Multivariate linkage analysis of blood pressure and body mass index

Bivariate traits association analysis using generalized estimating equations in family data

Genome wide association study (GWAS) is becoming fundamental in the arduous task of deciphering the etiology of complex diseases. The majority of the statistical models used to address the genes-disease association consider a single response variable. However, it is common for certain diseases to have correlated phenotypes such as in cardiovascular diseases. Usually, GWAS typically sample unrelated individuals from a population and the shared familial risk factors are not investigated. In this paper, we propose to apply a bivariate model using family data that associates two phenotypes with a genetic region. Using generalized estimation equations (GEE), we model two phenotypes, either discrete, continuous or a mixture of them, as a function of genetic variables and other important covariates. We incorporate the kinship relationships into the working matrix extended to a bivariate analysis. The estimation method and the joint gene-set effect in both phenotypes are developed in this work. We also evaluate the proposed methodology with a simulation study and an application to real data.

Comparison of Heritability Estimation and Linkage Analysis for Multiple Traits Using Principal Component Analyses

A disease trait often can be characterized by multiple phenotypic measurements that can provide complementary information on disease etiology, physiology, or clinical manifestations. Given that multiple phenotypes may be correlated and reflect common underlying genetic mechanisms, the use of multivariate analysis of multiple traits may improve statistical power to detect genes and variants underlying complex traits. The literature, however, has been unclear as to the optimal approach for analyzing multiple correlated traits. In this study, heritability and linkage analysis was performed for six obstructive sleep apnea hypopnea syndrome (OSAHS) related phenotypes, as well as principal components of the phenotypes and principal components of the heritability (PCHs) using the data from Cleveland Family Study, which include both African and European American families. Our study demonstrates that principal components generally result in higher heritability and linkage evidence than individual traits. Furthermore, the PCHs can be transferred across populations, strongly suggesting that these PCHs reflect traits with common underlying genetic mechanisms for OSAHS across populations. Thus, PCHs can provide useful traits for using data on multiple phenotypes and for genetic studies of trans-ethnic populations.

The Human Obesity Gene Map: The 2004 Update

This paper presents the eleventh update of the human obesity gene map, which incorporates published results up to the end of October 2004. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTLs) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2004, 173 human obesity cases due to single-gene mutations in 10 different genes have been reported, and 49 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 166 genes which, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 221. The number of human obesity QTLs derived from genome scans continues to grow, and we have now 204 QTLs for obesity-related phenotypes from 50 genome-wide scans. A total of 38 genomic regions harbor QTLs replicated among two to four studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably with 358 findings of positive associations with 113 candidate genes. Among them, 18 genes are supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. Overall, >600 genes, markers, and chromosomal regions have been associated or linked with human obesity phenotypes. The electronic version of the map with links to useful publications and genomic and other relevant sites can be found at http://obesitygene.pbrc.edu.

The genetics of obesity and the metabolic syndrome

In this review, we discuss the genetic architecture of obesity and the metabolic syndrome, highlighting recent advances in identifying genetic variants and loci responsible for a portion of the variation in components of the metabolic syndrome, namely, adiposity traits, serum HDL and triglycerides, blood pressure, and glycemic traits. We focus particularly on recent progress from large-scale genome-wide association studies (GWAS), by detailing their successes and how lessons learned can pave the way for future discovery. Results from recent GWAS coalesce with earlier work suggesting numerous interconnections between obesity and the metabolic syndrome, developed through several potentially pleiotropic effects. We detail recent work by way of a case study on the cadherin 13 gene and its relation with adiponectin in the HyperGEN and the Framingham Heart Studies, and its association with obesity and the metabolic syndrome. We provide also a gene network analysis of recent variants related to obesity and metabolic syndrome discovered through genome-wide association studies, and 4 gene networks based on searching the NCBI database.

