Obesity is a growing health problem in the U.S. As a complex trait, obesity involves multiple genes and gene-gene and gene-environment interactions that contribute to its pathogenesis. Here we report significant linkage from a scan of a large sample segregating extreme obesity and normal weight. We have used 382 microsatellite markers in 1,297 individuals from 260 European-American families. We conducted nonparametric linkage (NPL) analyses for dichotomous BMI (using BMI ≥27, ≥30, ≥35, and ≥40 kg/m2) using Genehunter. We also analyzed quantitative traits (BMI, percentage of fat, and waist circumference) by the family regression method using Merlin_regress. We found evidence for linkage on chromosome 12 (125 cM, D12S2070, logarithm of odds [LOD] 3.79, P = 0.00001 for percentage of fat; LOD 2.98, P = 0.0001 for BMI; and LOD 2.86, P = 0.00014 for waist circumference) by family regression analyses. Adding three additional markers to the intervals flanking the chromosome 12 peak yielded an LOD score of 4.08 (P = 0.00001) for percentage of fat at 116 cM and LOD scores of 3.57 (P = 0.00003) and 3.05 (P = 0.00009) for BMI and waist circumference, respectively, at 125 cM. We also obtained other suggestive linkages on chromosomes 2, 3, 7, 8, 9, 12, 13, and 21. Our results suggest multiple loci that could influence obesity, particularly a locus in chromosome region 12q23-24.

Obesity is a worldwide health problem with a rapidly increasing prevalence. The average BMI of the U.S. population is in the overweight range (28.1 kg/m2), and >30% of the people in the U.S. are obese (>30 kg/m2) (1,2). Obesity is associated with hypertension, diabetes, cardiovascular diseases, and cancer.

Over 100 years’ worth of scientific research has demonstrated that heredity plays a major role in the development of body size and obesity (3,4). Family studies (46) demonstrate that obesity and thinness follow family lines. Studies (3,7) examining several obesity thresholds have found that the relative risk increases with more extreme obesity, from 1.5–3.0 for moderate obesity (90th percentile for BMI or >30 kg/m2) to 3.0–9.0 for extreme obesity (>40 kg/m2). Twin and adoption studies (5, 6, 8) have found that identical twins are more highly correlated in body composition than fraternal twins. Correlations for dizygotic twins are similar to those of other first-degree relatives. Adoption studies (6,9,10) found that adoptees resembled their biological relatives in body composition but not members of their adoptive families. Studies of twins reared apart (11,12) confirmed this pattern: they resemble each other to a similar degree whether raised together or apart. Genes account for as much as two-thirds of individual differences in obesity in adults, with the remainder due to idiosyncratic influences from outside the family.

Major gene effects are uncommon. After a worldwide search by many investigators (13), few obese individuals have been found who carry major gene mutations in six genes. Of the six genes, mutations were extremely rare in all except one, MC4R. Individual major gene mutations appear to be too infrequent to make any significant contribution to common forms of obesity, although the cumulative proportion due to major gene mutations in all causative genes could account for a small but clinically significant proportion of cases of extreme obesity (13a). The pattern of inheritance of obesity, combined with the low frequency of mutations in known major genes, suggests a complex mode of inheritance involving multiple genes. It is likely that many genes have relatively small effects on common obesity phenotypes, and expression almost certainly depends on both genetic background and environmental conditions.

A number of linkage studies have been reported for obesity-related phenotypes. Chagnon et al. (13), in the annual obesity gene map update, report >300 genetic loci linked or associated with obesity in humans and animal models. These include 9 major genes from animal models (plus 24 knockouts), 6 major genes in humans, 33 genetic syndromes, 168 animal quantitative trait loci (QTLs), 71 candidate genes with reported associations in humans, and 68 loci with at least one report of possible linkage. Considering all studies, every human chromosome has been implicated except the Y.

The linkage results come from over 60 separate publications, including 29 genome scans. Some of the scans involved reanalysis of the same cohort with different phenotypes. The number of loci (>68) is large, and at least some may well be false-positives. Eighteen of the linkages have achieved at least minimal replication through an independent report of linkage to the same region. Often, however, the regions are large and/or poorly defined. These “replications” are concentrated in seven regions, including 2p22, 3q27, 6p21.3-p21.1, 10p12-p11, 11q23-q24, 17p12, and 18q21.

Many of the linkage results (including our own) have been weak and/or based on small samples. There have been a total of ∼150 separate reports of linkage in these >60 studies. Most of the reports were based on small sample sizes. Of the reports, 43% were based on <500 subjects and 87% were based on <1,000. When these numbers are compared with theoretical works on the sample size requirements for mapping human QTLs, they are very small indeed. Only 11 (7.3%) of the 150 reported linkages reached a genome-wide level of significance (logarithm of odds [LOD] >3.6). One would expect that even large LOD scores based on small sample sizes would be unstable. Only 3 (2%) of 150 reports of linkage achieved a genome-wide level of significance based on a sample size of ≥1,000 subjects. The generally weak nature of linkage results is not unique to obesity but generalizes to all complex traits (14).

Here we report the results from a second-generation scan of 260 European-American families having 1,297 individuals. The families were highly selected to segregate extreme obesity, with normal weight individuals also found in two generations. With this large and unique cohort we detected significant linkage to a region of chromosome 12q23-24.

Subjects.

Two hundred sixty European-American families (1,297 subjects) were chosen, as previously described (15,16). Briefly, all family probands (BMI ≥40 kg/m2) had at least one obese sibling (≥30 kg/m2) and at least one parent and one sibling who were of normal weight (<27 kg/m2). All subjects gave informed consent, and the protocol was approved by the Committee on Studies Involving Human Beings at the University of Pennsylvania.

Phenotypes.

BMI was calculated based on measured height and weight: BMI = weight (in kilograms)/height (in meters)2. Percentage of fat was measured by bioelectric impedance (Tanita TBF310 Pro Body Composition Analyzer; Tanita, Arlington Heights, IL). Waist circumference was measured when blood samples were collected. All quantitative variables were adjusted for linear effects of age within generation and sex using SPSS version 11.0.

