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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2006-0549
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The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 10 4001-4005
Copyright © 2006 by The Endocrine Society

BRIEF REPORT

Assessment of Genetic Linkage and Parent-of-Origin Effects on Obesity

Yan-fang Guo, Hui Shen, Yong-jun Liu, Wei Wang, Dong-hai Xiong, Peng Xiao, Yao-zhong Liu, Lan-juan Zhao, Robert R. Recker and Hong-wen Deng

Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics (Y.G., W.W., H.D.), School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, People’s Republic of China; Laboratory of Molecular and Statistical Genetics (Y.G., H.D.), College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, People’s Republic of China; Osteoporosis Research Center and Department of Biomedical Sciences (Y.G., Yo.L., W.W., D.X., P.X., Ya.L., L.Z., R.R.R.), Creighton University, Omaha, Nebraska 68131; and Departments of Orthopedic Surgery and Basic Medical Sciences (H.S., H.D.), University of Missouri-Kansas City, Kansas City, Missouri 64108

Address all correspondence and requests for reprints to: Hong-Wen Deng, Ph.D., Departments of Orthopedic Surgery and Basic Medical Sciences, University of Missouri-Kansas City, 2411 Holmes Street, Room M3-C03, Kansas City, Missouri 64108-2792. E-mail: dengh{at}umkc.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: Obesity is a growing health care problem worldwide and is a major underlying risk factor for common diseases such as diabetes. Parent-of-origin effect has been reported to be involved in the development of obesity. But the genes with imprinting effects related to obesity are largely unknown.

Objective: The objective of the study was to identify obesity-related genetic loci, both with and without imprinting effects.

Design and Subjects: We conducted genome-wide linkage analyses for obesity with and without consideration of imprinting effects in a large sample including more than 4000 individuals. In addition to body mass index (BMI), we also used a more stringent and accurate obesity definition, which simultaneously considers BMI and percentage of fat mass (PFM) in a gender-specific manner. Simulations were performed to identify the genome-wide significant and suggestive significant thresholds.

Results: In nonimprinted linkage analyses, we detected suggestive linkage at 2q31 (LOD = 2.23) and 16q22 (LOD = 1.87) for BMI and 2q37 (LOD = 2.23) for BMI and PFM. Interestingly, 2q37 also achieved a significant maternal linkage with BMI and PFM (LOD=3.34) in imprinted linkage analyses. Imprinted linkage analyses revealed suggestive linkage evidence for BMI at three additional genomic regions, including 3p14 (LOD = 2.20, paternal), 3q24 (LOD = 1.97, maternal), and 19q13 (LOD = 1.81, maternal).

Conclusion: We reported linkage and imprinting effects for obesity on several chromosome regions and suggested the potential importance of parent-of-origin effects and phenotype definition of obesity in delineating the genetic basis of obesity.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
OBESITY IS A growing health care problem worldwide. The prevalence of obesity has increased so rapidly that the obese population in developed countries has more than doubled over the last decade (1). The susceptibility of obesity is under strong genetic control (1). Genomic imprinting, a phenomenon involving a preferential paternal or maternal inheritance of a susceptibility allele to affected individuals (2), may play an important role in affecting the propensity to obesity (1). Nevertheless, few genetic studies have investigated the imprinting effects on obesity in humans (3, 4, 5, 6).

Body mass index (BMI) is a widely used measure of obesity due to its measurement convenience and low cost (7). However, BMI may not always be an appropriate phenotype for studies on obesity because it may not reflect the actual body composition (7). For example, a person may be overweight (i.e. BMI > 25 kg/m2) but be muscular along with only 10–15% body fat. Therefore, it may be more appropriate to consider both BMI and percentage of fat mass (PFM, the ratio of fat mass to body weight) as a combined phenotype in obesity research (8, 9). So far, genetic studies on combined BMI/PFM are rare.

