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A Follow-Up Linkage Study for Quantitative Trait Loci Contributing to Obesity-Related Phenotypes

Osteoporosis Research Center (Y.-J.L., F.-H.X., H.S., Y.-Z.L., H.-Y.D., L-J.Z., Q.-Y.H., V.D., T.C., K.M.D., R.R.R., H.-W.D.) and Department of Biomedical Sciences (Y.-J.L., F.-H.X., H.S., Y.-Z.L., L.-J.Z., H.-W.D.), Creighton University, Omaha, Nebraska 68131; Laboratory of Molecular and Statistical Genetics (H.-W.D.), College of Life Sciences Hunan Normal University, Changsha, 410081 Hunan, China; and Center for Medical Informatics (J.-L.L.), School of Medicine, Yale University, New Haven, Connecticut 06520-8009

Address all correspondence and requests for reprints to: Hong-Wen Deng, Ph.D., Osteoporosis Research Center, Creighton University, 601 North 30th Street, Suite 6787, Omaha, Nebraska 68131. E-mail: deng{at}creighton.edu.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
We have recently reported a whole-genome scan in a sample of 630 subjects from 53 extended pedigrees, in which several genomic regions that may contain quantitative trait loci (QTLs) for obesity were suggested. In the present study, with an attempt to confirm our previous findings, we performed a follow-up linkage study in an expanded sample of 79 pedigrees with 1816 subjects (including expanded previous 53 pedigrees and 26 newly recruited pedigrees containing 1058 subjects). A new set of microsatellite markers spanning previously identified regions were selected, with the average genomic distance narrowed from approximately 10 cM to approximately 5 cM in this study. Using a variance component method, we performed two- and multipoint linkage analyses in the following three sample sets: expanded previous 53 pedigrees (758 subjects), 26 new pedigrees, and 79 total pedigrees. For body mass index, analyses of the expanded 53 pedigrees attained a LOD score of 2.32 near marker D1S468 in two-point analysis and a maximum LOD score (MLS) of 2.21 in multipoint analysis; 2q14 near marker D2S347 attained a LOD score of 3.42 in two-point analysis and a MLS of 3.93 in multipoint analysis. The linkage peaks at 1p36 and 2q14 were further supported in the analyses of all 79 pedigrees, with multipoint MLS being 1.38 and 0.90, respectively. For fat mass, genomic region 6q27 achieved a LOD score of 1.24 in two-point analysis and an MLS of 0.92 in multipoint analysis in all 79 pedigrees. Our data support that 1p36, 2q14, and 6q27 are promising regions that may harbor QTLs for obesity phenotypes.

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
SUBSTANTIAL PROGRESS HAS been made in deciphering the pathogenesis of obesity over the past few years, with the well-established knowledge that obesity is under strong genetic control (1, 2). As one of the major approaches to detecting obesity genes, whole-genome scans have been extensively performed over the past few years, resulting in the publication of more than 29 genome scans in various populations (3). The human quantitative trait loci (QTLs) identified from these studies have been expanded from three to 68 during the past 6 years (3). Several genomic regions (e.g. 1p36, 1p31–p21, 2p21, 3q27, 10p12, 11q23–24, and 20q) have been replicated across some different studies (3, 4), whereas others remain to be confirmed. As the number of QTLs uncovered continues to increase, there is a growing need to rigorously validate these candidate regions.

We have recently reported a whole-genome linkage scan for QTLs for obesity phenotypes on 53 extended Caucasian pedigrees that contained more than 10,000 relative pairs (including 1,249 sibling pairs) informative for linkage analyses (5). That study includes a large number of subjects with body composition [fat mass, percentage fat mass (PFM), and lean mass] assessed by dual x-ray absorptiometry (DXA), a contemporary technique that yields homogeneous measurements of body composition with excellent precision. Because PFM relates differentially to body mass index (BMI) in various ethnic groups (6) and the validity of BMI as an index of obesity remains uncertain and controversial (7), a study using different obesity-related phenotypes may offer potential advantages in detecting obesity genes.

In the present study, with the aim of confirming our previous whole-genome scan findings, we conducted a follow-up linkage study with denser markers spanning the previously localized regions. Within six regions (i.e. 1p36, 2q14, 4q12, 6q27, 12q14, and 20q13) that achieved maximum multipoint LOD scores > 1.5 in our initial genome scan, we genotyped a set of 31 microsatellite markers in an expanded sample of 79 pedigrees (with 53 pedigrees previously reported and 26 new pedigrees) that contained 1816 subjects, narrowing the average intermarker genomic distance from approximately 10 cM to approximately 5 cM. Based on two-point and multipoint linkage analyses, our studies support that chromosomes 1p36 and 2q14 may harbor QTLs for BMI variation, whereas chromosome 6q27 may harbor QTL for fat mass variation. However, linkage peaks at other loci were generally reduced or even disappeared, reflecting the difficulties and challenges that face the genetic mapping of complex traits.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Subjects

