Genome-wide linkage scan for the metabolic syndrome: the GENNID study.

In the United States, the metabolic syndrome (MetS) constitutes a major public health problem with over 47 million persons meeting clinical criteria for MetS. Numerous studies have suggested genetic susceptibility to MetS. The goals of this study were (i) to identify susceptibility loci for MetS in well-characterized families with type 2 diabetes (T2D) in four ethnic groups and (ii) to determine whether evidence for linkage varies across the four groups. The GENNID study (Genetics of NIDDM) is a multicenter study established by the American Diabetes Association in 1993 and comprises a comprehensive, well-characterized resource of T2D families from four ethnic groups (whites, Mexican Americans, African Americans, and Japanese Americans). Principal component factor analysis (PCFA) was used to define quantitative phenotypes of the MetS. Variance components linkage analysis was conducted using microsatellite markers from a 10-cM genome-wide linkage scan, separately in each of the four ethnic groups. Three quantitative MetS factors were identified by PCFA and used as phenotypes for MetS: (i) a weight/waist factor, (ii) a blood pressure factor, and (iii) a lipid factor. Evidence for linkage to each of these factors was observed. For each ethnic group, our results suggest that several regions harbor susceptibility genes for the MetS. The strongest evidence for linkage for MetS phenotypes was observed on chromosome 2 (2q12.1-2q13) in the white sample and on chromosome 3 (3q26.1-3q29) in the Mexican-American sample. In conclusion, the results suggest that several regions harbor MetS susceptibility genes and that heterogeneity may exist across groups.

Variation in inflammation-related genes and risk of incident nonfatal myocardial infarction or ischemic stroke.

BACKGROUND: From initiation to plaque rupture, immune system components contribute to atherosclerosis. We investigated variation in inflammation-related genes - interleukin (IL)-1beta, IL-6, C-reactive protein (CRP), IL-10, IL-18, and the tumor necrosis factor (TNF) superfamily [lymphotoxin(LT)-alpha, TNF-alpha, LT-beta] - with respect to nonfatal incident myocardial infarction (MI) or ischemic stroke risk. METHODS AND RESULTS: A population-based case-control study recruited postmenopausal and/or hypertensive Group Health members aged 30-79 years. We chose a subset of single nucleotide polymorphisms (SNPs) to describe common gene-wide variation on the basis of linkage disequilibrium. 36 SNPs, describing 38 common haplotypes for 5 genes and a 3-gene cluster, were genotyped among 856 MI cases, 368 stroke cases, and 2688 controls. Associations of SNPs or PHASE-inferred haplotypes and risk were estimated using logistic regression; significance of gene-level associations was assessed with global Wald tests and permutation tests. Gene-wide IL-18 variation was associated with higher MI risk and an IL-1B haplotype was associated with lower stroke risk. In secondary analyses of SNPs, we observed associations of several IL-1B polymorphisms with risk of MI or stroke. IL-6, CRP, IL-10, and TNF superfamily gene variation was not associated with MI or stroke risk. CONCLUSIONS: Our results support prior reports associating an IL-18 gene variant and MI risk, contribute additional evidence to reports of IL-1B and cardiovascular risk, and fail to confirm risk differences previously observed for CRP, IL-6, and TNF-alpha promoter variants.

Renin-angiotensin system haplotypes and the risk of myocardial infarction and stroke in pharmacologically treated hypertensive patients.

The products of the renin-angiotensin system (RAS) play an important role in the pathogenesis of cardiovascular disease. Studies examining RAS gene variants and cardiovascular disease have focused on single-nucleotide polymorphisms (SNPs) rather than haplotypes, which better characterize the patterns of genetic variation. The authors conducted a population-based, case-control study at Group Health (Seattle, Washington) between 1995 and 1999 to determine whether common haplotypes in the angiotensinogen gene (AGT), the renin gene, the angiotensin-converting enzyme gene, and the angiotensin II receptor type 1 and receptor type 2 genes were associated with the risk of myocardial infarction and stroke among pharmacologically treated hypertensive patients. SNP discovery was done using 23 European-origin samples. Thirty tagSNPs (the minimum sets of SNPs that capture most of the haplotype diversity within a block) were genotyped in cases and controls. Haplotypes were inferred using the program PHASE (http://www.stat.washington.edu/stephens/software.html). The authors used weighted logistic regression to estimate associations and conducted a permutation test to estimate the probability of a chance finding. AGT haplotype B was associated with the risk of myocardial infarction (odds ratio = 1.58, 95% confidence interval: 1.06, 2.35); however, results were not statistically significant given the number of tests performed (permutation p = 0.17). In this case-control study, RAS gene haplotypes were not significantly associated with increased risks of myocardial infarction or stroke.

