Estimates of life expectancy gains from reducing/eliminating major causes of death in the USA: An update analysis for population in 2001--2008

Evaluation of the impact of a disease on life expectancy is an important part of public health. Potential gains in life expectancy (PGLE) that can properly take into account the competing risks are an effective indicator for measuring the impact of the multiple causes of death. This study aimed to measure the PGLEs from reducing/eliminating the major causes of death in the USA from 2001 to 2008. To calculate the PGLEs due to the elimination of specific causes of death, the age-specific mortality rates for heart disease, malignant neoplasms, Alzheimer disease, kidney diseases and HIV/AIDS and life table constructing data were obtained from the National Center for Health Statistics, and the multiple decremental life tables were constructed. The PGLEs by elimination of heart disease, malignant neoplasms or HIV/AIDS continued decreasing from 2001 to 2008, but the PGLE by elimination of Alzheimer's disease or kidney diseases revealed increased trends. The PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of heart disease 2001–2008 were 0.336–0.299, 0.327–0.301, 0.344–0.295, 0.360–0.315, 0.349–0.317, 0.371–0.316,0.278–0.251, 0.272–0.255, and 0.282–0.246 respectively. Similarly, the PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of malignant neoplasms, Alzheimer's disease, kidney disease or HIV/AIDS 2001–2008 were also uncovered, respectively. Most diseases affect specific population, such as, HIV/AIDS tends to have a greater impact on people of working age, heart disease and malignant neoplasms have a greater impact on people over 65 years of age, but Alzheimer's disease and kidney diseases have a greater impact on people over 75 years of age. To measure the impact of these diseases on life expectancy in people of working age, partial multiple decremental life tables were constructed and the PGLEs were computed by partial or complete elimination of various causes of death during the working years. Thus, the results of the study outlined a picture of how each single disease could affect the life expectancy in age-, race-, or sex-specific population in USA. Therefore, the findings would not only assist to evaluate current public health improvements, but also provide useful information for future research and disease control programs.^

Trastuzumab use and risk of congestive heart failure in older breast cancer patients

Trastuzumab is a humanized-monoclonal antibody, developed specifically for HER2-neu over-expressed breast cancer patients. Although highly effective and well tolerated, it was reported associated with Congestive Heart Failure (CHF) in clinical trial settings (up to 27%). This leaves a gap where, Trastuzumab-related CHF rate in general population, especially older breast cancer patients with long term treatment of Trastuzumab remains unknown. This thesis examined the rates and risk factors associated with Trastuzumab-related CHF in a large population of older breast cancer patients. A retrospective cohort study using the existing Surveillance, Epidemiology and End Results (SEER) and Medicare linked de-identified database was performed. Breast cancer patients ≥ 66 years old, stage I-IV, diagnosed in 1998-2007, fully covered by Medicare but no HMO within 1-year before and after first diagnosis month, received 1st chemotherapy no earlier than 30 days prior to diagnosis were selected as study cohort. The primary outcome of this study is a diagnosis of CHF after starting chemotherapy but none CHF claims on or before cancer diagnosis date. ICD-9 and HCPCS codes were used to pool the claims for Trastuzumab use, chemotherapy, comorbidities and CHF claims. Statistical analysis including comparison of characteristics, Kaplan-Meier survival estimates of CHF rates for long term follow up, and Multivariable Cox regression model using Trastuzumab as a time-dependent variable were performed. Out of 17,684 selected cohort, 2,037 (12%) received Trastuzumab. Among them, 35% (714 out of 2037) were diagnosed with CHF, compared to 31% (4784 of 15647) of CHF rate in other chemotherapy recipients (p<.0001). After 10 years of follow-up, 65% of Trastuzumab users developed CHF, compared to 47% in their counterparts. After adjusting for patient demographic, tumor and clinical characteristics, older breast cancer patients who used Trastuzumab showed a significantly higher risk in developing CHF than other chemotherapy recipients (HR 1.69, 95% CI 1.54 - 1.85). And this risk is increased along with the increment of age (p-value < .0001). Among Trastuzumab users, these covariates also significantly increased the risk of CHF: older age, stage IV, Non-Hispanic black race, unmarried, comorbidities, Anthracyclin use, Taxane use, and lower educational level. It is concluded that, Trastuzumab users in older breast cancer patients had 69% higher risk in developing CHF than non-Trastuzumab users, much higher than the 27% increase reported in younger clinical trial patients. Older age, Non-Hispanic black race, unmarried, comorbidity, combined use with Anthracycline or Taxane also significantly increase the risk of CHF development in older patients treated with Trastuzumab. ^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^

Performance of HbA1c for classifying diabetes and diagnostic threshold for diabetes among the Mexican American border community

