Tests of linkage and/or association of the LEPR gene polymorphisms with obesity phenotypes in Caucasian nuclear families
Yong-Jun Liu1,2,
Sonia M.S. Rocha-Sanchez1,
Peng-Yuan Liu1,
Ji-Rong Long1,
Yan Lu1,
Leo Elze1,
Robert R. Recker1 and
Hong-Wen Deng1,2,3
1 Osteoporosis Research Center, Creighton University, Omaha, Nebraska 68131
2 Department of Biomedical Sciences, Creighton University, Omaha, Nebraska 68131
3 Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, Peoples Republic of China
 |
ABSTRACT
|
---|
Genetic variations in the leptin receptor (LEPR) gene have been conceived to affect body weight in general populations. In this study, using the tests implemented in the statistical package QTDT, we evaluated association and/or linkage of the LEPR gene with obesity phenotypes in a large sample comprising 1,873 subjects from 405 Caucasian nuclear families. Obesity phenotypes tested include body mass index (BMI), fat mass, percentage fat mass (PFM), and lean mass, with the latter three measured by dual-energy X-ray absorptiometry (DXA). Three single nucleotide polymorphisms (SNPs), namely Lys109Arg (A/G), Lys656Asn (G/C), Pro1019Pro (G/A), in the LEPR gene were analyzed. Significant linkage disequilibrium (0.394
|D'|
0.688, P < 0.001) was observed between pairs of the three SNPs. No significant population stratification was found for any SNP/phenotype. In single-locus analyses, evidence of association was observed for Lys656Asn with lean mass (P = 0.002) and fat mass (P = 0.015). The contribution of this polymorphism to the phenotypic variation of lean mass and fat mass was 2.63% and 1.15%, respectively. Subjects carrying allele G at the Lys656Asn site had, on average, 3.16% higher lean mass and 2.71% higher fat mass than those without it. In the analyses for haplotypes defined by the three SNPs, significant associations were detected between haplotype GCA (P = 0.005) and lean mass. In addition, marginally significant evidence of association was observed for this haplotype with fat mass (P = 0.012). No statistically significant linkage was found, largely due to the limited power of the linkage approach to detect small genetic effects in our data sets. Our results suggest that the LEPR gene polymorphisms contribute to variation in obesity phenotypes.
leptin receptor; quantitative transmission disequilibrium test
 |
INTRODUCTION
|
---|
THE DISCOVERY OF LEPTIN and its receptor, LEPR, has defined a novel molecular pathway for energy metabolism and regulation of body weight (14, 35, 42). Leptin is a fat tissue-derived cytokine that reports nutritional information and regulates energy expenditure by activating LEPR in the hypothalamus (14). LEPR is a single transmembrane protein signaling through the JAK-signal transducer and activator of transcription (STAT) pathway (7, 8, 35). Functional mutation in the LEPR gene in animal models and humans could cause extreme obesity (9, 20, 24). However, such mutations are extremely rare and are not likely to be responsible for common obesity. It is conceivable that common polymorphisms in the LEPR gene could modify the function of LEPR and lead to variation in leptin levels and body weight in the general population.
A number of common polymorphisms and rare variants of the human LEPR gene have been identified. Their potential associations with obesity were evaluated in different populations, largely using the population-based association approach (reviewed in Ref. 5). Up to the present, consistent associations have been difficult to demonstrate. For example, association between a polymorphism Gln223Arg and obesity phenotypes was observed in middle-aged Caucasian males (6), postmenopausal Caucasian women (29), a Mediterranean population (41), and young Dutch adults (38), but not in American Caucasians (33), Pima Indians (36), blacks (6), and Japanese (22). Linkage between LEPR gene locus and adiposity was reported in French Caucasians (4), whereas no linkage was detected in Pima Indians (26) and Mexican Americans (3). Our whole genome linkage scan in 53 Caucasian pedigrees (11) suggested a major quantitative trait locus (QTL) on chromosome 1p, a region harboring the LEPR gene. Our follow-up linkage study by genotyping denser markers in an expanded sample provided further support to this finding (21).