The study subjects came from an expanding database being created for studies to search for genes underlying the risk to osteoporosis and obesity in the Osteoporosis Research Center of Creighton University. All the study subjects were Caucasians of European origin. We adopted an exclusion criterion that had been elaborated before (5). In brief, subjects with diseases, treatments, or conditions that would be a nongenetic cause for low bone mass were excluded. Our initial genome scan (5) was based on 630 subjects from 53 multigenerational pedigrees. In the follow-up study, all the subjects in the initial genome scan were included in the analyses. In addition, 128 additional subjects who were members of the previous 53 pedigrees were included, resulting in a total of 758 subjects in the 53 pedigrees. We recruited 26 new pedigrees consisting of 1058 subjects. Thus, our total sample comprises 79 pedigrees with 1816 subjects. The pedigrees vary in size from four to 416 individuals, with a mean of 31.9 individuals (SD =; 48.9; Table 1). Each pedigree was identified through a proband having bone mineral density (BMD) Z score of either -1.28 or +1.28 at the hip or spine. Among them, 50 pedigrees were ascertained through probands having extremely low BMD (Z score < -1.28 at the hip or spine), 25 pedigrees were ascertained through extremely high BMD (Z score > +1.28 at the hip or spine), and four pedigrees were ascertained without regard to BMD. BMD values expressed as Z scores adjust for age, gender, and ethnic difference in general healthy referent populations. Our ascertainment scheme through the proband of extremely low or high BMD may offer higher statistical power to identify QTLs for BMD than would be achieved through random sampling (8, 9). Because BMD and BMI are significantly correlated (5), the sampling scheme of our study pedigrees for detecting QTLs for BMD variation may also have a similar effect (if any) on BMI and related obesity phenotypes. Actually, the probands having extremely low BMD tend to have BMI that are lower than the average in their pedigrees, whereas the probands having extremely high BMD tend to have BMI that are higher than the average in their pedigrees (see Results).

The procedures of genotyping have been described previously (5). In this study, we genotyped 31 microsatellite markers (six markers from our previous study) flanking six regions (i.e. 1p36, 2q14, 4q12, 6q27, 12q14, and 20q13) in which multipoint LOD scores greater than 1.5 were attained in the initial genome scan, narrowing the average space from approximately 10 cM to approximately 5 cM. PCR cycling conditions followed those suggested in the ABI PRISM Linkage Mapping Sets Version 2.5 (Applied Biosystems, Foster City, CA). Genotyping was performed using ABI PRISM 3700 DNA Analyzer running the Genescan and GENOTYPER softwares (Applied Biosystems) for allele identification and sizing. Because the genotyping system has been upgraded from ABI PRISM 377 DNA Sequencer to ABI PRISM 3700 DNA Analyzer in the present study, we retyped the markers that were used in the initial genome scan for the previous sample (53 pedigrees), with a purpose to avoid potential problems introduced by binning adjustment due to systemic change and to keep the genotyping data consistent. A genetic database management system (GenoDB) (10) was used to manage the phenotype and genotype data, as well as allele binning. PedCheck (available at http://watson.hgen.pitt.edu/register/soft_doc.html) was used to check the conformation of Mendelian inheritance pattern at all the marker loci and the relationships of family members within pedigrees. The genotyping data set was 99.7% complete, with 0.3% genotypes missing due to PCR failure, unsolved PedCheck errors, and microsatellite mutation.

Measurement

BMI was recorded as body weight (in kilograms) divided by the square of height (in meters). Fat mass and lean mass were measured by DXA. As mentioned by Deng et al. (5), in our initial genome scan, only 289 subjects from 38 pedigrees had the data of fat mass, PFM, and lean mass. In the present study, all 1058 subjects from the 26 newly recruited pedigrees and 128 additional subjects who are members of the previous 53 pedigrees have the data on fat mass, PFM, and lean mass. The PFM is recorded as the ratio of fat mass to body weight, which is obtained from DXA (i.e. the sum of fat mass, lean mass, and bone mass).

Statistical analyses

The statistical analyses have been detailed previously (5). In brief, a variance component linkage analysis (11, 12) for quantitative traits was used. Using SOLAR program version 1.7 (12), we performed two- and multipoint linkage analyses in the expanded 53 pedigrees, the 26 new pedigrees, and the 79 total pedigrees, respectively. The pedigree-ascertainment scheme, based on the low or high BMD values of probands, leads to corresponding lower-than-average or higher-than-average BMI values (see Results). The ascertainment scheme is accounted for in the analyses by identifying the probands and their phenotypic values for each pedigree. The built-in modules of the SOLAR program will then be able to account for the ascertainment scheme by using conditional likelihood in LOD score computation. The updated version of SOLAR (version 2.0) has the function of evaluating genetic heterogeneity by incorporating the program HOMO. HOMO performs a test for heterogeneity using an admixture model (13) and is, in principle, quite similar to Jurg Ott’s program HOMOG (14). In this study, loci achieving LOD scores more than 2.0 in the analyses of the 79 total pedigrees were analyzed for linkage heterogeneity using the HOMO program.