The genetics of blood pressure and hypertension: the role of rare variation

The role of heredity in influencing blood pressure and risk of hypertension is well recognized. However, progress in identifying specific genetic variation that contributes to heritability is very limited. This is in spite of completion of the human genome sequence, the development of extraordinary amounts of information about genome sequence variation and the investigation of blood pressure inheritance in linkage analysis, candidate gene studies and, most recently genome-wide association studies. This paper considers the progress of this research and the obstacles that have been encountered. This work has made clear that the genetic architecture of blood pressure regulation in the population is not likely to be shaped by commonly occurring genetic variation in a discrete set of blood pressure-influencing genes. Rather heritability may be accounted for by rare variation that has its biggest impact within pedigrees rather than on the population at large. Rare variants in a wide range of genes are likely to be the focus of high blood pressure genetics for the next several years and the emerging strategies that can be applied to uncover this genetic variation and the problems that must confronted are considered.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins

AIMS/HYPOTHESIS

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population.

METHODS

Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model.

RESULTS

Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes.

CONCLUSIONS/INTERPRETATION

Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Genome-wide linkage analysis for uric acid in families enriched for hypertension

BACKGROUND

Uric acid is heritable and associated with hypertension and insulin resistance. We sought to identify genomic regions influencing serum uric acid in families in which two or more siblings had hypertension.

METHODS

Uric acid levels and microsatellite markers were assayed in the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort (1075 whites and 1333 blacks) and the Hypertension Genetic Epidemiology Network (HyperGEN) cohort (1542 whites and 1627 blacks). Genome-wide linkage analyses of uric acid and bivariate linkage analyses of uric acid with an additional surrogate of insulin resistance were completed. Pathway analysis explored gene sets enriched at loci influencing uric acid.

RESULTS

In the GENOA white cohort, loci influencing uric acid were identified on chromosome 8 at 135 cM [multipoint logarithm of odds score (MLS) = 2.4], on chromosome 9 at 113 cM (MLS = 3.7) and on chromosome 16 at 93 cM (MLS = 2.3), but did not replicate in HyperGEN. At these loci, there was evidence of pleiotropy with other surrogates of insulin resistance and genes in the fructose and mannose metabolism pathway were enriched. In the HyperGEN-black cohort, there was some evidence of a locus for uric acid on chromosome 4 at 135 cM (MLS = 2.4) that had modest replication in GENOA (MLS = 1.2).

CONCLUSIONS

Several novel loci linked to uric acid were identified but none showed clear replication. Widespread diuretic use, a medication that raises uric acid levels, was an important study limitation. Bivariate linkage analyses and pathway analysis were consistent with genes regulating insulin resistance and fructose metabolism contributing to the heritability of uric acid.

A bivariate whole genome linkage study identified genomic regions influencing both BMD and bone structure

Areal BMD (aBMD) and areal bone size (ABS) are biologically correlated traits and are each important determinants of bone strength and risk of fractures. Studies showed that aBMD and ABS are genetically correlated, indicating that they may share some common genetic factors, which, however, are largely unknown. To study the genetic factors influencing both aBMD and ABS, bivariate whole genome linkage analyses were conducted for aBMD-ABS at the femoral neck (FN), lumbar spine (LS), and ultradistal (UD)-forearm in a large sample of 451 white pedigrees made up of 4498 individuals. We detected significant linkage on chromosome Xq27 (LOD = 4.89) for LS aBMD-ABS. In addition, we detected suggestive linkages at 20q11 (LOD = 3.65) and Xp11 (LOD = 2.96) for FN aBMD-ABS; at 12p11 (LOD = 3.39) and 17q21 (LOD = 2.94) for LS aBMD-ABS; and at 5q23 (LOD = 3.54), 7p15 (LOD = 3.45), Xq27 (LOD = 2.93), and 12p11 (LOD = 2.92) for UD-forearm aBMD-ABS. Subsequent discrimination analyses indicated that quantitative trait loci (QTLs) at 12p11 and 17q21 may have pleiotropic effects on aBMD and ABS. This study identified several genomic regions that may contain QTLs important for both aBMD and ABS. Further endeavors are necessary to follow these regions to eventually pinpoint the genetic variants affecting bone strength and risk of fractures.