Motivated by the potential importance of LEPR gene in energy homeostasis and body weight regulation, yet with rather inconsistent results so far, we performed a study to test the LEPR gene as a QTL for obesity. We surveyed the literature and searched the public database for single-nucleotide polymorphisms (SNPs) in the LEPR gene. SNPs were selected based on a comprehensive consideration of 1) functional relevance and importance; 2) degree of heterozygosity, i.e., allele frequencies (we included those with minor allele frequencies >0.15 in the analyses); and 3) their use in previous genetic studies. We examined three highly polymorphic SNPs [i.e., Lys109Arg (exon 4), Lys656Asn (exon 14), and Pro1019Pro (exon 20)] and haplotypes defined by these for linkage and/or association with obesity phenotypes in a large sample with 1,873 subjects from 405 Caucasian nuclear families. Important to this study is that we employed a robust approach, the family-based transmission disequilibrium test (TDT), which is immune to population stratification, a potential confounding factor to population-based association studies (1, 34).
 |
MATERIALS AND METHODS
|
---|
Subjects
The study subjects came from an expanding database being created for studies to search for genes underlying the risk to osteoporosis and obesity at the Osteoporosis Research Center of Creighton University. The study was approved by the Creighton University Institutional Review Board. All subjects were Caucasians of European origin. Only healthy people were included in the study with the exclusion criteria that were detailed elsewhere (12). In brief, individuals having serious chronic diseases/conditions that may have potential influence on bone mass were excluded. These diseases/conditions have general influence on human endocrine and metabolism. Application of the exclusion criteria may help minimize, empirically, nongenetic influence on obesity phenotypes. No restrictions were imposed in terms of body weight or diet history. Individuals who were overweight [body mass index (BMI) > 25 kg/m2] or obese (BMI > 30 kg/m2) but permitted under the exclusion criteria entered the study. For each study subject, the information on age, sex, medical history, and family history was acquired. A total of 405 nuclear families comprising 1,873 subjects were recruited, including 740 parents, 744 daughters, and 389 sons. Among these, 341 families were composed of both parents and at least one offspring. In the remaining 64 families, there were at least two children with either one or no parent. The average family size was 4.62 ± 1.78 (mean ± SD), ranging from 3 to 12, and there were a total of 1,512 sib pairs. The descriptive characteristics of the study subjects are presented in Table 1.
Genotyping
DNA was extracted from whole blood using a commercial isolation kit (Gentra Systems, Minneapolis, MN) following the procedure detailed in the kit. The genotyping procedure for all SNPs was similar, involving polymerase chain reaction (PCR) and invader assay reaction (Third Wave Technology, Madison, WI). PCR was performed in a 10-µl reaction volume with 35 ng genomic DNA, 0.2 mM each dNTP, 1x PCR buffer and 1.5 mM MgCl2, 0.4 µM of each the primers, and 0.35 U Taq polymerase [Applied Biosystems (ABI), Foster City, CA]. The following procedure was used: 95°C for 5 min, 30 cycles of 94°C for 1 min, 50°C for 1 min, 72°C for 1 min, and then 72°C for 5 min. After amplification, an invader reaction was performed in a 7.5-µl reaction volume, with 3.75 µl diluted PCR product (1:20), 1.5 µl probe mix, 1.75 µl Cleavase FRET mix, and 0.5 µl Cleavase enzyme/MgCl2 solution (Third Wave Technology). The reaction mix was overlaid with 15 µl mineral oil and denatured at 95°C for 5 min, then incubated at 63°C for 20 min. All the PCR and invader assay reactions were performed on thermal cyclers (ABI model 9700). After the invader reaction, the fluorescence intensity for both colors (FAM dye and Red dye) was read using a multi-well plate reader (Cytofluor model 4000, ABI). The data were then loaded to the software Invader Analyzer (Third Wave Technology), and the genotype for every sample was called according to the ratio of the fluorescence intensity of the two dyes. PedCheck software (28) was used to verify Mendelian inheritance of the alleles within each family. The overall genotyping error and missing rate was
1%.
