Chapter 10. Genome-wide linkage scan for the metabolic syndrome reveals a major quantitative trait locus on chromosome 15q: The Quebec Family Study.

Yohan Bossé, Jean-Pierre Després, Yvon C Chagnon, Treva Rice, DC Rao, Claude Bouchard, Louis Pérusse, Marie-Claude Vohl

L’objectif de cette étude était d’identifier les régions chromosomiques contenant les gènes de prédisposition au syndrome métabolique. Une analyse factorielle a été effectuée avec huit phénotypes reliés au syndrome métabolique incluant la circonférence de taille, l’indice de masse corporelle, la tension artérielle systolique et diastolique, ainsi que les mesures plasmatiques de glucose, d’insuline, de triglycérides et de cholestérol-HDL. Cette analyse a produit trois facteurs interprétés comme un facteur de syndrome métabolique, de tension artérielle et de lipides. Le facteur syndrome métabolique avait un haut degré de saturation factorielle (>0.40) avec tous les phénotypes. La cellule familiale expliquait 45.6% de la variance de ce facteur. Un criblage génomique effectué sur ce dernier indique la présence d’un locus quantitatif majeur localisé sur le chromosome 15 (86 cM, LOD = 3.15). Des évidences suggestives (LOD > 1.75) ont aussi été observées sur les régions 1p, 3p, 3q, 6q, 7p, 19q et 21q.

A quantitative trait locus on 15q for a composite metabolic syndrome variable derived from factor analysis in the Quebec Family Study.

Y. Bossé1,2, J.-P. Després2,3, Y. C. Chagnon4, T. Rice5, D.C. Rao5, C. Bouchard6, L. Pérusse7, M.-C. Vohl1,2

1-Lipid Research Center, CHUL Research Center, Quebec, Canada; 2- Department of Food Science and Nutrition, Laval University, Quebec, Canada; 3- The Quebec Heart Institute, Quebec, Canada; 4- Laval University Robert-Giffard Research Center, Quebec, Canada; 5- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri; 6- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA; 7- Division of Kinesiology, Department of Social and Preventive Medicine, Laval University, Quebec, Canada.

Running title: Genome scan for the metabolic syndrome.

Address all correspondence to:

Marie-Claude Vohl, Ph.D., Lipid Research Center, CHUL Research Center, TR-93, 2705, Laurier Blvd, Sainte-Foy, Quebec, G1V 4G2, Canada. Fax: (418) 654-2145; Tel: (418) 656-4141 extension 48280; E-mail: marie-claude.vohl@crchul.ulaval.ca

Abstract

The metabolic syndrome represents a cluster of cardiovascular risk factors co-occurring in the same individual. The aim of this study was to identify chromosomal regions encoding genes predisposing to the metabolic syndrome using composite factors derived from maximum likelihood-based factor analysis. Genetic data were obtained from the Quebec Family Study and included 707 subjects from 264 nuclear families. Factor analysis were performed on eight metabolic syndrome-related phenotypes including waist circumference, body mass index, systolic and diastolic blood pressure as well as plasma insulin, glucose, triglyceride and HDL-cholesterol levels. Three factors were identified and interpreted as a general metabolic syndrome, blood pressure and blood lipids, respectively. The metabolic syndrome factor had high factor loadings (>0.4) for all phenotypes and explained 42% of the total variance. An ANOVA testing for familial aggregation revealed that the family lines accounted for 45.6% of the metabolic syndrome factor variance. A genome-wide linkage scan performed with this first factor revealed the existence of a quantitative trait locus (QTL) on chromosome 15 (86 cM) with a logarithm of odds (LOD) score of 3.15. Suggestive evidences of linkage (LOD > 1.75) were also observed on chromosomes 1p, 3p, 3q, 6q, 7p, 19q, 21q. These QTLs may harbor genes contributing to the clustering of the metabolic syndrome-related phenotypes.

