Characteristics of a spina bifida population including North American Caucasian and Hispanic individuals.

BACKGROUND: Meningomyelocele (MM) is a common human birth defect. MM is a disorder of neural development caused by contributions from genes and environmental factors that result in the NTD and lead to a spectrum of physical and neurocognitive phenotypes. METHODS: A multidisciplinary approach has been taken to develop a comprehensive understanding of MM through collaborative efforts from investigators specializing in genetics, development, brain imaging, and neurocognitive outcome. Patients have been recruited from five different sites: Houston and the Texas-Mexico border area; Toronto, Canada; Los Angeles, California; and Lexington, Kentucky. Genetic risk factors for MM have been assessed by genotyping and association testing using the transmission disequilibrium test. RESULTS: A total of 509 affected child/parent trios and 309 affected child/parent duos have been enrolled to date for genetic association studies. Subsets of the patients have also been enrolled for studies assessing development, brain imaging, and neurocognitive outcomes. The study recruited two major ethnic groups, with 45.9% Hispanics of Mexican descent and 36.2% North American Caucasians of European descent. The remaining patients are African-American, South and Central American, Native American, and Asian. Studies of this group of patients have already discovered distinct corpus callosum morphology and neurocognitive deficits that associate with MM. We have identified maternal MTHFR 667T allele as a risk factor for MM. In addition, we also found that several genes for glucose transport and metabolism are potential risk factors for MM. CONCLUSIONS: The enrolled patient population provides a valuable resource for elucidating the disease characteristics and mechanisms for MM development.

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Drug metabolism in human brain: high levels of cytochrome P4503A43 in brain and metabolism of anti-anxiety drug alprazolam to its active metabolite.

Cytochrome P450 (P450) is a super-family of drug metabolizing enzymes. P450 enzymes have dual function; they can metabolize drugs to pharmacologically inactive metabolites facilitating their excretion or biotransform them to pharmacologically active metabolites which may have longer half-life than the parent drug. The variable pharmacological response to psychoactive drugs typically seen in population groups is often not accountable by considering dissimilarities in hepatic metabolism. Metabolism in brain specific nuclei may play a role in pharmacological modulation of drugs acting on the CNS and help explain some of the diverse response to these drugs seen in patient population. P450 enzymes are also present in brain where drug metabolism can take place and modify therapeutic action of drugs at the site of action. We have earlier demonstrated an intrinsic difference in the biotransformation of alprazolam (ALP) in brain and liver, relatively more alpha-hydroxy alprazolam (alpha-OHALP) is formed in brain as compared to liver. In the present study we show that recombinant CYP3A43 metabolizes ALP to both alpha-OHALP and 4-hydroxy alprazolam (4-OHALP) while CYP3A4 metabolizes ALP predominantly to its inactive metabolite, 4-OHALP. The expression of CYP3A43 mRNA in human brain samples correlates with formation of relatively higher levels of alpha-OH ALP indicating that individuals who express higher levels of CYP3A43 in the brain would generate larger amounts of alpha-OHALP. Further, the expression of CYP3A43 was relatively higher in brain as compared to liver across different ethnic populations. Since CYP3A enzymes play a prominent role in the metabolism of drugs, the higher expression of CYP3A43 would generate metabolite profile of drugs differentially in human brain and thus impact the pharmacodynamics of psychoactive drugs at the site of action.

Drug metabolism in human brain: high levels of cytochrome P4503A43 in brain and metabolism of anti-anxiety drug alprazolam to its active metabolite.

Cytochrome P450 (P450) is a super-family of drug metabolizing enzymes. P450 enzymes have dual function; they can metabolize drugs to pharmacologically inactive metabolites facilitating their excretion or biotransform them to pharmacologically active metabolites which may have longer half-life than the parent drug. The variable pharmacological response to psychoactive drugs typically seen in population groups is often not accountable by considering dissimilarities in hepatic metabolism. Metabolism in brain specific nuclei may play a role in pharmacological modulation of drugs acting on the CNS and help explain some of the diverse response to these drugs seen in patient population. P450 enzymes are also present in brain where drug metabolism can take place and modify therapeutic action of drugs at the site of action. We have earlier demonstrated an intrinsic difference in the biotransformation of alprazolam (ALP) in brain and liver, relatively more alpha-hydroxy alprazolam (alpha-OHALP) is formed in brain as compared to liver. In the present study we show that recombinant CYP3A43 metabolizes ALP to both alpha-OHALP and 4-hydroxy alprazolam (4-OHALP) while CYP3A4 metabolizes ALP predominantly to its inactive metabolite, 4-OHALP. The expression of CYP3A43 mRNA in human brain samples correlates with formation of relatively higher levels of alpha-OH ALP indicating that individuals who express higher levels of CYP3A43 in the brain would generate larger amounts of alpha-OHALP. Further, the expression of CYP3A43 was relatively higher in brain as compared to liver across different ethnic populations. Since CYP3A enzymes play a prominent role in the metabolism of drugs, the higher expression of CYP3A43 would generate metabolite profile of drugs differentially in human brain and thus impact the pharmacodynamics of psychoactive drugs at the site of action.