Measurement
Fat mass and lean mass were measured by dual-energy X-ray absorptiometry (DXA) with a scanner (model 2000+ or 4500; Hologic, Bedford, MA). Both machines were calibrated daily. The body composition bar was used on every whole body scan on the Hologic 2000+. On the Hologic 4500, the bar was not needed for the body scans; instead, it was scanned every week. Percentage fat mass (PFM) is the ratio of fat mass to body weight (i.e., the sum of fat mass + lean mass + bone mass). At the same visit as body composition scan, weight was measured in light indoor clothing, using a calibrated balance beam scale, and height was measured using a calibrated stadiometer. The measurement precision of BMI as reflected by the coefficient of variation was 0.2%. The coefficients of variation for fat mass, PFM, and lean mass were 2.2%, 2.2%, and 1.0%, respectively, for measurements obtained on the Hologic 2000+, and were 1.2%, 1.1%, and 0.7%, respectively, for measurements on the Hologic 4500. Members of the same nuclear family were usually measured on the same type of machine. Except for lean mass and fat mass, the phenotypes are correlated significantly. The phenotypic correlations were 0.88 (between BMI and fat mass), 0.49 (between BMI and lean mass), 0.54 (between BMI and PFM), 0.12 (between fat mass and lean mass), 0.81 (between fat mass and PFM), and 0.34 (between lean mass and PFM).
Statistical Analyses
Single-locus analyses.
The allele frequencies of each SNP were estimated in all the subjects of the nuclear families via a maximum-likelihood method implemented in the program SOLAR (available at http://www.sfbr.org/sfbr/public/software/solar).
Under the flexible variance-component framework, tests of population stratification, linkage, total association, and within-family association between each of the SNPs and obesity phenotypes were implemented in the statistical software package QTDT ("quantitative trait disequilibrium tests") (available at http://www.sph.umich.edu/csg/abecasis/QTDT/). The orthogonal model of Abecasis et al. (1) was adopted in the analyses, where the genotype score is decomposed into orthogonal between-family (ßb) and within-family (ßw) components. Population stratification is examined by testing whether ßb = ßw (15). Linkage tests are based on the standard variance-component methods and the identity by descent (IBD) among relatives. Total association tests use all information including ßb and ßw components and may yield false-positive/false-negative results due to population stratification. Within-family association (via TDT), however, is significant only if there is linkage disequilibrium (LD), and it is robust to population stratification. When both linkage and association are detected, Fulker et al. (15) suggested testing linkage while simultaneously modeling association to evaluate whether the tested marker is the functional mutation underlying the trait. When significant association is observed, the approximate phenotypic variation due to the detected marker is calculated as 2p(1 p)a2, where p is the allele frequency of the marker, and a is the estimate of additive effect, i.e., E(ßw) = a (Ref. 1). It is noteworthy that the trade-off for conducting the within-family association test is a reduction in power to detect allelic association (37). In the absence of population stratification, total association confers more power and can be more sensitive than within-family association to detect correlation between a marker and a specific trait (1). In light of such power consideration, we first tested population stratification. If no evidence of population stratification was observed, then the more powerful test of total association was utilized. Alternatively, an orthogonal model robust to population stratification was used.
Variance-component methods implemented in the QTDT make a critical assumption that the underlying trait follows a multivariate normal distribution. In this study, the four obesity phenotypes demonstrated departure from normal distribution and were transformed to approach normality via the Box-Cox procedure implemented in the statistical software Minitab (Minitab, State College, PA).
Haplotype analyses.
Haplotype reconstruction in the nuclear families was carried out using the program Genehunter version 2.1 (http://www.hgmp.mrc.ac.uk/Registered/Option/genehunter.html). Genehunter extracts complete multipoint inheritance information to infer maximum-likelihood haplotypes for all individuals in nuclear families (19). The SNPs were arranged in the order of Lys109Arg-Lys656Asn-Pro1019Pro in haplotype reconstruction. Haplotype frequencies were estimated using unrelated subjects only (parents from each nuclear family). Pair-wise LD between the SNPs was calculated by the normalized measure, D' (13). The statistical significance of the observed LD was examined by Monte-Carlo approximation of Fishers exact test (31). For QTDT haplotype analyses, we first utilized the QTDT multi-allelic tests option which include alleles in one test as a categorical variable with allele frequencies of <5% being pooled to give a global significance. Then we performed the allele-wise test on each allele separately, with all other alleles being pooled into another category. Employing the aforementioned sequential tests within QTDT,
2 and P values were computed for those haplotypes present in at least 30 informative offspring.
