The Human Obesity Gene Map

Obesity (2006) 14, 529–644; doi: 10.1038/oby.2006.71

The Human Obesity Gene Map: The 2005 Update

Tuomo Rankinen*, Aamir Zuberi†, Yvon C. Chagnon‡, S. John Weisnagel§, George Argyropoulos, Brandon Walts*, Louis Pérusse§ and Claude Bouchard*,*

  1. *Human Genomics, Pennington Biomedical Research Center, Baton Rouge, Louisiana
  2. †Functional Genomics, Pennington Biomedical Research Center, Baton Rouge, Louisiana
  3. Energy Balance Genomics Laboratories, Pennington Biomedical Research Center, Baton Rouge, Louisiana
  4. ‡Psychiatric Genetic Unit, Laval University Robert-Giffard Research Center, Quebec, Canada
  5. §Division of Kinesiology, Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Sainte-Foy, Quebec, Canada.

Correspondence: Claude Bouchard Human Genomics Laboratory, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70808-4124. E-mail: Bouchac@PBRC.edu

*See Appendix for list of study centers.

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Abstract

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.

Keywords:

human obesity gene map, association, linkages, Mendelian disorders, quantitative trait loci, candidate genes

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Introduction

This paper represents the 12th in a series (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) on the status of the human obesity gene map, the 11th report published in Obesity. As in previous reports, we reviewed the literature published up to the end of October 2005 searching for the relevant publications through a variety of sources: PubMed using a combination of key words, authors, and journals; continuous reviews of obesity and genetics journals; personal collection of reprints; and papers made available to us by colleagues from around the world. Publications dealing with a wide variety of phenotypes pertaining to obesity, such as BMI, body fat mass, percentage of body fat, abdominal fat, fat-free mass, skinfolds, resting metabolic rates, plasma leptin levels, and other components of fat distribution and energy balance, were retained. As in previous reports, negative findings are not systematically reviewed but are briefly introduced when such data were available to us.

Each collaborating author was assigned one section of the report for an in-depth review. In addition to an introduction and a brief discussion (C.B), the report includes sections dealing with monogenic obesity cases (G.A.), Mendelian disorders exhibiting obesity as clinical feature (J.W.), murine gene-deficient [ knockout (KO)1/floxed] , transgenic models in which altered expression of a gene (or genes) results in phenotypes relevant to obesity and quantitative trait loci (QTL) from murine models (A.Z.), QTLs from other animal model studies and gene-drug interactions (Y.C.), association studies in humans with specific candidate genes (T.R.), and human linkage studies including genome scans performed to identify QTLs of obesity or obesity-related phenotypes (L.P.). The other collaborating author (B.W.) is involved in the management of the database, the generation of the tables and the map from the database, and the electronic version of the human obesity gene map (http://obesitygene.pbrc.edu). Readers are referred to previous publications (9, 11) for detailed information on the electronic version of the map and on browsing and querying capabilities of the online Obesity Gene Map Database.

As in the past, the published references for each entry in the current human obesity gene map are provided for convenience. We are using gene symbols and chromosomal locations given in the Entrez Gene database (http://www.ncbi.nlm.nih.gov/) available from the National Center for Biotechnology Information. The appendix provides a complete list of genes and map locations cited in this paper.

Although the authors have taken every possible effort to provide correct information, in the rapidly changing world of genetics and bioinformatics and the ever-present world of human fallibility, it is almost inevitable that inaccuracies will emerge. The full responsibility for errors is ours. Furthermore, we seek your indulgence in errors of omission and hope you will notify us of any oversights. All correspondence to maximize the precision and quality of the map is welcomed and, indeed, solicited.

Sadly, we have to inform the readership that this is likely to be the last time that we are able to publish the review of the human obesity gene map. We have tried unsuccessfully to obtain the funding to support the enormous amount of work that is necessary every year to prepare this popular review. The printed version of the map in Obesity is highly cited, and the e-version is accessed approx200,000 times a year by approx40,000 unique users based mainly in academic institutions and pharmacological or biotechnology companies. Although we recognize that the yearly review in its printed and electronic versions is a valuable tool for those involved in this field, the project has become too large to be handled solely by us without support staff.

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Monogenic Effects and Mendelian Disorders

Monogenics Section

The majority of disorders previously summarized in Table 2 have now been associated with a candidate gene or a genetic defect. Therefore, this year they are being merged with the monogenic obesity cases into a new table, Table 1.