Simple estimates of haplotype relative risks in case-control data.

Methods of varying complexity have been proposed to efficiently estimate haplotype relative risks in case-control data. Our goal was to compare methods that estimate associations between disease conditions and common haplotypes in large case-control studies such that haplotype imputation is done once as a simple data-processing step. We performed a simulation study based on haplotype frequencies for two renin-angiotensin system genes. The iterative and noniterative methods we compared involved fitting a weighted logistic regression, but differed in how the probability weights were specified. We also quantified the amount of ambiguity in the simulated genes. For one gene, there was essentially no uncertainty in the imputed diplotypes and every method performed well. For the other, approximately 60% of individuals had an unambiguous diplotype, and approximately 90% had a highest posterior probability greater than 0.75. For this gene, all methods performed well under no genetic effects, moderate effects, and strong effects tagged by a single nucleotide polymorphism (SNP). Noniterative methods produced biased estimates under strong effects not tagged by an SNP. For the most likely diplotype, median bias of the log-relative risks ranged between -0.49 and 0.22 over all haplotypes. For all possible diplotypes, median bias ranged between -0.73 and 0.08. Results were similar under interaction with a binary covariate. Noniterative weighted logistic regression provides valid tests for genetic associations and reliable estimates of modest effects of common haplotypes, and can be implemented in standard software. The potential for phase ambiguity does not necessarily imply uncertainty in imputed diplotypes, especially in large studies of common haplotypes.

Genetic Susceptibility to Prostate Cancer: Prostate-specific Antigen and its Interaction with the Androgen Receptor (United States).

OBJECTIVE: To determine whether directly observed prostate-specific antigen (PSA) promoter diploid haplotype, either alone or in conjunction with androgen receptor (AR) genotype, is associated with prostate cancer risk. METHODS: We conducted a case-control study nested within the US population-based Cardiovascular Health Study cohort. Incident prostate cancers were identified by linkage to cancer registry records for the years 1989-2000. We genotyped 193 cases and 391 controls for the PSA -252 G/A and -158 G/A SNPs and the AR CAG microsatellite, and developed methods to directly determine proximal PSA promoter haplotypes. Exact logistic regression was used to estimate odds ratios and significance levels. RESULTS: No significant associations were observed between PSA diplotype and prostate cancer overall. Short (< 20) AR CAG repeat lengths were associated with modest increases in the risk of prostate cancer (OR, 1.46; 95% CI, 0.97-2.19; p = 0.071) that were significant for advanced disease (OR, 1.82; 95% CI, 1.02-3.26; p = 0.044). Men who possessed two copies of the PSA*2 (-252G/-158G) haplotype and short AR CAG repeat lengths had a 4-fold (95% CI, 1.05-20.75; exact p = 0.040) increased risk of prostate cancer, and a 7-fold (95% CI, 1.25-39.78; exact p = 0.026) increased risk of advanced disease. CONCLUSIONS: We found evidence that the PSA*2*2 diplotype in combination with short AR CAG alleles increases a man's risk of developing prostate cancer. These findings support an etiologic role in prostate cancer of genetic interactions between polymorphisms that increase AR transactivation strength and those that alter the regulatory regions of target genes such as PSA that are responsive to androgen stimulation.

Sequence variation in NPC1L1 and association with improved LDL-cholesterol lowering in response to ezetimibe treatment.

