Genetics of obesity in adult adoptees and their biological siblings. British Medical Journal (1989) 298, 87-90.
6. Farooqi I.S., O'Rahilly S., Monogenic human obesity syndromes. Recent Prog. Horm. Res. 59 (2004) 409-424.
7. Lander E.S., The new genomics: global views of biology. Science 1996, 274 536-539.
8. J.K. Pritchard, Are rare variants responsible for susceptibility to complex diseases?, Am. J. Hum. Genet. 2001, 69 124-137
9. Clement K.: Genetics of human obesity. C R Biol. 2006 Aug;329(8):608-22.
10. Perusse L, Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, Snyder EE, Bouchard C The human obesity gene map: the 2004 update. Obes Res 2005 13:381-490
11. Bell CG, Walley AJ, Froguel P. The genetics of human obesity. Nat Rev Genet. 2005, 6 :221-34.
12. Montague CT, Farooqi IS, Whitehead JP, Soos MA, Rau H, Wareham NJ, Sewter CP, Digby JE, Mohammed SN, Hurst JA, Cheetham CH, Earley AR, Barnett AH, Prins JB, O'Rahilly S. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature. 1997 26;387, 903-8.
13. Strobel A, Issad T, Camoin L, Ozata M, Strosberg AD A leptin missense mutation associated with hypogonadism and morbid obesity. Nat Genet. 1998 Mar;18, :213-5.
14. Ozata M, Ozdemir IC, Licinio J. Human leptin deficiency caused by a missense mutation: multiple endocrine defects, decreased sympathetic tone, and immune system dysfunction indicate new targets for leptin action, greater central than peripheral resistance to the effects of leptin, and spontaneous correction of leptin-mediated defects. J Clin Endocrinol Metab. 1999, 84,:3686-95.
15. Clement K, Vaisse C, Lahlou N, Cabrol S, Pelloux V, Cassuto D, Gourmelen M, Dina C, Chambaz J, Lacorte JM, Basdevant A, Bougneres P, Lebouc Y, Froguel P, Guy-Grand B A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature. 1998, 26;392:398-401.
16. Krude H, Biebermann H, Luck W, Horn R, Brabant G, Gruters A Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat Genet. 1998, 19, :155-7
17. Challis BG, Pritchard LE, Creemers JW, Delplanque J, Keogh JM, Luan J, Wareham NJ, Yeo GS, Bhattacharyya S, Froguel P, White A, Farooqi IS, O'Rahilly S A missense mutation disrupting a dibasic prohormone processing site in pro-opiomelanocortin (POMC) increases susceptibility to early-onset obesity through a novel molecular mechanism. Hum Mol Genet. 2002, 11, 1997-2004
18. Jackson RS, Creemers JW, Ohagi S, Raffin-Sanson ML, Sanders L, Montague CT, Hutton JC, O'Rahilly S. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nat Genet. 1994,16:303-6
19. Jackson RS, Creemers JW, Farooqi IS, Raffin-Sanson ML, Varro A, Dockray GJ, Holst JJ, Brubaker PL, Corvol P, Polonsky KS, Ostrega D, Becker KL, Bertagna X, Hutton JC, White A, Dattani MT, Hussain K, Middleton SJ, Nicole TM, Milla PJ, Lindley KJ, O'Rahilly S. Small-intestinal dysfunction accompanies the complex endocrinopathy of human proprotein convertase 1 deficiency. J Clin Invest. 2003;112:1550-60
20. Gibson WT, Pissios P, Trombly DJ, Luan J, Keogh J, Wareham NJ, Maratos-Flier E, O'Rahilly S, Farooqi IS. Melanin-concentrating hormone receptor mutations and human obesity: functional analysis. Obes Res. 2004, 12:743-9
21. Lee YS, Poh LK, Loke KY. A novel melanocortin 3 receptor gene (MC3R) mutation associated with severe obesity. J Clin Endocrinol Metab. 2002, 87:1423-6.
