Varghese George

Telomere Length and Pulse Pressure in Newly Diagnosed, Antipsychotic-Naive Patients With Nonaffective Psychosis

INTRODUCTION Recent studies suggest that in addition to factors such as treatment side effects, suicide, and poor health habits, people with schizophrenia may have an increased risk of diabetes prior to antipsychotic treatment. Diabetes is associated with an increased pulse pressure (PP) and a shortened telomere. We tested the hypothesis that prior to antipsychotic treatment, schizophrenia and related disorders are associated with a shortened telomere, as well as an increased PP. METHODS Telomere content (which is highly correlated with telomere length) and PP were measured in newly diagnosed, antipsychotic-naive patients with schizophrenia and related disorders on first clinical contact and in matched control subjects. Both groups were also administered an oral glucose tolerance test. RESULTS Compared with control subjects, the patients with psychosis had decreased telomere content and an increased PP. As previously reported, they also had increased glucose concentrations at 2 hours. These differences could not be attributed to differences in age, ethnicity, smoking, gender, body mass index, neighborhood of residence, socioeconomic status, aerobic conditioning, or an increased cortisol concentration in the psychotic subjects. DISCUSSION These results suggest that prior to antipsychotic use, nonaffective psychosis is associated with reduced telomere content and increased PP, indices that have been linked to an increased risk of diabetes and hypertension.

Can dysfunctional HDL explain high coronary artery disease risk in South Asians

BACKGROUND Coronary artery disease (CAD) is the leading cause of mortality and morbidity in United States, and South Asian immigrants (SAIs) have a higher risk for CAD compare to Caucasians. Traditional risk factors do not completely explain high risk, and some of the unknown risk factors need to be explored. We assessed dysfunctional pro-inflammatory high density lipoprotein (HDL) in SAIs and assessed its association with sub-clinical CAD using carotid intima-media thickness (IMT) as a surrogate marker for atherosclerosis. METHODS Cross-sectional study on SAIs aged 40-65 years. Sub-clinical CAD was measured using carotid intima media thickness (IMT) as a surrogate marker of atherosclerosis. Dysfunctional or pro-inflammatory HDL was determined by novel cell free assay and HDL inflammatory Index. RESULTS Dysfunctional HDL was found in the 50% participants, with HDL-inflammatory index of >or=1.00, suggesting pro-inflammatory HDL (95% CI, 0.8772-1.4333). The prevalence of sub-clinical CAD using carotid IMT (>or=0.80 mm) was seen in 41.4% (95% CI, 0.2347-0.5933). On logistic regression analysis, positive carotid IMT was found to be associated with dysfunctional HDL after adjusting for age, family history of cardiovascular disease, and hypertension (p=0.030). CONCLUSIONS The measurement of HDL level as well as functionality plays an important role in CAD risk assessment. Those SAIs with dysfunctional HDL and without known CAD can be a high risk group requiring treatment with lipid lowering drugs to reduce future risk of CAD. Further large studies are required to explore association of dysfunctional HDL with CAD and identify additional CAD risk caused by dysfunctional HDL.

Mapping Multiple Quantitative Trait Loci for Ordinal Traits

Many complex traits in humans and other organisms show ordinal phenotypic variation but do not follow a simple Mendelian pattern of inheritance. These ordinal traits are presumably determined by many factors, including genetic and environmental components. Several statistical approaches to mapping quantitative trait loci (QTL) for such traits have been developed based on a single-QTL model. However, statistical methods for mapping multiple QTL are not well studied as continuous traits. In this paper, we propose a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm to map multiple QTL for ordinal traits in experimental crosses. We model the ordinal traits under the multiple threshold model, which assumes a latent continuous variable underlying the ordinal phenotypes. The ordinal phenotype and the latent continuous variable are linked through some fixed but unknown thresholds. We adopt a standardized threshold model, which has several attractive features. An efficient sampling scheme is developed to jointly generate the threshold values and the values of latent variable. With the simulated latent variable, the posterior distributions of other unknowns, for example, the number, locations, genetic effects, and genotypes of QTL, can be computed using existing algorithms for normally distributed traits. To this end, we provide a unified approach to mapping multiple QTL for continuous, binary, and ordinal traits. Utility and flexibility of the method are demonstrated using simulated data.

Structural Equation Modeling of Gene–Environment Interactions in Coronary Heart Disease

Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene–environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two‐level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

A Method to Detect Differentially Methylated Loci With Next-Generation Sequencing

Epigenetic changes, especially DNA methylation at CpG loci have important implications in cancer and other complex diseases. With the development of next‐generation sequencing (NGS), it is feasible to generate data to interrogate the difference in methylation status for genome‐wide loci using case‐control design. However, a proper and efficient statistical test is lacking. There are several challenges. First, unlike methylation experiments using microarrays, where there is one measure of methylation for one individual at a particular CpG site, here we have the counts of methylation allele and unmethylation allele for each individual. Second, due to the nature of sample preparation, the measured methylation reflects the methylation status of a mixture of cells involved in sample preparation. Therefore, the underlying distribution of the measured methylation level is unknown, and a robust test is more desirable than parametric approach. Third, currently NGS measures methylation at over 2 million CpG sites. Any statistical tests have to be computationally efficient in order to be applied to the NGS data. Taking these challenges into account, we propose a test for differential methylation based on clustered data analysis by modeling the methylation counts. We performed simulations to show that it is robust under several distributions for the measured methylation levels. It has good power and is computationally efficient. Finally, we apply the test to our NGS data on chronic lymphocytic leukemia. The results indicate that it is a promising and practical test.