Oncologic outcomes in diabetic patients with colon cancer

Type II diabetes mellitus is a growing problem worldwide and although its association with increased cardiovascular morbidity and mortality is well known, its role in the development of cancer is now being further elucidated. Recently, there has been increasing evidence that not only are diabetics more susceptible towards development of particular types of cancer, but also have worse oncologic outcomes. This retrospective chart review investigates whether diabetics with colon cancer have a poorer prognosis than their nondiabetic counterparts. Patients with high risk Stage II and Stage III colon cancer who were diagnosed and/or treated at The University of Texas M.D. Anderson Cancer Center from 1/1/2000 till 12/1/2004 were included in our study. We carried out a survival analysis using Kaplan-Meier method and multivariable analysis to assess differences in outcomes of the two population groups. We found that the decreased overall survival in diabetics did not reach statistical significance but this could be due to a lower event rate in our study. Larger studies are required to investigate this further. ^

A geographic analysis of liver cancer mortality and alcohol dependence or abuse in Texas and the U.S., 1980--2003

Background. Liver cancer mortality continues to be a significant factor in deaths worldwide and in the U.S., yet there remains a lack of studies on how mortality burden is impacted by racial groups or by heavy alcohol use. This study evaluated the geographic distribution of liver cancer mortality across population groups in Texas and the U.S. over a 24-year period, as well as determining whether alcohol dependence or abuse correlates with mortality rates. ^ Methods. The Spatial Scan Statistic was used to identify regions of excess liver cancer mortality in Texas counties and the U.S. from 1980 to 2003. The statistic was conducted with a spatial cluster size of 50% of the population at risk, and all analyses used publicly available data. Alcohol abuse data by state and ethnicity were extracted from SAMHSA datasets for the study period 2000–2004. ^ Results. The results of the geographic analysis of liver cancer mortality in both Texas and the U.S. indicate that there were four and seven regions, respectively, that were identified as having statistically significant excess mortality rates with elevated relative risks ranging from 1.38–2.07 and 1.05–1.623 (p = 0.001), respectively. ^ Conclusion. This study revealed seven regions of excess mortality of liver cancer mortality across the U.S. and four regions of excess mortality in Texas between 1980–2003, as well as demonstrated a correlation between elevated liver cancer mortality rates and reporting of alcohol dependence among Hispanics and Other populations. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Factors affecting prenatal care use and pregnancy outcome in Texas: A comparative study among and within Black, Anglo, and Hispanic populations

This research project is a study in the field of public health to test the relationships of demographic, socioeconomic, behavioral, and biological factors with (1) prenatal care use and (2) pregnancy outcome, measured by birth weight. It has been postulated that demographic, socioeconomic, and behavioral factors are associated with differences in the use of prenatal care services. It has also been postulated that differences in demographic, socioeconomic, behavioral, and biological factors result in differences in birth weight. This research attempts to test these two basic conceptual frameworks. At the same time, an attempt is made to determine the population groups and subgroups that are at increased risk (1) of using fewer prenatal care visits, and (2) of displaying a higher incidence of low birth weight babies. An understanding of these relationships of the demographic, socioeconomic, behavioral, and biological factors in the use of prenatal care visits and pregnancy outcome, measured by birth weight, will potentially offer guidance in the planning and policy development of maternal and child health services. The research considers four major components of maternal characteristics: (1) Demographic factors. Ethnicity, household size, maternal parity, and maternal age; (2) Socioeconomic factors. Maternal education, family income, maternal employment, health insurance coverage, and household dwelling; (3) Behavioral factors. Maternal smoking, attendance at child development classes, mother's first prenatal care visit, total number of prenatal care visits, and adequacy of care; and, (4) Biological factors. Maternal weight gain during pregnancy.^ The research considers 16 independent variables and two dependent variables.^ It was concluded that: (1) Generally, differences in demographic, socioeconomic, and behavioral factors were associated with differences in the average number of prenatal care visits between and within population groups and subgroups. The Hispanic mothers were the lowest users of prenatal care services. (2) In some cases, differences in demographic, socioeconomic, behavioral, and biological factors demonstrated differences in the average birth weight of infants between and within population groups and subgroups. (3) Differences in demographic, socioeconomic, behavioral, and biological factors resulted in differences in the rates of low birth weight babies between and within population groups and subgroups. The Black mothers delivered the highest incidence of low birth weight infants.^ These findings could provide guidance in the formulation of public health policies such as MCH services, an increase in the use of prenatal care services by prospective mothers, resulting in reduction of the incidence of low birth weight babies, and consequently aid in reducing the rates of infant mortality. ^