DNA preparation and genotyping.

DNA was extracted using a high-salt method (17) and diluted to 10 ng/μl for genotyping. Three hundred eighty-two polymorphic Marshfield microsatellite markers from the Marshfield Screening Set 11 were genotyped by the Marshfield Center for Medical Genetics. Map distances were taken from the Marshfield Database (http://research.marshfieldclinic.org/genetics/). One family was duplicated (coded as different family) as an inner control for genotyping. Sex-specific PCR markers were amplified to verify sex. Mendel checks were performed by Merlin (18), and all errors were corrected or dropped.

Linkage analysis for qualitative (dichotomous) phenotypes.

We performed nonparametric multipoint linkage analyses using Genehunter version 1.3 (19) for BMI. We used dichotomous obesity affection status: BMI ≥27, ≥30, ≥35, and ≥40 kg/m2. Gene frequencies were estimated by counting alleles, using all individuals who provided DNA. This approach gives asymptotically unbiased estimates of the allele frequencies (20). All Mendelian inheritance errors were checked and resolved using Merlin and Genehunter 1.3. Any genetically unrelated parents and siblings were excluded, as were all half-siblings.

Pedigree-wide regression analyses by Merlin.

Quantitative phenotypes (BMI, percentage of fat, and waist circumference) were analyzed using the family regression test (Merlin_regress) (21). We used phenotypes after linear correction for age within generation and sex. Following this standardization, higher-order age effects were not significant. Outliers (more than four times the SD) were dropped in our analyses.

Variance components.

Using sex, age, and the interaction age × sex as covariates, a variance-components approach was performed with Solar 2.6.6 (22). We corrected for ascertainment based on the primary proband (23).

Simulation.

We estimated empirical significance levels for the analyses of qualitative phenotypes. We used the Simulate program (24) to generate 500 replicates of simulated markers genotypes based on real family structures, map distance, and allele frequencies, as we used in the Genehunter analyses. Simulated genotypes were analyzed using Genehunter 1.3. Empirical P values were calculated by dividing the number of replicates that exceeded the observed nonparametric linkage (NPL) score by the number of replicates (500 replicates × 382 markers = 191,000 replicates). Empirical P values (Pstep-down) were also given by a step-down method to control for multiple testing (25).

For quantitative traits, we simulated 500 replicates of our datasets for family regression analyses using the “simulate” function in Merlin. As we did for Genehunter, empirical P values were computed by dividing the number of replicates that exceeded the observed LOD score by the number of replicates (i.e., n = 191,000).

Additional genotyping on 12q23-24.

After detecting linkage on 12q23-24, three additional microsatellite markers IGF-I (108 cM), D12S1339 (116 cM), and D12S349 (135 cM) were picked to fill the gaps flanking the peak from the genome scan. Genotyping of microsatellite markers was performed as previously described (15).

Gap filling on 10p.

To verify our previous linkage result on 10p12, two microsatellite markers (D10S197 and D10S193) were manually genotyped after the Marshfield genome scan finished. Data analyses were carried out using Genehunter and Merlin after new genotyping data were merged.

Characteristics of samples.

Among 1,297 European-American individuals, the average BMI values (±SD) for probands, fathers, mothers, sisters, and brothers were 48.7 ± 9.1, 29.1 ± 6.2, 32.3 ± 9.1, 33.6 ± 10.0, and 30.7 ± 7.7 kg/m2, respectively. Most probands were female (237 of 251), and about two-thirds of all subjects were female (895 of 1,297). We had DNA available from both parents for 143 families, one parent for 111 families, and no parent for 6 families. Sibship size ranged from 2 to 8, and most families (n = 244) had 2–6 sibs (1–15 sibpairs), with a median sibship size of 3. Numbers of affected sib pairs for BMI ≥27, ≥30, ≥35, and ≥40 kg/m2 were 658, 472, 282, and 134, respectively. The numbers of families having at least one affected sibpair were 224 (224 of 260, 86.15%) for BMI ≥27, 203 (78.08%) for BMI ≥30, 163 (62.69%) for BMI ≥35, and 100 (38.46%) for BMI ≥40 kg/m2, respectively. Clinical characteristics (including BMI, percentage of fat, and waist circumference) of all samples, probands, fathers, mothers, sisters, and brothers are shown in Table 1. To demonstrate distributions of BMI, percentage of fat, and waist circumference, Table 1 also includes skewness and kurtosis for reference.

Genotyping.

The 1,297 European-American individuals were genotyped by Marshfield Medical Genetics Center using 382 microsatellite markers across the human genome. Marker heterozygosity was from 0.49 to 0.92 and averaged 0.76 ± 0.06. The average interval between markers was 8.94 cM, with the maximum gap of 17.5 cM on chromosome 18.

Discrete data analysis.

Using Genehunter 1.3, we obtained the highest NPL score (2.52, P = 0.0039) at the marker D7S1799 (114 cM, BMI ≥27 kg/m2) on chromosome 7. NPL scores as well as nominal and empirical P values are shown in Table 2. In addition to chromosome 7, chromosomes 3 (D3S2045, 124 cM), 8 (D8S2324, 94 cM), 9 (D9S910, 104 cM), 12 (GATA49D12N and PAH, 18 and 109 cM, respectively), and 13 (D13S894, 33 cM) also had linkage signals (NPL scores >1.85) (Table 2). Simulation tests yielded empirical P values that were all <0.05 for those loci.

Pedigree regression analyses (Merlin_regress).

We obtained significant results on chromosomes 4 (D4S1644, 143 cM), 12 (D12S2070, 125 cM), 13 (D13S800 and D13S779, 55 and 83 cM, respectively), and 21 (57.8 cM) (Table 3) (Fig. 1). We also found suggestive linkage on chromosome 2 (38 cM, LOD 1.70, P = 0.003) for BMI (Fig. 1). The most significant result was from chromosome 21 (D21S1446, 57.8 cM) for percentage of fat (LOD 4.27, P = 0.00001). Marker D12S2070 (125 cM) revealed significant linkage for BMI (2.98, P = 0.0001), percentage of fat (3.79, P = 0.00001), and waist circumference (2.86, P = 0.00014). Empirical genome-wide P values based on simulations (500 simulations each for 382 markers) ranged from 0.00002 to 0.0003 at the same genomic location, 125 cM from the p-terminus in chromosome region 12q23.