This year, there has been relatively nominal reporting of monogenic cases of obesity. The majority of the monogenic obesity cases remain those with a genetic defect (mutation, deletion, or insertion) in the melanocortin receptor 4 (MC4R) gene. Table 1 summarizes all of the cases that were reported in previous years. A publication by Farooqi and O'Rahilly (12) elegantly summarizes cases of monogenic obesity that received treatment for the mutated gene that resulted in improvement of the health status of the patients. These cases were covered in the 2004 Obesity Gene Map report. The same group recently described a new rare mutation in the receptor of the neurotrophin brain-derived neurotrophic factor (BDNF) gene, TrkB (13).

Neurotrophic Tyrosine Receptor Kinase 2 (NTRK2)

In humans, the receptor of the murine BDNF gene, TrkB, is encoded by the NTRK2 gene. A study was reported by Yeo and colleagues (13) whereby a de novo heterozygous mutation arose in a child with severe early-onset obesity and hyperphagia. The A-to-G transition resulted in amino acid substitution of the tyrosine residue at position 722 by a cysteine (Y722C) (Table 1). An additional cohort of 192 alleles and the proband's parents were screened for the presence of this rare mutation, but nobody was found to carry it. In vitro functional studies showed that the mutation impaired activation of MAPK when cells were treated with BDNF (13). This new rare mutation provides another example of single-gene mutations in genes involved in energy balance regulation that result in severe and early onset obesity. In another preliminary study of 288 individuals with a history of early onset obesity, five missense mutations were identified in NTRK2 (A74T, I98V, M354V, P660L, T821L) that have yet to be functionally characterized and described in greater detail (13).

Mendelian Disorders

Since last year's review, there has been limited development in the area of Mendelian disorders related to obesity, although many novel mutations in known genes have been reported. Updated references on new mutations for the Albright hereditary osteodystrophy (AHO), Bardet-Biedl, Berardinelli-Seip congenital lipodystrophy, Borjeson-Forssman-Lehmann, familial partial lipodystrophy, multiple endocrine neoplasia (type 1), and WAGR syndromes are provided (see Table 1).

In the present review, we now properly report AHO in the context of all disorders related to parathyroid hormone resistance, as described by DeSanctis et al. (131). To date, the AHO phenotype is always associated with mutations in GNAS1. In the AHO-like syndrome linked to 2q37, a French group narrowed down the critical region to a 4-megabasepair interval delimited by D2S2338 (present) and D2S2253 (deleted) (149).

A new mutation was discovered for familial partial lipodystrophy, Dunnigan type (167). The affected 21-year-old woman had a great excess of subcutaneous fat on the face, neck, trunk, and abdomen, with relative lack on the gluteal region, arms, and legs. She was insulin resistant and had the metabolic syndrome and type 2 diabetes. She was heterozygous for a novel A>G mutation at position - 14 of intron B, upstream of PPARG exon 1 within the promoter of the PPARgamma4 isoform, implicating this isoform as being potentially important in adipocyte biology.

Finally, in recent clinical reviews of large groups of Alstrom (58) and WAGR (229) syndrome patients, the central role of childhood obesity and hyperinsulinism in Alstrom syndrome was confirmed, as well as a significant prevalence of obesity (of 18% ) in WAGR subjects. In this last syndrome, the new acronym WAGRO (obesity) has even been suggested (227).

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Transgenics and KOs

The murine obesity gene map identifies 248 genes (Table 2) that, when mutated or expressed as transgenes in the mouse, result in phenotypes affecting body weight (BW) and adiposity. We include genes that promote obesity and genes that promote leanness, with the exception of genes that seem to promote failure-to-thrive phenotypes or mutant genes impacting developmental issues affecting multiple organs systems during embryogenesis or early growth. The list was compiled from the primary literature, accessible through PubMed and corroborated with information captured by the Mouse Genome Informatics (MGI) group (www.informatics.org). Official gene nomenclature rules have been followed, even where the use of this nomenclature differs from the gene name used in the primary publication. We have attempted to capture common synonyms, but the list is not exhaustive. Readers are directed to MGI for a more complete list of synonyms and nomenclature history.

