Biomedical subjects

Young Ju Suh

Publications and source records attributed to Young Ju Suh.

5 recordsLinked to original sources

PubMed2006

GEDA: new knowledge base of gene expression in drug addiction.

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and nonredundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.

Animals↗

PubMed2005

A Bayesian approach for applying Haseman-Elston methods.

The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDX1 variable as our response.

Alcoholism↗

PubMed2004

Human leukocyte antigen class II alleles influence levels of antibodies to the Plasmodium falciparum asexual-stage apical membrane antigen 1 but not to merozoite surface antigen 2 and merozoite surface protein 1.

The apical membrane antigen 1 (AMA1), merozoite surface antigen 2 (MSA2), and merozoite surface protein 1 (MSP1) are asexual-stage proteins currently being evaluated for inclusion in a vaccine for Plasmodium falciparum. Accordingly, it is important to understand factors that control antibody responses to these antigens. Antibody levels in plasma from residents of Etoa, Cameroon, between the ages of 5 and 70 years, were determined using recombinant AMA1, MSA2, and the N-terminal region of MSP1 (MSP1-190L). In addition, antibody responses to four variants of the C-terminal region of MSP1 (MSP1(19)) were assessed. Results showed that all individuals produced antibodies to AMA1, MSA2, and MSP1-190L; however, a proportion of individuals never produced antibodies to the MSP1(19) variants, although the percentage of nonresponders decreased with age. The influence of age and human leukocyte antigen (HLA)-DRB1/DQB1 alleles on antibody levels was evaluated using two-way analysis of variance. Age was correlated with levels of antibodies to AMA1 and MSP1(19) but not with levels of antibodies to MSA2 and MSP1-190L. No association was found between a single HLA allele and levels of antibodies to MSA2, MSP1-190L, or any of the MSP1(19) variants. However, individuals positive for DRB1*1201 had higher levels of antibodies to the variant of recombinant AMA1 tested than did individuals of all other HLA types. Since the effect was seen across all age groups, HLA influenced the level but not the rate of antibody acquisition. This association for AMA1, combined with the previously reported association between HLA class II alleles and levels of antibodies to rhoptry-associated protein 1 (RAP1) and RAP2, indicates that HLA influences the levels of antibodies to three of the five vaccine candidate antigens that we have evaluated.

Adolescent↗

PubMed2003

Linkage analysis of longitudinal data.

BACKGROUND: We propose a statistical model for linkage analysis of the longitudinal data. The proposed model is a mixed model based on the new Haseman and Elston model and allows several random effects. Specifically, the proposed model includes a random effect for correlation among sib pairs having one sibling in common, and one for the correlation among siblings from the same parents. RESULTS: The proposed model was applied to the analysis of the Genetic Analysis Workshop 13 simulated data set for a quantitative trait of the systolic blood pressure. A simple independence model and two kinds of random effects models yielded good power for detecting linkage for these data sets, while the random effects models performed slightly better than the independence model. Both random effects models showed similar performance. CONCLUSIONS: The proposed models seem not only quite useful in detecting linkage with the longitudinal data for the trait but also quite flexible. They can handle a wide class of correlation structures. Models with a more general class of covariance structure are desirable.

Adult Children↗

PubMed2003

A method for evaluating the results of Bayesian model selection: application to linkage analyses of attributes determined by two or more genes.

OBJECTIVES: We apply and evaluate the intrinsic Bayes factor (IBF) of Berger and Pericchi [J Am Stat Assoc 1996;91:109-122; Bayesian Statistics, Oxford University Press, vol 5, 1996] to linkage analyses done using the stochastic search variable selection (SSVS) method of George and McCulloch [J Am Stat Assoc 1993;88:881-889] as proposed by Suh et al. [Genet Epidemiol 2001;21(suppl 1):S706-S711]. METHODS: We consider 20 simulations of linkage data obtained under two different generating models. The SSVS is applied to a multiple regression extension [Genet Epidemiol 2001;21(suppl 1): S706-S711] of the Haseman-Elston [Behav Genet 1972;2:3-19; Genet Epidemiol 2000;19:1-17] methods. Four prior distributions are considered. We apply the IBF criterion to those samples where different prior distributions result in different top models. RESULTS: In those samples where three different models were obtained using the four priors, application of the IBFs eliminated one of the two wrong models in 4 out of 5 situations. Further elimination using the IBF criterion for situations with two different subsets did not serve as well. CONCLUSIONS: When different priors result in three or more different subsets of markers, one can use the IBF to get this number down to two for consideration. When two subsets result we recommend that both be considered.

Bayes Theorem↗