Comparison of Tumor Shrinkage and Cumulative Dose Distribution for Lung Cancers

Background: The physical characteristic of protons is that they deliver most of their radiation dose to the target volume and deliver no dose to the normal tissue distal to the tumor. Previously, numerous studies have shown unique advantages of proton therapy over intensity-modulated radiation therapy (IMRT) in conforming dose to the tumor and sparing dose to the surrounding normal tissues and the critical structures in many clinical sites. However, proton therapy is known to be more sensitive to treatment uncertainties such as inter- and intra-fractional variations in patient anatomy. To date, no study has clearly demonstrated the effectiveness of proton therapy compared with the conventional IMRT under the consideration of both respiratory motion and tumor shrinkage in non-small cell lung cancer (NSCLC) patients. Purpose: This thesis investigated two questions for establishing a clinically relevant comparison of the two different modalities (IMRT and proton therapy). The first question was whether or not there are any differences in tumor shrinkage between patients randomized to IMRT versus passively scattered proton therapy (PSPT). Tumor shrinkage is considered a standard measure of radiation therapy response that has been widely used to gauge a short-term progression of radiation therapy. The second question was whether or not there are any differences between the planned dose and 5D dose under the influence of inter- and intra-fractional variations in the patient anatomy for both modalities. Methods: A total of 45 patients (25 IMRT patients and 20 PSPT patients) were used to quantify the tumor shrinkage in terms of the change of the primary gross tumor volume (GTVp). All patients were randomized to receive either IMRT or PSPT for NSCLC. Treatment planning goals were identical for both groups. All patients received 5 to 8 weekly repeated 4-dimensional computed tomography (4DCT) scans during the course of radiation treatments. The original GTVp contours were propagated to T50 of weekly 4DCT images using deformable image registration and their absolute volumes were measured. Statistical analysis was performed to compare the distribution of tumor shrinkage between the two population groups. In order to investigate the difference between the planned dose and the 5D dose with consideration of both breathing motion and anatomical change, we re-calculated new dose distributions at every phase of the breathing cycle for all available weekly 4DCT data sets which resulted 50 to 80 individual dose calculations for each of the 7 patients presented in this thesis. The newly calculated dose distributions were then deformed and accumulated to T50 of the planning 4DCT for comparison with the planned dose distribution. Results: At the end of the treatment, both IMRT and PSPT groups showed mean tumor volume reductions of 23.6% ( 19.2%) and 20.9% ( 17.0 %) respectively. Moreover, the mean difference in tumor shrinkage between two groups is 3% along with the corresponding 95% confidence interval, [-8%, 14%]. The rate of tumor shrinkage was highly correlated with the initial tumor volume size. For the planning dose and 5D dose comparison study, all 7 patients showed a mean difference of 1 % in terms of target coverage for both IMRT and PSPT treatment plans. Conclusions: The results of the tumor shrinkage investigation showed no statistically significant difference in tumor shrinkage between the IMRT and PSPT patients, and the tumor shrinkage between the two modalities is similar based on the 95% confidence interval. From the pilot study of comparing the planned dose with the 5D dose, we found the difference to be only 1%. Overall impression of the two modalities in terms of treatment response as measured by the tumor shrinkage and 5D dose under the influence of anatomical change that were designed under the same protocol (i.e. randomized trial) showed similar result.