Chromosome 13 also showed positive linkage signals: LOD 2.70 (P = 0.0002) and 2.82 (P = 0.0002) for BMI on 55 cM (D13S800) and 83 cM (D13S779), respectively. We also found LOD 1.80 (P = 0.002) for marker D13S779 for waist circumference. Estimates of trait mean, variance, and heritability had little effect on results from the family regression analyses (data not shown).

Variance components (Solar).

Variance components analyses showed suggestive linkages on chromosomes 12, 13, and 21 (Table 3). The most significant result was on chromosome 13 (83 cM, D13S779), with LOD 3.11 for BMI. We obtained LOD scores of 2.33, 2.40, and 2.11 on chromosome 12 (D12S2070, 125 cM) for BMI, percentage of fat, and waist circumference, respectively.

Fine mapping on 12q23-24.

After we added three additional markers to the 12q23-24 region, regression analysis using Merlin_regress gave a LOD score of 4.08 (P = 0.00001) for percentage of fat on the marker D12S1339 (116 cM). D12S2070 yielded LOD scores of 3.57 (P = 0.00003) and 3.05 (P = 0.00009) for BMI and waist circumference, respectively. For qualitative BMI, we obtained an NPL score of 1.60 at the marker D12S1339 (BMI ≥30 kg/m2). The results of the fine mapping on chromosome 12 are shown in Fig. 2.

Gap filling on 10p.

After five gap-filling markers (D10S197 and D10S193) were added, Genehunter NPL scores improved from 0.84 to 2.58 (D10S197, BMI ≥27 kg/m2).

We have identified strong evidence for linkage of obesity-related phenotypes to markers in chromosome region 12q23-24. The evidence for linkage is consistent across correlated obesity phenotypes and analytic methods, and the peak LOD score meets criteria for genome-wide significance (LOD 4.08, P = 0.00001). The result was based on a large and unique sample segregating extreme obesity and normal weight.

The results for chromosome 12 gave LOD scores based on regression analyses of 3.79, 2.98, and 2.86 for the correlated phenotypes, percentage of fat, BMI, and waist circumference, respectively. After adding three microsatellite markers (IGF-I, D12S1339, and D12S349) to the 12q23-24 region, LOD scores for percentage of fat, BMI, and waist circumference increased from 3.79 to 4.07, 2.98 to 3.56, and 2.86 to 3.05, respectively. The peaks for BMI and waist circumference remained unchanged, whereas the peak of percentage of fat shifted to the adjacent (and newly added) marker, D12S1339.

Empirical genome-wide P values based on simulations (500 simulations each for 382 markers) ranged from 0.00002 to 0.0003 at the same genomic location, 125 cM from the p-terminus in chromosome region 12q23. Qualitative analyses revealed an overlapping peak ∼16 cM centromeric for BMI ≥30 kg/m2, with an NPL score of 1.92 and a genome-wide P value of 0.02. Because of a gap in the marker density, this second peak is at the next proximal centromeric marker. Although the multiple phenotypes examined are correlated, we assumed four independent phenotypes in the simulations. Overall, results were consistent across regression analyses for three related quantitative phenotypes and NPL analyses of BMI qualitative thresholds.

Perusse et al. (26) reported a genome scan for abdominal fat based on computed tomography scans for 156 European-American families from Quebec. Approximately one-half of the families were ascertained through an obese individual (BMI >32 kg/m2). They found linkage to markers in chromosome region 12q22-24, with a maximum LOD of 2.88. In our original scan with 92 families (15) we detected a similar peak, but with a Z score of only 1.4 in 12q23-24 (a proximal peak had a somewhat higher score in the earlier scan). Because the 1.4 value did not reach our threshold for suggestive linkage, we did not report the result at that time.

There have been two possibly relevant QTLs that map to the homologous regions in mouse and rat. One was the QTL (Qsbw) for body weight identified in a cross between C57BL/6J and Quackenbush-Swiss mice (27). Another study (28) reported a QTL (Weight1 or bw/gk1) with a strong influence on body weight in a GKxBN rat cross. Both QTLs had large effects, with Weight1 accounting for 24% of the phenotypic variance and the Qsbw locus causing an increase of 0.4 SD units. Both QTLs are located very near the gene for IGF-I, which in humans maps to chromosome 12q23-24. Another rat QTL (Dmo7p) for fat weight is also located in the region (29).

In addition to the human linkage and mouse QTL studies, there have been two reported associations of obesity-related phenotypes to genes located in this region of chromosome 12q22-24. Sun et al. (30) reported that percentage of body fat and other body fat-related variables were associated with polymorphisms in the IGF-I gene. One study (31) reported a possible association between BMI and the lipoprotein receptor SCARB1 (scavenger receptor class B, member 1), which appears to be located distal to IGF-I in 12q24, probably too far to contribute to reported linkage results in humans.

A candidate gene ACACB (acetyl-CoA carboxylase-β) localizes within 0.5 Mb of the marker D12S1339, our peak LOD score for percentage of fat. ACACB controls fatty acid oxidation in skeleton and heart muscle cells. ACACB knockout mice (AccII−/−) have a higher fatty acid oxidation rate and lower amounts of fat than normal controls (32).

In addition to ACACB, there are several plausible candidate genes located within the linked region on chromosome 12q23-24, including PMCH (promelanin-concentrat-ing hormone) and IGF-I. MCH (melanin-concentrating hormone) knockout mice showed reduced body weight (due to hypophagia) (33). However, there are 39 genes or expressed sequence tags within a 20-Mb region for which we obtained a LOD score >2.0. At present, this locus is insufficiently localized to go beyond hypothesizing about possible causative genes.