Objective. In 2009, the International Expert Committee recommended the use of HbA1c test for diagnosis of diabetes. Although it has been recommended for the diagnosis of diabetes, its precise test performance among Mexican Americans is uncertain. A strong “gold standard” would rely on repeated blood glucose measurement on different days, which is the recommended method for diagnosing diabetes in clinical practice. Our objective was to assess test performance of HbA1c in detecting diabetes and pre-diabetes against repeated fasting blood glucose measurement for the Mexican American population living in United States-Mexico border. Moreover, we wanted to find out a specific and precise threshold value of HbA1c for Diabetes Mellitus (DM) and pre-diabetes for this high-risk population which might assist in better diagnosis and better management of patient diabetes. ^ Research design and methods. We used CCHC dataset for our study. In 2004, the Cameron County Hispanic Cohort (CCHC), now numbering 2,574, was established drawn from randomly selected households on the basis of 2000 Census tract data. The CCHC study randomly selected a subset of people (aged 18-64 years) in CCHC cohort households to determine the influence of SES on diabetes and obesity. Among the participants in Cohort-2000, 67.15% are female; all are Hispanic. ^ Individuals were defined as having diabetes mellitus (Fasting plasma glucose [FPG] ≥ 126 mg/dL or pre-diabetes (100 ≤ FPG < 126 mg/dL). HbA1c test performance was evaluated using receiver operator characteristic (ROC) curves. Moreover, change-point models were used to determine HbA1c thresholds compatible with FPG thresholds for diabetes and pre-diabetes. ^ Results. When assessing Fasting Plasma Glucose (FPG) is used to detect diabetes, the sensitivity and specificity of HbA1c≥ 6.5% was 75% and 87% respectively (area under the curve 0.895). Additionally, when assessing FPG to detect pre-diabetes, the sensitivity and specificity of HbA1c≥ 6.0% (ADA recommended threshold) was 18% and 90% respectively. The sensitivity and specificity of HbA1c≥ 5.7% (International Expert Committee recommended threshold) for detecting pre-diabetes was 31% and 78% respectively. ROC analyses suggest HbA1c as a sound predictor of diabetes mellitus (area under the curve 0.895) but a poorer predictor for pre-diabetes (area under the curve 0.632). ^ Conclusions. Our data support the current recommendations for use of HbA1c in the diagnosis of diabetes for the Mexican American population as it has shown reasonable sensitivity, specificity and accuracy against repeated FPG measures. However, use of HbA1c may be premature for detecting pre-diabetes in this specific population because of the poor sensitivity with FPG. It might be the case that HbA1c is differentiating the cases more effectively who are at risk of developing diabetes. Following these pre-diabetic individuals for a longer-term for the detection of incident diabetes may lead to more confirmatory result.^

Using spatial linear models with SAR and CAR structure to examine Texas lung cancer incidence rates

Scholars have found that socioeconomic status was one of the key factors that influenced early-stage lung cancer incidence rates in a variety of regions. This thesis examined the association between median household income and lung cancer incidence rates in Texas counties. A total of 254 individual counties in Texas with corresponding lung cancer incidence rates from 2004 to 2008 and median household incomes in 2006 were collected from the National Cancer Institute Surveillance System. A simple linear model and spatial linear models with two structures, Simultaneous Autoregressive Structure (SAR) and Conditional Autoregressive Structure (CAR), were used to link median household income and lung cancer incidence rates in Texas. The residuals of the spatial linear models were analyzed with Moran's I and Geary's C statistics, and the statistical results were used to detect similar lung cancer incidence rate clusters and disease patterns in Texas.^

Impact of donor-recipient gender and race mismatching on patient survival in patients with end-stage liver diseases undergoing liver transplantation