Before linkage analyses, the effects of covariates (sex and age) on obesity phenotype were evaluated in polygenic models, with only the significant covariates (P 0.05) being retained in the model and included in the linkage analyses. SOLAR is a flexible tool that can accommodate the effect of a common household in the polygenic model. This may increase statistical power to detect linkage by decreasing the proportion of the residual phenotypic variation attributable to random environmental factors. However, because the information of household in our large pedigrees is incomplete, it was not included in the analyses. Because we have some large multigenerational pedigrees, generation may also be incorporated in the analyses as a covariate. Generally, the variance component analyses implemented in SOLAR are quite robust to deviation of normality (15). However, some types of nonnormality of the data (e.g. extreme leptokurtosis of two or more) may inflate the type 1 error rate (16). For our total sample (79 pedigrees), the values of skewness of BMI, fat mass, PFM, and lean mass are 0.68, 0.83, 0.08, and 0.41, respectively, and values of kurtosis of BMI, fat mass, PFM, and lean mass are 0.56, 1.00, -0.57, and -0.73, respectively. These values are quite similar to those of the original 53 pedigrees and 26 new pedigrees (data not shown).

In SOLAR, a multipoint approximation for pedigrees of unlimited size and complexity was developed that uses a linear function of identity by descent (IBD) values at genotyped markers to estimate IBD sharing at arbitrary chromosomal locations (12). The IBD estimation procedure has been shown to be efficient and compares favorably to other multipoint methods suitable for use in pedigrees (12). So far, there have been few software programs capable of handling large and complex pedigrees (>200 individuals in a pedigree) with regard to multipoint linkage analysis. SimWalk, a statistical genetics computer application that uses Markov chain Monte Carlo and simulated annealing algorithms to perform linkage analysis, is able to deal with pedigree of any size (17). To investigate the potential difference between the methods implemented in the two computer programs, we used SimWalk to perform linkage analyses and compared the results with the results from SOLAR.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The basic characteristics of the study subjects for BMI, fat mass, PFM, and lean mass are summarized in Table 2. The BMI value is significantly lower (P < 0.01) in the probands who have extremely low BMD, whereas the BMI value is significantly higher (P < 0.01) in the probands who have extremely high BMD. Information about the family structure of the study pedigrees is outlined in Table 3. The 79 total pedigrees provide a large number of relative pairs that are informative for linkage analyses. For BMI, there are 3,846 sibling pairs, 3,718 grandparent-grandchild pairs, 7,170 avuncular pairs, and 10,916 first cousin pairs; for fat mass, PFM, and lean mass, there are 3,304 sibling pairs, 3,276 grandparent-grandchild pairs, 6,278 avuncular pairs, and 9,722 first cousin pairs. It is notable that the number of informative relationships in the 26 new pedigrees is much larger than that in the original 53 pedigrees. This is mainly due to a large pedigree composed of 416 subjects (with six generations), which also accounts for the dramatic increase of the second cousin pairs (one removed; Table 3). The estimated heritabilities (± SE) of BMI, fat mass, PFM, and lean mass in the total 79 pedigrees are 0.41 (± 0.04), 0.53 (± 0.05), 0.54 (± 0.05), and 0.58 (± 0.04), respectively, after adjusting for significant covariates (age and sex). The correlations between BMI and spine and hip BMD are 0.25 and 0.38, respectively (both significant at P < 0.01).

View larger version (16K): [in this window] [in a new window]   FIG. 1. Multipoint linkage analysis results for chromosome 1 (A) and chromosome 2 (B) in different subsamples. The dashed line represents the 53 expanded pedigrees with 128 new individuals; the dot-dash line represents the 26 new pedigrees; and the solid black line represents the total 79 pedigrees.

For fat mass, marginal evidence of linkage was observed at 6q27 in the 79 total pedigrees. A LOD of 1.24 was achieved near marker D6S11696 in two-point analysis, and an MLS of 0.92 was achieved at 6q27 in multipoint analysis. For PFM, linkage peaks diminished dramatically at 2q14, with LOD scores less than 1.0 in all of the three sample sets. A similar tendency was observed for lean mass.

Because the largest pedigree contained 416 subjects with six generations, we performed additional linkage analyses in this family but found no significant evidence of linkage for any of the obesity phenotypes. We also performed separate linkage analyses in the subsample that excludes the 416 subjects. We found that the results were in perfect agreement with that of the expanded 53 pedigrees (data not shown).

Incorporating generation as a covariate in the analyses slightly changed the results. To keep the results comparable with our previous whole-genome linkage scan, the results were presented without considering it in the analyses. We also performed linkage analysis using the program SimWalk and compared the results with that of SOLAR. We found no significant differences between them, and the linkage results demonstrated reasonable consistency (data not shown).