Previous linkages have been reported to ∼70 locations in 150 reports from 60 articles, including 29 genome scans (30 including this one) (13). However, most studies were based on small samples, and most results were at most suggestive. Only three previously reported linkages achieved genome-wide significance (LOD >3.6) based on a sample of 1,000 or more subjects. This fact should dramatize the need for large samples in genome scans for complex traits.

We detected linkage at locations previously identified in other studies on earlier subsets of our cohort based on 78–220 families, particularly on chromosome 7q (3436) and 20q (15). Linkage results from the scan were not as strong as in previous studies, apparently because of lower marker coverage in the linked regions.

We also found suggestive linkage on chromosomes 2, 3, 4, 7, 8, 9, and 13 for both qualitative and quantitative obesity phenotypes. For chromosome 21, we found a high LOD score and significance level. However, the result was based on a single marker that had a relatively low information content, which was compounded by the fact that it is a terminal marker. The linkage results for this location were not consistent across phenotypes or analytic methods, and we suspect the result may not be reliable.

One candidate gene on chromosome 13 is IRS2 (insulin receptor substrate 2, close to marker D13S779 at 83 cM). IRS2-disrupted mice have type 2 diabetes (37), and case-control studies in human subjects also showed associations between IRS2 polymorphisms and type 2 diabetes (38) or obesity (39). Table 3 shows another peak of linkage on chromosome 13 around 55 cM (GATA64F08 and D13S317). Feitosa et al. (40) found an LOD 3.0 for BMI on marker GATA11C08 (8 cM upstream) and the flanking region on 13q14-22. They suggested that HTR2A (5-hydroxytryptamine receptor 2A) could be a candidate gene in this region.

As we reported in our fine mapping study (36), chromosome 7 (114 cM) showed linkage for dichotomous BMI in this study. Because of the lower marker density (the marker GATA23F05 locates 4.5 Mb upstream of our previous peak D7S692), it is not surprising that the LOD score is slightly lower in this study compared with the previous results from fine mapping, especially for quantitative traits. There were multiple linkage peaks found in 7q by different groups of researchers within or outside the leptin (LEP) gene region for obesity and related phenotypes (34,4144).

Although there are no previously reported human linkage reports near our chromosome 3, 4, 8, and 9 peaks, multiple QTL and association studies suggested that obesity-related genes may lie in those chromosome regions. We found suggestive linkage for multiple phenotypes for a marker (D4S1644, 4q31.21) only 0.25 Mb downstream of uncoupling protein 1 (UCP1). Both animal models (45) and human association studies (46,47) indicate that the key role UCP1 plays in energy balance could influence common obesity. The chromosome 8 linkage peak (D8S2324, 8q21.11) localizes within the mouse QTL Hlq4 (48), and the chromosome 9 peak (D9S910, 9q22.23) overlaps with mouse QTL Qlw4 (49,50).

Importantly, we missed a previously reported linkage peak on chromosome 10p (51,52). Although the Marshfield marker set has an average coverage of 10 cM, there is variability in the density of coverage. This peak linkage falls within a 16-cM gap. To validate the previous linkage result and exclude the possibility that the lack of results in the current scan might be due to failure to replicate in the more recently accrued families, we filled the gap between Marshfield markers on 10p. We then found a maximum NPL score of 2.58, a more than threefold increase from the initial genome scan maximum value of 0.84. The two additional markers increased the information content at the peak from <60 to >90%. The fact that an established linkage result can be missed in a 10-cM (average) scan supports the development and use of finer maps in genome scans of complex traits. Although genotyping costs will be higher, it appears to be the only way to fully extract information from family cohorts already collected at great expense.

Much progress has been made in methods for analyses of quantitative traits in the 5 years since our first genome scan. Three major methods are widely used: Haseman-Elston-based regression, variance components (20,21), and the family regression method by Sham et al. (21). The first two methods are sensitive to departures from normality, particularly variance components. Using simulated, nonnormally distributed data, Sham et al. (21) found that their family regression method was insensitive to distribution properties. Because our families were selected through multiple probands, we used the family regression method to minimize any effects of the ascertainment. We used variance components (Solar) to verify our family regression results. While the estimates of variance accounted for cannot be interpreted based on analyses of our sample, the localization of linkage should be relatively robust. The results matched very well with those from the family regression analyses (Table 3).

Homogeneity is very important in family selection for linkage analyses. To minimize admixture, we only selected non-Hispanic, European-American subjects in this study. It has been shown that European and European-American populations appear to have minimum population structure (53).

Our chromosome 10 results provide a dramatic demonstration of the effect of marker density on linkage detection for complex traits. Risch (54) examined the issue of linkage detection from the standpoint of marker information content. Using scheme 2 in an article by Risch (54), where parental information is used to infer identity-by-descent status, his results (see Fig. 3 of that study) showed that the percentage of maximal expected LOD score as compared with fully informative markers is approximately proportional to the polymorphism information content of the marker under study. Therefore, near the true disease locus, we expect the LOD score to on average increase by a factor of 1.2 to 1.6 when the polymorphism information content is increased from 60–80 to 90–95%. This will also result in the reduction of the CI length, although the extent of this reduction depends on the strength of the true signal. Obviously, when the markers are located farther from the disease locus, the effect can be much more pronounced, threefold in this case.

Two recently published studies have addressed the issue of map density from the standpoint of CIs rather than linkage detection. They examined the question from very different perspectives and found consistent results but drew opposite conclusions. Schulze et al. (55) provided a proof-of-principle demonstration by going from a 10-cM to a 1-cM map in linkage analyses of bipolar disorder. Their LOD score increased from 4.7 to 5.4, and the 1 LOD CI decreased from 12 to 9 cM, i.e., by roughly 25%. Atwood and Heard-Costa (56) examined the effect of map density on location errors for quantitative traits analyzed by variance components methods through a series of simulations. Their largest sample size was one-third smaller than ours, 200 families with five members. For QTL accounting for 20–40% of the variance, they found decreases in location error of 7–40% by going from a 10-cM to a 1-cM map, values well within the range found in the bipolar study. They concluded that fine linkage mapping was of little value. However, their view seems to ignore the effort and expense involved in genotyping. On the other hand, higher marker density and its resulting information increases the power for detection and reduces CIs.