Background. End-stage liver disease (ESLD) is an irreversible condition that leads to the imminent complete failure of the liver. Orthotopic liver transplantation (OLT) has been well accepted as the best curative option for patients with ESLD. Despite the progress in liver transplantation, the major limitation nowadays is the discrepancy between donor supply and organ demand. In an effort to alleviate this situation, mismatched donor and recipient gender or race livers are being used. However, the simultaneous impact of donor and recipient gender and race mismatching on patient survival after OLT remains unclear and relatively challenging to surgeons. ^ Objective. To examine the impact of donor and recipient gender and race mismatching on patient survival after OLT using the United Network for Organ Sharing (UNOS) database. ^ Methods. A total of 40,644 recipients who underwent OLT between 2002 and 2011 were included. Kaplan-Meier survival curves and the log-rank tests were used to compare the survival rates among different donor-recipient gender and race combinations. Univariate Cox regression analysis was used to assess the association of donor-recipient gender and race mismatching with patient survival after OLT. Multivariable Cox regression analysis was used to model the simultaneous impact of donor-recipient gender and race mismatching on patient survival after OLT adjusting for a list of other risk factors. Multivariable Cox regression analysis stratifying on recipient hepatitis C virus (HCV) status was also conducted to identify the variables that were differentially associated with patient survival in HCV + and HCV − recipients. ^ Results. In the univariate analysis, compared to male donors to male recipients, female donors to male recipients had a higher risk of patient mortality (HR, 1.122; 95% CI, 1.065–1.183), while in the multivariable analysis, male donors to female recipients experienced an increased mortality rates (adjusted HR, 1.114; 95% CI, 1.048–1.184). Compared to white donors to white recipients, Hispanic donors to black recipients had a higher risk of patient mortality (HR, 1.527; 95% CI, 1.293–1.804) in the univariate analysis, and similar result (adjusted HR, 1.553; 95% CI, 1.314–1.836) was noted in multivariable analysis. After the stratification on recipient HCV status in the multivariable analysis, HCV + mismatched recipients appeared to be at greater risk of mortality than HCV − mismatched recipients. Female donors to female HCV − recipients (adjusted HR, 0.843; 95% CI, 0.769–0.923), and Hispanic HCV + recipients receiving livers from black donors (adjusted HR, 0.758; 95% CI, 0.598–0.960) had a protective effect on patient survival after OLT. ^ Conclusion. Donor-recipient gender and race mismatching adversely affect patient survival after OLT, both independently and after the adjustment for other risk factors. Female recipient HCV status is an important effect modifier in the association between donor-recipient gender combination and patient survival.^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Metabolic syndrome in Mexican American females---prevalence and impact of age, socioeconomic status, and family history of type II diabetes

This thesis presents an analysis of data from Molecular Epidemiology of Type II Diabetes Mellitus in Mexican Americans. The study included 294 families. Among the participating families were 500 Mexican American females aged 19 to 86 who provided information on characteristics such as height, weight, and a variety of biochemical indicators. The research questions for this thesis are: (1) How strong is the association between indicators of the metabolic syndrome in study participants and their family histories of type II diabetes; and (2) How is an individual's family history of type II diabetes, age and socioeconomic status associated with the metabolic syndrome? In this thesis education status of the participants is used as an indicator of socioeconomic status. Answers to these questions are provided through the analysis of women's responses to written questionnaires and biochemical data. ^

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

Exploration of genetic susceptibility to pancreatic cancer: A GWAS approach

Pancreatic cancer is the 4th most common cause for cancer death in the United States, accompanied by less than 5% five-year survival rate based on current treatments, particularly because it is usually detected at a late stage. Identifying a high-risk population to launch an effective preventive strategy and intervention to control this highly lethal disease is desperately needed. The genetic etiology of pancreatic cancer has not been well profiled. We hypothesized that unidentified genetic variants by previous genome-wide association study (GWAS) for pancreatic cancer, due to stringent statistical threshold or missing interaction analysis, may be unveiled using alternative approaches. To achieve this aim, we explored genetic susceptibility to pancreatic cancer in terms of marginal associations of pathway and genes, as well as their interactions with risk factors. We conducted pathway- and gene-based analysis using GWAS data from 3141 pancreatic cancer patients and 3367 controls with European ancestry. Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using the logistic kernel machine (LKM) test, we analyzed 17906 genes defined by University of California Santa Cruz (UCSC) database. Using the likelihood ratio test (LRT) in a logistic regression model, we analyzed 177 pathways and 17906 genes for interactions with risk factors in 2028 pancreatic cancer patients and 2109 controls with European ancestry. After adjusting for multiple comparisons, six pathways were marginally associated with risk of pancreatic cancer ( P < 0.00025): Fc epsilon RI signaling, maturity onset diabetes of the young, neuroactive ligand-receptor interaction, long-term depression (Ps < 0.0002), and the olfactory transduction and vascular smooth muscle contraction pathways (P = 0.0002; Nine genes were marginally associated with pancreatic cancer risk (P < 2.62 × 10−5), including five reported genes (ABO, HNF1A, CLPTM1L, SHH and MYC), as well as four novel genes (OR13C4, OR 13C3, KCNA6 and HNF4 G); three pathways significantly interacted with risk factors on modifying the risk of pancreatic cancer (P < 2.82 × 10−4): chemokine signaling pathway with obesity ( P < 1.43 × 10−4), calcium signaling pathway (P < 2.27 × 10−4) and MAPK signaling pathway with diabetes (P < 2.77 × 10−4). However, none of the 17906 genes tested for interactions survived the multiple comparisons corrections. In summary, our current GWAS study unveiled unidentified genetic susceptibility to pancreatic cancer using alternative methods. These novel findings provide new perspectives on genetic susceptibility to and molecular mechanisms of pancreatic cancer, once confirmed, will shed promising light on the prevention and treatment of this disease. ^

A comparison of adherence with minocycline treatment in Parkinson's disease and rheumatoid arthritis clinical trials