Niemann-Pick C1-like 1 (NPC1L1) is an intestinal cholesterol transporter and the molecular target of ezetimibe, a cholesterol absorption inhibitor demonstrated to reduce LDL-cholesterol (LDL-C) both as monotherapy and when co-administered with 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins). Interestingly, significant interindividual variability has been observed for rates of intestinal cholesterol absorption and LDL-C reductions at both baseline and post ezetimibe treatment. To test the hypothesis that genetic variation in NPC1L1 could influence the LDL-C response to ezetimibe, we performed extensive resequencing of the gene in 375 apparently healthy individuals and genotyped hypercholesterolemic patients from clinical trial cohorts. No association was observed between NPC1L1 single-nucleotide polymorphism and baseline cholesterol. However, significant associations to LDL-C response to treatment with ezetimibe were observed in patients treated with ezetimibe in two large clinical trials. Our data demonstrate that DNA sequence variants in NPC1L1 are associated with an improvement in response to ezetimibe pharmacotherapy and suggest that detailed analysis of genetic variability in clinical trial cohorts can lead to improved understanding of factors contributing to variable drug response.

Comparison of single-nucleotide polymorphisms and microsatellites in detecting quantitative trait loci for alcoholism: the Collaborative Study on the Genetics of Alcoholism.

BACKGROUND: The feasibility of effectively analyzing high-density single nucleotide polymorphism (SNP) maps in whole genome scans of complex traits is not known. The purpose of this study was to compare variance components linkage results using different density marker maps in data from the Collaborative Study on the Genetics of Alcoholism (COGA). Marker maps having an average spacing of 10 cM (microsatellite), 0.78 cM (SNP1), and 0.31 cM (SNP2) were used to identify quantitative trait loci (QTLs) affecting maximum number of alcoholic drinks consumed in a 24-hour period (Inmaxalc). RESULTS: Heritability of Inmaxalc was estimated to be 15%. Multipoint variance components linkage analysis revealed similar linkage patterns among the three marker panels, with the SNP maps consistently yielding higher LOD scores. Robust LOD scores > 1.0 were observed on chromosomes 1 and 13 for all three marker maps. Additional LODs > 1.0 were observed on chromosome 4 with both SNP maps and on chromosomes 18 and 21 with the SNP2 map. Peak LOD scores for Inmaxalc were observed on chromosome 1, although none reached genome-wide statistical significance. Quantile-quantile plots revealed that the multipoint distribution of SNP results appeared to fit the asymptotic null distribution better than the twopoint results. CONCLUSION: In conclusion, variance-components linkage analysis using high-density SNP maps provided higher LOD scores compared with the standard microsatellite map, similar to studies using nonparametric linkage methods. Widespread application of SNP maps will depend on further improvements in the computational methods implemented in current software packages.

Genome-wide scan for quantitative trait loci influencing LDL size and plasma triglyceride in familial hypertriglyceridemia.

Small, dense LDLs and hypertriglyceridemia, two highly correlated and genetically influenced risk factors, are known to predict for risk of coronary heart disease. The objective of this study was to perform a whole-genome scan for linkage to LDL size and triglyceride (TG) levels in 26 kindreds with familial hypertriglyceridemia (FHTG). LDL size was estimated using gradient gel electrophoresis, and genotyping was performed for 355 autosomal markers with an average heterozygosity of 76% and an average spacing of 10.2 centimorgans (cMs). Using variance components linkage analysis, one possible linkage was found for LDL size [logarithm of odds (LOD) = 2.1] on chromosome 6, peak at 140 cM distal to marker F13A1 (closest marker D6S2436). With adjustment for TG and/or HDL cholesterol, the LOD scores were reduced, but remained in exactly the same location. For TG, LOD scores of 2.56 and 2.44 were observed at two locations on chromosome 15, with peaks at 29 and 61 cM distal to marker D15S822 (closest markers D15S643 and D15S211, respectively). These peaks were retained with adjustment for LDL size and/or HDL cholesterol. These findings, if confirmed, suggest that LDL particle size and plasma TG levels could be caused by two different genetic loci in FHTG.

Genetics of gene expression surveyed in maize, mouse and man.

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.