Introduction

The metabolic syndrome is defined as a cluster of interrelated cardiovascular risk factors observed in the same individual (1-3). There has been an increased in the number of abnormalities associated with this syndrome since its introduction more than 15 years ago (4; 5). However, all definitions include markers of glucose, lipid and blood pressure abnormalities. Obesity, and particularly abdominal obesity, is also an integral feature of the syndrome and is thought to be a major contributor to the metabolic abnormalities (1; 6).

The correlations among the multiple risk factors suggest the existence of common aetiologies. A large number of studies have used factor analysis to disentangle the metabolic and physiological basis of this clustering (7-31). They have identified two (7-13), three (14-25), four (25-31) and up to seven (31) independent factors underlying the metabolic syndrome architecture. However, several issues make the comparison among these studies difficult, including: 1-differences in study population, 2-the nature and number of variables chosen for inclusion in the modeling, 3-the number of factors extracted, and 4-the threshold for interpreting loadings (32). Nevertheless, in the aggregate, these studies suggest a three- to four-factor model including factors interpreted as representing insulin resistance, obesity, blood lipids and blood pressure with the insulin resistance and the obesity factors frequently found together. This putative metabolic syndrome factor structure was confirmed using confirmatory factor analysis (33; 34).

Heritability studies have shown that composite factors of the metabolic syndrome derived from factor analysis are under genetic influences (16; 23; 24; 30; 35). It has been postulated that a common gene, or a set of genes, may mediate the clustering of metabolic syndrome-related traits (36-38). By combining factor analysis and a candidate gene approach, Edwards et al. (39) found significant linkage between the apolipoprotein E gene and a weight/fat factor (loading on body weight, waist circumference (WC) and fasting insulin) and also between the cholesterol ester transfer protein gene and a lipid factor (loading on triglyceride, HDL-cholesterol and LDL peak particle diameter). More recently, Arya et al. (23) performed a genome-wide linkage scan on three composite factors extracted by factor analysis in nondiabetic Mexican-American families. Factor 1, loading on body mass index (BMI), fasting insulin and leptin levels (interpreted as an adiposity-insulin factor) yielded significant evidence of linkage on two loci on chromosome 6q. A third QTL was observed on chromosome 7q for factor 3 with high loadings on HDL-cholesterol and triglyceride levels. In the present study, we have used subjects of the Quebec Family Study and performed a genome-wide linkage scan on a composite quantitative trait derived from factor analysis. The aim of this study was to identify genomic regions harboring genes influencing the variance of the metabolic syndrome-related phenotypes.

Research Design and Methods

Population

Subjects were participants of the Quebec Family Study (QFS) which is an ongoing project of French Canadian families designed to investigate the genetics of obesity and its comorbidities (40). The QFS represented a mixture of random sampling and ascertainment through obese probands (BMI ≥ 32 kg/m2). In the present study, a total of 707 individuals from 264 nuclear families had complete data for the eight variables used in the identification of the metabolic syndrome factor. None of these subjects had fasting glycemia above 7.0 mmol/L or had a two hours post-glucose challenge glycemia above 11.1 mmol/L. Characteristics of the subjects are presented in Table 1. The Medical Ethics Committee of Laval University approved the protocol, and written consent was obtained from each subjects after the nature of the procedure was explained.

Phenotypes

Body weight, height, and WC were measured following standardized procedures (41). BMI was measured as weight (kg)/height (m2). Fasting blood samples were collected, and cholesterol (42) as well as triglyceride (43) concentrations were determined enzymatically using a Technicon RA-500 automated analyzer (Bayer, Tarrytown, NY). HDL fraction was obtained after precipitation of LDL in the infranatant (>1.006 g/ml) with heparin and MnCl2 (44). Plasma glucose and insulin levels were measured by standard procedures as previously described (45; 46). Subjects underwent systolic and diastolic blood pressure (SBP and DBP) measurements with a mercury sphygmomanometer and stethoscope according to the American Heart Association recommendations (47). SBP was defined as the first detectable sound, whereas DBP was measured at the disappearance of Korotkoff’s sound. The blood pressure value was the mean of two consecutive measurements.