In summary, we completed a genome scan of a large sample of families segregating extreme obesity and normal weight. We obtained consistent support for linkage to markers in chromosome region 12q21.2-23 across obesity phenotypes and analytic methods. The peak LOD score met genome-wide significance based on simulations. This result appears to be one of the strongest yet reported for an obesity trait. Obviously, further study and independent replications are needed.

FIG. 1.

Genome scan results (LOD or NPL score) of family regression (BMI, percentage of fat, and waist circumference) and Genehunter (dichotomous BMI) analyses for 22 chromosomes. The figure key denotes, in order, family regression analysis for BMI by Merlin (BMI), family regression analysis for percentage of fat by Merlin (%Fat), family regression analysis for waist circumference by Merlin (Waist), and NPL analysis for BMI ≥27 and ≥30 kg/m2 by Genehunter.

FIG. 1.

Genome scan results (LOD or NPL score) of family regression (BMI, percentage of fat, and waist circumference) and Genehunter (dichotomous BMI) analyses for 22 chromosomes. The figure key denotes, in order, family regression analysis for BMI by Merlin (BMI), family regression analysis for percentage of fat by Merlin (%Fat), family regression analysis for waist circumference by Merlin (Waist), and NPL analysis for BMI ≥27 and ≥30 kg/m2 by Genehunter.

FIG. 2.

Fine mapping results of chromosome 12 quantitative and qualitative analyses. The figure key denotes, in order, family regression analysis for BMI by Merlin (BMI), family regression analysis for percentage of fat by Merlin (% fat), family regression analysis for waist circumference by Merlin (waist), and NPL analysis for BMI ≥27 and ≥30 kg/m2 by Genehunter.

FIG. 2.

Fine mapping results of chromosome 12 quantitative and qualitative analyses. The figure key denotes, in order, family regression analysis for BMI by Merlin (BMI), family regression analysis for percentage of fat by Merlin (% fat), family regression analysis for waist circumference by Merlin (waist), and NPL analysis for BMI ≥27 and ≥30 kg/m2 by Genehunter.

TABLE 1

Clinical characteristics of all samples, probands, fathers, mothers, sisters, and brothers

nMinimumMaximumMean ± SDSkewnessKurtosis
All samples       
 Sex 1,297 402 (male) 895 (female)    
 Age (years) 1,296 14 91 48.3 ± 15.2 0.42 0.61 
 BMI (kg/m21,282 17 83 35.2 ± 11.1 0.91 0.71 
 % Fat 1,057 10* 63 38.4 ± 12.0 −0.32 −0.79 
 Waist circumference (cm) 1,199 61 225 103.9 ± 19.8 0.53 0.90 
Probands*       
 Sex 251 14 (male) 237 (female)    
 Age (years) 250 16 66 40.4 ± 8.4 −0.14 0.54 
 BMI (kg/m2249 29 83 48.7 ± 9.1 0.99 1.45 
 % Fat 227 24 62 49.4 ± 6.3 −0.78 0.83 
 Waist circumference (cm) 247 86 225 121.5 ± 16.5 1.39 6.16 
Fathers       
 Age (years) 162 41 86 67.5 ± 8.8 −0.60 0.14 
 BMI (kg/m2163 17 53 29.1 ± 6.2 1.23 1.71 
 % Fat 128 12 52 27.0 ± 7.9 0.36 −0.21 
 Waist circumference (cm) 151 79 133 102.9 ± 12.1 0.46 0.31 
Mothers       
 Age (years) 240 41 91 65.9 ± 9.2 0.004 0.04 
 BMI (kg/m2237 19 66 32.3 ± 9.1 0.91 0.50 
 % Fat 196 15 61 41.8 ± 8.9 −0.26 −0.30 
 Waist circumference (cm) 225 65 154 97.4 ± 18.1 0.57 −0.31 
Sisters       
 Age (years) 418 14 70 39.6 ± 9.2 −0.01 0.34 
 BMI (kg/m2409 18 78 33.6 ± 10.0 0.86 1.20 
 % Fat 324 15 58 39.9 ± 9.6 −0.42 −0.55 
 Waist circumference (cm) 371 61 168 96.7 ± 19.8 0.59 −0.01 
Brothers       
 Age (years) 226 15 69 40.5 ± 9.5 0.26 −0.08 
 BMI (kg/m2224 20 65 30.7 ± 7.7 1.63 3.40 
 % Fat 182 10* 56 26.3 ± 9.3 0.30 0.14 
 Waist circumference (cm) 205 73 167 103.6 ± 16.5 0.96 0.89 
nMinimumMaximumMean ± SDSkewnessKurtosis
All samples       
 Sex 1,297 402 (male) 895 (female)    
 Age (years) 1,296 14 91 48.3 ± 15.2 0.42 0.61 
 BMI (kg/m21,282 17 83 35.2 ± 11.1 0.91 0.71 
 % Fat 1,057 10* 63 38.4 ± 12.0 −0.32 −0.79 
 Waist circumference (cm) 1,199 61 225 103.9 ± 19.8 0.53 0.90 
Probands*       
 Sex 251 14 (male) 237 (female)    
 Age (years) 250 16 66 40.4 ± 8.4 −0.14 0.54 
 BMI (kg/m2249 29 83 48.7 ± 9.1 0.99 1.45 
 % Fat 227 24 62 49.4 ± 6.3 −0.78 0.83 
 Waist circumference (cm) 247 86 225 121.5 ± 16.5 1.39 6.16 
Fathers       
 Age (years) 162 41 86 67.5 ± 8.8 −0.60 0.14 
 BMI (kg/m2163 17 53 29.1 ± 6.2 1.23 1.71 
 % Fat 128 12 52 27.0 ± 7.9 0.36 −0.21 
 Waist circumference (cm) 151 79 133 102.9 ± 12.1 0.46 0.31 
Mothers       
 Age (years) 240 41 91 65.9 ± 9.2 0.004 0.04 
 BMI (kg/m2237 19 66 32.3 ± 9.1 0.91 0.50 
 % Fat 196 15 61 41.8 ± 8.9 −0.26 −0.30 
 Waist circumference (cm) 225 65 154 97.4 ± 18.1 0.57 −0.31 
Sisters       
 Age (years) 418 14 70 39.6 ± 9.2 −0.01 0.34 
 BMI (kg/m2409 18 78 33.6 ± 10.0 0.86 1.20 
 % Fat 324 15 58 39.9 ± 9.6 −0.42 −0.55 
 Waist circumference (cm) 371 61 168 96.7 ± 19.8 0.59 −0.01 
Brothers       
 Age (years) 226 15 69 40.5 ± 9.5 0.26 −0.08 
 BMI (kg/m2224 20 65 30.7 ± 7.7 1.63 3.40 
 % Fat 182 10* 56 26.3 ± 9.3 0.30 0.14 
 Waist circumference (cm) 205 73 167 103.6 ± 16.5 0.96 0.89 