In the present study, using BMI and BMI/PFM as phenotypes, we performed a genome-wide linkage scan to search for loci with or without imprinting effects for obesity.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Study samples

The sample contains a total of 4489 phenotyped subjects from 451 Caucasians pedigrees of European origin, of which 4247 subjects were genotyped. The sampling scheme and exclusion criteria have been detailed earlier (10). The study was approved by the Creighton University Institutional Review Board. All the study subjects signed informed consent documents before entering the project.

Phenotype measurement and definition of obesity affected status

BMI was recorded as body weight (kilograms) divided by the square of height (meters). Weight was measured in light indoor clothing using a calibrated balance beam scale, and height was measured using a calibrated stadiometer. PFM was calculated as the ratio of fat mass to body weight. Fat mass was measured by dual-energy x-ray absorptiometry. The measurement precisions of BMI and PFM as reflected by the coefficient of variation were 0.2 and 1.1%, respectively.

We used two categories of definition for obesity affected status. First, we used the criteria proposed by the World Health Organization, defining subjects (after adjustment for age and sex) with BMI of 30 kg/m2 or greater as obese. Second, we applied a supposedly more accurate and stringent definition of obesity (7), by which after adjusting for age, obese female was defined as BMI of 30 kg/m2 and PFM of 39% or more, and obese male was defined as BMI of 30 kg/m2 or greater and PFM of 26% or greater (8, 9).

Genotyping

For each subject, DNA was extracted from peripheral blood by using the Puregene DNA isolation kit (Gentra Systems, Inc., Minneapolis, MN). Three hundred ninety-three microsatellite markers from the Marshfield screening set 14 were successfully genotyped by Marshfield Center for Medical Genetics (hppt://research.marshfieldclinic.org/genetics/sets/set14FinalForWeb.xls), with genotype missing and error rate approximately 0.3%. These markers have an average population heterozygosity of 0.75 and spaced on average 8.9 cM. PedCheck (10) was used to check Mendelian inheritance pattern at all the marker loci.

Statistical analyses

Our sample was mainly made up of pedigrees of medium to large sizes (11), which are too large to be handled by any available imprinted analysis software. Therefore, using MEGA2 (12), we split large pedigrees into nuclear families (i.e. parents plus children). Eventually 883 nuclear families (ranging from 4 to14 subjects per family) with 4918 subjects were used for subsequent statistical analyses (some individuals were included twice in different nuclear families, e.g. an individual may be a parent in one nuclear family and a child in another one).

Using the program ALLEGRO (9), we conducted nonimprinted and imprinted linkage analyses for BMI and BMI/PFM, respectively. The program computes nonparametric, multipoint, affected-only, and allele-sharing LOD scores on the basis of the S-pair scoring function (9) and an exponential allele-sharing model (9). Families were weighted halfway on the log scale between weighting families equally and weighting all pairs of affected relatives equally. For imprinted linkage analyses, ALLEGRO used an imprinting-based scoring function (13), which allows us to assign weights to allele sharing specific to parental origins. Because the difference in recombination rates between males and females may lead to false evidence of imprinting (14), we used sex-specific genetic maps (15) in imprinted linkage analyses.

Simulation

We conducted simulations to estimate empirical genome-wide significance levels. For each trait, data of 1000 simulated genome scans were generated using MERLIN (16) under the assumption of no susceptibility loci. Simulations were performed using the pedigree structures and the genotyping patterns observed in the data set, taking account of uneven marker spacing and informativeness. Each simulated replicate was analyzed in the same way as the observed data, and the highest peaks for each chromosome were recorded. The empirical significance level of an observed LOD score was then estimated by counting the proportion of genome scans containing one or more peaks of that size. The cutoff for suggestive linkage was calculated as the corresponding LOD score that was observed on average once per genome scan (i.e. the 1000th highest peak LOD score across the 1000 replicates), thus representing the average maximum peak size expected once per genome scan by chance alone (17). The significant linkage threshold was defined as the score occurring with probability 0.05 in a genome scan (17) (i.e. the 50th highest LOD score across the 1000 simulations).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Among the 4918 subjects (2140 males, 2778 females), 438 males and 586 females had BMI of 30 kg/m2 or greater, whereas 285 males and 380 females met the BMI/PFM obese criteria. For BMI, our sample contained 4247 sibling pairs, including 379 affected sibling pairs for linkage analyses. When using BMI/PFM criteria, the corresponding numbers of sibling pairs were 2815 and 166, respectively.