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
As a complex disease, obesity is characterized by convincing evidence of a genetic component, strong environmental effects, uncertain mode of inheritance, and potential multiple genes of varying effect that act independently and/or cooperatively (2). The arduous task of identifying the susceptibility genes of obesity is thus complicated by the fact that the interaction of these factors results in a variety of disease etiologies. This complex situation may account for why, despite a number of genome-wide scans for obesity during the past decade, the success of the identification of a major susceptibility gene to date has been limited, as reflected by the largely inconsistent results across different studies (3, 4). Therefore, significant or suggestive linkage claims should be subjected to extensive extension or replication studies to test for validity. A stringent, although widely accepted, threshold of LOD greater than 3.3 has been proposed to claim significant linkage with a genome-wide P < 0.05 (19). However, confirmation of a previously reported linkage in an independent study involves P value criterion different from a whole-genome scan. It has been proposed that a point-wise P value of approximately 0.01 is needed for an interval-wide significance level of 5% (18). In this study, we attempted to confirm our previous findings (5) in an expanded sample with denser markers spanning several putative regions. A LOD score of 1.0 (P = 0.016) was adopted as a criterion for tentative evidence of confirmation.

It is known that the power and the robustness of the linkage results critically rely on the study sample sizes used, among several other factors. Compared with the commonly used sibling-pair linkage approach (20), the variance components method used in the present study provides a more powerful test, which can be expanded to handle pedigrees of arbitrary size and complexity (21). Generally, the larger and more complex the sample, the more powerful the method. Compared with our initial genome scan, the present study sample has been expanded greatly; and accordingly, the numbers of informative relative pairs have been enhanced greatly. This large sample allows us to perform extension studies in the expanded 53 pedigrees (758 subjects) and the 79 total pedigrees, as well as independent replication analyses in the 26 newly recruited pedigrees.

Our study sample was initially recruited based on BMD with the purpose of efficiently locating genes underlying BMD (9), and BMD and body weight are known to be correlated (22). Because bone mass is a part of body mass, BMD and BMI may also share some genes underlying their variation, although the significance of these shared genetic determinations is unknown. In linkage analyses, the ascertainment scheme has been accounted for in the analyses by identifying the probands and their phenotypic values for each pedigree. As we know, a significant and high phenotypic correlation does not necessarily imply or translate into a significant and high genetic correlation, which indexes the degree of shared genetic determination of two phenotypes. This has been demonstrated by our earlier whole-genome linkage scans for BMD (9) and obesity phenotypes (5), in which the significant genomic regions for BMD vs. BMI were largely nonoverlapped. However, when two correlated traits show linkage to the same region, it is necessary to differentiate between pleiotropic effects of a single locus influencing all the traits and separate tightly clustered loci each influencing a single trait. In this case, bivariate quantitative trait linkage analysis is proposed to be pursued (23).

Based on our analyses, chromosomes 1p36 and 2q14 are the most promising regions that are likely to harbor QTLs for BMI variation. In a whole-genome scan, Stone et al. (24) reported suggestive linkage to severe obesity in females at 1p36, with a multipoint heterogeneity LOD score of 2.5 achieved near marker D1S468. In another study in Pima Indians, Norman et al. (25) reported linkage to percentage body fat at 1p31–21 (LOD = 2.8), a region adjacent to 1p36. The chromosome 1p36 contains an important candidate gene TNF2, the allelic polymorphisms of which were associated with obesity, leptin levels, and insulin resistance (26). The leptin receptor gene, LEPR, another prominent candidate for obesity, maps to this region. Polymorphisms of the LEPR gene have been associated with variation in obesity phenotypes in various populations (27, 28, 29). The NR0B2 (nuclear receptor subfamily 0, group B, member 2) gene is also located at 1p36, and mutations of NR0B2 were related with early-onset mild obesity (30). In addition, evidence of linkage was revealed between the 6-phosphogluconate dehydrogenase locus at 1p36 and suprailiac skinfolds (31). Chromosome 2q14 seems to be a novel locus that has not been replicated by other linkage studies in humans. Nonetheless, a recent HERITAGE Family Study in black and white families reported linkage to abdominal visceral fat at 2q22.1, a region that is approximately 20 cM apart from 2q14 (32). Furthermore, the importance of this region to obesity is likely to be corroborated by several mouse studies, which suggested the QTLs influence body weight and fatness in regions homologous to human chromosome 2q (3).

Chromosome 6q27 appears to be a promising region that may harbor a QTL underlying variation of fat mass. A recent study was performed on eight insulin resistance syndrome-related phenotypes using phenotypic data from 261 nondiabetic subjects distributed across 27 Mexican-American extended families (33). Using principal component factor analyses, they found significant evidence of linkage for adiposity-insulin factor 1 (BMI, leptin, and fasting-specific insulin) to two regions on chromosome 6p near markers D6S403 (LOD = 4.2) and D6S264 (LOD = 4.9), which are close to 6q27. Moreover, several earlier QTL mapping studies from animal models suggested regions homologous to human chromosome 6q25–27 (34). One of the strong candidate genes located within this region is estrogen receptor-. Genetic polymorphism at the restriction enzyme PvuII site of the estrogen receptor- gene was associated with BMI variation in a sample of postmenopausal Caucasian women (35). Other potential candidate genes within this region may include IGF2R and acetyl-coenzyme A acetyltransferase 2.

