Principal component for metabolic syndrome risk maps to chromosome 4p in Mexican Americans: the San Antonio Family Heart Study

Metabolic syndrome refers to the clustering of disease conditions such as insulin resistance, hyperinsulinemia, dyslipidemia, hypertension, and obesity. To explore the genetic predispositions of this complex syndrome, we conducted a principal components analysis using data on 14 phenotypes related to the risk of developing metabolic syndrome. The subjects were 566 nondiabetic Mexican Americans, distributed in 41 extended families from the San Antonio Family Heart Study. The factor scores obtained from these 14 phenotypes were used in multipoint linkage analysis using SOLAR. Factors were identified that accounted for 73% of the total variance of the original variables: body size-adiposity, insulin-glucose, blood pressure, and lipid levels. Each factor exhibited evidence for either significant or suggestive linkage involving four factor-specific chromosomal regions relating to chromosomes 1, 3, 4, and 6. Significant evidence for linkage of the lipid factor was found on chromosome 4 near marker D4S403 (LOD = 3.52), where the cholecystokinin A receptor (CCKAR) and ADP-ribosyl cyclase 1 (CD38) genes are located. Suggestive evidence for linkage of the body size-adiposity factor to chromosome 1 near marker D1S1597 (LOD = 2.53) in the region containing the nuclear receptor subfamily 0, group B, member 2 gene (NROB2) also was observed. The insulin-glucose and blood pressure factors were linked suggestively to regions on chromosome 3 near marker D3S1595 (LOD = 2.20) and on chromosome 6 near marker D6S 1031 (LOD = 2.08), respectively. In summary, our findings suggest that the factor structures for the risk of metabolic syndrome are influenced by multiple distinct genes across the genome.     

Male-like sexual behavior of female mouse lacking fucose mutarotase

Background

Quantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers.

Results

We propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes.

Conclusions

PCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.     

Exploring pleiotropy using principal components

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.     

Exploring Trans-acting regulators of gene expression associated with metabolic syndrome: a coupled application of factor analysis and linkage analysis

Metabolic syndrome is an established public health problem because it increases the risk of cardiovascular disease. Although many genes contribute to the etiology of metabolic syndrome, the effect size of each gene is much weaker than predicted by heritability. Exploring the regulation of gene expression levels can bridge this gap. Here, we aimed to explore genomic loci for a trans-regulator of gene expression associated with metabolic syndrome via factor analysis and linkage analysis. Forty-nine gene-expression quantitative traits (eQTs) associated with metabolic syndrome were selected and clustered into three latent eQTs by factor analysis. These included insulin-related, lipid-related, and glucose-related latent eQTs. Linkage analyses were performed for 49 original eQTs and 3 latent eQTs. Two original eQTs, adipose differentiation-related protein and IRS2, showed the highest LOD scores (3.09 and 3.08, respectively). We observed that the insulin-related and the lipid-related latent eQTs had stronger linkage evidence than the original eQTs, suggesting the presence of common gene regulators captured as latent eQTs. Several single nucleotide polymorphism markers located on the peaks were co-localized with specific genes. Five of them were related to the neuronal system, which plays a role in the metabolic syndrome pathway. The findings related those eQTs could contribute to our understanding regarding the genetic pathway for insulin and lipid metabolism, particularly the regulation of gene expression.     

Quantitative trait locus on 15q for a metabolic syndrome variable derived from factor analysis

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 analyses were performed on eight metabolic syndrome-related phenotypes including waist circumference; BMI; systolic and diastolic blood pressure; and plasma insulin, glucose, triglyceride, and high-density lipoprotein-cholesterol levels. Three factors were identified and interpreted as general metabolic syndrome, blood pressure, and blood lipids, respectively. The general metabolic syndrome factor had high factor loadings (>0.4) for all phenotypes and explained 42% of the total variance, and family membership accounted for 45.6% of the factor variance. A genome-wide linkage scan performed with this first factor revealed the existence of a quantitative trait locus on chromosome 15 (86 cM) with a logarithm of odds score of 3.15. Suggestive evidence of linkage (logarithm of odds > 1.75) was also observed on chromosomes 1p, 3p, 3q, 6q, 7p, 19q, and 21q. These quantitative trait loci may harbor genes contributing to the clustering of the metabolic syndrome-related phenotypes.     

Lipoprotein candidate genes for multivariate factors of the insulin resistance syndrome: a sib-pair linkage analysis in women twins.

