Interactions among the glucocorticoid receptor, lipoprotein lipase, and adrenergic receptor genes and plasma insulin and lipid levels in the Quebec Family Study

Genetic variants associated with fasting blood lipids in the U.S. population: Third National Health and Nutrition Examination Survey

BACKGROUND

The identification of genetic variants related to blood lipid levels within a large, population-based and nationally representative study might lead to a better understanding of the genetic contribution to serum lipid levels in the major race/ethnic groups in the U.S. population.

METHODS

Using data from the second phase (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III), we examined associations between 22 polymorphisms in 13 candidate genes and four serum lipids: high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TG). Univariate and multivariable linear regression and within-gene haplotype trend regression were used to test for genetic associations assuming an additive mode of inheritance for each of the three major race/ethnic groups in the United States (non-Hispanic white, non-Hispanic black, and Mexican American).

RESULTS

Variants within APOE (rs7412, rs429358), PON1 (rs854560), ITGB3 (rs5918), and NOS3 (rs2070744) were found to be associated with one or more blood lipids in at least one race/ethnic group in crude and adjusted analyses. In non-Hispanic whites, no individual polymorphisms were associated with any lipid trait. However, the PON1 A-G haplotype was significantly associated with LDL-C and TC. In non-Hispanic blacks, APOE variant rs7412 and haplotype T-T were strongly associated with LDL-C and TC; whereas, rs5918 of ITGB3 was significantly associated with TG. Several variants and haplotypes of three genes were significantly related to lipids in Mexican Americans: PON1 in relation to HDL-C; APOE and NOS3 in relation to LDL-C; and APOE in relation to TC.

CONCLUSIONS

We report the significant associations of blood lipids with variants and haplotypes in APOE, ITGB3, NOS3, and PON1 in the three main race/ethnic groups in the U.S. population using a large, nationally representative and population-based sample survey. Results from our study contribute to a growing body of literature identifying key determinants of plasma lipoprotein concentrations and could provide insight into the biological mechanisms underlying serum lipid and cholesterol concentrations.

Searching for epistasis and linkage heterogeneity by correlations of pedigree-specific linkage scores

Recognizing that multiple genes are likely responsible for common complex traits, statistical methods are needed to rapidly screen for either interacting genes or locus heterogeneity in genetic linkage data. To achieve this, some investigators have proposed examining the correlation of pedigree linkage scores between pairs of chromosomal regions, because large positive correlations suggest interacting loci and large negative correlations suggest locus heterogeneity (Cox et al. [1999]; Maclean et al. [1993]). However, the statistical significance of these extreme correlations has been difficult to determine due to the autocorrelation of linkage scores along chromosomes. In this study, we provide novel solutions to this problem by using results from random field theory, combined with simulations to determine the null correlation for syntenic loci. Simulations illustrate that our new methods control the Type-I error rates, so that one can avoid the extremely conservative Bonferroni correction, as well as the extremely time-consuming permutational method to compute P-values for non-syntenic loci. Application of these methods to prostate cancer linkage studies illustrates interpretation of results and provides insights into the impact of marker information content on the resulting statistical correlations, and ultimately the asymptotic P-values.

Peripheral mechanisms contributing to the glucocorticoid hypersensitivity in proopiomelanocortin null mice treated with corticosterone

Proopiomelanocortin (POMC) deficiency causes severe obesity through hyperphagia of hypothalamic origin. However, low glucocorticoid levels caused by adrenal insufficiency mitigate against insulin resistance, hyperphagia and fat accretion in Pomc-/- mice. Upon exogenous glucocorticoid replacement, corticosterone-supplemented (CORT) Pomc-/- mice show exaggerated responses, including excessive fat accumulation, hyperleptinaemia and insulin resistance. To investigate the peripheral mechanisms underlying this glucocorticoid hypersensitivity, we examined the expression levels of key determinants and targets of glucocorticoid action in adipose tissue and liver. Despite lower basal expression of 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1), which generates active glucocorticoids within cells, CORT-mediated induction of 11beta-HSD1 mRNA levels was more pronounced in adipose tissues of Pomc-/- mice. Similarly, CORT treatment increased lipoprotein lipase mRNA levels in all fat depots in Pomc-/- mice, consistent with exaggerated fat accumulation. Glucocorticoid receptor (GR) mRNA levels were selectively elevated in liver and retroperitoneal fat of Pomc-/- mice but were corrected by CORT in the latter depot. In liver, CORT increased phosphoenolpyruvate carboxykinase mRNA levels specifically in Pomc-/- mice, consistent with their insulin-resistant phenotype. Furthermore, CORT induced hypertension in Pomc-/- mice, independently of adipose or liver renin-angiotensin system activation. These data suggest that CORT-inducible 11beta-HSD1 expression in fat contributes to the adverse cardiometabolic effects of CORT in POMC deficiency, whereas higher GR levels may be more important in liver.

