A candidate gene study of obstructive sleep apnea in European Americans and African Americans.

RATIONALE: Obstructive sleep apnea (OSA) is hypothesized to be influenced by genes within pathways involved with obesity, craniofacial development, inflammation, and ventilatory control. OBJECTIVES: We conducted the first candidate gene study of OSA using family data from European Americans and African Americans, selecting biologically plausible genes from within these pathways. METHODS: A total of 1,080 single nucleotide polymorphisms (SNPs) were genotyped in 729 African Americans and 505 SNPs were genotyped in 694 European Americans. Coding for SNPs additively, association testing on the apnea-hypopnea index (AHI) as a continuous trait, and OSA as a dichotomous trait (AHI ≥15) was conducted using methods that account for familial correlations in models adjusted for age, age-squared, and sex, with and without body mass index. MEASUREMENTS AND MAIN RESULTS: In European Americans, variants within C-reactive protein (CRP) and glial cell line-derived neurotrophic factor (GDNF) were associated with AHI (CRP: ß = 4.6; SE = 1.1; P = 0.0000402) (GDNF: ß = 4.3; SE = 1; P = 0.0000201) and with the dichotomous OSA trait (CRP: odds ratio = 2.4; 95% confidence interval, 1.5-3.9; P = 0.000170) (GDNF: odds ratio = 2; 95% confidence interval, 1.4-2.89; P = 0.0000433). In African Americans, rs9526240 within serotonin receptor 2a (HTR2A: odds ratio = 2.1; 95% confidence interval, 1.5-2.9; P = 0.00005233) was associated with OSA. CONCLUSIONS: This candidate gene analysis identified the potential role of genes operating through intermediate disease pathways to influence sleep apnea phenotypes, providing a framework for focusing future replication studies.

Genetic association tests: a method for the joint analysis of family and case-control data.

With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.

Genome-wide association scan of Dupuytren's disease.

PURPOSE: Dupuytren's disease (DD) has a strong genetic component that is suggested by population studies and family clustering. Genetic studies have yet to identify the gene(s) involved in DD. The purpose of this study was to identify regions of the entire genome (chromosomes 1-23) associated with the disease by performing a genome-wide association scan on DD patients and controls. METHODS: We isolated genomic DNA from saliva collected from 40 unrelated DD patients and 40 unaffected controls. We conducted the genotyping using CytoSNP-Infinium HD Ultra genotyping assay on the Illumina platform. Using both log regression and mapping by admixture linkage disequilibrium analysis methods, we analyzed the single nucleotide polymorphism genotyping data. RESULTS: Single nucleotide polymorphism analysis revealed a significant association in regions for chromosomes 1, 3 through 6, 11, 16, 17, and 23. Mapping by admixture linkage disequilibrium analysis showed ancestry-associated regions in chromosomes 2, 6, 8, 11, 16, and 20, which may harbor DD susceptibility genes. Both analysis methods revealed loci association in chromosomes 6, 11, and 16. CONCLUSIONS: Our data suggest that chromosomes 6, 11, and 16 may contain the genes for DD and that multiple genes may be involved in DD. Future genetic studies on DD should focus on these areas of the genome.

Haseman-Elston regression in ascertained samples: importance of dependent variable and mean correction factor selection.

OBJECTIVE: One of the first tools for performing linkage analysis, Haseman-Elston regression (HE), has been successfully used to identify linkages to several disease traits. A recent explosion in extensions of HE leaves one faced with the task of choosing a flavor of HE best suited for a given situation. This paper puts this dilemma into perspective and proposes a modification to HE for highly ascertained samples (BLUP-PM). METHODS: Using data simulated for a range of models, we evaluated type I error and power of several dependent variables in HE, including the novel BLUP-PM. RESULTS: When analyzing a continuous trait, even in highly ascertained samples, type I error is stable and approximately nominal across dependent variables. When analyzing binary traits in highly ascertained samples, type I error is elevated and unstable for all except BLUP-PM. Regardless of trait type, the optimally weighted HE regression and BLUP-PM have the greatest power. CONCLUSIONS: Ascertained samples do not always reflect the population from which they are drawn and therefore choice of dependent variable in HE becomes increasingly important. Our results do not reveal a single, universal choice, but offer criteria by which to choose and demonstrate BLUP-PM performs well in most situations.

