A QTL genome scan of the metabolic syndrome and its component traits

BACKGROUND: Because high blood pressure, altered lipid levels, obesity, and diabetes so frequently occur together, they are sometimes collectively referred to as the metabolic syndrome. While there have been many studies of each metabolic syndrome trait separately, few studies have attempted to analyze them combined, i.e., as one composite variable, in quantitative trait linkage or association analysis. We used genotype and phenotype data from the Framingham Heart Study to perform a full-genome scan for quantitative trait loci underlying the metabolic syndrome. RESULTS: Heritability estimates for all of the covariate-adjusted and age- and gender-standardized individual traits, and the composite metabolic syndrome trait, were all fairly high (0.39–0.62), and the composite trait was among the highest at 0.61. The composite trait yielded no regions with suggestive linkage by Lander and Kruglyak's criteria, although there were several noteworthy regions for individual traits, some of which were also observed for the composite variable. CONCLUSION: Despite its high heritability, the composite metabolic syndrome trait variable did not increase the power to detect or localize linkage peaks in this sample. However, this strategy and related methods of combining correlated individual traits deserve further investigation, particularly in settings with complex causal pathways

Dietary Sodium Restriction Reverses Vascular Endothelial Dysfunction in Middle-Aged/Older Adults With Moderately Elevated Systolic Blood Pressure

ObjectivesThis study sought to determine the efficacy of dietary sodium restriction (DSR) for improving vascular endothelial dysfunction in middle-aged/older adults with moderately elevated systolic blood pressure (SBP) (130–159 mm Hg) and the associated physiological mechanisms.BackgroundVascular endothelial dysfunction develops with advancing age and elevated SBP, contributing to increased cardiovascular risk. DSR lowers BP, but its effect on vascular endothelial function and mechanisms involved are unknown.MethodsSeventeen subjects (11 men and 6 women; mean age, 62 ± 7 years) completed a, randomized crossover study of 4 weeks of both low (DSR) and normal sodium intake. Vascular endothelial function (endothelium-dependent dilation; EDD), nitric oxide (NO)/tetrahydrobiopterin (BH4) bioavailability, and oxidative stress-associated mechanisms were assessed following each condition.ResultsUrinary sodium excretion was reduced by ∼50% (to 70 ± 30 mmol/day), and conduit (brachial artery flow-mediated dilation [FMDBA]) and resistance (forearm blood flow responses to acetylcholine [FBFACh]) artery EDD were 68% and 42% (peak FBFACh) higher following DSR (p < 0.005). Low sodium markedly enhanced NO-mediated EDD (greater ΔFBFACh with endothelial NO synthase inhibition) without changing endothelial NO synthase expression/activation (Ser 1177 phosphorylation), restored BH4 bioactivity (less ΔFMDBA with acute BH4), abolished tonic superoxide suppression of EDD (less ΔFMDBA and ΔFBFACh with ascorbic acid infusion), and increased circulating superoxide dismutase activity (all p < 0.05). These effects were independent of ΔSBP. Other subject characteristics/dietary factors and endothelium-independent dilation were unchanged.ConclusionsDSR largely reversed both macro- and microvascular endothelial dysfunction by enhancing NO and BH4 bioavailability and reducing oxidative stress. Our findings support the emerging concept that DSR induces “vascular protection” beyond that attributable to its BP-lowering effects

Genomic screening in family-based association testing

Due to the recent gains in the availability of single-nucleotide polymorphism data, genome-wide association testing has become feasible. It is hoped that this additional data may confirm the presence of disease susceptibility loci, and identify new genetic determinants of disease. However, the problem of multiple comparisons threatens to diminish any potential gains from this newly available data. To circumvent the multiple comparisons issue, we utilize a recently developed screening technique using family-based association testing. This screening methodology allows for the identification of the most promising single-nucleotide polymorphisms for testing without biasing the nominal significance level of our test statistic. We compare the results of our screening technique across univariate and multivariate family-based association tests. From our analyses, we observe that the screening technique, applied to different settings, is fairly consistent in identifying optimal markers for testing. One of the identified markers, TSC0047225, was significantly associated with both the ttth1 (p = 0.004) and ttth1-ttth4 (p = 0.004) phenotype(s). We find that both univariate- and multivariate-based screening techniques are powerful tools for detecting an association

Comparison of linkage and association strategies for quantitative traits using the COGA dataset

Genome scans using dense single-nucleotide polymorphism (SNP) data have recently become a reality. It is thought that the increase in information content for linkage analysis as a result of the denser scans will help refine previously identified linkage regions and possibly identify new regions not identifiable using the sparser, microsatellite scans. In the context of the dense SNP scans, it is also possible to consider association strategies to provide even more information about potential regions of interest. To circumvent the multiple-testing issues inherent in association analysis, we use a recently developed strategy, implemented in PBAT, which screens the data to identify the optimal SNPs for testing, without biasing the nominal significance level. We compare the results from the PBAT analysis to that of quantitative linkage analysis on chromosome 4 using the Collaborative Study on the Genetics of Alcoholism data, as released through Genetic Analysis Workshop 14

Incommensurate magnetism near quantum criticality in CeNiAsO

Two phase transitions in the tetragonal strongly correlated electron system CeNiAsO were probed by neutron scattering and zero field muon spin rotation. For $T <T_{N1}$ = 8.7(3) K, a second order phase transition yields an incommensurate spin density wave with wave vector $\textbf{k} = (0.44(4), 0, 0)$. For $T < T_{N2}$ = 7.6(3) K, we find co-planar commensurate order with a moment of $0.37(5)~\mu_B$, reduced to $30 \%$ of the saturation moment of the $|\pm\frac{1}{2}\rangle$ Kramers doublet ground state, which we establish by inelastic neutron scattering. Muon spin rotation in $\rm CeNiAs_{1-x}P_xO$ shows the commensurate order only exists for x $\le$ 0.1 so the transition at $x_c$ = 0.4(1) is from an incommensurate longitudinal spin density wave to a paramagnetic Fermi liquid

Erratum: Testing the key assumption of heritability estimates based on genome-wide genetic relatedness

Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin- and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than .025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus non-urban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal

Gene–environment interactions related to body mass: School policies and social context as environmental moderators

This paper highlights the role of institutional resources and policies, whose origins lie in political processes, in shaping the genetic etiology of body mass among a national sample of adolescents. Using data from Waves I and II of the National Longitudinal Study of Adolescent Health, we decompose the variance of body mass into environmental and genetic components. We then examine the extent to which the genetic influences on body mass are different across the 134 schools in the study. Taking advantage of school differences in both health-related policies and social norms regarding body size, we examine how institutional resources and policies alter the relative impact of genetic influences on body mass. For the entire sample, we estimate a heritability of .82, with the remaining .18 due to unique environmental factors. However, we also show variation about this estimate and provide evidence suggesting that social norms and institutional policies often mask genetic vulnerabilities to increased weight. Empirically, we demonstrate that more-restrictive school policies and policies designed to curb weight gain are also associated with decreases the proportion of variance in body mass that is due to additive genetic influences

Is the Gene-Environment Interaction Paradigm Relevant to Genome-Wide Studies? The Case of Education and Body Mass Index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk

<p>Abstract</p> <p>Background</p> <p>Traditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs.</p> <p>Methods</p> <p>Using data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability.</p> <p>Results</p> <p>While no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously.</p> <p>Conclusion</p> <p>The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.</p