Genotyping

DNA preparation, polymerase chain reaction conditions, and genotyping are described in detail elsewhere (48). Genotypes were typed with automatic DNA sequencers and the computer software SAGA from LICOR. A total of 443 markers spanning the 22 autosomal chromosomes with an average intermaker distance of 7.2 centimorgans were available for this genome scan. These markers included 337 microsatellite markers and 106 polymorphisms in 65 candidate genes. The results were stored in a local database, GENEMARK, which inspects results for Mendelian inheritance incompatibilities.

Statistical analysis

Eight metabolic syndrome-related variables were chosen for factor analysis: WC, BMI, fasting insulin and glucose levels, SBP and DBP, as well as triglyceride and HDL-cholesterol levels. Five of them, BMI, glycemia, insulinemia, SBP and triglycerides, were log10 transformed to normalize their distribution. The factors were extracted by maximum-likelihood using PROC FACTOR procedure implemented in SAS (version 8.2, Cary, NC). This procedure generated orthogonal factors that are linear combination of the original variables. Factors were interpreted on the basis of the factor loading patterns describing the correlations between the emerging factors and the original variables.

The factor (factor 1) that accounted for the largest amount of variance had factor loadings > 0.4 for all variables and was labeled as an “overall metabolic syndrome factor”. Factor scores were then obtained for each individual and constituted the phenotype for linkage analysis. Prior to linkage analysis, the factor scores were adjusted for the effect of age (up to the cubic polynomial) in age-(<30, 30-50 and ≥50) by-sex (male and female) specific models using a stepwise multiple regression procedure retaining only significant terms (p < 0.05). Regression parameters were estimated after exclusion of outliers (± 3 SD), and residuals were computed for all subjects. Subjects whose residual values were greater then 4 SD from the mean and were separated by more than 1 SD from the nearest internal score were excluded from the analysis.

The presence of familial aggregation was tested using an ANOVA comparing the between-family to the within-family variances. This test was performed with the general linear model with the overall metabolic syndrome factor as the dependent variable, and the family lines (family number) as the independent variable.

Linkage was performed with a variance component model using the quantitative transmission disequilibrium test (QTDT) computer program (49). Under this model, a phenotype is influenced by the additive effects of a QTL (q), a residual familial component due to polygenes (g) and a residual nonfamilial component (e). Hypothesis testing was performed by the likelihood ratio test. The likelihood of the null hypothesis is obtained by restricting the additive genetic variance due to the QTL (σq) equal to zero (σq = 0). The test is conducted by contrasting this restricted model with the alternative where σq is estimated (σq ≠ 0). The difference in minus twice the log-likelihoods between the null and alternate models is approximately distributed as a χ2 which allowed LOD score computation as χ2/(2 loge 10). We have taken a LOD score of ≥ 3.00 (p ≤ 0.0001) as evidence of linkage and a LOD of ≥ 1.75 (p ≤ 0.0023) as evidence of suggestive linkage (50).

Results

Three factors accounted for 63% of the total variance (Table 2). Factor one explained 67% of the common variance and 42% of the total variance. This factor had high loadings (> 0.4) for all eight variables and can then be interpreted as the general metabolic syndrome factor. The correlation of this first factor with WC (0.97) and BMI (0.93) was especially high. Factor two had large positive loadings for SBP (0.56) and DBP (0.77) suggesting a blood pressure factor. Factor 3 had relatively high correlation with HDL-cholesterol (0.73) and triglycerides (-0.27) indicative of a lipid factor. The final communality estimates showed that all variables are relatively well accounted by the three factors, with final communality ranging from 0.19 for glycemia to 0.95 for WC. Subsequent analyses were undertaken only on the general metabolic syndrome factor (factor one).