Several issues need to be addressed in the present study. It is noted that the strongest evidence of confirmation comes from the analyses in the expanded 53 pedigrees, whereas the linkage peaks reduce substantially in the analyses of the 26 new pedigrees. This seems to be unexpected, given the large amount of relationships and thus substantially increased statistical power of the 26 new pedigrees. There exists several potential explanations. First, our study subjects of European-origin Caucasians, although relatively homogeneous, may allow for potential population heterogeneity. In this context, it is possible that the QTLs identified in the previous pedigrees may not exist or only have minor effects in the new pedigrees and thus are hard to detect; or linkage findings detected in the initial genome scan may involve a weak effect, which turns out to be weaker in the follow-up study. Our simulations show that, assuming the total heritability of the phenotype to be 60%, the total sample (79 pedigrees) provides strong power (95%) to detect linkage (at LOD > 3.0) for a QTL accounting for 25% or more of the total trait variance. With this fairly high power, it is unlikely that the loss of linkage signals is simply due to statistical fluctuation. Instead, population heterogeneity is probably one of the potential reasons for failure of replication in the 26 new pedigrees. Second, compared with the previous sample (53 pedigrees), the newly recruited sample (26 pedigrees) comprises more complex families, in which more than 60% of them are made up by pedigrees with at least 85 family numbers. Among these, a large pedigree (with six generations) that contains 416 genotyped individuals may have significant impact on linkage results, as reflected by the separate analyses in this pedigree and the remaining subsample excluding the 416 subjects. This pedigree could share some nongenetic factors that could contribute to the phenotypes in consideration that have not been adjusted for in the statistical analysis. Third, several key individuals who are involved in a lot of pairs and have an extreme value in pedigrees may have a substantial impact on the results of linkage analysis. We found that the removal of these individuals from the analyses, to some extent, changed the LOD scores.

For BMI, the LOD score achieved on chromosome 1p36 in multipoint analysis (LOD = 1.34) is lower than the score achieved in two-point analysis (LOD = 2.34). This difference is also seen for some other genomic regions in this study, such as for fat mass at 6q27. Actually, this observation is not uncommon in linkage studies. Compared with two-point linkage analysis, multipoint linkage analysis, which combines marker information in the form of haplotypes, may increase the power to detect linkages and decrease the false-positive rate (12). When linkage is detected, multipoint analysis also allows support or confidence intervals to be determined for the location of a gene (12). However, several issues (e.g. the greater dependence on precise and accurate marker locations and more devastating effects of marker-locus genotyping errors, etc.) may compromise these advantages. In gene-mapping studies, genotyping errors may deflate the power and underestimation of the strength of correlation between trait- and marker-locus genotypes. In two-point analysis, these errors can be absorbed in an inflated recombination-fraction estimate, leaving the test statistic quite robust. In multipoint analysis, however, genotyping errors can easily result in false exclusion of the true location of a disease-predisposing gene (36, 37). In addition, in multipoint analysis, marker-locus maps must be assumed to be known accurate, although, in reality, fine-scale intermarker recombination fractions cannot be accurately estimated without thousands of informative meioses (36, 37). The position of markers on commonly used genetic maps (e.g. Marshfield) (38) are based on a limited number of meioses (39), which may lead to incorrect marker order and poor estimates of recombination fractions (40, 41). As a consequence, multipoint LOD scores could be lower than two-point LOD scores in the presence of linkage, despite the use of more information in the multipoint analysis.

In the follow-up study, the LOD scores obtained are generally lower than the scores of the initial genome scan. Theoretically, LOD scores can be added across studies but only when they are computed by the same method, with the same set of markers, and at the same map position (19). In the present study, the putative intervals are saturated with additional markers, which may affect the LOD scores. In addition, with more individuals (especially founders in the same family) genotyped, the IBD sharing of two relative pairs can be calculated with higher precision, which ultimately influences the LOD scores (36). Furthermore, genotyping errors may lead to the loss of power and underestimation of the strength of correlation between trait- and marker-locus genotypes (37). Despite the low genotyping error rate (0.3%) attained in our initial genome scan and follow-up study, the distributions of the errors might be altered, which unavoidably changes the LOD scores. The exact relevance of these issues to linkage analyses is one of our future interests in statistical genetics.