A regression based transmission/disequilibrium test for binary traits: the power of joint tests for linkage and association.

BACKGROUND: In this analysis we applied a regression based transmission disequilibrium test to the binary trait presence or absence of Kofendred Personality Disorder in the Genetic Analysis Workshop 14 (GAW14) simulated dataset and determined the power and type I error rate of the method at varying map densities and sample sizes. To conduct this transmission disequilibrium test, the logit transformation was applied to a binary outcome and regressed on an indicator variable for the transmitted allele from informative matings. All 100 replicates from chromosomes 1, 3, 5, and 9 for the Aipotu and the combined Aipotu, Karangar, and Danacaa populations were used at densities of 3, 1, and 0.3 cM. Power and type I error were determined by the number of replicates significant at the 0.05 level. RESULTS: The maximum power to detect linkage and association with the Aipotu population was 93% for chromosome 3 using a 0.3-cM map. For chromosomes 1, 5, and 9 the power was less than 10% at the 3-cM scan and less than 22% for the 0.3-cM map. With the larger sample size, power increased to 38% for chromosome 1, 100% for chromosome 3, 31% for chromosome 5, and 23% for chromosome 9. Type I error was approximately 7%. CONCLUSION: The power of this method is highly dependent on the amount of information in a region. This study suggests that single-point methods are not particularly effective in narrowing a fine-mapping region, particularly when using single-nucleotide polymorphism data and when linkage disequilibrium in the region is variable.

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps.

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.

Genome-wide linkage scan for genes affecting longitudinal trends in systolic blood pressure.

Only one genome scan to date has attempted to make use of the longitudinal data available in the Framingham Heart Study, and this attempt yielded evidence of linkage to a gene for mean systolic blood pressure. We show how the additional information available in these longitudinal data can be utilized to examine linkages for not only mean systolic blood pressure (SBP), but also for its trend with age and its variability. Prior to linkage analysis, individuals treated for hypertension were adjusted to account for right-censoring of SBP. Regressions on age were fitted to obtain orthogonal measures of slope, curvature, and residual variance of SBP that were then used as dependent variables in the model-free linkage program SIBPAL. We included mean age, gender, and cohort as covariates in the analysis. To improve power, sibling pairs were weighted for informativity using weights derived from both the marker and trait. The most significant results from our analyses were found on chromosomes 12, 15, and 17 for mean SBP, and chromosome 20 for both SBP slope and curvature.

Confirmation of linkage to and localization of familial colon cancer risk haplotype on chromosome 9q22.

Genetic risk factors are important contributors to the development of colorectal cancer. Following the definition of a linkage signal at 9q22-31, we fine mapped this region in an independent collection of colon cancer families. We used a custom array of single-nucleotide polymorphisms (SNP) densely spaced across the candidate region, performing both single-SNP and moving-window association analyses to identify a colon neoplasia risk haplotype. Through this approach, we isolated the association effect to a five-SNP haplotype centered at 98.15 Mb on chromosome 9q. This haplotype is in strong linkage disequilibrium with the haplotype block containing HABP4 and may be a surrogate for the effect of this CD30 Ki-1 antigen. It is also in close proximity to GALNT12, also recently shown to be altered in colon tumors. We used a predictive modeling algorithm to show the contribution of this risk haplotype and surrounding candidate genes in distinguishing between colon cancer cases and healthy controls. The ability to replicate this finding, the strength of the haplotype association (odds ratio, 3.68), and the accuracy of our prediction model (approximately 60%) all strongly support the presence of a locus for familial colon cancer on chromosome 9q.

Mendelian randomization in family data.

The phrase "mendelian randomization" has become associated with the use of genetic polymorphisms to uncover causal relationships between phenotypic variables. The statistical methods useful in mendelian randomization are known as instrumental variable techniques. We present an approach to instrumental variable estimation that is useful in family data and is robust to the use of weak instruments. We illustrate our method to measure the causal influence of low-density lipoprotein on high-density lipoprotein, body mass index, triglycerides, and systolic blood pressure. We use the Framingham Heart Study data as distributed to participants in the Genetics Analysis Workshop 16.

Comparison of univariate and multivariate linkage analysis of traits related to hypertension.

Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis.

Multivariate association analysis of the components of metabolic syndrome from the Framingham Heart Study.