The results of familial aggregation revealed more than two times more variance between families than within families. The family lines accounted for 45.6% of the variance in the general metabolic syndrome factor (F value = 2.18, p < 0.0001). Thus, the metabolic syndrome factor extracted from factor analysis significantly aggregates within families.

An overview of the linkage results for the general metabolic syndrome factor is given in Figure 1. The strongest evidence of linkage was found on chromosome 15q25 (D15S171) with a LOD score of 3.15 at 86 cM. The 1-LOD support interval extends from 69 to 95 cM. The second highest LOD score (2.60) was detected on chromosome 3p (49 cM) with marker D3S1581. Approximately, 6 cM downstream from that marker, a polymorphism located within the PPARγ gene gave highly suggestive evidence of linkage with a LOD score of 2.56. Suggestive evidences of linkage (LOD > 1.75) were also observed on chromosome 1p, 3q, 6q, 7p, 19q and 21q (Figure 1).

The linkage profile observed on chromosome 15 was then compared to that of each of the eight original variables (Figure 2). WC and BMI have a similar linkage pattern to the general metabolic syndrome factor, with LOD scores of 3.06 and 2.28, respectively, at marker D15S171. LOD scores above 1.0 were also observed on chromosome 15q for triglyceride and insulin levels as well as for DBP. LOD scores of 0.98 and 0.89 were observed for SBP and fasting glucose levels, respectively. However, no evidence of linkage was observed in the 15q region for HDL-cholesterol levels.

Discussion

The metabolic syndrome is recognized as a constellation of metabolic disturbances present in the same individual, which tends to include obesity, hypertension, dyslipidemia and insulin/glucose disturbances (1-3). The metabolic and physiological bases for this clustering are not well elucidated but it has been hypothesized that it may have a common etiology. In the present study, we performed a genome-wide linkage scan on a general metabolic syndrome factor derived from factor analysis in order to identify genetic loci influencing the syndrome in a nondiabetic cohort. Factor analysis identified one underlying factor (labeled the general metabolic syndrome factor) with high loadings on each of the eight metabolic syndrome-related phenotypes. This suggests that a single weighted combination of these variables accounts for a large fraction of the clustering. Assessment of familial aggregation reveals that the general metabolic syndrome factor exhibits significant familial clustering. A major QTL was found on chromosome 15q suggesting the presence of a gene (or genes) contributing to the shared variance among the original variables. Evidences of linkage for WC and BMI considered individually were also observed at the same location. The stronger genetic signals with obesity-related phenotypes compared to the other traits may indicate that there is a gene acting through obesity.

The three factor model found in the present study is consistent with most of the literature (7-31). However, what is less consistent is the fact that all eight metabolic syndrome-related phenotypes had high factor loadings (>0.40) on the first factor. This discrepancy might be explained by the fact that most studies have used orthogonal rotation (varimax) following factor extraction in order to produce interpretable factors. In fact, the few studies that have not used such a rotation procedure have reported high factor loadings with the first factor for all, or almost all, metabolic syndrome-related phenotypes (13; 30). These results are in accordance with the concept that one unifying biological/physiological process underlies the clustering of cardiovascular risk variables.