Correction for multiple testing.
Since multiple markers and phenotypes were analyzed, a correction for multiple testing is required. Bonferroni correction seems to be too conservative and potentially damaging to statistical power (27). This is because statistical tests here are highly correlated in that 1) SNPs in the LEPR gene are in significant LD (shown in the results below) and 2) the tested phenotypes are significantly correlated. As a less stringent yet suitable adjustment, Monte-Carlo permutation was performed to circumvent multiple testing (23). This method was proposed by McIntyre et al. (23) and has been incorporated in the QTDT. After 1,000 times of permutations, an empirical threshold, P
0.01, was established for an individual test to achieve a global significance level of 0.05. All the statistical analyses were performed on the transformed data with age and sex as covariates.
 |
RESULTS
|
---|
The frequencies of the alleles and haplotypes for the LEPR gene are presented in Table 2. The minor allele frequencies of Lys109Arg, Lys656Asn, and Pro1019Pro are 29.1%, 18.2%, and 35.3%, respectively. The heterozygosity of the three SNPs is 0.40, 0.26, and 0.47, respectively. No deviation from Hardy-Weinberg equilibrium was observed in the parents group. Significant LD (0.394
|D'|
0.688, P < 0.001) exists between pairs of three SNPs.
Table 3 presents a summary of the results of QTDT analyses. For single-locus analyses, since no population stratification was detected, we then performed total association tests. Evidence of total association, even after permutation tests, was observed for Lys656Asn with lean mass (P = 0.002) and fat mass (P = 0.015). The contribution of this polymorphism to the phenotypic variation of lean mass and fat mass was 2.63% and 1.15%, respectively. Subjects carrying allele G had, on average, 3.16% higher lean mass and 2.71% higher fat mass than those without it. We did not find significant evidence of linkage or within-family association at individual SNPs for each phenotype. Since no evidence of linkage between any of the SNPs and the phenotypes was detected, as was expected, no significant results for the tests of linkage while modeling association were found (data not shown).
View this table:
[in this window]
[in a new window]
|
Table 3. P values of tests of population stratification, total association, within-family association, and linkage for individual SNPs and the haplotypes
|
|
Haplotype analyses yielded interesting and compatible results (Table 3). All the eight possible haplotypes were observed, with the most common form of haplotype AGG accounting for almost half of the frequencies. In multi-allelic tests, no significant results were obtained for population stratification, linkage, or within-family association. We observed significant total associations for lean mass (P = 0.006) and marginally significant association for fat mass (P = 0.020). In allele-wise tests, consistent with multi-allelic tests, we did not find significant population stratification or linkage for any individual haplotypes. Significant total associations were detected between lean mass and haplotype GCA (P = 0.005). In addition, for fat mass, marginally significant evidence of total associations was observed with this haplotype (P = 0.012). For within-family association tests, although the significance for these haplotypes with obesity phenotypes was not observed, some evidence was still found for haplotype GCA (P = 0.061) with fat mass and lean mass (P = 0.085). The contribution of haplotype GCA to the phenotypic variation of lean mass and fat mass was 2.23% and 1.74%, respectively. Individuals carrying haplotype GCA showed, on average, 4.41% higher lean mass and 3.50% higher fat mass than those without it. For those unmentioned haplotypes, either no significant results were detected, or these were not tested because there were not sufficient informative subjects.
 |
DISCUSSION
|
---|
This is a nuclear family-based study using the TDT approach. The TDT directly tests for LD between a trait and a marker locus and is not susceptible to false-positive results due to population stratification (1, 34). The commonly used population-based association approach or case-control study, while valuable, are limited in that spurious associations may be yielded due to very recent admixture or population samples that are stratified with respect to genetically differentiated groups (10). The linkage approach, which may reveal genomic regions harboring QTLs without prior knowledge for underlying traits, is often of limited statistical power to detect genes of small effects (30, 32). The TDT is much more powerful compared with the traditional linkage approach in testing linkage of specific candidate genes to complex traits (2). Although the ideal study design for detecting SNP/phenotype association is currently debatable, the present study represents our efforts to test the importance of the LEPR gene on variation of obesity phenotypes by employing a robust approach.