Of the new genes added to the list this year, three are imprinted. Maternal inheritance of the Gnas KO allele (400), a KO of the paternally expressed Peg3 gene (493), and transgenic overexpression of the paternally expressed Mest (Peg1) in adipose tissue all promote obesity. Imprinted loci are well documented in the mouse genome, but the degree of imprinting can also be tissue dependent. Clearly, the role of imprinted genes in the development of obesity-related phenotypes must be considered in cases where simple Mendelian inheritance relationships seem uninformative. Three new genes listed for the first time this year are relevant to the molecular characterization of three well-known human obesity syndromes: Alstroms, Bardel-Biedl, and McKusick-Kaufman. The respective murine homologs, Alms1, Bbs2, and Mkks, all present obesity phenotypes when mutated in mice. Interestingly, Bbs2-deficient mice weigh less than controls at birth, suggesting an additional effect on early development. These three mutants will provide valuable model systems to study the roles of these genes in the development of these polygenic syndromes.

One particularly interesting addition to the gene list is the murine Clock gene. The CLOCK transcription factor is a key component of the molecular circadian clock within pacemaker neurons of the hypothalamic suprachiasmatic nucleus. Characterization of murine Clock mutants reveals an obesity phenotype that is accelerated during feeding with high-fat diet. Causative factors include an attenuated diurnal feeding rhythm, hyperphagia, and perturbation of the expression of hypothalamic peptides associated with the regulation of feeding behavior and energy balance. The effects of the CLOCK transcription factor seem to be associated with growth and development only after weaning because no differences in BW are observed in newborn pups or 3- or 4-week weaned mice.

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Animal QTLs

The murine QTL information in Table 3 has been completely revised this year. Primarily, the names assigned to quantitative trait loci (QTLs) have been changed to conform to currently utilized nomenclature, and, in an attempt to more specifically define the location of the QTL on the mouse genome, we have included the genetic location of the peak logarithm of the odds ratio (LOD) score (or other statistical measure utilized) and a confidence interval (usually the 1 LOD interval). Information presented has been summarized from the primary literature and also from the MGI group at the Jackson Laboratory (www.informatics.jax.org). Clearly, the concept of QTL significance plays a large role in the identification of a QTL. We have attempted to adopt a uniform standard that identifies QTLs if they satisfy a genome-wide significance level below 0.05. QTLs that do not meet this are termed suggestive, and we have listed only suggestive QTLs that have either been corroborated in follow-up studies or replicated in another study using the same mouse strains. In cases of uncertainty, we have erred on the side of caution and listed the QTLs. For some recent studies, evidence for interactions between QTLs has been presented, despite no evidence of significance for the individual loci alone. Nomenclature rules may need to be revisited to describe these interactions. In the majority of the cases, QTL names listed in the table are not identical to those listed in the primary publication. In these cases, the names were changed by the MGI group to maintain conformity with existing nomenclature. Thus, names that have been listed in previous years may have been altered in this year's table.


QTLs may be identified from several different types of genetic crosses. We have listed this information in this year's table. Typically, F2 intercrosses or backcrosses are utilized. However, there is likely to be an increasing use of recombinant inbred strains, advanced intercross lines, and congenic strains (that contain a specific donor genetic segment on a different background strain). It must be remembered that QTLs identified from phenotyping and genotyping of crosses between two strains define only a statistical probability of a polymorphic gene residing in a defined genetic interval. Follow-up studies are necessary to confirm this likelihood. Congenic (and subcongenic) strains have been generated for some of these QTLs, supporting the existence and magnitude of some of these phenotypes. Some cases include the characterization of the Fob3 QTL (645, 646). Congenic strains containing a chromosome 15 region from the lean L strain were introgressed onto the F genetic background. Interestingly the characterization of subcongenic lines suggests that Fob3 contains two contributory regions: Fob3a and Fob3b, conferring late and early onset phenotypes, respectively. Expression analysis of genes positioned within the Fob3b segment by microarray screening identifies a candidate gene, Sqle (squalene epoxidase). This gene is involved in the regulation of cholesterol biosynthesis. Interestingly, the expression of other genes of the cholesterol biosynthesis pathway mapping outside of the Fob3b region are also perturbed, suggesting that the changes in activity of this pathway may be responsible for the phenotypic differences between the F parental and the F.L<Chr15> congenic strains. Other murine QTL regions for which candidate genes have been implicated include Bw19 (Gpc3, Glypican 3) (606) and the QTL on chromosome 7 associated with adiposity (ATP10a, encodes ATPase, class V, type 10A) (706). A candidate gene for a rat QTL Niddm24 is Pnlip (encodes pancreatic lipase). The continued generation of congenic mouse strains and expression screening and single nucleotide polymorphism genotyping analysis should continue to implicate specific genes with well-characterized QTL regions.