Proband DNA was not available in nine families.

TABLE 2

Summary of Genehunter analyses for dichotomous BMI

PhenotypeChromosomePosition (cM)NPL score*Nominal PPsinglePstep-down
BMI ≥27 kg/m2 114 2.52 0.0039 0.0040 0.0354 
 12 18 2.12 0.0128 0.0080 0.0393 
 13 33 1.88 0.0236 0.0200 0.0588 
BMI ≥30 kg/m2 124 1.88 0.0178 0.0240 0.0703 
 114 2.04 0.0110 0.0060 0.0412 
 104 2.09 0.0010 0.0100 0.0394 
 12 109 1.92 0.0151 0.0180 0.0530 
BMI ≥35 kg/m2 114 2.25 0.0024 0.0040 0.0316 
 94 1.90 0.0083 0.0060 0.0354 
PhenotypeChromosomePosition (cM)NPL score*Nominal PPsinglePstep-down
BMI ≥27 kg/m2 114 2.52 0.0039 0.0040 0.0354 
 12 18 2.12 0.0128 0.0080 0.0393 
 13 33 1.88 0.0236 0.0200 0.0588 
BMI ≥30 kg/m2 124 1.88 0.0178 0.0240 0.0703 
 114 2.04 0.0110 0.0060 0.0412 
 104 2.09 0.0010 0.0100 0.0394 
 12 109 1.92 0.0151 0.0180 0.0530 
BMI ≥35 kg/m2 114 2.25 0.0024 0.0040 0.0316 
 94 1.90 0.0083 0.0060 0.0354 

Data are simulated based on the real genetic distances, availability of the genotype, allele number, and frequency.

*

Only NPL scores >1.85 are shown. Psingle, based on phenotype- and locus-specific NPL scores in 500 replicates (divided by 500); Pstep-down, based on Psingle adjusted by step-down method (α = (1 − (1 − K(g,α))g) (g is the rank among 9 tests based on Psingle) (25).

TABLE 3

Summary of analyses using Merlin_regress and variance components (Solar) for quantitative traits in European Americans

TraitsChromosomePosition (cM)Merlin_regressNominal P valueEmpirical P value*Variance components (Solar)
BMI 143.3 1.71 0.002 0.003691 1.79 
 12 125 2.98 0.0001 0.000168 2.33 
 13 55 2.70 0.0002 0.000377 2.69 
 13 83 2.82 0.0002 0.000264 3.11 
% Fat 12 125 3.79 0.00001 0.000018 2.40 
 21 57.8 4.27 0.00001 0.000005 2.54 
Waist circumference 12 125 2.86 0.00014 0.000264 2.11 
 13 83 1.80 0.002 0.002945 1.82 
TraitsChromosomePosition (cM)Merlin_regressNominal P valueEmpirical P value*Variance components (Solar)
BMI 143.3 1.71 0.002 0.003691 1.79 
 12 125 2.98 0.0001 0.000168 2.33 
 13 55 2.70 0.0002 0.000377 2.69 
 13 83 2.82 0.0002 0.000264 3.11 
% Fat 12 125 3.79 0.00001 0.000018 2.40 
 21 57.8 4.27 0.00001 0.000005 2.54 
Waist circumference 12 125 2.86 0.00014 0.000264 2.11 
 13 83 1.80 0.002 0.002945 1.82 
*

Multiple test, empirical P values were calculated by dividing the number of replicates that exceeded the observed LOD score by total number of replicates (382 markers × 500 replicates = 191,000).

This work was supported by the National Institutes of Health (R01DK44073, R01DK48095, and R01DK56210 to R.A.P. and R01GM59507 to H.Z.). Except for five markers, genotypes were completed by the National Heart, Lung, and Blood Institute-supported Marshfield Genotyping Service, and we thank James L. Weber, Director, Donna Dorshorst, and Ying Fan.

We acknowledge the cooperation of our subjects. We thank Quan Cao, Jeffrey Hannah, Balasahib Shinde, Elizabeth Joe, Jan Merideth, Cameron Braswell, Cathleen Garrigan, and Kye Yun for technical assistance.