Using linkage analysis to identify quantitative trait loci for sleep apnea in relationship to body mass index

To understand the genetics of sleep apnea, we evaluated the relationship between the apnea hypopnea index (AHI) and body mass index (BMI) through linkage analysis to identify genetic loci that may influence AHI and BMI jointly and AHI independent of BMI. Haseman-Elston sibling regression was conducted on AHI, AHI adjusted for BMI and BMI in African-American and European-American pedigrees. A comparison of the magnitude of linkage peaks was used to assess the relationship between AHI and BMI. In EAs, the strongest evidence for linkage to AHI was on 6q23-25 and 10q24-q25, both decreasing after BMI adjustment, suggesting loci with pleiotropic effects. Also, a promising area of linkage to AHI but not BMI was observed on 6p11-q11 near the orexin-2 receptor, suggesting BMI independent pathways. In AAs the strongest evidence of linkage for AHI after adjusting for BMI was on chromosome 8p21.3 with linkage increasing after BMI adjustment and on 8q24.1 with linkage decreasing after BMI adjustment. Novel linkage peaks were also observed in AAs to both BMI and AHI on chromosome 13 near the serotonin-2a receptor. These analyses suggest genetic loci for sleep apnea that operate both independently of BMI and through BMI-related pathways.

Genetic influences on growth traits of BMI: a longitudinal study of adult twins

OBJECTIVE

To investigate the interplay between genetic factors influencing baseline level and changes in BMI in adulthood.

METHODS AND PROCEDURES

A longitudinal twin study of the cohort of Finnish twins (N = 10,556 twin individuals) aged 20-46 years at baseline was conducted and followed up 15 years. Data on weight and height were obtained from mailed surveys in 1975, 1981, and 1990.

RESULTS

Latent growth models revealed a substantial genetic influence on BMI level at baseline in males and females (heritability (h(2)) 80% (95% confidence interval 0.79-0.80) for males and h(2) = 82% (0.81, 0.84) for females) and a moderate-to-high influence on rate of change in BMI (h(2) = 58% (0.50, 0.69) for males and h(2) = 64% (0.58, 0.69) for females). Only very weak evidence for genetic pleiotropy was observed; the genetic correlation between baseline and rate of change in BMI was very modest (-0.070 (-0.13, -0.068) for males and 0.04 (0.00, 0.08) for females.

DISCUSSION

Our population-based results provide a basis for identifying genetic variants for change in BMI, in particular weight gain. Furthermore, they demonstrate for the first time that such genetic variants for change in BMI are likely to be different from those affecting level of BMI.

Meta-analysis of genome-wide linkage studies in BMI and obesity

OBJECTIVE

The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis.

RESEARCH METHODS AND PROCEDURES

We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry.

RESULTS

Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1-qter and 12p11.21-q23 (p < 0.01).

CONCLUSION

Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.

Bivariate genome-wide scan for metabolic phenotypes in non-diabetic Chinese individuals from the Stanford, Asia and Pacific Program of Hypertension and Insulin Resistance Family Study

AIMS/HYPOTHESIS

Hypertension, obesity, impaired glucose tolerance and dyslipidaemia are metabolic abnormalities that often cluster together more often than expected by chance alone. Since these metabolic variables are highly heritable and are at least partially genetically determined, the clustering of defects in these traits implies that pleiotropic effects, where a common set of genes influences more than one trait simultaneously, are likely.

METHODS

We conducted bivariate linkage analyses for highly correlated traits, aiming to dissect the genetic architecture affecting these traits, in 411 Chinese families participating in the Stanford Asia-Pacific Program of Hypertension and Insulin Resistance Study.

RESULTS

We confirmed the pleiotropic effects of the locus at 37 cM on chromosome 20 on the following pairs: (1) fasting insulin and insulin AUC (empirical p = 0.0006); (2) fasting insulin and homeostasis model assessment of beta cell function (HOMA-beta) (empirical p = 0.0051); and (3) HOMA of insulin resistance (IR) and HOMA-beta (empirical p = 0.0044). In addition, the peak logarithm of the odds (LOD) scores of linkage between a chromosomal locus and a trait for the pair fasting insulin and HOMA-IR rose to 5.10 (equivalent LOD score in univariate analysis, LOD([1]) = 4.01, empirical p = 8.0 x 10(-5)) from 3.67 and 3.42 respectively for these two traits in univariate analysis. Additional significant linkage evidence, not shown in single-trait analysis, was identified at 45 cM on chromosome 16 for the pair 1 h insulin and the AUC for insulin, with a LOD score of 4.29 (or LOD([1]) = 3.27, empirical p = 2.0 x 10(-4)). This new locus is also likely to harbour the common genes regulating these two traits (p = 1.73 x 10(-6)).