22. Vaisse C, Clement K, Guy-Grand B, Froguel P. A frameshift mutation in human MC4R is associated with a dominant form of obesity Nat Genet. 1998, 20:113-4
23. 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, :111-2
24. Challis BG, Luan J, Keogh J, Wareham NJ, Farooqi IS, O'Rahilly S. Genetic variation in the corticotrophin-releasing factor receptors: identification of single-nucleotide polymorphisms and association studies with obesity in UK Caucasians. Int J Obes Relat Metab Disord. 2004, 28:442-6.
25. Holder JL Jr, Butte NF, Zinn AR. Profound obesity associated with a balanced translocation that disrupts the SIM1 gene. Hum Mol Genet. 2000, 9:101-8
26. Faivre L, Cormier-Daire V, Lapierre JM, Colleaux L, Jacquemont S, Genevieve D, Saunier P, Munnich A, Turleau C, Romana S, Prieur M, De Blois MC, Vekemans M Deletion of the SIM1 gene (6q16.2) in a patient with a Prader-Willi-like phenotype. J Med Genet. 2002 Aug;39(8):594-6. Erratum in: J Med Genet. 2004 Apr;41(4):320
27. Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue Nature. 1994 Dec 1;372(6505):425-32 Erratum in: Nature 1995 Mar 30;374(6521):479
28. Farooqi IS, Jebb SA, Langmack G, Lawrence E, Cheetham CH, Prentice AM, Hughes IA, McCamish MA, O'Rahilly S Effects of recombinant leptin therapy in a child with congenital leptin deficiency N Engl J Med. 1999 Sep 16;341(12):879-84
29. Considine RV, Caro JF Leptin in humans: current progress and future directions. Clin Chem. 1996 Jun;42(6 Pt 1):843-4
30. Ren J Leptin and hyperleptinemia - from friend to foe for cardiovascular function J Endocrinol. 2004 Apr;181(1):1-10.
31. Burd L, Vesely B, Martsolf J, Kerbeshian J. Prevalence study of Prader-Willi syndrome in North Dakota Am J Med Genet. 1990 Sep;37(1):97-9
32. Butler MG. Prader-Willi syndrome: current understanding of cause and diagnosis Am J Med Genet. 1990 Mar;35(3):319-32
33. Wilson FH, Hariri A, Farhi A, Zhao H, Petersen KF, Toka HR, Nelson-Williams C, Raja KM, Kashgarian M, Shulman GI, Scheinman SJ, Lifton RP. A cluster of metabolic defects caused by mutation in a mitochondrial tRNA. Science. 2004 Nov 12;306(5699):1190-4.
34. Farooqi IS, O'Rahilly S. Recent advances in the genetics of severe childhood obesity. Arch Dis Child. 2000 Jul;83(1):31-4.
35. 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 Jul;106(2):253-62.
36. Geller F, Reichwald K, Dempfle A, Illig T, Vollmert C, Herpertz S, Siffert W, Platzer M, Hess C, Gudermann T, Biebermann H, Wichmann HE, Schafer H, Hinney A, Hebebrand J Melanocortin-4 receptor gene variant I103 is negatively associated with obesity. Am J Hum Genet. 2004 Mar;74(3):572-81.
37. Heid IM, Vollmert C, Hinney A, Doring A, Geller F, Lowel H, Wichmann HE, Illig T, Hebebrand J, Kronenberg F; KORA Group Association of the 103I MC4R allele with decreased body mass in 7937 participants of two population based surveys. J Med Genet. 2005 Apr;42(4):e21.