Shifting the focus: Addressing breast cancer disparities at the health care organization level

The purpose of this study was to understand the scope of breast cancer disparities within the Texas Medical Center. The goal was to increase the awareness of breast cancer disparities at the health care organization level, and to foster the development of organizational interventions to reduce breast cancer disparities. The study seeks to answer the following questions: 1. Are hospitals in the Texas Medical Center implementing interventions to reduce breast cancer disparities? 2. What are their interventions for reducing the effects of non clinical factors on breast cancer treatment disparities? 3. What are their measures for monitoring, continuously improving, and evaluating the success of their interventions? ^ This research project was designed as a mixed methods case study. Quantitative breast cancer data for the years 2000-2009 was obtained from the Texas Cancer Registry (TCR). Qualitative data collection and analysis was done by conducting a total of 20 semi-structured interviews of administrators, physicians and nurses at five hospitals (A, B, C, D and E) in the Texas Medical Center (TMC). For quantitative analysis, the study was limited to early stage breast cancer patients: local and regional. The dependent variable was receipt of standard treatment: Surgery (Yes/No), BCS vs Mastectomy, Chemotherapy (Yes/No) and Radiation after BCS (Yes/No). The main independent variable was race: non-Hispanic White (NHW) , non-Hispanic Black (NHB), and Hispanic. Other covariates included age at diagnosis, diagnosis date, percent poverty, grade, stage, and regional nodes. Multivariate logistic regression was used to test the adjusted association between receipt of standard care and race. Qualitative data was analyzed with the Atlas.ti7 software (ATLAS.ti GmbH, Berlin). ^ Though there were significant differences by race for all dependent variables when the data was analyzed as a single group of all hospitals; at the level of the individual hospitals the results were not consistent by race/ethnicity across all dependent variables for hospitals A, B, and E. There were no racial differences in adjusted analysis for receipt of chemotherapy for the individual hospitals of interest in this study. For hospitals C and D, no racial disparities in treatment was observed in adjusted multivariable analysis. All organizations in this study were aware of the body of research which shows that there are disparities in breast cancer outcomes for patient population groups. However, qualitative data analysis found that there were differences in interest among hospitals in addressing breast cancer disparities in their patient population groups. Some organizations were actively implementing directed measures to reduce the breast cancer disparity gap in outcomes for patients, and others were not. Despite the differences in levels of interest, quantitative data analysis showed that organizations in the Texas Medical Center were making progress in reducing the burden of breast cancer disparities in the patient populations being served.^

Evaluating the disparity of female breast cancer mortality among racial groups - a spatiotemporal analysis

Background The literature suggests that the distribution of female breast cancer mortality demonstrates spatial concentration. There remains a lack of studies on how the mortality burden may impact racial groups across space and over time. The present study evaluated the geographic variations in breast cancer mortality in Texas females according to three predominant racial groups (non-Hispanic White, Black, and Hispanic females) over a twelve-year period. It sought to clarify whether the spatiotemporal trend might place an uneven burden on particular racial groups, and whether the excess trend has persisted into the current decade. Methods The Spatial Scan Statistic was employed to examine the geographic excess of breast cancer mortality by race in Texas counties between 1990 and 2001. The statistic was conducted with a scan window of a maximum of 90% of the study period and a spatial cluster size of 50% of the population at risk. The next scan was conducted with a purely spatial option to verify whether the excess mortality persisted further. Spatial queries were performed to locate the regions of excess mortality affecting multiple racial groups. Results The first scan identified 4 regions with breast cancer mortality excess in both non-Hispanic White and Hispanic female populations. The most likely excess mortality with a relative risk of 1.12 (p = 0.001) occurred between 1990 and 1996 for non-Hispanic Whites, including 42 Texas counties along Gulf Coast and Central Texas. For Hispanics, West Texas with a relative risk of 1.18 was the most probable region of excess mortality (p = 0.001). Results of the second scan were identical to the first. This suggested that the excess mortality might not persist to the present decade. Spatial queries found that 3 counties in Southeast and 9 counties in Central Texas had excess mortality involving multiple racial groups. Conclusion Spatiotemporal variations in breast cancer mortality affected racial groups at varying levels. There was neither evidence of hot-spot clusters nor persistent spatiotemporal trends of excess mortality into the present decade. Non-Hispanic Whites in the Gulf Coast and Hispanics in West Texas carried the highest burden of mortality, as evidenced by spatial concentration and temporal persistence.

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.