QTLs from Cross-Breeding Experiments Other Than Rodents

Syntenic regions in humans have been picked up directly from the original papers or determined from the U.S. Livestock Genome Mapping Projects (NAGRP03). Four new chromosomes were targeted according to QTL analysis in chicken, one in pig, and one in sheep (Table 3). In a cross between Landrace and Iberian pig strains, a QTL for fatness was reported on pig chromosome 4 in the region of the AFABP gene (692) corresponding to the human fatty acid-binding protein 4 (adipocyte) gene located at 8q24. A QTL for fat was reported in a sheep Texel times Texel cross at the growth differentiation factor 8 gene (670), which is located at 2q32.2 in humans. The main QTL region detected for fat on chicken chromosome 7 from the cross White Leghorn layer times commercial broiler (591) corresponded to human chromosome 2q21. The cross of Rhode Island Red layer with itself produced a QTL for BW on chromosome 4 (707) corresponding to human chromosome 17q11.1-q12, whereas the White Plymouth Rock cross produced a QTL for weight on chromosome 5 (594) corresponding to human 22q13.1-q13.31 but also to 12p13-q23 reported in the cross WL times RIR (593). Finally, a QTL for weight was reported on chicken chromosome 1 (596) that corresponds to human chromosome 21q22.

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Associations with Candidate Genes

The evidence for associations between candidate genes and obesity-related phenotypes is summarized in Table 4. A total of 416 studies covering 127 candidate genes have reported significant associations. Of these, 57 studies (40 candidate genes) were published during the past year. This year's update includes 14 new candidate gene entries.


Associations with BW, BMI, Overweight, and Obesity

BW, BMI, overweight, and obesity were associated with DNA sequence variation in ACE (710, 711), ADIPOQ (718, 719, 720), ADRB2 (744), BDNF (814), COMT (822), CYP11B2 (824), DRD4 (836), ENPP1 (839), ESR1 (841), ESR2 (841), FOXC2 (850, 851), GAD2 (855), GHRHR (859), HTR2C (884), LIPC (951), MC4R (971), MCHR1 (876, 877), NPY (981), NTRK2 (998), NPY2R (984), PLIN (1112), PPARG (1012, 1021, 1027), PPARGC1A (1042), PYY (984, 1046), RETN (1051), SERPINE1 (1055), UCP1 (1084), and VDR (1110).

Associations with Body Composition and Fat Distribution Phenotypes

Body composition-related phenotypes (fat mass, fat-free mass, percentage body fat, sum of skinfolds) showed associations with markers in ACE (712), UCP1 (1079), LEPR (937), LIPC (951), PLIN (1113), PPARG (1021), GFPT1 (858), AR (809), DIO1 (830), IGF2 (899), FOXC2 (850), and COMT (822). Phenotypes reflecting body fat distribution [ abdominal visceral and subcutaneous fat, waist-to-hip ratio (WHR), waist circumference, sagittal diameter] were associated with ACE (710), ADIPOQ (719), ADRB2 (744), APOA2 (792), FABP2 (847), LTA (964), MTTP (976), PLIN (1113), PPARG (1021), and UCP1 (1079).

Associations with Changes in BW and Body Composition

Eight studies reported associations between seven candidate genes and changes in BW and body composition. The ADRB1 (736), NMB (978), and PPARG (1016) loci showed associations with spontaneous changes in BW and adiposity over time. Markers in the PPARG (1032) gene were reported to be associated with exercise training-induced weight loss, whereas sequence variation in the APOA5 (797) and MC4R (972) loci modified weight loss in response to a low-fat diet and bariatric surgery, respectively. The ADIPOQ (721) and LEPR (949) loci were reported to be associated with changes in BW during a 3-year diabetes prevention trial with acarbose.