The insulin resistance syndrome (IRS) is characterized by a combination of interrelated coronary heart disease risk factors, including low high-density lipoprotein cholesterol (HDLC) levels, obesity and increases in triglyceride (TG), systolic and diastolic blood pressure (BP), small low-density lipoprotein particles (LDL-size), and fasting and postload plasma insulin and glucose. Using factor analysis, we previously identified multivariate factors based on data from women participating in the Kaiser Permanente Women Twins Study: 1) Weight/Fat, 2) Insulin/Glucose, 3) Lipids, and 4) BP. The purpose of this study is to evaluate evidence for genetic linkage between the multivariate factors and candidate genes. Quantitative sib-pair analysis based on the factor scores with markers for 9 candidate genes was carried out based on data from 126 pairs of dizygotic (DZ) women twins from the second exam of the Kaiser Permanente Women Twins study. Suggestive evidence for linkage was found for the Weight/fat factor and the Apo E gene (p = 0.01), and stronger evidence for linkage with the Lipid factor and the cholesterol ester transfer protein (p = 0.002) gene. Therefore, the CETP gene appears to influence covariation in LDL size, TG, and HDL, and may account for a portion of the well-established statistical and metabolic associations observed between these risk factors.     

家族性不宁腿综合征候选基因的连锁分析

不宁腿综合征（restless legs syndrome,RLS)是以下肢部出现蚁行样及酸、麻、胀等不适感而使肢体不得休息为特征的一组病症。由于症状常在晚间发作并导致运动不安,患者长期入睡困难,经受严重的继发性失眠。作为一种常见的神经系统疾病,RLS发病率高达5％,其中原发性RLS多呈阳性家族史,表现为单基因决定的常染色体显性遗传。现在,人们普遍认为RLS的发生很可能与神经系统内多巴胺能功能异常和脑内铁缺乏有关,并初步建立了脑铁－多巴胺能系统的致病模型。为了探求脑铁－多巴胺能系统在RLS中的作用,选择了与脑铁－多巴胺能系统相关的16个疾病侯选基因,在每个候选基因附近染色体区域内选取若干个微卫星多态标记,应用微卫星引物荧光标记－基因扫描技术,对一个汉族家族性不宁腿综合征家系进行了基因分型和常染色体显性遗传模式下的连锁分析,试图从分子遗传学层面上确认或排除一些可能与RLS相关的重要侯选基因。结果显示,当重组系数θ=0.00时,LOD值均小于－2.00,所选位点与家族性不宁腿综合征不连锁。由此得出结论,在本家系中,所有候选基因均与家族性不宁腿综合征的发病无关,家族性不宁腿综合征可能是由其他多巴胺传导和脑铁代谢相关基因所致,或是存在全新的致病机制参与RLS的发生。     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.     

Domestication of plants in the Americas: insights from Mendelian and molecular genetics

BACKGROUND: Plant domestication occurred independently in four different regions of the Americas. In general, different species were domesticated in each area, though a few species were domesticated independently in more than one area. The changes resulting from human selection conform to the familiar domestication syndrome, though different traits making up this syndrome, for example loss of dispersal, are achieved by different routes in crops belonging to different families. GENETIC AND MOLECULAR ANALYSES OF DOMESTICATION: Understanding of the genetic control of elements of the domestication syndrome is improving as a result of the development of saturated linkage maps for major crops, identification and mapping of quantitative trait loci, cloning and sequencing of genes or parts of genes, and discoveries of widespread orthologies in genes and linkage groups within and between families. As the modes of action of the genes involved in domestication and the metabolic pathways leading to particular phenotypes become better understood, it should be possible to determine whether similar phenotypes have similar underlying genetic controls, or whether human selection in genetically related but independently domesticated taxa has fixed different mutants with similar phenotypic effects. CONCLUSIONS: Such studies will permit more critical analysis of possible examples of multiple domestications and of the origin(s) and spread of distinctive variants within crops. They also offer the possibility of improving existing crops, not only major food staples but also minor crops that are potential export crops for developing countries or alternative crops for marginal areas.     

The cholinesterases: analysis by pharmacogenomics in man

We have undertaken a study on variations in cholinesterase (ChE) genes in relation to cardiovascular (CV) function and the metabolic syndrome. Peripheral and central nervous system control of cardiovascular (CV) function mediated through cholinergic pathways is critical in homeostatic maintenance of blood pressure and responsiveness to stress. For acetylcholinesterase (AChE; EC 3.1.1.7) our focus is to identify single nucleotide polymorphisms (SNPs) in the gene that are linked to cardiovascular function. For butyrylcholinesterase (BChE; EC 3.1.1.8) we examined whether BChE activity correlated with parameters of the metabolic syndrome and cardiovascular function. ChE can be found in whole blood enabling a characterization of biochemical phenotype in addition to correlating genotype with phenotypic physiologic responses. Analysis of enzymatic activity was determined spectrophotometrically in blood samples from twin and other subject registries. Correlation analysis revealed significant relationships between enzyme activity and certain CV endpoints. Linkage analysis with data from a dizygotic (DZ) twin set showed a suggestive linkage at the BChE locus, and statistical analysis revealed a high correlation between BChE activity and variables associated with cardiovascular risk and the metabolic syndrome. Pattern of within-pair twin correlations by zygosity and the ACE model-fitting findings suggest the major source of this variation (65%) is attributable to an additive genetic component. To date 19 SNPs have been identified by the re-sequencing of AChE including four nonsynonymous coding SNPs (cSNPs).     