Two-stage two-locus models in genome-wide association

Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

Although there is growing appreciation that attempting to map genetic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association where the number of potential comparisons is enormous. In this article, the authors compare the performance of four different search strategies to detect loci which interact in genome-wide association—a single-locus search, an exhaustive two-locus search, and two, two-stage procedures in which a subset of loci initially identified with single-locus tests are analyzed using a full two-locus model. Their results show that when loci interact, an exhaustive two-locus search across the genome is superior to a two-stage strategy, and in many situations can identify loci which would not have been identified solely using a single-locus search. Their findings suggest that an exhaustive search involving all pairwise combinations of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.

Multilocus analyses of Renin-Angiotensin-aldosterone system gene variants on blood pressure at rest and during behavioral stress in young normotensive subjects

The renin-angiotensin-aldosterone system (RAAS) is a proteolytic cascade that regulates and maintains blood pressure (BP). This study aimed to explore the interactive and integrative effects of multiple RAAS polymorphisms on BP at rest and during behavioral stress in a normotensive population. A total of 920 young white and black twins (age: 12 to 30 years; 45% blacks) was subjected to three 10-minute stress tasks. Thirteen potential functional polymorphisms from 4 major RAAS genes were genotyped. We performed multilocus prediction allowing for genetic modification effects (gene-gene, gene-gender, gene-ethnicity, and gene-body mass index) using Multivariate Adaptive Regression Splines and generalized estimating equations. Single polymorphism analyses showed modest effects of M235T (angiotensinogen) and A-239T (angiotensin I-converting enzyme; P value range: 0.005 to 0.036), accounting for approximately 1% of the total variance of systolic BP at rest and during stress. Compared with this, the best multilocus models revealed multiple independent genetic modification effects (gene-gene, gene-gender, and gene-body mass index; P value range: 0.003 to 0.009), accounting for 2.5% and 7.3% of the total variance for systolic BP levels at rest and during stress, respectively. Our data support the hypothesis that multiple RAAS genetic modifications account for BP variation. We conclude that the RAAS genetic modifications may contribute more to the dynamic BP regulation in response to behavioral stress compared with the static BP value. In addition, we reported a gene-gene interaction between M235T (angiotensinogen) and A1159G (angiotensin I-converting enzyme) on stress systolic BP levels. We proposed a viable approach to test for the multiple genetic contributions to BP and hypertension.

Epistasis and the genetics of human diseases

Epistasis or modifier genes, that is, gene-gene interactions of non-allelic partners, play a major role in susceptibility to common human diseases. This old genetic concept has experienced a major renaissance recently. Interestingly, epistatic genes can make the disease less severe, or make it more severe. Hence, most diseases are of different intensities in different individuals and in different ethnicities. This phenomenon affects sickle-cell anemia carriers and other hemoglobinopathies, systemic lupus erythematosus, cystic fibrosis, complex autoimmune diseases, venous thromboembolism, and many others. It is likely, and fortunate, than 20 years form now, patients entering a medical facility will be subjected to a genomic scanning, including pathogenic genes as well as epistatic genes.

The beta(2)-adrenergic receptor Arg16-gly polymorphism and interactions involving beta(2)- and beta(3)-adrenergic receptor polymorphisms are associated with variations in longitudinal serum lipid profiles: the Bogalusa Heart Study

We examined the effects of combined genotypes of the beta(2)-adrenergic receptor (AR) Arg(16)-Gly and beta(3)-AR Trp(64)-Arg polymorphisms on longitudinal serum total (T-C) and low-density lipoprotein cholesterol (LDL-C) profiles in 1,198 subjects examined multiple times (6,488 observations) from 1973 to 1996 in the Bogalusa Heart Study, at ages from 4.5 to 38 years. Within 5-year age groups, T-C was significantly (P <.05) higher in beta(2)-AR Arg(16)/Arg(16) homozygotes than in Gly(16) carriers among those 4 to 8 (171.4 +/- 30.0 v 161.5 +/- 27.7 mg/dL), 9 to 13 (167.7 +/- 28.6 v 162.4 +/- 27.4 mg/dL), and 14 to 18 (158.8 +/- 29.6 v 154.7 +/- 27.5 mg/dL) years of age, but not in those 19 to 23, 24 to 28, 29 to 33, or 34 to 38 years of age. The beta(3)-AR polymorphism was not associated with variation in either T-C or LDL-C. In multilevel polynomial growth curve models, the combination of the beta(2)-AR Arg(16)/Arg(16) genotype with either the beta(3)-AR Arg(64)/Arg(64) or Trp(64)/Arg(64) genotypes, denoted AA/AX, was associated with variation in longitudinal T-C (P <.01) and LDL-C (P <.01) profiles. The association between combined beta(2)/beta(3)-AR genotype and lipid profiles differed among race/sex groups, being most marked in black females, in whom the AA/AX combination was associated with higher T-C and LDL-C profiles across all ages. In White males, the AA/AX combination was most strongly associated with higher lipids in adults. In black males and white females, lipid profiles differed little between genotype groups. Our findings suggest that the beta(2)-AR Arg(16)-Gly genotype influences T-C and LDL-C levels in an age-specific manner, that it may interact with beta(3)-AR Trp(64)-Arg genotypes to influence longitudinal T-C and LDL-C profiles, and that the effect of combined beta(2)/beta(3)-AR genotypes on T-C and LDL-C profiles may differ among race/sex groups.