Negative Associations with Obesity-Related Phenotypes

In addition to the positive studies summarized above, we identified 92 studies dealing with 58 genes in which there was no evidence of associations between DNA sequence variations and obesity-related phenotypes. Among these studies, the most frequent ones were those pertaining to markers of PPARG (1012, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126) (14 studies), ADIPOQ (1127, 1128, 1129, 1130), ADRB3 (1084, 1121, 1131, 1132), IL6 (1129, 1133, 1134, 1135) (four studies each), and ESR1 (1136, 1137, 1138) (three studies). Other markers yielding negative findings were those related to ACE (710, 1032), ACTN (1139), ADIPOR1 (1140), ADIPOR2 (1140), ADRB1 (1132), ADRB2 (1132), AGER (1141), AHSG (1142), APOA4 (1143), APOE (1144, 1145), AR (1146), BDNF (1147), CASQ1 (1148), COL1A1 (1134), CRP (1149), ENPP1 (1150), FABP2 (1151), GNAS (1152), GNB3 (1152, 1153), GPR40 (1154), H6PD (1155), HSD11B1 (1155, 1156), ICAM1 (1157), IGF1 (1158), IL6R (1159), INS (1160, 1161), KCNJ11 (1120), KL (1146), LEP (1129), LEPR (1162), LIPC (1163), LPL (1164), LTA (964), MKKS (1165), MT-DLOOP (1166), MTHFR (1167), MTTP (1168), NOS3 (1169, 1170), NPY (1171), NR0B2 (1172), PARD6A (1173), PLIN (1174), PPARGC1A (1115, 1175), PRDM2 (1176), PTPN1 (1177), SCD (1178), SELE (1179), TAS2R38 (1180), TNF (1181), UCP1 (1084), UCP2 (1182, 1183), UCP3 (1182, 1184), and VDR (1134, 1185).

Drug-Induced BW Gain and Obesity

Unintentional weight gain and weight loss are potential side effects associated with several pharmacological therapies. In previous editions of the human obesity gene map, these studies were summarized within the association studies section. However, because the number of reports addressing the contribution of DNA sequence variation in specific candidate genes to the drug-induced weight changes has increased, these studies will be reviewed in a specific section from now on.

Drug-induced weight gain and obesity have been observed after insulin therapy in patients with type 1 or 2 diabetes; in psychiatric therapy using anti-psychotics, anti-depressants, or mood stabilizers; in neurological treatments with anti-epileptic drugs; and in hypertension or steroid hormone therapies (for review, see (1186)). Drug-induced weight changes could range from a loss of weight to a gain of >50 kg in patients on anti-epileptic, anti-depressant, or anti-psychotic medication (1186). Because modest weight losses of 5% to 10% of initial BW are clinically significant (1187), it is clear that even modest weight gain is an undesirable side-effect of drugs.

Response to anti-psychotic treatment is considered to be a complex trait in which many genes, each with a small effect, are expected to play a role (1188). Few genes have yet to be studied in relation to BW gain under anti-psychotics (Table 4). The functional - 759C>T variant (1189) in the serotonin receptor 2C gene (HTR2C) was studied in Chinese anti-psychotic-naïve schizophrenic patients. Carriers of the - 759T variant showed three times lower anti-psychotic-induced weight gain than those not carrying the T allele (886). This result was confirmed in anti-psychotic-naïve Chinese men (893) but not in a third sample of anti-psychotic-resistant Chinese (1190). However, in a group of anti-psychotic-resistant African-American, white, and Hispanic individuals, the association of the - 759T variant with a smaller weight gain was confirmed recently (891), as in anti-psychotic-naïve whites (892). In contrast, Basile et al. (889) reported that carriers of the - 759T allele gained more weight than non-carriers in a mixed population of anti-psychotic-resistant white and African-American patients. A Cys23Ser variant of the HTR2C locus showed no association with BW gain in clozapine-treated anti-psychotic-naïve or resistant schizophrenics of white or African American descent (1191, 1192, 1193).

A significant effect of the cytochrome P450, subfamily IID, polypeptide 6 (CYP2D6) genotypes on the percentage change of BMI was reported in white men taking olanzapine and carrying the poor *4 and intermediate *1/*3 metabolizer genotypes (827, 1194). On the other hand, no association with BW changes in African Americans and whites taking clozapine was observed with a dinucleotide repeat polymorphism of the cytochrome P450 subfamily I, polypeptide 2 (CYP1A2) gene (1191). Chinese anti-psychotic-naïve schizophrenic homozygotes for the A allele of the - 2548A>G polymorphism of the LEP gene showed higher changes in BW than patients carrying A/G and G/G genotypes (933). An opposite result was observed in anti-psychotic-naïve whites showing a higher BMI change in homozygotes for the G allele (892). In two recent studies on Chinese schizophrenic patients treated with clozapine, the G/G homozygotes of the - 1291C>G variant of the adrenergic alpha2A receptor (ADRA2A) locus showed a 3 times greater weight gain than the C/C genotype (732). Furthermore, a 2 to 3 times greater weight gain was reported in the TT genotype of the GNB3 825C>T variant in contrast to carriers of the C allele (1195). Negative results were reported previously for these two genes (1191, 1196). Finally, 12 genes showed negative results with anti-psychotic-induced weight changes. Those were the tumor necrosis factor alpha (1191), the serotonin 1A and 2A receptors, the histamine H1 and H2 receptors, the beta3 and alpha1a-adrenergic receptors (1191, 1192, 1196, 1197), the serotonin transporter and the serotonin receptor 6 (1192), the dopamine receptor 4 (1198), the cytochrome P450 1A2 (CYP1A2), which is different from the CYP2D6 that had shown some association, and the 25-kDa synaptosomal-associated protein (1199).