38. Boutin P, Dina C, Vasseur F, Dubois S, Corset L, Seron K, Bekris L, Cabellon J, Neve B, Vasseur-Delannoy V, Chikri M, Charles MA, Clement K, Lernmark A, Froguel P. GAD2 on chromosome 10p12 is a candidate gene for human obesity. PLoS Biol. 2003 Dec;1(3):E68
39. Meyre D, Boutin P, Tounian A, Deweirder M, Aout M, Jouret B, Heude B, Weill J, Tauber M, Tounian P, Froguel P. Is glutamate decarboxylase 2 (GAD2) a genetic link between low birth weight and subsequent development of obesity in children? J Clin Endocrinol Metab. 2005 Apr;90(4):2384-90.
40. Meyre D, Bouatia-Naji N, Tounian A, Samson C, Lecoeur C, Vatin V, Ghoussaini M, Wachter C, Hercberg S, Charpentier G, Patsch W, Pattou F, Charles MA, Tounian P, Clement K, Jouret B, Weill J, Maddux BA, Goldfine ID, Walley A, Boutin P, Dina C, Froguel P. Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes. Nat Genet. 2005 Aug;37(8):863-7.
41. Swarbrick MM, Waldenmaier B, Pennacchio LA, Lind DL, Cavazos MM, Geller F, Merriman R, Ustaszewska A, Malloy M, Scherag A, Hsueh WC, Rief W, Mauvais-Jarvis F, Pullinger CR, Kane JP, Dent R, McPherson R, Kwok PY, Hinney A, Hebebrand J, Vaisse C. Lack of support for the association between GAD2 polymorphisms and severe human obesity. PLoS Biol. 2005 Sep;3(9):e315. Epub 2005 Aug 30
42. Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PW, D'Agostino RB. Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study. Am J Hum Genet. 2002 Nov;71(5):1044-50. Epub 2002 Sep 27.
43. Meyre D, Lecoeur C, Delplanque J, Francke S, Vatin V, Durand E, Weill J, Dina C, Froguel P. A genome-wide scan for childhood obesity-associated traits in French families shows significant linkage on chromosome 6q22.31-q23.2 Diabetes. 2004 Mar;53(3):803-11.
44. Duggirala R, Blangero J, Almasy L, Arya R, Dyer TD, Williams KL, Leach RJ, O'Connell P, Stern MP 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, 68:1149-64.
45. Abney M, Ober C, McPeek MS. Quantitative-trait homozygosity and association mapping and empirical genomewide significance in large, complex pedigrees: fasting serum-insulin level in the Hutterites Am J Hum Genet. 2002, 70:920-34.  
46. Ehm MG, Karnoub MC, Sakul H, Gottschalk K, Holt DC, Weber JL, Vaske D, Briley D, Briley L, Kopf J, McMillen P, Nguyen Q, Reisman M, Lai EH, Joslyn G, Shepherd NS, Bell C, Wagner MJ, Burns DK; American Diabetes Association GENNID Study Group. Genetics of NIDDM Genomewide search for type 2 diabetes susceptibility genes in four American populations Am J Hum Genet. 2000, 66:1871-81
47. Ghosh S, Watanabe RM, Valle TT, Hauser ER, Magnuson VL, Langefeld CD, Ally DS, Mohlke KL, Silander K, Kohtamaki K, Chines P, Balow Jr J, Birznieks G, Chang J, Eldridge W, Erdos MR, Karanjawala ZE, Knapp JI, Kudelko K, Martin C, Morales-Mena A, Musick A, Musick T, Pfahl C, Porter R, Rayman JB. The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes Am J Hum Genet. 2000, 67:1174-85.
48. Demenais F, Kanninen T, Lindgren CM, Wiltshire S, Gaget S, Dandrieux C, Almgren P, Sjogren M, Hattersley A, Dina C, Tuomi T, McCarthy MI, Froguel P, Groop LC. A meta-analysis of four European genome screens (GIFT Consortium) shows evidence for a novel region on chromosome 17p11.2-q22 linked to type 2 diabetes. Hum Mol Genet. 2003 Aug 1;12:1865-73.