Can novel Apo A-I polymorphisms be responsible for low HDL in South Asian immigrants?

Coronary artery disease (CAD) is the leading cause of death in the world. Even though its rates have decreased worldwide over the past 30 years, event rates are still high in South Asians. South Asians are known to have low high-density lipoprotein (HDL) levels. The objective of this study was to identify Apolipoprotein A-I (Apo A-I) polymorphisms, the main protein component of HDL and explore its association with low HDL levels in South Asians. A pilot study on 30 South Asians was conducted and 12-h fasting samples for C-reactive protein, total cholesterol, HDL, low-density lipoprotein (LDL), triglycerides, Lipoprotein (a), Insulin, glucose levels, DNA extraction, and sequencing of Apo A-I gene were done. DNA sequencing revealed six novel Apo A-I single nucleotide polymorphisms (SNPs) in South Asians, one of which (rs 35293760, C938T) was significantly associated with low (<40 mg/dl) HDL levels (P = 0.004). The association was also seen with total cholesterol (P = 0.026) and LDL levels (P = 0.032). This pilot work has highlighted some of the gene-environment associations that could be responsible for low HDL and may be excess CAD in South Asians. Further larger studies are required to explore and uncover these associations that could be responsible for excess CAD risk in South Asians.

COGA phenotypes and linkages on chromosome 2

An initial linkage analysis of the alcoholism phenotype as defined by DSM-III-R criteria and alcoholism defined by DSM-IV criteria showed many, sometimes striking, inconsistencies. These inconsistencies are greatly reduced by making the definition of alcoholism more specific. We defined new phenotypes combining the alcoholism definitions and the latent variables, defining an individual as affected if that individual is alcoholic under one of the definitions (either DSM-III-R or DSM-IV), and indicated having a symptom defined by one of the latent variables. This was done for each of the two alcoholism definitions and five latent variables, selected from a canonical discriminant analyses indicating they formed significant groupings using the electrophysiological variables. We found that linkage analyses utilizing these latent variables were much more robust and consistent than the linkage results based on DSM-III-R or DSM-IV criteria for definition of alcoholism. We also performed linkage analyses on two first prinicipal components derived phenotypes, one derived from the electrophysiolocical variables, and the other derived from the latent variables. A region on chromosome 2 at 250 cM was found to be linked to both of these derived phenotypes. Further examination of the SNPs in this region identified several haplotypes strongly associated with these derived phenotypes.

Variable selection method for quantitative trait analysis based on parallel genetic algorithm

Selection of important genetic and environmental factors is of strong interest in quantitative trait analyses. In this study, we use parallel genetic algorithm (PGA) to identify genetic and environmental factors in genetic association studies of complex human diseases. Our method can take account of both multiple markers across the genome and environmental factors, and also can be used to do fine mapping based on the results of haplotype analysis to select the markers that are associated with the quantitative traits. Using both simulated and real examples, we show that PGA is able to choose the variables correctly and is also an easy‐to‐use variable selection tool.

A new measure of population structure using multiple single nucleotide polymorphisms and its relationship with FST.

BackgroundLarge-scale genome-wide association studies are promising for unraveling the genetic basis of complex diseases. Population structure is a potential problem, the effects of which on genetic association studies are controversial. The first step to systematically quantify the effects of population structure is to choose an appropriate measure of population structure for human data. The commonly used measure is Wrights FST. For a set of subpopulations it is generally assumed to be one value of FST. However, the estimates could be different for distinct loci. Since population structure is a concept at the population level, a measure of population structure that utilized the information across loci would be desirable.FindingsIn this study we propose an adjusted C parameter according to the sample size from each sub-population. The new measure C is based on the c parameter proposed for SNP data, which was assumed to be subpopulation-specific and common for all loci. In this study, we performed extensive simulations of samples with varying levels of population structure to investigate the properties and relationships of both measures. It is found that the two measures generally agree well.ConclusionThe new measure simultaneously uses the marker information across the genome. It has the advantage of easy interpretation as one measure of population structure and yet can also assess population differentiation.

A Monte Carlo test of linkage disequilibrium for single nucleotide polymorphisms

BackgroundGenetic association studies, especially genome-wide studies, make use of linkage disequilibrium(LD) information between single nucleotide polymorphisms (SNPs). LD is also used for studying genome structure and has been valuable for evolutionary studies. The strength of LD is commonly measured by r2, a statistic closely related to the Pearsons χ2 statistic. However, the computation and testing of linkage disequilibrium using r2 requires known haplotype counts of the SNP pair, which can be a problem for most population-based studies where the haplotype phase is unknown. Most statistical genetic packages use likelihood-based methods to infer haplotypes. However, the variability of haplotype estimation needs to be accounted for in the test for linkage disequilibrium.FindingsWe develop a Monte Carlo based test for LD based on the null distribution of the r2 statistic. Our test is based on r2 and can be reported together with r2. Simulation studies show that it offers slightly better power than existing methods.ConclusionsOur approach provides an alternative test for LD and has been implemented as a R program for ease of use. It also provides a general framework to account for other haplotype inference methods in LD testing.