Treatment with lithium has long been recognized to be associated with adverse metabolic effects, notably weight gain (1200). No evidence for an association has been observed between two polymorphisms (+35A>G in intron 3 and +7T>G in intron 10) in the alpha subunit of the olfactory G-protein Golf gene and weight gain in response to lithium treatment (1201). The combination of glitazones with insulin may favor weight gain due to enhanced adipogenesis. Patients with the PPARG Pro12Ala genotype show a better response to rosiglitazone treatment than those with the Pro12Pro genotype do, with no difference in weight or BMI (1202).

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Human QTLs

Linkage Studies

Linkage studies with obesity-related phenotypes are summarized in Table 5. During the past year, 11 linkage studies were published: nine genome scans, one bivariate linkage analysis of metabolic syndrome phenotypes with markers on chromosome 7q (1203), and a meta-analysis of genome-wide linkage studies for BMI (1204).


Two genome scans for eating-related phenotypes were reported last year. The first was a genome scan for total caloric and macronutrient intakes assessed from a food frequency questionnaire in 816 subjects from the San Antonio Family Heart Study (1235). Evidence of linkage was found on chromosome 2p22-p21 near marker D2S1346 for total caloric intake and intakes of fat, saturated fat, and protein (LOD scores ranging from 2.09 to 2.62). The second was a genome scan of eating behaviors assessed from the Three-Factor Eating questionnaire in 660 subjects from the Quebec Family Study (978). Evidence of linkage was found on chromosomes 15q21-q23 (LIPC), 15q24-q25 (D15S206), and 17q22-q24 (D17S1306, D17S1290, D17S1351) for susceptibility to hunger and on chromosome 19p13 (D19S215) for disinhibition.

A genome-wide linkage analysis of obesity associated with the use of anti-psychotics in patients treated for psychoses was performed in 508 subjects from 21 multigenerational kindreds (1258). Obesity diagnosed from medical files was found to be 2.5 times more prevalent in patients treated with anti-psychotics than in untreated family members. Linkage with obesity and a set of 470 microsatellite markers was tested only in pedigrees with at least two occurrences of obesity. Evidence of linkage with obesity was found on chromosomes 6p23 (D6S260; LOD = 1.72), 8q22-q23 (D8S1136; LOD = 1.93), 9q34 (D9S282; LOD = 1.71), and 12q23.1-q24.23 (D12S1279-D12S366; LOD = 2.74).

Four genome scans reporting linkages with BMI and body fatness phenotype were published during the past year. In a study performed in West African families with type 2 diabetes (1236), linkage analysis of BMI and body composition assessed by bioelectric impedance revealed evidence of three QTLs affecting body fatness chromosomes 2p16-p13.3 (D2S2739-D2S441), 4q24 (D4S1647-D4S2623), and 5q14.3 (D5S1725). All linkages with BMI showed LOD scores below 1.7 (1236). A second genome scan for loci linked to BMI and percentage body fat assessed from bioelectric impedance was conducted in 3383 subjects from 1124 hypertensive African-American and white families (1227). Linkage to BMI and percentage body fat was tested separately in men and women and also in the combined sample. In the combined sample, evidence of linkage was found on chromosome 3q13.33 for BMI (LOD = 2.8) and on chromosome 12q24.3 for percentage body fat (LOD = 3.3). QTLs influencing both BMI and percentage body fat were found over a broad region [ 102 to 200 centimorgans (cM)] on chromosome 3 in men (3p12.2, 3q13.33, 3q26.33, and 3q27.3). Evidence of linkage with percentage body fat was also found on chromosomes 7q36.1 (LOD = 1.8), 15q25.3 (LOD = 3.0), and 18p11.22-p11.23 (LOD = 1.7) in men. In women, QTLs affecting percentage body fat were found on chromosomes 2p24.2 (LOD = 1.8), 12q24-q24.32 (LOD = 3.8), and 21q21.2 (LOD = 1.8), whereas linkage with BMI was found on chromosome 11p13 (LOD = 1.8). The third study was undertaken in a European-American sample of 1297 subjects from 260 families with the aim of detecting imprinted genetic loci influencing obesity-related traits (1224). Parent-specific linkage analyses of overweight (BMI greater than or equal to 27), obesity (BMI greater than or equal to 30), and obesity-related quantitative traits [ BMI, percentage body fat, and waist circumference (WC)] were performed with 391 microsatellite markers. Several QTLs influencing obesity were uncovered: a paternal effect for BMI and WC on 2p25.1, a maternal effect for percentage body fat on 3p24, a paternal effect for BMI on 3q12.3, a maternal effect for obesity on 9q22.33, a maternal effect for overweight on 10p12.2, a paternal effect for percentage body fat on 11q12 and 11q13.3, a maternal effect for BMI and WC on 12q24.21, a maternal effect for overweight on 13q13.3, and a paternal effect for BMI and WC on 13q31.3. The fourth scan was undertaken in the same sample of European American families with the aim of detecting epistatic interactions among QTLs (1226). QTLs influencing BMI were found on chromosomes 2p24.2 and 4q28.3 and over a broad region of chromosome 13q21.1-q32.2, whereas QTLs influencing percentage body fat were found on chromosomes 12q24.21 and 21q22.3. Linkages with different obesity affection status (BMI greater than or equal to 27, 30, 35, and 40) were found on chromosomes 3q12.3, 7q21.3, 7q22.1, 8q13.3, 9q22.33, 12p13.31, 12q23.1, 13q13.2, and 13q13.3. Significant evidence of interactions was found between loci on chromosome regions 2p25-p24 and 13q13-q21 (1226).