Few studies have attempted to localize the “metabolic syndrome genes” using a similar approach. Loos et al. (13) performed a genome-wide search on two principal components obtained from 456 whites and 217 blacks participants of the HERITAGE Family Study. Principal component analysis was carried out on seven metabolic syndrome-related phenotypes including percent body fat, visceral adipose tissue area assessed by computed tomography, mean arterial blood pressure, and plasma HDL-cholesterol, triglyceride, glucose, as well as insulin concentrations. Suggestive evidences of linkage (p < 0.0023) were found on 10p for principal component 1 and 19q for principal component 2 in Whites and on 1p for principal component 2 in Blacks. Similarly, Tang et al. (30) performed a genome scan on a metabolic syndrome factor using traditional (BMI, waist-to-hip ratio, triglyceride, HDL-cholesterol, and HOMA index) and non-traditional (PAI-1 and serum uric acid) metabolic syndrome-related phenotypes. A general metabolic syndrome factor was derived from maximum likelihood based factor analysis using the data from the NHLBI Family Heart Study. A significant signal was observed on 2q with additional lower signals observed on chromosomes 7, 12, 14 and 15. Most of these QTLs were not replicated in the present study. However, the suggestive linkage observed on 1p (57 cM) and 19q (50 cM) could represent possible replication of the suggestive linkage observed in Blacks (1p, 56 cM) and in Whites (19q, 60 cM), respectively, for principal component 2 in the HERITAGE Family Study (13). The identification of different QTLs among studies may be due to differences in study populations, the nature and number of variables used to define the metabolic syndrome and the number of factors extracted. The lack of replication for the QTL reported on 2q36 by Tang et al. (30) can also be explained by the lack of diabetic subjects in the present study. In fact, their linkage signal on 2q36 was attenuated after the exclusion of diabetic subjects suggesting that these individuals contribute substantially to the linkage. It is also worth mentioning that the suggestive linkage observed on 3q overlapped with the QTL reported by Kissebah et al. (51) for six individual traits of the metabolic syndrome.

The signal observed on 15q25 represented a novel QTL for the metabolic syndrome. Arya et al. (23) have reported a suggestive linkage (LOD = 2.0) for a blood pressure factor near the same location. No QTL for any individual component of the metabolic syndrome have been identified in that region except for a significant linkage signal for blood pressure in a Chinese population (52). However, the locus has been linked to LDL-C (53), familial hypercholesterolemia (54) and insulin-dependent diabetes mellitus (55; 56). We have also reported in the QFS cohort a QTL for fat-free mass on 15q25-q26 (48). Whether or not the linkage signal is cause by the same gene is unknown at that time. Several candidate genes are located under the peak signal, including among others, perilipin (PLIN), neuromedin B (NMB), hepatic lipase (LIPC) and insulin-like growth factor-1 receptor (IGF1R). Further studies will be required to test these putative candidate genes.

In conclusion, the present study revealed the presence of a genetic locus on chromosome 15q linked to a general metabolic syndrome factor accounting for about 42% of the variance shared by WC, BMI, fasting insulin, glucose, HDL-cholesterol and triglyceride levels, as well as SBP and DBP. Suggestive evidence of linkage was also found on chromosomes 1p, 3p, 3q, 6q, 7p, 19q and 21q. These QTLs may contain genes influencing the metabolic syndrome-related phenotypes.

Acknowledgments

This study was supported by the Canadian Institutes of Health Research (MOP-13960). We would like to express our gratitude to the subjects for their participation and to the staff of the Physical Activity Sciences Laboratory for their contribution to the study. We also wish to thank M. Claude Leblanc for is helpful assistance with data processing and computation. Y. Bossé is the recipient of a doctoral scholarship from the Canada Graduate Scholarships program. M.C. Vohl is a research scholar from the “Fonds de la recherche en santé du Québec”. C. Bouchard is supported in part by the George A. Bray Chair in Nutrition. J.P. Després is chair professor of human nutrition, lipidology and prevention of cardiovascular disease supported by Provigo and Pfizer Canada.

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Table 1. Phenotypic characteristics of study participants by sex and generation groups.

Table 2. Results of factor analysis.

Figure 1. Genome-wide linkage results on the metabolic syndrome factor for autosomal chromosomes (Chr). Logarithm of the odds (LOD) scores are presented on the y axis, and genetic distances are presented on the x axis in centimorgans. The horizontal dashed line represents a LOD score of 1.00. The markers with the highest LOD score for every genomic region with LOD score above 1.75 are shown. GYS1, glycogen synthase 1.

Figure 2. Results of linkage analysis on chromosome 15 for the metabolic syndrome factor and the eight original variables. Genetics markers used for linkage are indicated under the x axis. The horizontal dashed line represents a LOD score of 1.00.