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.     

Multivariate linkage analysis using the electrophysiological phenotypes in the COGA alcoholism data

Multivariate linkage analysis using several correlated traits may provide greater statistical power to detect susceptibility genes in loci whose effects are too small to be detected in univariate analysis. In this analysis, we apply a new approach and perform a linkage analysis of several electrophysiological phenotypes of the Collaborative Study on the Genetics of Alcoholism data of the Genetic Analysis Workshop 14. Our approach is based on a variance-component model to map candidate genes using repeated or longitudinal measurements. It can take into account covariate effects and time-dependent genetic effects in general pedigree data. We compare our results with the ones obtained by SOLAR using single measurement data. Our multivariate linkage analysis found linkage evidence on two regions on chromosome 4: around marker GABRB1 at 51.4 cM and marker FABP2 at 116.8 cM (unadjusted p-value = 0.00006).     

Impaired lung function is associated with obesity and metabolic syndrome in adults

Objective: Impaired lung function is associated with obesity and insulin resistance. In this study, we investigated the relationship between metabolic syndrome and impaired lung function in adults. Research Methods and Procedures: A total of 46,514 subjects 20 years and over (21,669 men and 24,845 women, mean age = 37.3 ± 11.2 and 37.0 ± 11.3 years, respectively) were recruited from four nationwide MJ Health Screening Centers in Taiwan from 1998 to 2000. Metabolic syndrome was defined using the National Cholesterol Education Panel (NCEP) metabolic syndrome criteria or America Heart Association/National Heart Lung Blood Institute (AHA/NHLBI) criteria. The relationship between metabolic syndrome and lung function test was examined using multivariate logistic regression analysis. Results: The prevalence of impaired lung function was 11.1% in men and 14.0% in women. The prevalence of metabolic syndrome was 5.8% using NCEP criteria and 12.8% using AHA/NHLBI criteria. In multivariate logistic regression analysis with adjustment for age, gender, BMI, smoking, alcohol drinking, and physical activity, restrictive lung impairment was independently associated with increased risk of having metabolic syndrome (p < 0.01, odds ratios = 1.221 using NCEP criteria and 1.150 using AHA/NHLBI criteria). Discussion: Obesity and metabolic syndrome were associated with impaired lung function in adults in Taiwan. Our results imply that obesity and insulin resistance may be the common pathways underlying lung function impairment and metabolic syndrome. Moreover, lung function test may be applied as an additional evaluation for metabolic syndrome in a clinical setting.     

Exclusion of COL1A1, COL1A2, and COL3A1 genes as candidate genes for Ehlers-Danlos syndrome type I in one large family

Summary Ehlers-Danlos syndrome (EDS) type I is a generalized connective tissue disorder, the major manifestations of which are soft, velvety hyperextensible skin and moderately severe joint hypermobility. The gene defect or defects causing EDS type I have not yet been defined, but previous observations suggested that the syndrome may be caused by mutations in the genes for type-I collagen (COL1A1 and COL1A2) or type-III collagen (COL3A1). Here, we performed linkage studies for these three genes in large Azerbaijanian family with EDS type I. Three polymorphisms in the COL3A1 gene, two in the COL1A1 gene, and one in the COL1A2 gene were tested using the polymerase chain reaction. The data obtained excluded linkage of any of the three genes to EDS type I in the family.On leave of absence from Institute of Human Genetics, National Research Center of Medical Genetics, Moskvorechie St., 1. Moscow 115478, USSR     

Multivariate segregation analysis for quantitative traits in line crosses

Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectation-maximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F(2) mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.     