1.
Flegal KM, Troiano RP: Changes in the distribution of body mass index of adults and children in the US population.
Int J Obes Relat Metab Disord
24
:
807
–818,
2000
2.
Friedman JM: A war on obesity, not the obese.
Science
299
:
856
–858,
2003
3.
Price RA, Lee JH: Risk ratios for obesity in families of obese African-American and Caucasian women.
Hum Hered
51
:
35
–40,
2001
4.
Price RA, Reed DR, Guido NJ: Resemblance for body mass index in families of obese African American and European American women.
Obes Res
8
:
360
–366,
2000
5.
Maes HH, Neale MC, Eaves LJ: Genetic and environmental factors in relative body weight and human adiposity.
Behav Genet
27
:
325
–351,
1997
6.
Price RA, Cadoret RJ, Stunkard AJ, Troughton E: Genetic contributions to human fatness: an adoption study.
Am J Psychiatry
144
:
1003
–1008,
1987
7.
Lee JH, Reed DR, Price RA: Familial risk ratios for extreme obesity: implications for mapping human obesity genes.
Int J Obes Relat Metab Disord
21
:
935
–940,
1997
8.
Grilo CM, Pogue-Geile MF: The nature of environmental influences on weight and obesity: a behavior genetic analysis.
Psychol Bull
110
:
520
–537,
1991
9.
Stunkard AJ, Sorensen TI, Hanis C, Teasdale TW, Chakraborty R, Schull WJ, Schulsinger F: An adoption study of human obesity.
N Engl J Med
314
:
193
–198,
1986
10.
Sorensen TI, Price RA, Stunkard AJ, Schulsinger F: Genetics of obesity in adult adoptees and their biological siblings.
BMJ
298
:
87
–90,
1989
11.
Price RA, Gottesman II: Body fat in identical twins reared apart: roles for genes and environment.
Behav Genet
21
:
1
–7,
1991
12.
Stunkard AJ, Harris JR, Pedersen NL, McClearn GE: The body-mass index of twins who have been reared apart.
N Engl J Med
322
:
1483
–1487,
1990
13.
Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ, Perusse L, Bouchard C: The human obesity gene map: the 2002 update.
Obes Res
11
:
313
–367,
2003
13a.
Clement K, Boutin P, Froguel P: Genetics of obesity.
Am J Pharmacogenomics
2
:
177
–187,
2002
14.
Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M: Genomewide scans of complex human diseases: true linkage is hard to find.
Am J Hum Genet
69
:
936
–950,
2001
15.
Lee JH, Reed DR, Li WD, Xu W, Joo EJ, Kilker RL, Nanthakumar E, North M, Sakul H, Bell C, Price RA: Genome scan for human obesity and linkage to markers in 20q13.
Am J Hum Genet
64
:
196
–209,
1999
[published erratum appears in Am J Hum Genet 66:1472, 2000]
16.
Price RA, Reed DR, Lee JH: Obesity related phenotypes in families selected for extreme obesity and leanness.
Int J Obes Relat Metab Disord
22
:
406
–413,
1998
17.
Lahiri DK, Nurnberger JI Jr: A rapid non-enzymatic method for the preparation of HMW DNA from blood for RFLP studies.
Nucleic Acid Res
19
:
5444
,
1991
18.
Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin: rapid analysis of dense genetic maps using sparse gene flow trees.
Nat Genet
30
:
97
–101,
2002
19.
Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES: Parametric and nonparametric linkage analysis: a unified multipoint approach.
Am J Hum Genet
58
:
1347
–1363,
1996
20.
Ott J: Strategies for characterizing highly polymorphic markers in human gene mapping.
Am J Hum Genet
51
:
283
–290,
1992
21.
Sham PC, Purcell S, Cherny SS, Abecasis GR: Powerful regression-based quantitative-trait linkage analysis of general pedigrees.
Am J Hum Genet
71
:
238
–253,
2002
22.
Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees.
Am J Hum Genet
62
:
1198
–1211,
1998
23.
Amos CI, de Andrade M: Genetic linkage methods for quantitative traits.
Stat Methods Med Res
10
:
3
–25,
2001
24.
Terwilliger JD, Speer M, Ott J: Chromosome-based method for rapid computer simulation in human genetic linkage analysis.
Genet Epidemiol
10
:
217
–224,
1993
25.
Ewens WJ, Grant GR: Computationally intensive methods. In Statistical Methods in Bioinformatics: An Introduction, 1st ed. Ewens WJ, Grant GR, Eds. New York, Springer-Verlag, 2001, p. 349–363
26.
Perusse L, Rice T, Chagnon YC, Despres JP, Lemieux S, Roy S, Lacaille M, Ho-Kim MA, Chagnon M, Province MA, Rao DC, Bouchard C: A genome-wide scan for abdominal fat assessed by computed tomography in the Quebec Family Study.
Diabetes
50
:
614
–621,
2001
27.
Collins AC, Martin IC, Kirkpatrick BW: Growth quantitative trait loci (QTL) on mouse chromosome 10 in a Quackenbush-Swiss x C57BL/6J backcross.
Mamm Genome
4
:
454
–458,
1993
28.
Galli J, Li LS, Glaser A, Ostenson CG, Jiao H, Fakhrai-Rad H, Jacob HJ, Lander ES, Luthman H: Genetic analysis of non-insulin dependent diabetes mellitus in the GK rat.
Nat Genet
12
:
31
–37,
1996
29.
Watanabe TK, Okuno S, Oga K, Mizoguchi-Miyakita A, Tsuji A, Yamasaki Y, Hishigaki H, Kanemoto N, Takagi T, Takahashi E, Irie Y, Nakamura Y, Tanigami A: Genetic dissection of “OLETF,” a rat model for non-insulin-dependent diabetes mellitus: quantitative trait locus analysis of (OLETF x BN) x OLETF.
Genomics
58
:
233
–239,
1999
30.
Sun G, Gagnon J, Chagnon YC, Perusse L, Despres JP, Leon AS, Wilmore JH, Skinner JS, Borecki I, Rao DC, Bouchard C: Association and linkage between an insulin-like growth factor-1 gene polymorphism and fat free mass in the HERITAGE Family Study.
Int J Obes Relat Metab Disord
23
:
929
–935,
1999
31.