49. Xiang KS, Jia WP, Lu JX, Tang JL, Li J Obesity in type 2 diabetes, its features and specificity Zhonghua Yi Xue Za Zhi. 2004, 84:1768-72
50. Maddux BA, Sbraccia P, Kumakura S, Sasson S, Youngren J, Fisher A, Spencer S, Grupe A, Henzel W, Stewart TA, et al Membrane glycoprotein PC-1 and insulin resistance in non-insulin-dependent diabetes mellitus. Nature. 1995, 373:448-51.
51. Maddux BA, Goldfine ID Membrane glycoprotein PC-1 inhibition of insulin receptor function occurs via direct interaction with the receptor alpha-subunit. Diabetes. 2000, 49:13-9
52. Cohen PG, Holbrook JM Other pathways to the manifestations of the metabolic syndrome in males. Obes Res. 2004, 12:1536
53. Frikke-Schmidt R, Nordestgaard BG, Jensen GB, Tybjaerg-Hansen A. Genetic variation in ABC transporter A1 contributes to HDL cholesterol in the general population. J Clin Invest. 2004, 114:1343-53
54. Pajukanta P. Do DNA sequence variants in ABCA1 contribute to HDL cholesterol levels in the general population? J Clin Invest. 2004, 114:1244-7
55. Pajukanta P, Lilja HE, Sinsheimer JS, Cantor RM, Lusis AJ, Gentile M, Duan XJ, Soro-Paavonen A, Naukkarinen J, Saarela J, Laakso M, Ehnholm C, Taskinen MR, Peltonen L Familial combined hyperlipidemia is associated with upstream transcription factor 1 (USF1). Nat Genet. 2004, 36:371-6.
56. Huertas-Vazquez A, Aguilar-Salinas C, Lusis AJ, Cantor RM, Canizales-Quinteros S, Lee JC, Mariana-Nunez L, Riba-Ramirez RM, Jokiaho A, Tusie-Luna T, Pajukanta P Familial combined hyperlipidemia in Mexicans: association with upstream transcription factor 1 and linkage on chromosome 16q24.1. Arterioscler Thromb Vasc Biol. 2005, 25:1985-91.
57. Hokanson JE Lipoprotein lipase gene variants and risk of coronary disease: a quantitative analysis of population-based studies Int J Clin Lab Res. 1997;27:24-34
58. Wittrup HH, Tybjaerg-Hansen A, Nordestgaard BG Lipoprotein lipase mutations, plasma lipids and lipoproteins, and risk of ischemic heart disease. A meta-analysis Circulation. 1999, 99:2901-7
59. Zannis VI, Breslow JL. Human very low density lipoprotein apolipoprotein E isoprotein polymorphism is explained by genetic variation and posttranslational modification. Biochemistry. 1981, 20:1033-41
60. Dallongeville J, Lussier-Cacan S, Davignon J Modulation of plasma triglyceride levels by apoE phenotype: a meta-analysis. Lipid Res. 1992, 33:447-54.
61. Pennacchio LA, Olivier M, Hubacek JA, Cohen JC, Cox DR, Fruchart JC, Krauss RM, Rubin EM. An apolipoprotein influencing triglycerides in humans and mice revealed by comparative sequencing. Science. 2001 Oct 5;294(5540):169-73
62. Talmud PJ, Hawe E, Martin S, Olivier M, Miller GJ, Rubin EM, Pennacchio LA, Humphries SE Relative contribution of variation within the APOC3/A4/A5 gene cluster in determining plasma triglycerides Hum Mol Genet. 2002, 11:3039-46
63. Deeb SS, Fajas L, Nemoto M, Pihlajamaki J, Mykkanen L, Kuusisto J, Laakso M, Fujimoto W, Auwerx J A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 1998, 20:284-7
64. Altshuler D, Pollara VJ, Cowles CR, Van Etten WJ, Baldwin J, Linton L, Lander ES. An SNP map of the human genome generated by reduced representation shotgun sequencing Nature. 2000;407:513-6.
65. Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner TH, Mashima H, Schwarz PE, del Bosque-Plata L, Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky KS, Wei S, Concannon P, Iwasaki N, Schulze J, Baier LJ, Bogardus C, Groop L, Boerwinkle E, Hanis CL, Bell GI. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus Nat Genet. 2000, 26:163-75.
66. Weedon MN, Schwarz PE, Horikawa Y, Iwasaki N, Illig T, Holle R, Rathmann W, Selisko T, Schulze J, Owen KR, Evans J, Del Bosque-Plata L, Hitman G, Walker M, Levy JC, Sampson M, Bell GI, McCarthy MI, Hattersley AT, Frayling TM. Meta-analysis and a large association study confirm a role for calpain-10 variation in type 2 diabetes susceptibility. Am J Hum Genet. 2003, 73:1208-12
67. Love-Gregory LD, Wasson J, Ma J, Jin CH, Glaser B, Suarez BK, Permutt MA A common polymorphism in the upstream promoter region of the hepatocyte nuclear factor-4 alpha gene on chromosome 20q is associated with type 2 diabetes and appears to contribute to the evidence for linkage in an Ashkenazi Jewish population. Diabetes. 2004, 53:1134-40
68. Silander K, Mohlke KL, Scott LJ, Peck EC, Hollstein P, Skol AD, Jackson AU, Deloukas P, Hunt S, Stavrides G, Chines PS, Erdos MR, Narisu N, Conneely KN, Li C, Fingerlin TE, Dhanjal SK, Valle TT, Bergman RN, Tuomilehto J, Watanabe RM, Boehnke M, Collins FS Genetic variation near the hepatocyte nuclear factor-4 alpha gene predicts susceptibility to type 2 diabetes. Diabetes. 2004, 53:1141-9
69. Davignon J, Lussier-Cacan S, Gattereau A, Moll PP, Sing CF Interaction of two lipid disorders in a large French-Canadian kindred. Arteriosclerosis. 1983, 3:13-22
70. Sing CF, Haviland MB, Templeton AR, Zerba KE, Reilly SL. Biological complexity and strategies for finding DNA variations responsible for inter-individual variation in risk of a common chronic disease, coronary artery disease. Ann Med. 1992 ;24:539-47.
71. Sing CF, Zerba KE, Reilly SL. Traversing the biological complexity in the hierarchy between genome and CAD endpoints in the population at large. Clin Genet. 1994, 46(1 Spec No):6-14.
72. Sing CF, Haviland MB, Reilly SL. Genetic architecture of common multifactorial diseases. Ciba Found Symp. 1996;197:211-29; discussion 229-32.
73. Sing CF, Moll PP. Genetics of atherosclerosis. Annu Rev Genet. 1990;24:171-87.
74. Atchley, W. R.,  Rutledge JJ. Genetic components of size and shape variation. I. Dynamics of components of phenotypic variability and covariability during ontogeny in the laboratory rat. Evolution 1980, 34:1161-1174.
75. Kirkpatrick, M.,  Lofsvold D. Measuring selection and constraint in the evolution of growth. Evolution 1992, 46: 954-971.
76. Chase, K., D. R. Carrier, F. R. Adler, T. Jarvik, E. A. Ostrander et al., Genetic basis for systems of skeletal quantitative traits: principal component analysis of the canid skeleton. Proc. Natl.Acad. Sci. USA 2002 , 99: 9930-9935.
77. Kim, J. C. et al. The Bardet-Biedl protein BBS4 targets cargo to the pericentriolar region and is required for microtubule anchoring and cell cycle progression. Nature Genet. 2004.36, 462-470 
78. Feinleib, M. et al. The NHLBI twin study of cardiovascular disease risk factors: methodology and summary of results.Am. J. Epidemiol. 1977, 106, 284-285