Here, we report association for Lys656Asn with lean mass and fat mass. The variant Lys656Asn is located in the extracellular region of the LEPR gene. It causes a change in charge (lysine to asparagine at codon 656) and may have potential effects on the signaling capacity of LEPR. In this study, subjects carrying allele G had, on average, 3.16% higher lean mass and 2.71% higher fat mass than those without it. Our results are in agreement with that of the Quebec Family Study (4), in which female carriers of the C allele had 3 kg less of fat-free mass and 5 kg less of fat mass than noncarriers. In a group of overweight and obese postmenopausal Caucasian women, carriers of the C allele had increased hip circumference, total abdominal fat, and subcutaneous fat measured by computed tomography (CT) scan (39). Moreover, Lys656Asn was associated with fasting insulin and fasting glucose as well as in response to the oral glucose tolerance test in postmenopausal women with impaired glucose tolerance (40). These studies suggest that Lys656Asn may play a role in body weight regulation and may exert peripheral effects on regulation of glucose and insulin levels. It is noteworthy that the total association detected in this study should be robust and reliable, although we did not find correspondingly within-family association (via TDT). This is because total association confers more power than within-family association given the absence of population stratification. Our simulation shows that, assuming strong LD (|D'| = 0.9) between a marker and a functional mutation having 2% effect, we have more than 80% power to detect association via the TDT. However, this power is estimated under ideal conditions without taking into account of genetic interaction, heterogeneity, etc. Hence, significant total association could be detected without within-family association. In this study, further analyses in subsamples stratified by age or gender were not pursued, because of the consideration that results might be affected with the limited number of subjects remaining in subgroups.
Some studies, however, did not detect this allelic association. Negative results were reported for Lys656Asn in Caucasian populations (6, 33, 37, 41) and some other racial populations such as blacks (6) and Japanese (22). A meta-analysis suggested that there was no compelling evidence that any of the three polymorphisms (i.e., Lys109Arg, Gln223Arg, and Lys656Asn) are associated with BMI or waist circumference (17). The conflicting findings might arise from the inadequate power of small sample sizes, population structure, varying effects of disease-predisposing variants, or gene-environment interactions. For example, in a Mediterranean Caucasian population (41), the minor allele frequency of Lys656Asn (23.7%) was higher than that of our study population (18.2%). As we know, genetic variance due to a functional mutation is highly related with its allele frequency, and, generally, the more frequent the allele, the higher the heritability attributable to this functional mutation. In the HERITAGE Family Study (6), although the minor allele frequencies of Lys656Asn in Caucasians (18%) and blacks (17%) were comparable to ours, the analytical approach, sample sizes, and tested phenotypes were quite different. Finally, given the relatively small effects of a variant contributing to a complex trait, as reflected in our study (
2%), a potential association may be detected in some studies but missing in others.
Haplotype analysis, considering all of the variants segregating at multiple loci, can provide additional precision in genetic dissection of complex traits (25). However, in this study a potential limitation of the haplotype analyses is that only three SNPs were analyzed across a large gene spanning >200 kb. Nevertheless, as the three SNPs were in significant LD, haplotype analyses may still offer increased information. Indeed, in both multi-allelic and allele-wise tests, we detected significant total association, confirming the findings of single-locus analyses. Even for within-family association tests, suggestive evidence was found for haplotype GCA with fat mass (P = 0.061) and lean mass (P = 0.085).
We did not find linkage, which seems to be unexpected given the significant association detected. There are several possible reasons. First, the linkage approach is usually of limited statistical power with currently used sample sizes, especially in the case of small genetic effects. Second, SNPs as used in this study generally have lower heterozygosity and are less informative than microsatellite markers for linkage analyses. Assuming the overall heritability of a trait is 0.6 and the QTL effect is 2%, the power to detect linkage is only
20% with this study sample. Since the association approach is generally more powerful and sensitive than the linkage approach, association could be detected without linkage.