CONCLUSIONS/INTERPRETATION

These data help provide a better understanding of the genomic structure underlying the metabolic syndrome.

Regression-based multivariate linkage analysis with an application to blood pressure and body mass index

Multivariate linkage analysis has been suggested for the analysis of correlated traits, such as blood pressure (BP) and body mass index (BMI), because it may offer greater power and provide clearer results than univariate analyses. Currently, the most commonly used multivariate linkage methods are extensions of the univariate variance component model. One concern about those methods is their inherent sensitivity to the assumption of multivariate normality which cannot be easily guaranteed in practice. Another problem possibly related to all multivariate linkage analysis methods is the difficulty in interpreting nominal p-values, because the asymptotic distribution of the test statistic has not been well characterized. Here we propose a regression-based multivariate linkage method in which a robust score statistic is used to detect linkage. The p-value of the statistic is evaluated by a simple and rapid simulation procedure. Theoretically, this method can be used for any number and type of traits and for general pedigree data. We apply this approach to a genome linkage analysis of blood pressure and body mass index data from the Beaver Dam Eye Study.

Linkage analysis of circulating levels of adiponectin in Hispanic children

INTRODUCTION

Adiponectin, a hormone produced exclusively by adipose tissue, is inversely associated with insulin resistance and proinflammatory conditions. The aim of this study was to find quantitative trait loci (QTLs) that affect circulating levels of adiponectin in Hispanic children participating in the VIVA LA FAMILIA Study by use of a systematic genome scan.

METHODS

The present study included extended families with at least one overweight child between 4 and 19 years old. Overweight was defined as body mass index (BMI) 95th percentile. Fasting blood was collected from 466 children from 127 families. Adiponectin was assayed by radioimmunoassay (RIA) technique in fasting serum. A genome-wide scan on circulating levels of adiponectin as a quantitative phenotype was conducted using the variance decomposition approach.

RESULTS

The highest logarithm of odds (LOD) score (4.2) was found on chromosome 11q23.2-11q24.2, and a second significant signal (LOD score=3.0) was found on chromosome 8q12.1-8q21.3. In addition, a signal suggestive of linkage (LOD score=2.5) was found between 18q21.3 and 18q22.3. After adjustment for BMI-Z score, the LOD score on chromosome 11 remained unchanged, but the signals on chromosomes 8 and 18 dropped to 1.6 and 1.7, respectively. Two other signals suggestive of linkage were found on chromosome 3 (LOD score=2.1) and 10 (LOD score=2.5). Although the region on chromosome 11 has been associated with obesity and diabetes-related traits in adult populations, this is the first observation of linkage in this region for adiponectin levels. Our suggestive linkages on chromosomes 10 and 3 replicate results for adiponectin seen in other populations. The influence of loci on chromosomes 18 and 8 on circulating adiponectin seemed to be mediated by BMI in the present study.

CONCLUSION

Our genome scan in children has identified a novel QTL and replicated QTLs in chromosomal regions previously shown to be linked with obesity and type 2 diabetes (T2D)-related phenotypes in adults. The genetic contribution of loci to adiponectin levels may vary across different populations and age groups. The strong linkage signal on chromosome 11 is most likely underlain by a gene(s) that may contribute to the high susceptibility of these Hispanic children to obesity and T2D.

Gene by environment QTL mapping through multiple trait analyses in blood pressure salt-sensitivity: identification of a novel QTL in rat chromosome 5

BACKGROUND

The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene x environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem.

METHODS

We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data.

RESULTS

We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach.

CONCLUSION

Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness.

