Impact of DWCRA programme

Emotional intelligence and adjustment among adolescents

Mentally retarded primary school children

Age changes in physique

Preschool children in home and daycare environments

Many common diseases such as obesity and type-2 diabetes have a significant genetic component that contributes to susceptibility. Peroxisome proliferator activated receptors (PPARs) are nuclear receptors that heterodimerize with the retinoid X receptors (RXRs) to influence the expression of many genes involved in adipocyte differentiation and lipid metabolism such as the fatty acid binding proteins (FABPs) and the uncoupling proteins (UCPs). Genetic variation in any of these gene families could potentially alter metabolic traits related to obesity and type-2 diabetes. The goal of this project is to identify genetic variation in the PPARs and RXRs and then to determine if this variation is associated to quantitative traits related to obesity and type-2 diabetes using a multi-locus analysis approach. In this study, three sets of regression models were constructed: the first containing polymorphisms in just the PPARs or RXRs; the second with variants from all four gene families; and the third using polymorphisms from the gene isoforms showing the highest level of expression in each of three tissues. Some of the models were only able to account for small portions of the particular trait variation; however, many of the models accounted for a large amount of variation in the trait, up to 23.4% in the Hispanic female model for fasting free fatty acids. Multi-locus genotypes, as opposed to single locus effects, were found to be the best predictors of variation in almost all of the final models. These analyses confirmed the importance of gene-gene interactions on traits related to obesity and type-2 diabetes such as fasting free fatty acids and cholesterol; therefore, multiple polymorphisms should be considered together to fully understand their influence on a quantitative trait.

Obesity and type 2 diabetes are heterogeneous conditions caused by a combination of genetic and environmental factors. A number of candidate gene families have been identified that influence obesity- and diabetes-related traits, including the uncoupling proteins (UCPs) and fatty acids binding proteins (FABPs). The UCPs are mitochondrial transport proteins that promote proton leakage across the inner mitochondrial membrane, uncoupling oxidative phosphorylation from ATP production and releasing energy as heat. The FABPs are intracellular transporters of fatty acids that facilitate lipid metabolism and gene transcription regulation. The UCPs and FABPs influence energy metabolism, fuel substrate partitioning, glucose and lipid metabolism, and insulin action. In this study, we identified variation in UCP and FABP genes, explored the influence of that variation on phenotype through multi-locus analyses, and assayed the functional consequences of promoter variation on gene expression. Using the multi-locus analysis approach, we constructed regression models that explained a relatively large portion of the variation in phenotypes in comparison to the individual effects of single loci. Several of the models explained upwards of 10% of the variation in traits. This suggests that a multi-locus approach to studying complex disease is much more informative than considering single loci individually. In addition to the statistical analyses, functional studies were performed to assess the effects of promoter variation on gene expression. Variation in the FABP2 promoter region was associated with levels of promoter activity, suggesting a biological explanation for effects of this polymorphism on phenotype. This exploratory analysis identified a number of interesting multi-locus genetic effects on traits related to obesity and type 2 diabetes, suggesting that consideration of multiple gene effects is a more comprehensive approach to understanding complex disease.

Complex diseases such as type 2 diabetes, hypertension and psychiatric disorders have been major public health problems in US. In order to increase the power in the linkage analysis of complex traits, genetic heterogeneity has to be taken into account. During the past few years, several methods have been proposed for dealing with this issue by incorporating covariate information into the affected sib pair (ASP) analysis. However, it is still not clear how these approaches perform under different gene-environment (G x E) interactions. The covariate statistics evaluated in this study are: (1) mixture model; (2) general conditional-logistic model (LODPAL); (3) multinomial logistic regression models (MLRM under no dominance, no additive and min-max restriction); (4) extension of the maximum-likelihood-binomial approach (MLB); (5) ordered-subset analysis (OSA with three different rank orders: high-to-low, low-to-high and optimal-slice); (6) logistic regression modeling (COVLINK). Based on the chromosome-based approach, we have written simulation programs to generate data under various G x E models and disease models. We first define the empirical statistical significance thresholds using C2, the environmental risk factor, under the null hypothesis. We then evaluate the power of the covariate statistics when different covariates are used. We also compare the performance of the covariate statistics with the model-free methods (Sall and Spair). In all three G x E interaction models, most covariate methods perform better when using C1, the covariate with G x E interaction effect, than when using C2 or the random noise covariate C3, except for MLB and the low-to-high OSA method. Comparing with the model-free methods (using Sall as the baseline), mixture model and the high-to-low OSA method perform the best of the covariate statistics when using C1. However, when using C2 or C3, most covariate statistics provide less power than Sall. Only MLB has comparable power to Sall across all genetic models. According to our results, in different G x E interactions, one should apply the appropriate covariate statistic and include the suitable type of covariates carefully.

