Detection of nonrandom association of alleles from the distribution of the number of heterozygous loci in a sample.

The distribution of the number of heterozygous loci in two randomly chosen gametes or in a random diploid zygote provides information regarding the nonrandom association of alleles among different genetic loci. Two alternative statistics may be employed for detection of nonrandom association of genes of different loci when observations are made on these distributions: observed variance of the number of heterozygous loci (s2k) and a goodness-of-fit criterion (X2) to contrast the observed distribution with that expected under the hypothesis of random association of genes. It is shown, by simulation, that s2k is statistically more efficient than X2 to detect a given extent of nonrandom association. Asymptotic normality of s2k is justified, and X2 is shown to follow a chi-square (chi 2) distribution with partial loss of degrees of freedom arising because of estimation of parameters from the marginal gene frequency data. Whenever direct evaluations of linkage disequilibrium values are possible, tests based on maximum likelihood estimators of linkage disequilibria require a smaller sample size (number of zygotes or gametes) to detect a given level of nonrandom association in comparison with that required if such tests are conducted on the basis of s2k. Summarization of multilocus genotype (or haplotype) data, into the different number of heterozygous loci classes, thus, amounts to appreciable loss of information.

Measurement of the vascular input function in mice for DCE-MRI

DCE-MRI is an important technique in the study of small animal cancer models because its sensitivity to vascular changes opens the possibility of quantitative assessment of early therapeutic response. However, extraction of physiologically descriptive parameters from DCE-MRI data relies upon measurement of the vascular input function (VIF), which represents the contrast agent concentration time course in the blood plasma. This is difficult in small animal models due to artifacts associated with partial volume, inflow enhancement, and the limited temporal resolution achievable with MR imaging. In this work, the development of a suite of techniques for high temporal resolution, artifact resistant measurement of the VIF in mice is described. One obstacle in VIF measurement is inflow enhancement, which decreases the sensitivity of the MR signal to the presence of contrast agent. Because the traditional techniques used to suppress inflow enhancement degrade the achievable spatiotemporal resolution of the pulse sequence, improvements can be achieved by reducing the time required for the suppression. Thus, a novel RF pulse which provides spatial presaturation contemporaneously with the RF excitation was implemented and evaluated. This maximizes the achievable temporal resolution by removing the additional RF and gradient pulses typically required for suppression of inflow enhancement. A second challenge is achieving the temporal resolution required for accurate characterization of the VIF, which exceeds what can be achieved with conventional imaging techniques while maintaining adequate spatial resolution and tumor coverage. Thus, an anatomically constrained reconstruction strategy was developed that allows for sampling of the VIF at extremely high acceleration factors, permitting capture of the initial pass of the contrast agent in mice. Simulation, phantom, and in vivo validation of all components were performed. Finally, the two components were used to perform VIF measurement in the murine heart. An in vivo study of the VIF reproducibility was performed, and an improvement in the measured injection-to-injection variation was observed. This will lead to improvements in the reliability of quantitative DCE-MRI measurements and increase their sensitivity.

Hepatocellular carcinoma survival in uninsured and underinsured patients.

BACKGROUND: The incidence of hepatitis C virus (HCV) and hepatocellular carcinoma (HCC) is increasing. The purpose of this study is to establish baseline survival in a medically-underserved population and to evaluate the effect of HCV seropositivity on our patient population. MATERIALS AND METHODS: We reviewed clinicopathologic parameters from a prospective tumor registry and medical records from the Harris County Hospital District (HCHD). Outcomes were compared using Kaplan-Meier survival analysis and log-rank tests. RESULTS: A total of 298 HCC patients were identified. The median survival for the entire cohort was 3.4 mo. There was no difference in survival between the HCV seropositive and the HCV seronegative groups (3.6 mo versus 2.6 mo, P = 0.7). Patients with a survival <1 mo had a significant increase in>αfetoprotein (AFP), international normalized ratio (INR), model for end-stage liver disease (MELD) score, and total bilirubin and decrease in albumin compared with patients with a survival ≥ 1 mo. CONCLUSIONS: Survival for HCC patients in the HCHD is extremely poor compared with an anticipated median survival of 7 mo reported in other studies. HCV seropositive patients have no survival advantage over HCV seronegative patients. Poorer liver function at diagnosis appears to be related to shorter survival. Further analysis into variables contributing to decreased survival is needed.

Patient classification system: An integrated method for measuring nursing intensity and optimizing resource allocation

A patient classification system was developed integrating a patient acuity instrument with a computerized nursing distribution method based on a linear programming model. The system was designed for real-time measurement of patient acuity (workload) and allocation of nursing personnel to optimize the utilization of resources.^ The acuity instrument was a prototype tool with eight categories of patients defined by patient severity and nursing intensity parameters. From this tool, the demand for nursing care was defined in patient points with one point equal to one hour of RN time. Validity and reliability of the instrument was determined as follows: (1) Content validity by a panel of expert nurses; (2) predictive validity through a paired t-test analysis of preshift and postshift categorization of patients; (3) initial reliability by a one month pilot of the instrument in a practice setting; and (4) interrater reliability by the Kappa statistic.^ The nursing distribution system was a linear programming model using a branch and bound technique for obtaining integer solutions. The objective function was to minimize the total number of nursing personnel used by optimally assigning the staff to meet the acuity needs of the units. A penalty weight was used as a coefficient of the objective function variables to define priorities for allocation of staff.^ The demand constraints were requirements to meet the total acuity points needed for each unit and to have a minimum number of RNs on each unit. Supply constraints were: (1) total availability of each type of staff and the value of that staff member (value was determined relative to that type of staff's ability to perform the job function of an RN (i.e., value for eight hours RN = 8 points, LVN = 6 points); (2) number of personnel available for floating between units.^ The capability of the model to assign staff quantitatively and qualitatively equal to the manual method was established by a thirty day comparison. Sensitivity testing demonstrated appropriate adjustment of the optimal solution to changes in penalty coefficients in the objective function and to acuity totals in the demand constraints.^ Further investigation of the model documented: correct adjustment of assignments in response to staff value changes; and cost minimization by an addition of a dollar coefficient to the objective function. ^