Acton SL, Kozarsky KF, Rigotti A: The HDL receptor SR-BI: a new therapeutic target for atherosclerosis?
Mol Med Today
5
:
518
–524,
1999
32.
Abu-Elheiga L, Matzuk MM, Abo-Hashema KA, Wakil SJ: Continuous fatty acid oxidation and reduced fat storage in mice lacking acetyl-CoA carboxylase 2.
Science
291
:
2613
–2616,
2001
33.
Shimada M, Tritos NA, Lowell BB, Flier JS, Maratos-Flier E: Mice lacking melanin-concentrating hormone are hypophagic and lean.
Nature
396
:
670
–674,
1998
34.
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
45
:
691
–694,
1996
35.
Li WD, Reed DR, Lee JH, Xu W, Kilker RL, Sodam BR, Price RA: Sequence variants in the 5′ flanking region of the leptin gene are associated with obesity in women.
Ann Intern Med
63
:
227
–234,
1999
36.
Li WD, Li D, Wang S, Zhang S, Zhao H, Price RA: Linkage and linkage disequilibrium mapping of genes influencing human obesity in chromosome region 7q22.1-7q35.
Diabetes
52
:
1557
–1561,
2003
37.
Withers DJ, Gutierrez JS, Towery H, Burks DJ, Ren JM, Previs S, Zhang Y, Bernal D, Pons S, Shulman GI, Bonner-Weir S, White MF: Disruption of IRS-2 causes type 2 diabetes in mice.
Nature
391
:
900
–904,
1998
38.
Mammarella S, Romano F, Di Valerio A, Creati B, Esposito DL, Palmirotta R, Capani F, Vitullo P, Volpe G, Battista P, Della Loggia F, Mariani-Costantini R, Cama A: Interaction between the G1057D variant of IRS-2 and overweight in the pathogenesis of type 2 diabetes.
Hum Mol Genet
9
:
2517
–2521,
2000
39.
Lautier C, El Mkadem SA, Renard E, Brun JF, Gris JC, Bringer J, Grigorescu F: Complex haplotypes of IRS2 gene are associated with severe obesity and reveal heterogeneity in the effect of Gly1057Asp mutation.
Hum Genet
113
:
34
–43,
2003
40.
Feitosa MF, Borecki IB, Rich SS, Arnett DK, Sholinsky P, Myers RH, Leppert M, Province MA: 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
70
:
72
–82,
2002
41.
Feitosa MF, Rice T, Nirmala A, Rao DC: Major gene effect on body mass index: the role of energy intake and energy expenditure.
Hum Biol
72
:
781
–799,
2000
42.
Arya R, Blangero J, Williams K, Almasy L, Dyer TD, Leach RJ, O’Connell P, Stern MP, Duggirala R: Factors of insulin resistance syndrome-related phenotypes are linked to genetic locations on chromosomes 6 and 7 in nondiabetic Mexican-Americans.
Diabetes
51
:
841
–847,
2002
43.
Cheng LS, Davis RC, Raffel LJ, Xiang AH, Wang N, Quinones M, Wen PZ, Toscano E, Diaz J, Pressman S, Henderson PC, Azen SP, Hsueh WA, Buchanan TA, Rotter JI: Coincident linkage of fasting plasma insulin and blood pressure to chromosome 7q in hypertensive Hispanic families.
Circulation
104
:
1255
–1260,
2001
44.
Duggirala R, Stern MP, Mitchell BD, Reinhart LJ, Shipman PA, Uresandi OC, Chung WK, Leibel RL, Hales CN, O’Connell P, Blangero J: Quantitative variation in obesity-related traits and insulin precursors linked to the OB gene region on human chromosome 7.
Am J Hum Genet
59
:
694
–703,
1996
45.
Lowell BB, S Susulic V, Hamann A, Lawitts JA, Himms-Hagen J, Boyer BB, Kozak LP, Flier JS: Development of obesity in transgenic mice after genetic ablation of brown adipose tissue.
Nature
366
:
740
–742,
1993
46.
Oppert JM, Vohl MC, Chagnon M, Dionne FT, Cassard-Doulcier AM, Ricquier D, Perusse L, Bouchard C: DNA polymorphism in the uncoupling protein (UCP) gene and human body fat.
Int J Obes Relat Metab Disord
18
:
526
–531,
1994
47.
Clement K, Ruiz J, Cassard-Doulcier AM, Bouillaud F, Ricquier D, Basdevant A, Guy-Grand B, Froguel P: Additive effect of A→G (−3826) variant of the uncoupling protein gene and the Trp64Arg mutation of the beta 3-adrenergic receptor gene on weight gain in morbid obesity.
Int J Obes Relat Metab Disord
20
:
1062
–1066,
1996
48.
Moody DE, Pomp D, Nielsen MK, Van Vleck LD: Identification of quantitative trait loci influencing traits related to energy balance in selection and inbred lines of mice.
Genetics
152
:
699
–711,
1999
49.
Vaughn TT, Pletscher LS, Peripato A, King-Ellison K, Adams E, Erikson C, Cheverud JM: Mapping quantitative trait loci for murine growth: a closer look at genetic architecture.
Genet Res
74
:
313
–322,
1999
50.
Cheverud JM, Vaughn TT, Pletscher LS, Peripato AC, Adams ES, Erikson CF, King-Ellison KJ: Genetic architecture of adiposity in the cross of LG/J and SM/J inbred mice.
Mamm Genome
12
:
3
–12,
2001
51.
Price RA, Li WD, Bernstein A, Crystal A, Golding EM, Weisberg SJ, Zuckerman WA: A locus affecting obesity in human chromosome region 10p12.
Diabetologia
44
:
363
–366,
2001
52.
Dong C, Wang S, Li WD, Li D, Zhao H, Price RA: Interacting genetic loci on chromosomes 20 and 10 influence extreme human obesity.
Am J Hum Genet
72
:
115
–124,
2003
53.
Ardlie KG, Lunetta KL, Seielstad M: Testing for population subdivision and association in four case-control studies.
Am J Hum Genet
71
:
304
–311,
2002
54.
Risch N: Linkage strategies for genetically complex traits. III: the effect of marker polymorphism on analysis of affected relative pairs.
Am J Hum Genet
46
:
242
–253,
1990
55.
Schulze TG, Chen YS, Badner JA, McInnis MG, DePaulo JR, McMahon FJ: Additional, physically ordered markers increase linkage signal for bipolar disorder on chromosome 18q22.
Biol Psychiatry
53
:
239
–243,
2003
56.
Atwood LD, Heard-Costa NL: Limits of fine-mapping a quantitative trait.
Genet Epidemiol
24
:
99
–106,
2003