Quantitative trait loci influencing low density lipoprotein particle size in African Americans

Genomic regions that influence LDL particle size in African Americans are not known. We performed family-based linkage analyses to identify genomic regions that influence LDL particle size and also exert pleiotropic effects on two closely related lipid traits, high density lipoprotein cholesterol (HDL-C) and triglycerides, in African Americans. Subjects (n = 1,318, 63.0 +/- 9.5 years, 70% women, 79% hypertensive) were ascertained through sibships with two or more individuals diagnosed with essential hypertension before age 60. LDL particle size was measured by polyacrylamide gel electrophoresis, and triglyceride levels were log-transformed to reduce skewness. Genotypes were measured at 366 microsatellite marker loci distributed across the 22 autosomes. Univariate and bivariate linkage analyses were performed using a variance components approach. LDL particle size was highly heritable (h(2) = 0.78) and significantly (P < 0.0001) genetically correlated with HDL-C (rho(G) = 0.32) and log triglycerides (rho(G) = -0.43). Significant evidence of linkage for LDL particle size was present on chromosome 19 [85.3 centimorgan (cM), log of the odds (LOD) = 3.07, P = 0.0001], and suggestive evidence of linkage was present on chromosome 12 (90.8 cM, LOD = 2.02, P = 0.0011). Bivariate linkage analyses revealed tentative evidence for a region with pleiotropic effects on LDL particle size and HDL-C on chromosome 4 (52.9 cM, LOD = 2.06, P = 0.0069). These genomic regions may contain genes that influence interindividual variation in LDL particle size and potentially coronary heart disease susceptibility in African Americans.

Summary of contributions to GAW Group 12: multivariate methods

Here we summarize the contributions to Group 12 of Genetic Analysis Workshop (GAW) 14, held in Noordwijkerhout, The Netherlands. The theme of this group, multivariate methods, covered a broad range of statistical applications. Most of the contributors considered Problem 1 of the GAW. However, one paper considered the bivariate analysis of two binary phenotypes generated by the simulated data in Problem 2. Some contributors focused on statistical issues involved in considering multiple variables, and others on extensions to the variance-components methodology for analysis of quantitative traits. Applications to the Collaborative Study on the Genetics of Alcoholism data identified a single-nucleotide polymorphism (SNP) on chromosome 4 associated with the ttth1-ttth4 phenotypes, and replicated previous findings of linkage on chromosome 4 for alcohol consumption, using microsatellite and SNP data.

The human obesity gene map: the 2005 update

This paper presents the 12th update of the human obesity gene map, which incorporates published results up to the end of October 2005. Evidence from single-gene mutation obesity cases, Mendelian disorders exhibiting obesity as a clinical feature, transgenic and knockout murine models relevant to obesity, quantitative trait loci (QTL) from animal cross-breeding experiments, association studies with candidate genes, and linkages from genome scans is reviewed. As of October 2005, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes. There are 244 genes that, when mutated or expressed as transgenes in the mouse, result in phenotypes that affect body weight and adiposity. The number of QTLs reported from animal models currently reaches 408. The number of human obesity QTLs derived from genome scans continues to grow, and we now have 253 QTLs for obesity-related phenotypes from 61 genome-wide scans. A total of 52 genomic regions harbor QTLs supported by two or more studies. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has also increased considerably, with 426 findings of positive associations with 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies. The obesity gene map shows putative loci on all chromosomes except Y. The electronic version of the map with links to useful publications and relevant sites can be found at http://obesitygene.pbrc.edu.

Variance components analyses of multiple asthma traits in a large sample of Australian families ascertained through a twin proband

BACKGROUND

Intermediate phenotypes are often measured as a proxy for asthma. It is largely unclear to what extent the same set of environmental or genetic factors regulate these traits.

OBJECTIVE

Estimate the environmental and genetic correlations between self-reported and clinical asthma traits.

METHODS

A total of 3,073 subjects from 802 families were ascertained through a twin proband. Traits measured included self-reported asthma, airway histamine responsiveness (AHR), skin prick response to common allergens including house dust mite (Dermatophagoides pteronyssinus [D. pter]), baseline lung function, total serum immunoglobulin E (IgE) and eosinophilia. Bivariate and multivariate analyses of eight traits were performed with adjustment for ascertainment and significant covariates.