Metabolic syndrome, by definition, is the manifestation of multiple, correlated metabolic impairments. It is known to have both strong environmental and genetic contributions. However, isolating genetic variants predisposing to such a complex trait has limitations. Using pedigree data, when available, may well lead to increased ability to detect variants associated with such complex traits. The ability to incorporate multiple correlated traits into a joint analysis may also allow increased detection of associated genes. Therefore, to demonstrate the utility of both univariate and multivariate family-based association analysis and to identify possible genetic variants associated with metabolic syndrome, we performed a scan of the Affymetrix 50 k Human Gene Panel data using 1) each of the traits comprising metabolic syndrome: triglycerides, high-density lipoprotein, systolic blood pressure, diastolic blood pressure, blood glucose, and body mass index, and 2) a composite trait including all of the above, jointly. Two single-nucleotide polymorphisms within the cholesterol ester transfer protein (CETP) gene remained significant even after correcting for multiple testing in both the univariate (p < 5 x 10-7) and multivariate (p < 5 x 10-9) association analysis. Three genes met significance for multiple traits after correction for multiple testing in the univariate analysis, while five genes remained significant in the multivariate association. We conclude that while both univariate and multivariate family-based association analysis can identify genes of interest, our multivariate approach is less affected by multiple testing correction and yields more significant results.

Defining genetic determinants of the Metabolic Syndrome in the Framingham Heart Study using association and structural equation modeling methods.

The Metabolic Syndrome (MetSyn), which is a clustering of traits including insulin resistance, obesity, hypertension and dyslipidemia, is estimated to have a substantial genetic component, yet few specific genetic targets have been identified. Factor analysis, a sub-type of structural equation modeling (SEM), has been used to model the complex relationships in MetSyn. Therefore, we aimed to define the genetic determinants of MetSyn in the Framingham Heart Study (Offspring Cohort, Exam 7) using the Affymetrix 50 k Human Gene Panel and three different approaches: 1) an association-based "one-SNP-at-a-time" analysis with MetSyn as a binary trait using the World Health Organization criteria; 2) an association-based "one-SNP-at-a-time" analysis with MetSyn as a continuous trait using second-order factor scores derived from four first-order factors; and, 3) a multivariate SEM analysis with MetSyn as a continuous, second-order factor modeled with multiple putative genes, which were represented by latent constructs defined using multiple SNPs in each gene. Results were similar between approaches in that CSMD1 SNPs were associated with MetSyn in Approaches 1 and 2; however, the effects of CSMD1 diminished in Approach 3 when modeled simultaneously with six other genes, most notably CETP and STARD13, which were strongly associated with the Lipids and MetSyn factors, respectively. We conclude that modeling multiple genes as latent constructs on first-order trait factors, most proximal to the gene's function with limited paths directly from genes to the second-order MetSyn factor, using SEM is the most viable approach toward understanding overall gene variation effects in the presence of multiple putative SNPs.

Triglyceride levels and not adipokine concentrations are closely related to severity of nonalcoholic fatty liver disease in an obesity surgery cohort.

Although nonalcoholic fatty liver disease (NAFLD) is frequent in obesity, the metabolic determinants of advanced liver disease remain unclear. Adipokines reflect inflammation and insulin resistance associated with obesity and may identify advanced NAFLD. At the time of obesity surgery, 142 consecutive patients underwent liver biopsy and had their preoperative demographic and clinical data obtained. Liver histology was scored by the NAFLD activity score, and patients subdivided into four groups. Concentrations of retinol-binding protein 4 (RBP4), adiponectin, tumor necrosis factor-alpha (TNF-alpha), and leptin were determined approximately 1 week prior to surgery and results were related to liver histology. The prevalence of no NAFLD was 30%, simple steatosis 23%, borderline nonalcoholic steatohepatitis (NASH) 28%, and definitive NASH 18%. Type 2 diabetes mellitus (T2DM) and metabolic syndrome (MS) prevalence were 39 and 75%, respectively, and did not differ across the four histological groups (P = NS). Triglyceride (TG) and alanine transaminase (ALT) levels, strongly associated with advanced stages of NAFLD and NASH (P = 0.04). TG levels >150 mg/dl, increased the likelihood of NASH 3.4-fold, whereas high-density lipoprotein (HDL) levels predicted no NAFLD (P < 0.01). Concentrations of TNF-alpha, leptin, and RBP4 did not differ among histological groups and thus did not identify NASH; however, there was a trend for adiponectin to be lower in NASH vs. no NAFLD (P = 0.061). In summary, both TG and ALT levels assist in identification of NASH in an obesity surgery cohort. These findings underscore the importance of fatty acid delivery mechanisms to NASH development in severely obese individuals.