Marfan syndrome: exclusion of genetic linkage to five genes coding for connective tissue components in the long arm of chromosome 2

Summary Marfan syndrome represents a heterogeneous connective tissue disease, the symptoms arising in several tissues and organs. The defective gene(s) behind this autosomal dominant condition has not been found despite considerable research. The main targets of the research have been the genes coding for connective tissue components. Several of the candidate genes suspected to be defective in Marfan syndrome are located on the long arm of chromosome 2. These genes include a cluster of two genes coding for fibrillar collagens COL3A1 and COL5A2, and a third member of the collagen gene family: COL6A3. Furthermore, genes for elastin (ELN) and fibronectin (FN) are also located in this area of chromosome 2. We studied this chromosomal area using restriction fragment length polymorphism (RFLP) linkage analysis in five Finnish Marfan families with affected members in three generations. In two point linkage analyses, Lod scores of –3.192 ( = 0.1) to COL3A1, –1.683 ( = 0) to COL6A3 and –2.664 ( = 0.01) to FN were obtained, whereas the linkage analysis between elastin and the disease was non-informative (Lod score 0.444, = 0). With the multipoint linkage analysis that permits simultaneous examination of several loci and more efficient use of family data, we obtained an exclusion of all these loci as the site of the mutation leading to Marfan syndrome in these families.     

Maximum likelihood haplotyping for general pedigrees

Haplotype data is valuable in mapping disease-susceptibility genes in the study of Mendelian and complex diseases. We present algorithms for inferring a most likely haplotype configuration for general pedigrees, implemented in the newest version of the genetic linkage analysis system SUPERLINK. In SUPERLINK, genetic linkage analysis problems are represented internally using Bayesian networks. The use of Bayesian networks enables efficient maximum likelihood haplotyping for more complex pedigrees than was previously possible. Furthermore, to support efficient haplotyping for larger pedigrees, we have also incorporated a novel algorithm for determining a better elimination order for the variables of the Bayesian network. The presented optimization algorithm also improves likelihood computations. We present experimental results for the new algorithms on a variety of real and semiartificial data sets, and use our software to evaluate MCMC approximations for haplotyping.     

Mutation analysis of the MKKS gene in McKusick-Kaufman syndrome and selected Bardet-Biedl syndrome patients

McKusick-Kaufman syndrome comprises hydrometrocolpos, polydactyly, and congenital heart defects and overlaps with Bardet-Biedl syndrome, comprising retinitis pigmentosa, polydactyly, obesity, mental retardation, and renal and genital anomalies. Bardet-Biedl syndrome is genetically heterogeneous with three cloned genes ( BBS2, BBS4, and MKKS) and at least three other known loci ( BBS1, BBS3, and BBS5). Both McKusick-Kaufman syndrome and Bardet-Biedl syndrome are inherited in an autosomal recessive pattern, and both syndromes are caused by mutations in the MKKS gene. However, mutations in MKKS are found in only 4%-11% of unselected Bardet-Biedl syndrome patients. We hypothesized that an analysis of patients with atypical Bardet-Biedl syndrome and McKusick-Kaufman syndrome (Group I; 15 probands) and patients with Bardet-Biedl syndrome who had linkage results inconsistent with linkage to the other loci (Group II; 12 probands) could increase the MKKS mutation yield. Both mutant alleles were identified in only two families in Group II. Single (heterozygous) sequence variations were found in three Group I families and in two Group II families. Combining these results with previously published data showed that only one mutant allele was detected in nearly half of all patients screened to date, suggesting that unusual mutational mechanisms or patterns of inheritance may be involved. However, sequencing of the BBS2 gene in these patients did not provide any evidence of digenic or "triallelic" inheritance. The frequency of detected mutations in MKKS in Group II patients was 24%, i.e., six times higher than the published rate for unselected BBS patients, suggesting that small-scale linkage analyses may be useful in suitable families.     

Use of multivariate linkage analysis for dissection of a complex cognitive trait

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.     

Genome‐wide Linkage Analysis of Multiple Metabolic Factors: Evidence of Genetic Heterogeneity

The metabolic syndrome is a highly complex disease and has become one of the major public‐health challenges worldwide. We sought to identify genetic loci with potential influence on multiple metabolic factors in a white population in Beaver Dam, Wisconsin, and to explore the possibility of genetic heterogeneity by family history of diabetes (FHD). Three metabolic factors were generated using principal‐component factor analysis, and they represented: (i) glycemia, (ii) blood pressure, and (iii) combined (BMI, high‐density lipoprotein (HDL) cholesterol, and serum uric acid) factors. Multipoint model‐free linkage analysis of these factors with 385 microsatellite markers was performed on 1,055 sib‐pairs, using Haseman–Elston regression. Genome‐wide suggestive evidence of linkage was found at 30 cM on chromosome 22q (empirical P (P_e) = 0.0002) for the glycemia factor, at 188–191 cM on chromosome 1q (P_e = 0.0007) for the blood pressure factor, and at 82 cM on chromosome 17q (P_e = 0.0007) for the combined factor. Subset analyses of the families by FHD showed evidence of genetic heterogeneity, with divergent linkage signals in the subsets on at least four chromosomes. We found evidence of genetic heterogeneity by FHD for the three metabolic factors. The results also confirmed findings of previous studies that mapped components of the metabolic syndrome to a chromosome 1q region.