The effect of Lys656Asn on lean mass and fat mass in our study is not reflected on BMI and PFM, although these traits are phenotypically correlated. This may be partially due to the insufficient power to detect a specific genetic effect. On the other hand, a significant and high phenotypic correlation does not necessarily imply a significant and high genetic correlation (16), which indicates the degree of shared genetic determination of two phenotypes.
In summary, using a large sample and a robust analytical approach, this study provides support that the LEPR gene polymorphisms are associated with obesity phenotypes. Further studies by examining denser markers and determining LD patterns throughout the gene region are warranted to fully elucidate the role of the LEPR gene in predisposition to obesity (16, 18).
 |
GRANTS
|
---|
This study was partially supported by grants from Health Future Foundation of the USA, National Institute of Health Grants K01-AR-02170-01, R01-GM-60402-01 A1, and P01-DC-01813-07, State of Nebraska Cancer and Smoking Related Disease Research Program Grant LB595, State of Nebraska Tobacco Settlement Fund Grant LB692, and by United States Department of Energy Grant DE-FG03-00ER63000/A00.
 |
FOOTNOTES
|
---|
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).
Address for reprint requests and other correspondence: H.-W. Deng, Osteoporosis Research Center, Creighton Univ. Medical Center, 601 N. 30th St., Suite 6787, Omaha, NE 68131 (E-mail: deng{at}creighton.edu).
10.1152/physiolgenomics.00213.2003.
 |
REFERENCES
|
---|
- Abecasis GR, Cardon LR, and Cookson WO. A general test of association for quantitative traits in nuclear families. Am J Hum Genet 66: 279292, 2000.[CrossRef][ISI][Medline]
- Allison DB. Transmission-disequilibrium tests for quantitative traits. Am J Hum Genet 60: 676690, 1997.[ISI][Medline]
- Bray MS, Boerwinkle E, and Hanis CL. Linkage analysis of candidate obesity genes among the Mexican-American population of Starr County, Texas. Genet Epidemiol 16: 397411, 1999.[CrossRef][ISI][Medline]
- Chagnon YC, Chung WK, Perusse L, Chagnon M, Leibel RL, and Bouchard C. Linkages and associations between the leptin receptor (LEPR) gene and human body composition in the Quebec Family Study. Int J Obes Relat Metab Disord 23: 278286, 1999.[CrossRef][Medline]
- Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ, Perusse L, and Bouchard C. The human obesity gene map: the 2002 update. Obes Res 11: 313367, 2003.[Abstract/Free Full Text]
- Chagnon YC, Wilmore JH, Borecki IB, Gagnon J, Perusse L, Chagnon M, Collier GR, Leon AS, Skinner JS, Rao DC, and Bouchard C. Associations between the leptin receptor gene and adiposity in middle-aged Caucasian males from the HERITAGE family study. J Clin Endocrinol Metab 85: 2934, 2000.[Abstract/Free Full Text]
- Chen H, Charlat O, Tartaglia LA, Woolf EA, Weng X, Ellis SJ, Lakey ND, Culpepper J, Moore KJ, Breitbart RE, Duyk GM, Tepper RI, and Morgenstern JP. Evidence that the diabetes gene encodes the leptin receptor: identification of a mutation in the leptin receptor gene in db/db mice. Cell 84: 491495, 1996.[ISI][Medline]
- Chung WK, Power-Kehoe L, Chua M, Lee R, and Leibel RL. Genomic structure of the human OB receptor and identification of two novel intronic microsatellites. Genome Res 6: 11921199, 1996.[Abstract]
- Clement K, Vaisse C, Lahlou N, Cabrol S, Pelloux V, Cassuto D, Gourmelen M, Dina C, Chambaz J, Lacorte JM, Basdevant A, Bougneres P, Lebouc Y, Froguel P, and Guy-Grand B. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392: 398401, 1998.[CrossRef][ISI][Medline]
- Deng HW. Population admixture may appear to mask, change or reverse genetic effects of genes underlying complex traits. Genetics 159: 13191323, 2001.[Abstract/Free Full Text]
- Deng HW, Deng H, Liu YJ, Liu YZ, Xu FH, Shen H, Conway T, Li JL, Huang QY, Davies KM, and Recker RR. A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet 70: 11381151, 2002.[CrossRef][ISI][Medline]
- Deng HW, Shen-H, Xu FH, Deng HY, Conway T, Zhang HT, and Recker RR. Tests of linkage and/or association of genes for vitamin D receptor, osteocalcin, and parathyroid hormone with bone mineral density. J Bone Miner Res 17: 678686, 2002.[ISI][Medline]