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

Diffusion tensor imaging of rat spinal cord in vivo

Magnetic resonance imaging, with its exquisite soft tissue contrast, is an ideal modality for investigating spinal cord pathology. While conventional MRI techniques are very sensitive for spinal cord pathology, their specificity is somewhat limited. Diffusion MRI is an advanced technique which is a very sensitive and specific indicator of the integrity of white matter tracts. Diffusion imaging has been shown to detect early ischemic changes in white matter, while conventional imaging demonstrates no change. By acquiring the complete apparent diffusion tensor (ADT), tissue diffusion properties can be expressed in terms of quantitative and rotationally invariant parameters. ^ Systematic study of SCI in vivo requires controlled animal models such as the popular rat model. To date, studies of spinal cord using ADT imaging have been performed exclusively in fixed, excised spinal cords, introducing inevitable artifacts and losing the benefits of MRI's noninvasive nature. In vivo imaging reflects the actual in vivo tissue properties, and allows each animal to be imaged at multiple time points, greatly reducing the number of animals required to achieve statistical significance. Because the spinal cord is very small, the available signal-to-noise ratio (SNR) is very low. Prior spin-echo based ADT studies of rat spinal cord have relied on high magnetic field strengths and long imaging times—on the order of 10 hours—for adequate SNR. Such long imaging times are incompatible with in vivo imaging, and are not relevant for imaging the early phases following SCI. Echo planar imaging (EPI) is one of the fastest imaging methods, and is popular for diffusion imaging. However, EPI further lowers the image SNR, and is very sensitive to small imperfections in the magnetic field, such as those introduced by the bony spine. Additionally, The small field-of-view (FOV) needed for spinal cord imaging requires large imaging gradients which generate EPI artifacts. The addition of diffusion gradients introduces yet further artifacts. ^ This work develops a method for rapid EPI-based in vivo diffusion imaging of rat spinal cord. The method involves improving the SNR using an implantable coil; reducing magnetic field inhomogeneities by means of an autoshim, and correcting EPI artifacts by post-processing. New EPI artifacts due to diffusion gradients described, and post-processing correction techniques are developed. ^ These techniques were used to obtain rotationally invariant diffusion parameters from 9 animals in vivo, and were validated using the gold-standard, but slow, spinecho based diffusion sequence. These are the first reported measurements of the ADT in spinal cord in vivo . ^ Many of the techniques described are equally applicable toward imaging of human spinal cord. We anticipate that these techniques will aid in evaluating and optimizing potential therapies, and will lead to improved patient care. ^

MONITORING ENZYME REACTIONS BY FAST ATOM BOMBARDMENT MASS SPECTROMETRY

The potential for the direct analysis of enzyme reactions by fast atom bombardment (FAB) mass spectrometry has been investigated. Conditions are presented for the maintenance of enzymatic activity under FAB conditions along with FAB mass spectrometric data showing that these conditions can be applied to solutions of enzyme and substrate to follow enzymatic reactions inside the mass spectrometer in real-time. In addition, enzyme kinetic behavior under FAB mass spectrometric conditions is characterized using trypsin and its assay substrate, TAME, as an enzyme-substrate reaction model. These results show that two monitoring methods can be utilized to follow reactions by FAB mass spectrometry. The advantages of each method are discussed and illustrated by obtaining kinetic parameters from the direct analysis of enzyme reactions with assay or peptide substrates. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Renal Cryoablation: Investigation of Periprocedural Visualization Tools and Treatment Response Quantification

Cryoablation for small renal tumors has demonstrated sufficient clinical efficacy over the past decade as a non-surgical nephron-sparing approach for treating renal masses for patients who are not surgical candidates. Minimally invasive percutaneous cryoablations have been performed with image guidance from CT, ultrasound, and MRI. During the MRI-guided cryoablation procedure, the interventional radiologist visually compares the iceball size on monitoring images with respect to the original tumor on separate planning images. The comparisons made during the monitoring step are time consuming, inefficient and sometimes lack the precision needed for decision making, requiring the radiologist to make further changes later in the procedure. This study sought to mitigate uncertainty in these visual comparisons by quantifying tissue response to cryoablation and providing visualization of the response during the procedure. Based on retrospective analysis of MR-guided cryoablation patient data, registration and segmentation algorithms were investigated and implemented for periprocedural visualization to deliver iceball position/size with respect to planning images registered within 3.3mm with at least 70% overlap and a quantitative logit model was developed to relate perfusion deficit in renal parenchyma visualized in verification images as a result of iceball size visualized in monitoring images. Through retrospective study of 20 patient cases, the relationship between likelihood of perfusion loss in renal parenchyma and distance within iceball was quantified and iteratively fit to a logit curve. Using the parameters from the logit fit, the margin for 95% perfusion loss likelihood was found to be 4.28 mm within the iceball. The observed margin corresponds well with the clinically accepted margin of 3-5mm within the iceball. In order to display the iceball position and perfusion loss likelihood to the radiologist, algorithms were implemented to create a fast segmentation and registration module which executed in under 2 minutes, within the clinically-relevant 3 minute monitoring period. Using 16 patient cases, the average Hausdorff distance was reduced from 10.1mm to 3.21 mm with average DSC increased from 46.6% to 82.6% before and after registration.