RESULTS

Overall 2,716 participants completed an asthma questionnaire and 2,087 were clinically tested, including 1,289 self-reported asthmatics (92% previously diagnosed by a doctor). Asthma, AHR, markers of allergic sensitization and eosinophilia had significant environmental correlations with each other (range: 0.23-0.89). Baseline forced expiratory volume in 1 s (FEV(1)) showed low environmental correlations with most traits. Fewer genetic correlations were significantly different from zero. Phenotypes with greatest genetic similarity were asthma and atopy (0.46), IgE and eosinophilia (0.44), AHR and D. pter (0.43) and AHR and airway obstruction (-0.43). Traits with greatest genetic dissimilarity were FEV(1) and atopy (0.05), airway obstruction and IgE (0.07) and FEV(1) and D. pter (0.11).

CONCLUSION

These results suggest that the same set of environmental factors regulates the variation of many asthma traits. In addition, although most traits are regulated to great extent by specific genetic factors, there is still some degree of genetic overlap that could be exploited by multivariate linkage approaches.

Genome scans with gene-covariate interaction

Genetic models for gene-covariate interactions are described. Methods of linkage analysis that utilize special features of these models and the corresponding score statistics are derived. Their power is compared with that of simple genome scans that ignore these special features, and substantial gains in power are observed when the gene-covariate interaction is strong. Quantitative trait mapping in randomly ascertained sibships and affected sibpair mapping are discussed. For the latter case, a simpler statistic is proposed that has similar performance to the score statistic, but does not require the estimation of nuisance parameters. Since the nuisance parameters are not estimable solely from affected sib-pair data, this statistic would be much easier to apply in practice. Similarities with linkage analysis of models for longitudinal data and multivariate phenotypes are also briefly discussed. Approximations for the P-value and power are derived under the framework of local alternatives.

A genetic analysis of ambulatory cardiorespiratory coupling

This study assessed the heritability of ambulatory heart period, respiratory sinus arrhythmia (RSA), and respiration rate and tested the hypothesis that the well-established correlation between these variables is determined by common genetic factors. In 780 healthy twins and siblings, 24-h ambulatory recordings of ECG and thorax impedance were made. Genetic analyses showed considerable heritability for heart period (37%-48%), RSA (40%-55%), and respiration rate (27%-81%) at all daily periods. Significant genetic correlations were found throughout. Common genes explained large portions of the covariance between heart period and RSA and between respiration rate and RSA. During the afternoon and night, the covariance between respiration rate and RSA was completely determined by common genes. This overlap in genes can be exploited to increase the power of linkage studies to detect genetic variation influencing cardiovascular disease risk.

Genomic susceptibility loci for brain atrophy in hypertensive sibships from the GENOA study

We measured 366 microsatellite markers genome-wide to search for loci contributing to subcortical white matter ischemic damage (leukoariosis) and brain atrophy in 488 non-Hispanic white subjects (193 men, 295 women; mean age+/-SD=64.1+/-7.7 years; 79% hypertensive) from 223 sibships recruited through > or =2 members with essential hypertension diagnosed before age 60. Leukoariosis was quantitated by magnetic resonance imaging (MRI), brain atrophy by the difference between intracranial and brain volumes, and calculated mean arterial pressure and pulse pressure provided measures of steady-state level and pulsatile components of blood pressure. After adjustment for sex and age, variance components models estimated significant heritability of leukoariosis (0.72), brain atrophy (0.52), mean arterial pressure (0.084), and pulse pressure (0.294) (P<0.0001 for each trait). Univariate maximum logarithm of odds scores (MLS) were observed for leukoariosis on chromosome 5 (MLS=1.91; P=0.00150); for brain atrophy on 1q and 17p (MLS=2.76, P=0.00018); for mean arterial pressure on 11p (MLS=1.57; P=0.00354); and for pulse pressure on 11p (MLS=3.02; P=0.00070). Bivariate linkage analyses provided evidence of loci with pleiotropic effects on brain atrophy and pulse pressure on chromosomes 11p (MLS = 5.07 at 16 cM; P=0.00001) and 16q (MLS of 4.56 at 124 cM; P=0.00003). These results demonstrate usefulness of multivariate linkage analyses to detect loci with pleiotropic effects on genetically correlated traits and suggest overlap between the genes influencing blood pressure and those contributing to brain atrophy.