Multipoint LOD scores obtained from nonimprinted and imprinted analyses are shown in Fig. 1Go. The empirical thresholds obtained via simulation are listed in Table 1Go, with thresholds for significant linkage established at LOD ranging from 2.99 to 3.49 and suggestive linkage around LOD = 1.80. Genomic regions achieved at least suggestive linkage in any of the conducted analyses [LOD_N, nonimprinted multipoint LOD score; LOD_P and LOD_M, multipoint LOD score conditioning on paternal and maternal identical by descent (IBD) sharing, respectively] are also summarized in Table 1Go and illustrated in Fig. 2Go.


Figure 1
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  		FIG. 1. Genome-wide multipoint scan results for BMI and BMI/PFM. The y-axis presents multipoint LOD scores, and the x-axis shows the chromosome numbers. A nonimprinted scan is indicated as a solid line; a scan with consideration of only maternal transmission is indicated as a dot dashed line; and a scan with consideration of only paternal transmission is indicated as a dashed line.

 

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  		TABLE 1. Empirical genome-wide thresholds and genomic regions with at least suggestive linkage evidence

 

Figure 2
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  		FIG. 2. Multipoint LOD score for chromosome regions with linkage or imprinting evidence. The y-axis presents multipoint LOD scores, and the x-axis shows the chromosome position. A nonimprinted scan is indicated as a solid line; a scan with consideration of only maternal transmission is indicated as a dot dashed line; and a scan with consideration of only paternal transmission is indicated as a dashed line.

 
In nonimprinted linkage scan, we detected suggestive linkage for BMI on 2q37 (LOD_N = 2.31, Fig. 2AGo) and 16q22 (LOD_N = 1.87, Fig. 2DGo). For BMI/PFM, the most significant linkage result, a multipoint LOD score of 2.23 was achieved on 2q31 (Fig. 2BGo). Interestingly, when parent-of-origin effects were taken into account, we detected a significant maternal linkage at 2q31 (LOD_M = 3.34, genome-wide empirical P = 0.047) for BMI/PFM (Fig. 2BGo). For BMI, imprinted linkage analyses revealed suggestive linkage evidence on 3p14 with paternal IBD sharing (LOD_P = 2.20) (Fig. 2CGo) and 3p24 (LOD_M=1.97) and 19q13 (LOD_M = 1.81) (Fig. 2Go, C and E) with maternal IBD sharing.


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Chromosome 2q is the most promising genomic region identified in this study because the highest LOD score peaks for the two obesity-related phenotypes were both achieved in this region. For BMI, linkage evidence achieved a LOD score of 2.31 at 2q37, whereas for BMI/PFM, linkage evidence was mapped to 2q31 (LOD_N = 2.23, LOD_M = 3.34). The considerable increase in the LOD score at 2q31 suggests that this region may contain an imprinted gene(s) underlying obesity risk. The observed relatively large disparity between results from nonimprinted and imprinted linkage analyses is not totally unexpected. Previous studies demonstrated that linkage analysis without incorporating parent-of-origin models is more powerful to detect nonimprinted loci, whereas imprinted analysis may have higher power to identify large imprinting effects (18).