Location variation has been observed in this study. For example, in our initial scan, a peak MLS of 4.44 was achieved at 128 cM on chromosome 2 in multipoint analysis, whereas the peak MLS of 3.93 was attained at 115 cM in the follow-up study. This is not unusual in gene mapping of complex traits, as exemplified in linkage studies of type 1 diabetes, in which two studies reported evidence of a susceptibility locus on genomic region 14q, but the location estimates were approximately 33 cM apart (42, 43). Some studies indicate that, in independent replication studies, chance variation in the location estimate is substantial, even with relatively large numbers of multiplex families (44).

For PFM and lean mass, the present study did not lend strong support to our previous findings. As mentioned by Deng et al. (5), in our initial linkage scan, only 289 subjects from 38 pedigrees had the phenotypic data of fat mass, PFM, and lean mass. This modest sample size renders the linkage analysis of limited statistical power, which may result in false-positive findings or the missing of true linkages. Because the linkage approach adopted in this study is intended to detect linkage instead of linkage exclusion, the relevance of these regions to fat mass, PFM, and lean mass awaits future exclusion analyses and/or confirmation studies in larger samples. In gene-mapping studies of complex traits, due to polygenic inheritance, some QTLs may be detected in a whole-genome scan even if the power is low. However, to detect linkage of a specific QTL (e.g. to replicate a previous linkage finding) is a much harder task; even some well-designed, large-scale replication studies may not repeat prior true-positive findings (45, 46). It is likely that the robustness of linkage results and the power of linkage studies for complex traits generally require sample sizes far larger than those currently used. Our next effort of searching for QTLs underlying obesity will be performing a whole-genome linkage scan in our enlarged sample (the 79 total pedigrees, in which 400 microsatellite markers spaced at 10 cM on human genome will be genotyped for all the 1816 subjects). It would be interesting in the near future to compare the results to see whether the additional linkage peaks will be identified on the initial genome scan of the 53 pedigrees.

In summary, in a follow-up study, we confirmed some of our previous linkage findings for obesity phenotypes. In the meantime, the complexity of the genetic basis of obesity and the difficulties facing genetic mapping of complex traits are also implicated. Recently, topics such as "Genome-Wide Scans of Complex Human Diseases: True Linkage Is Hard to Find" (47) and "Limits of Fine-Mapping a Quantitative Trait" (48) have been receiving more attention. In the future, it is likely that endeavors from multidisciplinary approaches incorporating knowledge of pathophysiology, biochemistry, genetic epidemiology, genomics, expression profiling, and bio-informatics will eventually unravel the molecular determinants of obesity.

	Footnotes

 
This work was supported in part by grants from Health Future Foundation, National Institutes of Health Grants K01 AR02170-01, R01 AR45349-01, R01 GM60402-01A1, and P01 DC01813-07, grants from the State of Nebraska Cancer and Smoking Related Disease Research Program and the State of Nebraska Tobacco Settlement Fund, United States Department of Energy Grant DE-FG03-00ER63000/A00, Creighton University, grants from the National Science Foundation of China, a grant from the Huo Ying Dong Education Foundation, and grants from HuNan Normal University and the Ministry of Education of China.

Abbreviations: BMD, Bone mineral density; BMI, body mass index; DXA, dual x-ray absorptiometry; IBD, identity by descent; MLS, maximum LOD score; PFM, percentage fat mass; QTL, quantitative trait locus.

Received April 30, 2003.

Accepted October 20, 2003.

	References

Top Abstract Introduction Subjects and Methods Results Discussion References

 