- Devlin B and Risch N. A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29: 311322, 1995.[CrossRef][ISI][Medline]
- Friedman JM and Halaas JL. Leptin and the regulation of body weight in mammals. Nature 395: 763770, 1998.[CrossRef][ISI][Medline]
- Fulker DW, Cherny SS, Sham PC, and Hewitt JK. Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet 64: 259267, 1999.[CrossRef][ISI][Medline]
- Goldstein DB, Ahmadi KR, Weale ME, and Wood NW. Genome scans and candidate gene approaches in the study of common diseases and variable drug responses. Trends Genet 19: 615622, 2003.[CrossRef][ISI][Medline]
- Heo M, Leibel RL, Fontaine KR, Boyer BB, Chung WK, Koulu M, Karvonen MK, Pesonen U, Rissanen A, Laakso M, Uusitupa MI, Chagnon Y, Bouchard C, Donohoue PA, Burns TL, Shuldiner AR, Silver K, Andersen RE, Pedersen O, Echwald S, Sorensen TI, Behn P, Permutt MA, Jacobs KB, Elston RC, Hoffman DJ, Gropp E, and Allison DB. A meta-analytic investigation of linkage and association of common leptin receptor (LEPR) polymorphisms with body mass index and waist circumference. Int J Obes Relat Metab Disord 26: 640646, 2002.[CrossRef][Medline]
- Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, and Todd JA. Haplotype tagging for the identification of common disease genes. Nat Genet 29: 233237, 2001.[CrossRef][ISI][Medline]
- Kruglyak L, Daly MJ, Reeve-Daly MP, and Lander ES. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet 58: 13471363, 1996.[ISI][Medline]
- Lee GH, Proenca R, Montez JM, Carroll KM, Darvishzadeh JG, Lee JI, and Friedman JM. Abnormal splicing of the leptin receptor in diabetic mice. Nature 379: 632635, 1996.[CrossRef][ISI][Medline]
- Liu YJ, Xu FH, Shen H, Liu YZ, Deng HY, Zhao LJ, Huang QY, Dvornyk V, Conway T, Davies KM, Li JL, Recker RR, and Deng HW. A follow-up linkage study for quantitative trait loci contributing to obesity-related phenotypes. J Clin Endocrinol Metab 89: 875882, 2004.[Abstract/Free Full Text]
- Matsuoka N, Ogawa Y, Hosoda K, Matsuda J, Masuzaki H, Miyawaki T, Azuma N, Natsui K, Nishimura H, Yoshimasa Y, Nishi S, Thompson DB, and Nakao K. Human leptin receptor gene in obese Japanese subjects: evidence against either obesity-causing mutations or association of sequence variants with obesity. Diabetologia 40: 12041210, 1997.[CrossRef][ISI][Medline]
- McIntyre LM, Martin ER, Simonsen KL, and Kaplan NL. Circumventing multiple testing: a multilocus Monte Carlo approach to testing for association. Genet Epidemiol 19: 1829, 2000.[CrossRef][ISI][Medline]
- Montague CT, Farooqi IS, Whitehead JP, Soos MA, Rau H, Wareham NJ, Sewter CP, Digby JE, Mohammed SN, Hurst JA, Cheetham CH, Earley AR, Barnett AH, Prins JB, and ORahilly S. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387: 903908, 1997.[CrossRef][ISI][Medline]
- Morris RW and Kaplan NL. On the advantage of haplotype analysis in the presence of multiple disease susceptibility alleles. Genet Epidemiol 23: 221233, 2002.[CrossRef][ISI][Medline]
- Norman RA, Leibel RL, Chung WK, Power-Kehoe L, Chua SC Jr, Knowler WC, Thompson DB, Bogardus C, and Ravussin E. Absence of linkage of obesity and energy metabolism to markers flanking homologues of rodent obesity genes in Pima Indians. Diabetes 45: 12291232, 1996.[Abstract]
- Nyholt DR. Genetic case-control association studies: correcting for multiple testing. Hum Genet 109: 564567, 2001.[CrossRef][ISI][Medline]
- OConnell JR and Weeks DE. PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 63: 259266, 1998.[CrossRef][ISI][Medline]
- Quinton ND, Lee AJ, Ross RJ, Eastell R, and Blakemore AI. 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: 233236, 2001.[CrossRef][ISI][Medline]
- Risch N and Merikangas K. The future of genetic studies of complex human diseases. Science 273: 15161517, 1996.[ISI][Medline]
- Schneider S, Roessli D, and Excoffier L. A Software for Population Genetics Data Analysis, ARLEQUIN ver 2.000. Genetics and Biometry Laboratory, Dept. of Anthropology and Ecology, University of Geneva, Switzerland, 2000.