Addressing genetic heterogeneity in complex disease: finding seizure genes in systemic lupus erythematosus.

Complex genetic disease is inherently difficult to study due to an imperfect relationship between genotype and phenotype. One important reason for this imperfect relationship is genetic heterogeneity, the occurrence of different genetic factors underlying the same clinical syndrome. One method of addressing genetic heterogeneity is covariate-based linkage analysis, which allows the use of additional phenotypic features to define genetically distinct subsets of patients. Systemic lupus erythematosus (SLE) is one example of a complex genetic disease affecting multiple organ systems including the central nervous system. We report here the use of covariate-based linkage analysis to detect a potential genetic locus on chromosome 15 influencing the development of seizures in individuals with SLE. The use of covariates increases the power to detect linkage in the presence of genetic heterogeneity.

Metabolic syndrome in childhood predicts adult cardiovascular disease 25 years later: the Princeton Lipid Research Clinics Follow-up Study.

OBJECTIVE: The goal was to assess the association of metabolic syndrome in childhood with adult cardiovascular disease 25 years later. METHODS: Data from the National Heart, Lung, and Blood Institute Lipid Research Clinics Princeton Prevalence Study (1973-1976) and the Princeton Follow-up Study (2000-2004) were used. BMI was used as the obesity measure in childhood, because waist circumference was not measured in the Lipid Research Clinics study. The adult cardiovascular disease status of participants and their parents was obtained through participant report. A logistic analysis was used to predict adult cardiovascular disease; pediatric metabolic syndrome, age at the Princeton Follow-up Study, gender, race, and parental history of cardiovascular disease were potential explanatory variables. RESULTS: Ages ranged from 6 to 19 years in the Lipid Research Clinics study and from 30 to 48 years in the Princeton Follow-up Study. There were 17 cases of cardiovascular disease in the analysis cohort in the Princeton Follow-up Study. Pediatric metabolic syndrome and age at follow-up assessment were significant predictors of cardiovascular disease. Pediatric metabolic syndrome and changes in age-specific BMI percentile from childhood to adulthood were significant predictors of adult metabolic syndrome. CONCLUSIONS: Evaluating children for metabolic syndrome could identify patients at increased risk of adult cardiovascular disease, making targeted interventions possible.

Localization and replication of the systemic lupus erythematosus linkage signal at 4p16: interaction with 2p11, 12q24 and 19q13 in European Americans.

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by both population and phenotypic heterogeneity. Our group previously identified linkage to SLE at 4p16 in European Americans (EA). In the present study we replicate this linkage effect in a new cohort of 76 EA families multiplex for SLE by model-free linkage analysis. Using densely spaced microsatellite markers in the linkage region, we have localized the potential SLE susceptibility gene(s) to be telomeric to the marker D4S2928 by haplotype construction. In addition, marker D4S394 showed marginal evidence of linkage disequilibrium with the putative disease locus by the transmission disequilibrium test and significant evidence of association using a family-based association approach as implemented in the program ASSOC. We also performed both two-point and multipoint model-based analyses to characterize the genetic model of the potential SLE susceptibility gene(s), and the lod scores both maximized under a recessive model with penetrances of 0.8. Finally, we performed a genome-wide scan of the total 153 EA pedigrees and evaluated the possibility of interaction between linkage signals at 4p16 and other regions in the genome. Fourteen regions on 11 chromosomes (1q24, 1q42, 2p11, 2q32, 3p14.2, 4p16, 5p15, 7p21, 8p22, 10q22, 12p11, 12q24, 14q12, 19q13) showed evidence of linkage, among which, signals at 2p11, 12q24 and 19q13 also showed evidence of interaction with that at 4p16. These results provide important additional information about the SLE linkage effect at 4p16 and offer a unique approach to uncovering susceptibility loci involved in complex human diseases.

Genetic characterization and fine mapping of susceptibility loci for sarcoidosis in African Americans on chromosome 5.