1. Comuzzie AG, Allison DB 1998 The search for human obesity genes. Science 280:1374–1377[Abstract/Free Full Text]
2. Barsh GS, Farooqi IS, O’Rahilly S 2000 Genetics of body-weight regulation. Nature 404:644–651[Medline]
3. Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ, Perusse L, Bouchard C 2003 The human obesity gene map: the 2002 update. Obes Res 11:313–367[Medline]
4. Liu YJ, Araujo S, Robert R, Deng HW 2003 Molecular and genetic mechanisms of obesity: implications for future management. Curr Mol Med 3:345–360
5. Deng HW, Deng H, Liu YJ, Liu YZ, Xu FH, Shen H, Conway T, Li JL, Huang QY, Davies KM, Recker RR 2002 A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet 70:1138–1151[CrossRef][Medline]
6. Fernandez JR, Heo M, Heymsfield SB, Pierson Jr RN, Pi-Sunyer FX, Wang ZM, Wang J, Hayes M, Allison DB, Gallagher D 2003 Is percentage body fat differentially related to body mass index in Hispanic Americans, African Americans, and European Americans? Am J Clin Nutr 77:71–75[Abstract/Free Full Text]
7. Blew RM, Sardinha LB, Milliken LA, Teixeira PJ, Going SB, Ferreira DL, Harris MM, Houtkooper LB, Lohman TG 2002 Assessing the validity of body mass index standards in early postmenopausal women. Obes Res 10:799–808[Medline]
8. Risch N, Zhang H 1995 Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science 268:1584–1589[Abstract/Free Full Text]
9. Deng HW, Xu FH, Huang QY, Shen H, Deng H, Conway T, Liu YJ, Liu YZ, Li JL, Zhang HT, Davies KM, Recker RR 2002 A whole-genome linkage scan suggests several genomic regions potentially containing quantitative trait loci for osteoporosis. J Clin Endocrinol Metab 87:5151–5159[Abstract/Free Full Text]
10. Li JL, Deng HY, Lai DB, Recker RR, Deng HW 2001 Towards high-throughput genotyping: a dynamic and automatic software for manipulating large-scale genotype data using fluorescently labeled dinucleotide markers. Genome Res 11:1304–1314[Abstract/Free Full Text]
11. Amos CI 1994 Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54:535–543[Medline]
12. Almasy L, Blangero J 1998 Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211[CrossRef][Medline]
13. Smith CAB 1963 Testing heterogeneity of recombination fraction values in human genetics. Ann Hum Genet 27:175–182[Medline]
14. Ott J 1983 Linkage analysis and family classification under heterogeneity. Ann Hum Genet 47:311–320[Medline]
15. Williams JT, Van Eerdewegh P, Almasy L, Blangero J 1999 Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I Likelihood formulation and simulation results. Am J Hum Genet 65:1134–1147[CrossRef][Medline]
16. Allison DB, Neale MC, Zannolli R, Schork NJ, Amos CI, Blangero J 1999 Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci mapping procedure. Am J Hum Genet 65:531–544[CrossRef][Medline]
17. Sobel E, Lange K 1996 Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker sharing statistics. Am J Hum Genet 58:1323–1337[Medline]
18. Whittemore AS, Halpern J 2001 Problems in definition, interpretation, and evaluation of genetic heterogeneity. Am J Hum Genet 68:457–465[CrossRef][Medline]
19. Lander E, Kruglyak L 1995 Genetic dissection of complex traits: guidelines for interpreting the reporting results. Nat Genet 11:241–247[CrossRef][Medline]
20. Haseman JK, Elston RC 1972 The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 2:3–19[CrossRef][Medline]
21. Williams JT, Blangero J 1999 Power of variance component linkage analysis to detect quantitative trait loci. Ann Hum Genet 63:545–563[CrossRef][Medline]
22. Deng HW, Chen WM, Conway T, Zhou Y, Davies KM, Stegman MR, Deng H, Recker RR 2000 Determination of bone mineral density of the hip and spine in human pedigrees by genetic and life-style factors. Genet Epidemiol 19:160–177[CrossRef][Medline]
23. Almasy L, Dyer TD, Blangero J 1997 Bivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages. Genet Epidemiol 14:953–958[CrossRef][Medline]
24. Stone S, Abkevich V, Hunt SC, Gutin A, Russell DL, Neff CD, Riley R, Frech GC, Hensel CH, Jammulapati S, Potter J, Sexton D, Tran T, Gibbs D, Iliev D, Gress R, Bloomquist B, Amatruda J, Rae PM, Adams TD, Skolnick MH, Shattuck D 2002 A major predisposition locus for severe obesity, at 4p15–p14. Am J Hum Genet 70:1459–1468[CrossRef][Medline]
25. Norman RA, Tataranni PA, Pratley R, Thompson DB, Hanson RL, Prochazka M, Baier L, Ehm MG, Sakul H, Foroud T, Garvey WT, Burns D, Knowler WC, Bennett PH, Bogardus C, Ravussin E 1998 Autosomal genomic scan for loci linked to obesity and energy metabolism in Pima Indians. Am J Hum Genet 62:659–668[CrossRef][Medline]
26. Fernandez-Real JM, Vendrell J, Ricart W, Broch M, Gutierrez C, Casamitjana R, Oriola J, Richart C 2000 Polymorphism of the tumor necrosis factor- receptor 2 gene is associated with obesity, leptin levels, and insulin resistance in young subjects and diet-treated type 2 diabetic patients. Diabetes Care 23:831–837[Abstract/Free Full Text]