- Sham PC, Cherny SS, Purcell S, and Hewitt JK. Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data. Am J Hum Genet 66: 16161630, 2000.[CrossRef][ISI][Medline]
- Silver K, Walston J, Chung WK, Yao F, Parikh VV, Andersen R, Cheskin LJ, Elahi D, Muller D, Leibel RL, and Shuldiner AR. The Gln223Arg and Lys656Asn polymorphisms in the human leptin receptor do not associate with traits related to obesity. Diabetes 46: 18981900, 1997.[ISI][Medline]
- Spielman RS and Ewens WJ. The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet 59: 983989, 1996.[ISI][Medline]
- Tartaglia LA, Dembski M, Weng X, Deng N, Culpepper J, Devos R, Richards GJ, Campfield LA, Clark FT, Deeds J, Muir C, Sanker S, Moriarty A, Moore KJ, Smutk JS, Mays GG, Woolf EA, Monroe CA, and Tepper RI. Identification and expression cloning of a leptin receptor, OB-R. Cell 83: 12631271, 1995.[ISI][Medline]
- Thompson DB, Ravussin E, Bennett PH, and Bogardus C. Structure and sequence variation at the human leptin receptor gene in lean and obese Pima Indians. Hum Mol Genet 6: 675679, 1997.[Abstract/Free Full Text]
- van den Oord EJ. Association studies in psychiatric genetics: what are we doing? Mol Psychiatry 7: 827828, 2002.[CrossRef][ISI][Medline]
- van Rossum CT, Hoebee B, van Baak MA, Mars M, Saris WH, and Seidell JC. Genetic variation in the leptin receptor gene, leptin, and weight gain in young Dutch adults. Obes Res 11: 377386, 2003.[Abstract/Free Full Text]
- Wauters M, Mertens I, Chagnon M, Rankinen T, Considine RV, Chagnon YC, Van Gaal LF, and Bouchard C. Polymorphisms in the leptin receptor gene, body composition and fat distribution in overweight and obese women. Int J Obes Relat Metab Disord 25: 714720, 2001.
- Wauters M, Mertens I, Rankinen T, Chagnon M, Bouchard C, and Van Gaal L. Leptin receptor gene polymorphisms are associated with insulin in obese women with impaired glucose tolerance. J Clin Endocrinol Metab 86: 32273232, 2001.[Abstract/Free Full Text]
- Yiannakouris N, Yannakoulia M, Melistas L, Chan JL, Klimis-Zacas D, and Mantzoros CS. 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: 44344439, 2001.[Abstract/Free Full Text]
- Zhang Y, Leaves NI, Anderson GG, Ponting CP, Broxholme J, Holt R, Edser P, Bhattacharyya S, Dunham A, Adcock IM, Pulleyn L, Barnes PJ, Harper JI, Abecasis G, Cardon L, White M, Burton J, Matthews L, Mott R, Ross M, Cox R, Moffatt MF, and Cookson WO. Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nat Genet 34: 181186, 2003.[CrossRef][ISI][Medline]