A search for genes influencing BMI, WHR, and abdominal fat assessed by computed tomography scan was undertaken in 330 subjects from 154 African-American families and in 729 subjects from 275 Hispanic-American families (1210). In the African-American families, significant linkage to BMI was found on chromosomes 1p36 (LOD = 2.14) and 3p26.3 (LOD = 3.67). In the Hispanic-American families, a QTL for BMI was found on chromosome 17q23.2 and QTLs for WHR were found on chromosomes 8q24.11, 12q13.13-q15, 12q21-q21.33, and 12q22-q24.21. QTLs for abdominal fat were found on chromosomes 5q33.2-q35.1, 8q11.22-q12.1, and 17p13.3 for abdominal subcutaneous fat and on chromosome 11q12.13-q13.3 for abdominal visceral fat. The last genome scan study was a genome-wide linkage analysis of four factors related to the metabolic syndrome derived from a factor analysis of 10 risk factors (1247). Factor analysis yielded four different metabolic syndrome factors (obesity-insulin, blood pressure, lipids-insulin, and central obesity) that were tested for linkage with 400 microsatellite markers in four different ethnic groups (blacks, whites, Hispanics, Asians). Only results with the central obesity factor are reported in Table 5. Evidence of linkage was found on chromosomes 13q31.3, 13q32.2, 20p12.2, and 20p12.1 in blacks, on chromosomes 11q13.3, 21q21.3, and 21q22.12 in whites, and on chromosomes 3q22.1, 5q35.2, 6p25.1, 6p23, and 8p23.3 in Asians. No evidence of linkage was found in Hispanics (1247).

A bivariate linkage analysis of metabolic syndrome phenotypes (BMI, WC, lipids, and insulin) with 19 markers located on chromosome 7q11.22-q22.1 performed in 440 subjects from 27 Mexican-American families revealed evidence of univariate linkage for BMI (LOD = 2.4) and WC (LOD = 2.0) between markers D7S653 and D7S479 and linkages (LOD scores ranging from 1.86 to 4.21) for most of the bivariate traits (BMI-lipids, BMI-insulin, WC-lipids, WC-insulin, BMI-WC) to a 6-cM region near marker D7S653 (1203).