[Genetics of obesity and metabolic complications in the Quebec Family Study]

Considerable progress has been accomplished over the past 10 years in the understanding of the genetic basis of obesity and its related metabolic complications. It is now well established that obesity aggregates within families and that genes are partly responsible for this familial aggregation. The number of genes potentially involved in obesity continues to grow. This review summarizes the evidence accumulated so for regarding the contribution of genetic factors in obesity and the number of gene and loci linked to obesity in the Quebec Family Study.

[Remarks of the Zurich Discussion Panel on the gene test]

Leider besteht für diesen Artikel keine Zusammenfassung. Als Einstieg stellen wir den Textanfang zur Verfügung. Seit einiger Zeit wird ein Gentest angeboten, mit dem sich angeblich das individuelle Risiko für z.B. Herz-/Kreislauferkrankungen, Thrombosen, Osteoporose, Mammakarzinom oder Schwangerschaftskomplikationen durch die Analyse von 18 genetischen Parametern bzw. deren Polymorphismen erkennen lässt. Anhand der Ergebnisse dieses Tests soll es dann möglich sein, für jede Frau die optimale Hormonsubstitution festzulegen und die individuellen Krankheitsrisiken durch geeignete Massnahmen zu minimieren. Nach der Veröffentlichung einer kurzen Stellungnahme zu diesem Gentest im Addendum der jüngsten Empfehlungen des Zürcher Gesprächskreises zur Hormonsubstitution in ‘Frauenarzt’ 2/2002 gingen verschiedene Kommentare ein. Der Zürcher Gesprächskreis sieht sich deshalb veranlasst, zum Thema Polymorphismen eine ausführliche Stellungnahme abzugeben.

Family studies: their role in the evaluation of genetic cardiovascular risk factors

Early epidemiological studies showed that genetic factors contribute to the risk of cardiovascular disease. Genetic epidemiological studies based upon families can be used to investigate familial trait aggregation, to localize genes implicated in cardiovascular diseases in the human genome, and to establish the role of environmental factors. Family studies can be also used to identify the physiological role of candidate genes for cardiovascular diseases, and to characterize shared environmental risk factors and their impact on the expression of genetic predisposition. The present paper reviews the existing family studies with special emphasis on those which have studied healthy populations in relation to cardiovascular disease such as the Framingham Heart Study, the National Heart, Lung, and Blood Institute Family Heart Study, and the STANISLAS cohort.

Clustering of metabolic abnormalities in obese individuals: the role of genetic factors

The objective of this paper is to review the current evidence in support of genetic factors underlying the clustering of components of the metabolic syndrome in obese individuals. It has become clear that individual features of the metabolic syndrome are partially determined by familial factors some of which are unique to a given component and others that are shared among several features. A few candidate genes, encoding proteins of glucose, insulin and lipid metabolism, lipolytic cascade, fatty acid intestinal absorption, glucocorticoid metabolism, haemostasis and blood pressure, have been associated with a clustering of metabolic abnormalities, although the functional significance of these associations remains to be established. Furthermore, genetic polymorphisms, such as those detected at several lipoprotein metabolism loci, can modulate the relationships between different components of the metabolic syndrome. An overfeeding study conducted on identical twins has demonstrated that genetic factors play an important role in the responsiveness to changing energy balance conditions. Leptin receptor, beta2 adrenergic receptor and glucocorticoid receptor gene polymorphisms have been associated with an augmented clustering of metabolic abnormalities in response to overfeeding. Gene-gene interaction effects between markers of the alpha2A, beta2 and beta3 adrenergic receptor genes on components of the metabolic syndrome have been described. Genetic factors also seem to modify the responsiveness of metabolic syndrome features to endurance training. A growing understanding of the genetic architecture of the metabolic syndrome may help in the prevention of this condition. The reduction of excess body fat, the most common clinical feature among the cluster of metabolic abnormalities, should be the focus of the prevention and treatment of the metabolic syndrome.