A spatial-temporal approach to surveillance of prostate cancer disparities in population subgroups

BACKGROUND: Prostate cancer mortality disparities exist among racial/ethnic groups in the United States, yet few studies have explored the spatiotemporal trend of the disease burden. To better understand mortality disparities by geographic regions over time, the present study analyzed the geographic variations of prostate cancer mortality by three Texas racial/ethnic groups over a 22-year period. METHODS: The Spatial Scan Statistic developed by Kulldorff et al was used. Excess mortality was detected using scan windows of 50% and 90% of the study period and a spatial cluster size of 50% of the population at risk. Time trend was analyzed to examine the potential temporal effects of clustering. Spatial queries were used to identify regions with multiple racial/ethnic groups having excess mortality. RESULTS: The most likely area of excess mortality for blacks occurred in Dallas-Metroplex and upper east Texas areas between 1990 and 1999; for Hispanics, in central Texas between 1992 and 1996: and for non-Hispanic whites, in the upper south and west to central Texas areas between 1990 and 1996. Excess mortality persisted among all racial/ethnic groups in the identified counties. The second scan revealed that three counties in west Texas presented an excess mortality for Hispanics from 1980-2001. Many counties bore an excess mortality burden for multiple groups. There is no time trend decline in prostate cancer mortality for blacks and non-Hispanic whites in Texas. CONCLUSION: Disparities in prostate cancer mortality among racial/ethnic groups existed in Texas. Central Texas counties with excess mortality in multiple subgroups warrant further investigation.

Population amalgamation and genetic variation: observations on artificially agglomerated tribal populations of Central and South America.

The interpretation of data on genetic variation with regard to the relative roles of different evolutionary factors that produce and maintain genetic variation depends critically on our assumptions concerning effective population size and the level of migration between neighboring populations. In humans, recent population growth and movements of specific ethnic groups across wide geographic areas mean that any theory based on assumptions of constant population size and absence of substructure is generally untenable. We examine the effects of population subdivision on the pattern of protein genetic variation in a total sample drawn from an artificial agglomerate of 12 tribal populations of Central and South America, analyzing the pooled sample as though it were a single population. Several striking findings emerge. (1) Mean heterozygosity is not sensitive to agglomeration, but the number of different alleles (allele count) is inflated, relative to neutral mutation/drift/equilibrium expectation. (2) The inflation is most serious for rare alleles, especially those which originally occurred as tribally restricted "private" polymorphisms. (3) The degree of inflation is an increasing function of both the number of populations encompassed by the sample and of the genetic divergence among them. (4) Treating an agglomerated population as though it were a panmictic unit of long standing can lead to serious biases in estimates of mutation rates, selection pressures, and effective population sizes. Current DNA studies indicate the presence of numerous genetic variants in human populations. The findings and conclusions of this paper are all fully applicable to the study of genetic variation at the DNA level as well.

The association of genetic groups 1, 2, and 3 of Mycobacterium tuberculosis with pulmonary and extra-pulmonary tuberculosis

Objective. To investigate the association of the three major genetic groups of Mycobacterium tuberculosis with pulmonary and extra-pulmonary tuberculosis in clustered and non-clustered TB cases in the Houston area. ^ Study design. Secondary analysis of an ambi-directional study. ^ Study population. Three hundred fifty-eight confirmed cases of tuberculosis in the Houston that occurred between October 1995 and May 1997, who had been interviewed by the Houston T13 Initiative staff at Baylor College of Medicine, and whose isolates have had their DNA fingerprint and genetic group determined. ^ Exclusions. Individuals whose mycobacterial genotype was unknown, or whose data variables were unavailable. ^ Source of data. Laboratory results, patient interviews, and medical records at clinics and hospitals of the study population. ^ Results. In clustered cases, the majority of both, pulmonary and extra-pulmonary TB cases were caused by genetic group 1. Independent factors were assessed to determine the interactions that may influence the site of infection or increase the risk for one site or another. HIV negative males were protected against extra-pulmonary TB compared to HIV negative females. Individuals ages 1–14 years were at higher risk of having extra-pulmonary TB. Group 3 organisms were found less frequently in the total population in general, especially in extra-pulmonary disease. This supports the evidence in previous studies that this group is the least virulent and genetically distinct from the other two groups. Group 1 was found more frequently among African Americans than other ethnic groups, a trend for future investigations. ^ Among the non-clustered cases, group 2 organisms were the majority of the organisms found in both sites. They were also the majority of organisms found in African Americans, Caucasians, and Hispanics causing the majority of the infections at both sites. However, group 1 organisms were the overwhelming majority found in Asian/Pacific Islander individuals, which may indicate these organisms are either endemic to that area, or that there is an ethnic biological factor involved. This may also be due to a systematic bias, since isolates from individuals from that geographic region lack adequate copies of the insertion sequence IS6110, which leads to their placement in the non-clustered population. ^ The three genetic groups of Mycobacterium tuberculosis were not found equally distributed between sites of infection in both clustered and non-clustered cases. Furthermore, these groups were not distributed in the same patterns among the clustered and non-clustered cases, but rather in distinct patterns. ^