Finally, a meta-analysis of genome scans that used BMI as their primary obesity phenotype and were published before July 2003 was undertaken to identify QTLs influencing obesity (1204). A total of 29 genome scans were identified from the literature; of these studies, 13 analyzed BMI as a quantitative trait. Access to detailed results was requested from the authors of the 13 studies, and information was obtained in only 5 of the 13 studies. The results from these five studies, which included a total of 2814 individuals from 505 families, were jointly analyzed using a variance component approach. For the purpose of the analysis, the genome was divided into 121 30-cM regions called bins in such a way that the first bin on chromosome 1 (1.1) includes the results of markers tested between locations 0 and 30 cM, the second bin (1.2) encompasses the 30- to 60-cM region of chromosome 1, and so on for all chromosomes. For each scan, the bins were then sorted according to the maximum LOD score in that bin, and ranks were assigned with the lowest rank assigned to the bin with the highest LOD score. Within each study, the ranks were weighted according to the number of genotyped individuals in the sample, and the weighted average rank was then calculated for each bin across the five studies. The bin with the lowest weighted average rank for all studies corresponded to the region of the genome showing the most evidence of linkage across all studies retained in the meta-analysis. The results of the analysis revealed that the lowest weighted average rank was found in bin 8.1, suggesting that the best evidence of linkage to BMI across all five studies is found at the location 0 to 30 cM on chromosome 8 (8pter-p23.3). Based on permutation testing, this was the only region showing significant (p = 0.0005) evidence of linkage to BMI. Interestingly, only two of the five studies retained in the meta-analysis showed suggestive evidence of linkage to BMI in that region of chromosome 8.

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Conclusion

The 2005 human obesity gene map is depicted in Figure 1. The map includes >600 loci from single-gene mutations in mouse models of obesity, non-syndromic human obesity cases due to single-gene mutations, obesity-related Mendelian disorders that have been mapped, transgenic and KO mice models, QTLs from cross-breeding experiments and genome-wide scans, and genes or markers that have been shown to be associated or linked with an obesity phenotype. The map reveals that putative loci affecting obesity-related phenotypes are found on all chromosomes except Y. The number of genes and other markers associated or linked with human obesity phenotypes continues to increase, as indicated by the numbers collated in Table 6. Based on the various lines of evidence reviewed in the different sections of this report, there are now 135 different candidate genes that have been associated and/or linked with obesity-related phenotypes. The majority of the 127 candidate genes associated with obesity have been identified in association studies (Table 4). With the growing number of genes and loci indexed in the map, several genes and QTLs identified from association and genome scan studies have been replicated. We can now identify 22 different genes that have shown associations with obesity-related phenotypes in at least five studies. Among them, those showing replications in 10 studies and more include PPARG (30 studies), ADRB3 (29), ADRB2 (20), LEPR (16), GNB3 (14), UCP3 (12), ADIPOQ (11), LEP (11), UCP2 (11), HTR2C (10), NR3C1 (10), and UCP1 (10). The number of obesity QTLs identified from genome scans now reaches 253, which include 15 QTLs that have been replicated in at least three studies. The large number of genes and loci depicted in the obesity gene map is a good indication of the complexity of the task of identifying genes associated with the susceptibility to obesity. Although several of the genes listed in this report may be false positives, it is also clear that some genes are more important than others based on the numbers of replications from independent studies. A recent meta-analysis of genetic association studies concluded that, although false positive associations are abundant in the literature, 20% to 30% of genetic associations are real and have modest effects on risk of common diseases (1291). This would suggest that perhaps as many as 20 to 30 of the obesity candidate genes identified in this report might contribute to the risk of obesity in humans. Of course, the goal remains to identify the right combination of genes and mutations that are associated with this increased risk and to determine how environmental factors interact with these genes and mutations to determine the risk. We hope that the information provided in this publication will contribute in the years ahead to the resolution of this enormous challenge.

Figure 1.
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The 2005 human obesity gene map. The map includes all obesity-related genes and QTLs identified from the various lines of evidence reviewed in this article. This year's map consists of a 862-band-resolution cytogenetic map overlaid with build 35.1 of the human genome sequence available from National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov). This allows the human genes (as abbreviated in the tables and appendix and located to the right of each chromosome in this figure) to be placed at precise positions on both the sequence and the cytogenetic map. For all loci, we used the name preferred by UniSTS or Entrez Gene. The ruler to the left of each figure represents kilobasepairs. Chromosomes are drawn to scale only within a given page and not on the last page. These maps, along with information from this report, can be browsed and searched interactively at the Obesity Gene Map web site (http://obesitygene.pbrc.edu).

Full figure and legend (72K)


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Appendix

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Notes

1 Nonstandard abbreviations: KO, knockout; QTL, quantitative trait locus; MC4R, melanocortin receptor 4; BDNF, brain-derived neurotrophic factor; NTRK2, neurotrophic tyrosine receptor kinase 2; AHO, Albright Hereditary Osteodystrophy; BW, body weight; MGI, Mouse Genome Informatics; LOD, logarithm of the odds ratio; WHR, waist-to-hip ratio; cM, centimorgan(s); WC, waist circumference.

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