Sarcoidosis, a systemic granulomatous disease, likely results from both environmental agents and genetic susceptibility. Sarcoidosis is more prevalent in women and, in the United States, African Americans are both more commonly and more severely affected than Caucasians. We report a follow up of the first genome scan for sarcoidosis susceptibility genes in African Americans. Both the genome scan and the present study comprise 229 African American nuclear families ascertained through two or more sibs with sarcoidosis. Regions studied included those which reached a significance in the genome scan of 0.01 (2p25, 5q11, 5q35, 9q34, 11p15 and 20q13), 0.05 (3p25 and 5p15-13) or which replicated previous findings (3p14-11). We performed genotyping with additional markers in the same families used in the genome scan. We examined multi-locus models for epistasis and performed model-based linkage analysis on subsets of the most linked families to characterize the underlying genetic model. The strongest signal was at marker D5S407 (P=0.005) on 5q11.2, using both full and half sibling pairs. Our results support, in an African American population, a sarcoidosis susceptibility gene on chromosome 5q11.2, and a gene protective for sarcoidosis on 5p15.2. These fine mapping results further prioritize the importance of candidate regions on chromosomes 2p25, 3p25, 5q35, 9q34, 11p15 and 20q13 for African Americans. Additionally, our results suggest joint action of the effects of putative genes on chromosome 3p14-11 and 5p15.2. We conclude that multiple susceptibility loci for sarcoidosis exist in African Americans and that some may have interdependent effects on disease pathogenesis.

Reduction of sample heterogeneity through use of population substructure: an example from a population of African American families with sarcoidosis.

Sarcoidosis is a granulomatous inflammatory disorder of complex etiology with significant linkage to chromosome 5, and marginal linkage was observed to five other chromosomes in African Americans (AAs) in our previously published genome scan. Because genetic factors underlying complex disease are often population specific, genetic analysis of samples with diverse ancestry (i.e., ethnic confounding) can lead to loss of power. Ethnic confounding is often addressed by stratifying on self-reported race, a controversial and less-than-perfect construct. Here, we propose linkage analysis stratified by genetically determined ancestry as an alternative approach for reducing ethnic confounding. Using data from the 380 microsatellite markers genotyped in the aforementioned genome scan, we clustered AA families into subpopulations on the basis of ancestry similarity. Evidence of two genetically distinct groups was found: subpopulation one (S1) comprised 219 of the 229 families, subpopulation two (S2) consisted of six families (the remaining four families were a mixture). Stratified linkage results suggest that only the S1 families contributed to previously identified linkage signals at 1p22, 3p21-14, 11p15, and 17q21 and that only the S2 families contributed to those found at 5p15-13 and 20q13. Signals on 2p25, 5q11, 5q35, and 9q34 remained significant in both subpopulations, and evidence of a new susceptibility locus at 2q37 was found in S2. These results demonstrate the usefulness of stratifying on genetically determined ancestry, to create genetically homogeneous subsets--more reliable and less controversial than race-stratified subsets--in which to identify genetic factors. Our findings support the presence of sarcoidosis-susceptibility genes in regions identified elsewhere but indicate that these genes are likely to be ancestry specific.

Genetic linkage of systemic lupus erythematosus to 13q32 in African American families with affected male members.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving genetic and environmental factors. Previously, our group showed that SLE females with affected male relatives have higher prevalence of renal disease than SLE females with no affected male relatives in a sample of 372 individuals from 159 families. By adding 392 individuals from 181 new families, we replicated this finding in the largest collection of families with affected males, confirming our hypothesis that multiplex SLE families with at least one affected male member ("male families") comprise a distinct subpopulation of SLE multiplex families. We studied 64 male families by a genome-wide scan for SLE and found the largest signal (lod=3.08) at 13q32 in 18 African American male families using an affected-relative-pair model-free linkage method. Closer examination of IBD sharing at this region suggested a dominant mode of inheritance. Multipoint model-based linkage analysis generated a lod score of 3.13 in the same chromosomal region with a low-disease allele frequency of 0.0004 and a disease penetrance of 0.5 for the 18 African American male families. We performed fine mapping in these and three additional African American male families and the SLE predisposing locus was localized to a region tightly linked to the marker D13S892. We have therefore confirmed the linkage of SLE to 13q32, which was reported previously, and suggested that an SLE susceptibility gene in this region is specific to predisposition of African Americans to a specific form of SLE, with males at high risk.