Biomedical subjects

Dvora Shmulewitz

Publications and source records attributed to Dvora Shmulewitz.

4 recordsLinked to original sources

PubMed2006

Linkage analysis of quantitative traits for obesity, diabetes, hypertension, and dyslipidemia on the island of Kosrae, Federated States of Micronesia.

Obesity, diabetes, hypertension, and heart disease are highly heritable conditions that in aggregate are the major causes of morbidity and mortality in the developed world and are growing problems in developing countries. To map the causal genes, we conducted a population screen for these conditions on the Pacific Island of Kosrae. Family history and genetic data were used to construct a pedigree for the island. Analysis of the pedigree showed highly significant heritability for the metabolic traits under study. DNA samples from 2,188 participants were genotyped with 405 microsatellite markers with an average intermarker distance of 11 cM. A protocol using loki, a Markov chain Monte Carlo sampling method, was developed to analyze the Kosraen pedigree for height, a model quantitative trait. Robust quantitative trait loci for height were found on 10q21 and 1p31. This protocol was used to map a set of metabolic traits, including plasma leptin to chromosome region 5q35; systolic blood pressure to 20p12; total cholesterol to 19p13, 12q24, and 16qter; hip circumference to 10q25 and 4q23; body mass index to 18p11 and 20q13; apolipoprotein B to 2p24-25; weight to 18q21; and fasting blood sugar to 1q31-1q43. Several of these same chromosomal regions have been identified in previous studies validating the use of loki. These studies add information about the genetics of the metabolic syndrome and establish an analytical approach for linkage analysis of complex pedigrees. These results also lay the foundation for whole genome scans with dense sets of SNPs aimed to identifying causal genes.

Body Height↗

PubMed2005

Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships, gene interaction, linkage, association, disequilibrium, and ascertainment effects for a complex phenotype.

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker x marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium.

Computer Simulation↗

PubMed2004

Case-control association studies in mixed populations: correcting using genomic control.

OBJECTIVE: Case-control association studies in mixed populations can result in spurious disease-marker associations if subpopulation disease prevalence and marker frequencies both differ. Genomic control (GC) uses neutral loci to correct for spurious association (due to population stratification), but how well this works remains undetermined. METHODS: We simulated and mixed populations with different disease and marker frequencies but without marker-disease association. We generated case-control datasets, calculated the chi2 for disease association with each marker, and applied two GC procedures, dividing by the mean chi2 or median-chi2/0.456. RESULTS: Corrections became conservative (false positive rate [FPR] <5%) with increasing subpopulation prevalence and marker differences. The mean correction resulted in FPRs close to 5% at average subpopulation allele frequency differences <0.26, but inclusion of just a few markers with large frequency differences resulted in conservative FPRs. FPRs from the median correction were mostly conservative but became anticonservative when a few markers with large frequency differences were included. CONCLUSION: GC can both lead to a notable loss of power to detect a true association (conservative) in many circumstances or may fail to eliminate the spurious associations (anticonservative). The mean correction factor is useful in certain situations to correct population stratification, but it is difficult to know when those situations exist.

Alleles↗

PubMed2002

Candidate genes involved in cardiovascular risk factors by a family-based association study on the island of Kosrae, Federated States of Micronesia.

Altered plasma levels of lipids and lipoproteins, obesity, hypertension, and diabetes are major risk factors for atherosclerotic cardiovascular disease. To identify genes that affect these traits and disorders, we looked for association between markers in candidate genes (apolipoprotein AII (apo AII), apolipoprotein AI-CIII-AIV gene cluster (apo AI-CIII-AIV), apolipoprotein E (apo E), cholesteryl ester transfer protein (CETP), cholesterol 7alpha-hydroxylase (CYP7a), hepatic lipase (HL), and microsomal triglyceride transfer protein (MTP)) and known risk factors (triglycerides (Tg), total cholesterol (TC), apolipoprotein AI (apo AI), apolipoprotein AII (apo AII), apolipoprotein B (apo B), body mass index (BMI), blood pressure (BP), leptin, and fasting blood sugar (FBS) levels.) A total of 1,102 individuals from the Pacific island of Kosrae were genotyped for the following markers: Apo AII/MspI, Apo CIII/SstI, Apo AI/XmnI, Apo E/HhaI, CETP/TaqIB, CYP7a/BsaI, HL/DraI, and MTP/HhpI. After testing for population stratification, family-based association analysis was carried out. Novel associations found were: 1) the apo AII/MspI with apo AI and BP levels, 2) the CYP7a/BsaI with apo AI and BMI levels. We also confirmed the following associations: 1) the apo AII/MspI with Tg level; 2) the apo CIII/SstI with Tg, TC, and apo B levels; 3) the Apo E/HhaI E2, E3, and E4 alleles with TC, apo AI, and apo B levels; and 4) the CETP/TaqIB with apo AI level. We further confirmed the connection between the apo AII gene and Tg level by a nonparametric linkage analysis. We therefore conclude that many of these candidate genes may play a significant role in susceptibility to heart disease.

Adult↗