Inheritance of DNA methylation on imprinted genes depends on the Dnmt1 (cytosine-5-) methyltransferase protein. Methylation patterns on imprinted genes are maintained by oocytespecific Dnmt1o isoform at the 8-cell stage of preimplantation development. Methylation patterns in postimplantation embryos are maintained by the Dnmt1s isoform. To determine if Dnmt1s can functionally replace Dnmt1o, we expressed Dnmt1s in oocytes and discovered that Dnmt1s can maintain genomic imprints in the absence of Dnmt1o. However, the ability of Dnmt1s to maintain imprinting is dependant on the level of oocyte Dnmt1s. Though Dnmt1s and Dnmt1o have equivalent maintenance methyltransferase functions in oocytes, the unstable nature of oocyte Dnmt1s, in comparison to oocyte Dnmt1o, leads to levels lower than what are required to maintain methylation at the 8-cell stage. We also determined that in cloned embryos, Dnmt1o undergoes none of its expected trafficking to 8-cell stage nuclei. Instead, these embryos exhibit a mosaic pattern of Dnmt1s expression. Defects in intracellular trafficking of Dnmt1o and misexpression of Dnmt1s, along with the intrinsic instability of Dnmt1s, might contribute to iv aberrant DNA methylation in cloned embryos, thus raising concerns about the use of current cloning technologies for therapeutic cloning. Molecular mechanisms involved in the formation of ovarian teratomas were also analyzed. Unfertilized oocytes arrest at the MII stage of meiotic maturation. After fertilization, oocytes continue into cell division. Premature activation of MII oocytes without fertilization, can lead to ovarian teratoma formation. To better understand mechanisms governing the prevention of spontaneous oocyte activation, we investigated the molecular defects leading to formation of ovarian teratomas in the Tgkd mouse model. Tgkd is a transgene insertional mutation that leads to reduced levels of the Inpp4b protein in MII oocytes of hemizygous Tgkd females. Wildtype GV oocytes have less Inpp4b protein than MII oocytes, and a significant decrease in Inpp4b is also seen after fertilization. Also, the dependence of ovarian teratoma formation on the mouse strain, emphasizes the role of a strain-specific modifier on chromosome 6, possibly IP31. Thus, it is possible that oocyte Inpp4b normally suppresses spontaneous MII oocyte activation, possibly by reducing levels of IP3, an intermediate in the oocyte activation mechanism, that occurs following fertilization.

Human capital theory is accepted as one of the foundational theories of socioeconomic development. Although, according to founding scholars, any acquired qualities and abilities that help individuals and groups be economically productive can be considered as individual or group human capital, the classical human capital model focuses on schooling and training as the major factors comprising human capital on individual, group, and national levels. Consequently, current human capital measurement tools generally assess only educational attainment on these levels. Because of this overly simplified approach, the present manner in which human capital is commonly measured by national and international entities creates difficulty in accurately assessing the strengths and weaknesses of human capital within and between countries. A major challenge to improvement of human capital variables is identification and availability of data. The factors suggested to have significant impact on human capital are mostly intangible. Collecting such data is cost prohibitive for many developing countries. Consequently, national policy-makers, multinational corporations and international aid organizations use simplified estimates of human capital. The purpose of this dissertation is to construct and validate a more comprehensive human capital index. Study research questions include: 1) What are the significant factors that affect national human capital as revealed in the literature? 2) Can an expanded measure of national human capital be developed to reflect adequate content of HC identified in the literature? 3) What is the preliminary evidence supporting the validity of the newly developed human capital index? This analysis resulted in the formation of a new human capital index, which is expanded due to the incorporation of new variables together with the routinely used education measures. The sample panel data is from 163 countries for the years 2000-2005. Literature content analysis, factor analysis and regression analyses are used to support the exploration of the research questions. The results of the analyses suggest that a human capital model, which includes additional variables together with currently used education variables, predicts the level of national economic development significantly better than the model which includes only education measures. These results have implications for human resource development, corporate human capital management, national education, and international aid policies.