27. Chagnon YC, Wilmore JH, Borecki IB, Gagnon J, Perusse L, Chagnon M, Collier GR, Leon AS, Skinner JS, Rao DC, Bouchard C 2000 Associations between the leptin receptor gene and adiposity in middle-aged Caucasian males from the HERITAGE family study. J Clin Endocrinol Metab 85:29–34[Abstract/Free Full Text]
28. Quinton ND, Lee AJ, Ross RJ, Eastell R, Blakemore AI 2001 A single nucleotide polymorphism (SNP) in the leptin receptor is associated with BMI, fat mass and leptin levels in postmenopausal Caucasian women. Hum Genet 108:233–236[CrossRef][Medline]
29. Yiannakouris N, Yannakoulia M, Melistas L, Chan JL, Klimis-Zacas D, Mantzoros CS 2001 The Q223R polymorphism of the leptin receptor gene is significantly associated with obesity and predicts a small percentage of body weight and body composition variability. J Clin Endocrinol Metab 86:4434–4439[Abstract/Free Full Text]
30. Nishigori H, Tomura H, Tonooka N, Kanamori M, Yamada S, Sho K, Inoue I, Kikuchi N, Onigata K, Kojima I, Kohama T, Yamagata K, Yang Q, Matsuzawa Y, Miki T, Seino S, Kim MY, Choi HS, Lee YK, Moore DD, Takeda J 2001 Mutations in the small heterodimer partner gene are associated with mild obesity in Japanese subjects. Proc Natl Acad Sci USA 98:575–580[Abstract/Free Full Text]
31. Wilson AF, Elston RC, Tran LD, Siervogel RM 1991 Use of the robust sib-pair method to screen for single-locus, multiple-locus, and pleiotropic effects: application to traits related to hypertension. Am J Hum Genet 48:862–872[Medline]
32. Rice T, Chagnon YC, Perusse L, Borecki IB, Ukkola O, Rankinen T, Gagnon J, Leon AS, Skinner JS, Wilmore JH, Bouchard C, Rao DC 2002 A genomewide linkage scan for abdominal subcutaneous and visceral fat in black and white families: The HERITAGE Family Study. Diabetes 51:848–855[Abstract/Free Full Text]
33. Arya R, Blangero J, Williams K, Almasy L, Dyer TD, Leach RJ, O’Connell P, Stern MP, Duggirala R 2002 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[Abstract/Free Full Text]
34. Taylor BA, Phillips SJ 1997 Obesity QTLs on mouse chromosome 2 and 17. Genomics 43:249–257[CrossRef][Medline]
35. Deng HW, Li J, Li JL, Johnson M, Dowd R, Gong G, Deng H, Recker RR 2000 Association of estrogen receptor-alpha (ER) genotypes with body mass index in normal healthy postmenopausal caucasian women. J Clin Endocrinol Metab 85:2748–2751[Abstract/Free Full Text]
36. Goring HH, Terwilliger JD 2000 Linkage analysis in the presence of errors III: marker loci and their map as nuisance parameters. Am J Hum Genet 66:1298–1309[CrossRef][Medline]
37. Goring HH, Terwilliger JD 2000 Linkage analysis in the presence of errors II: marker-locus genotyping errors modeled with hypercomplex recombination fractions. Am J Hum Genet 66:1107–1118[CrossRef][Medline]
38. Broman KW, Murray JC, Sheffield VC, White RL, Weber JL 1998 Comprehensive human genetic maps: individual and sex-specific variation in recombination. Am J Hum Genet 63:861–869[CrossRef][Medline]
39. Yu A, Zhao C, Fan Y, Jang W, Mungall AJ, Deloukas P, Olsen A, Doggett NA, Ghebranious N, Broman KW, Weber JL 2001 Comparison of human genetic and sequence-based physical maps. Nature 409:951–953[CrossRef][Medline]
40. Terwilliger JD, Ding Y, Ott J 1992 On the relative importance of marker heterozygosity and intermarker distance in gene mapping. Genomics 13:951–956[CrossRef][Medline]
41. Leal SM 2003 Genetic maps of microsatellite and single-nucleotide polymorphism markers: are the distances accurate? Genet Epidemiol 24:243–252[CrossRef][Medline]
42. Field LL, Tobias R, Thomson G, Plon S 1996 Susceptibility to insulin-dependent diabetes mellitus maps to a locus (IDDM11) on human chromosome 14q24.3-q31. Genomics 33:1–8[CrossRef][Medline]
43. Mein CA, Esposito L, Dunn MG, Johnson GC, Timms AE, Goy JV, Smith AN, Sebag-Montefiore L, Merriman ME, Wilson AJ, Pritchard LE, Cucca F, Barnett AH, Bain SC, Todd JA 1998 A search for type 1 diabetes susceptibility genes in families from the United Kingdom. Nat Genet 19:297–300[CrossRef][Medline]
44. Roberts SB, MacLean CJ, Neale MC, Eaves LJ, Kendler KS 1999 Replication of linkage studies of complex traits: an examination of variation in location estimates. Am J Hum Genet 65:876–884[CrossRef][Medline]
45. Suarez BK, Hampe CL, Van Eerdewegh P 1994 Problems of replicating linkage claims in psychiatry. In: Gershon ES, Cloninger CR, eds. Genetic approaches to mental disorders. Washington, DC: American Psychiatric Press; 23–46
46. Deng HW, Xu FH, Conway T, Deng XT, Li JL, Davies KM, Deng H, Johnson M, Recker RR 2001 Is population bone mineral density variation linked to the marker D11S987 on chromosome 11q12–13? J Clin Endocrinol Metab 86:3735–3741[Abstract/Free Full Text]
47. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M 2001 Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 69:936–950[CrossRef][Medline]
48. Atwood LD, Heard-Costa NL 2003 Limits of fine-mapping a quantitative trait. Genet Epidemiol 24:99–106[CrossRef][Medline]

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