However, the linkage peaks on 2q from the two different definitions of obesity may have arisen from linkage to a common locus, which may be demonstrated by an estimation of the size of the region supported by the linkage findings in either instance. In a previous search for quantitative trait loci controlling obesity in this study population, linkage was identified on 2q14 (10). This finding potentially adds support for the strength of the linkage finding on 2q or possibly that both quantitative and qualitative linkage analysis findings may also be evidence of linkage to a common gene within a large support interval on 2q. This further underscores the need for further mapping of support intervals underlying the 2q linkage peaks.

Chromosome 3 is also noteworthy. When nonimprinted linkage analyses were performed for BMI, two nominal linkage peaks were observed on this chromosome, 3p14 with LOD_N = 1.33 and 3q24 with LOD_N = 1.16. Interestingly, when considering imprinting effect, the two peaks largely increased; as such 3p14 showed suggestive paternal linkage (LOD_P = 2.20) and 3q24 showed suggestive maternal linkage (LOD_M = 1.97). On the region of 3q24, five studies (19) reported linkage evidence for BMI, with LOD scores ranging from 1.8 to 4.3, mapped to within 20 Mb of our locus. Currently, the extent to which the human genome or which specific genomic regions are imprinted is unknown. All the potentially imprinted regions reported in this study hence are novel and important.

Among the reported linkage studies for obesity-related phenotypes, only four (3, 4, 5, 6) of them investigated parent-of-origin effects, including three studies investigating BMI (3, 4, 5) and one studying PFM (4). Results from different studies are largely inconsistent. The lack of replication may be due to, for example, the differences in sample ethnicity, phenotype, sample size, and assessing method. Extension and/or replication studies with more samples and denser markers are needed to confirm the results. Our study for the first time used comprehensive obesity criteria in linkage analyses, which take into account both BMI and PFM as well as gender difference in PFM. Such criteria may be more accurate in defining obesity (8, 9) and hence may generate a more homogenous sample set for linkage analyses.

A few aspects of our study may need further discussion. First, dividing large pedigrees into small families may generally result in a loss of statistical power (20). However, our dividing strategy retained all sibling pairs contained in the original pedigrees, and the linkage analysis method we used considered only information from sibling pairs; thus, the loss of power might be minimum. Second, the newly created nuclear families are not totally independent, which may inflate the false-positive rate. Nevertheless, our simulation results implied that such effects would be small because our empirical genome-wide thresholds are comparable with those established by Lander and Kruglyak (17). The ultimate way to avoid the problem of pedigree fragmentation is to modify the software [such as Loki (21), Solar (22)], which can handle large extended pedigrees, to allow imprinting effect assessment.

In summary, we identified linkage and imprinting effects for obesity on several chromosome regions in a large sample. A susceptibility locus on 2q37 may be involved in the pathogenesis of obesity through maternal genomic imprinting. Our findings also suggest that loci on 2q31, 16q22, 3p14, 3q24, and 19q13 may be involved in the development of obesity. This study implies the importance of parent-of-origin effects and phenotype definition in delineating the genetic basis of obesity.


   Footnotes
 
This work was partially supported by grants from the National Institutes of Health (K01 AR02170-01, R01 AR050496-01, and R01 GM60402-01A1) and an LB595 grant from the state of Nebraska. The study was also supported by grants from the National Science Foundation of China, Huo Ying Dong Education Foundation, HuNan Province, Xi’an Jiaotong University, and the Ministry of Education of China. The genotyping experiment was performed by Marshfield Center for Medical Genetics and supported by National Heart, Lung, and Blood Institute Mammalian Genotyping Service (Contract no. HV48141).

Disclosure summary: the authors have nothing to disclose.

First Published Online July 11, 2006

Abbreviations: BMI, Body mass index; IBD, identical by descent; LOD_M, multipoint LOD score conditioning on maternal transmission; LOD_N, nonimprinted multipoint LOD score; LOD_P, multipoint LOD score conditioning on paternal transmission; PFM, percentage of fat mass.

Received March 10, 2006.

Accepted July 5, 2006.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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