Background: Little is known about the effects on patient adherence when the same study drug is administered in the same dose in two populations with two different diseases in two different clinical trials. The Minocycline in Rheumatoid Arthritis (MIRA) trial and the NIH Exploratory Trials in Parkinson's disease (NET-PD) Futility Study I provide a unique opportunity to do the above and to compare methods measuring adherence. This study may increase understanding of the influence of disease and adverse events on patient adherence and will provide insights to investigators selecting adherence assessment methods in clinical trials of minocycline and other drugs in future.^ Methods: Minocycline adherence by pill count and the effect of adverse events was compared in the MIRA and NET-PD FS1 trials using multivariable linear regression. Within the MIRA trial, agreement between assay and pill count was compared. The association of adverse events with assay adherence was examined using multivariable logistic regression.^ Results: Adherence derived from pill count in the MIRA and NET-PD FS1 trials did not differ significantly. Adverse events potentially related to minocycline did not appear useful to predict minocycline adherence. In the MIRA trial, adherence measured by pill count appears higher than adherence measured by assay. Agreement between pill count and assay was poor (kappa statistic = 0.25).^ Limitations: Trial and disease are completely confounded and hence the independent effect of disease on adherence to minocycline treatment cannot be studied.^ Conclusion: Simple pill count may be preferred over assay in the minocycline clinical trials to measure adherence. Assays may be less sensitive in a clinical setting where appointments are not scheduled in relation to medication administration time, given assays depend on many pharmacokinetic and instrument-related factors. However, pill count can be manipulated by the patient. Another study suggested that self-report method is more sensitive than pill count method in differentiating adherence from non-adherence. An effect of medication-related adverse events on adherence could not be detected.^

Survival analysis of circulating tumor cells in triple-negative breast cancer

Background. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% (1.38 million) of the total new cancer cases and 14% (458,400) of the total cancer deaths in 2008. [1] Triple-negative breast cancer (TNBC) is an aggressive phenotype comprising 10–20% of all breast cancers (BCs). [2-4] TNBCs show absence of estrogen, progesterone and HER2/neu receptors on the tumor cells. Because of the absence of these receptors, TNBCs are not candidates for targeted therapies. Circulating tumor cells (CTCs) are observed in blood of breast cancer patients even at early stages (Stage I & II) of the disease. Immunological and molecular analysis can be used to detect the presence of tumor cells in the blood (Circulating tumor cells; CTCs) of many breast cancer patients. These cells may explain relapses in early stage breast cancer patients even after adequate local control. CTC detection may be useful in identifying patients at risk for disease progression, and therapies targeting CTCs may improve outcome in patients harboring them. Methods . In this study we evaluated 80 patients with TNBC who are enrolled in a larger prospective study conducted at M D Anderson Cancer Center in order to determine whether the presence of circulating tumor cells is a significant prognostic factor in relapse free and overall survival . Patients with metastatic disease at the time of presentation were excluded from the study. CTCs were assessed using CellSearch System™ (Veridex, Raritan, NJ). CTCs were defined as nucleated cells lacking the presence of CD45 but expressing cytokeratins 8, 18 or 19. The distribution of patient and tumor characteristics was analyzed using chi square test and Fisher's exact test. Log rank test and Cox regression analysis was applied to establish the association of circulating tumor cells with relapse free and overall survival. Results. The median age of the study participants was 53years. The median duration of follow-up was 40 months. Eighty-eight percent (88%) of patients were newly diagnosed (without a previous history of breast cancer), and (60%) of patients were chemo naïve (had not received chemotherapy at the time of their blood draw for CTC analysis). Tumor characteristics such as stage (P=0.40), tumor size (P=69), sentinel nodal involvement (P=0.87), axillary lymph node involvement (P=0.13), adjuvant therapy (P=0.83), and high histological grade of tumor (P=0.26) did not predict the presence of CTCs. However, CTCs predicted worse relapse free survival (1 or more CTCs log rank P value = 0.04, at 2 or more CTCs P = 0.02 and at 3 or more CTCs P < 0.0001) and overall survival (at 1 or more CTCs log rank P value = 0.08, at 2 or more CTCs P = 0.01 and at 3 or more CTCs P = 0.0001. Conclusions. The number of circulating tumor cells predicted worse relapse free survival and overall survival in TNBC patients.^

Identification of genetic risk factors for cerebellar mutism in pediatric brain tumor patients

Following posterior fossa surgery for resection of childhood medulloblastoma and primitive neuroectodermal tumor (M/PNET), cerebellar mutism (CM) may develop. This is a condition of absent or diminished speech in a conscious patient with no evidence of oral apraxia, which can be accompanied by other symptoms of the posterior fossa syndrome complex, which includes ataxia and hypotonia. Little is known about the etiology. Therefore, we conducted a SNP, gene, and pathway-level analysis to assess the role of host genetic variation on the risk of CM in M/PNET subjects following treatment. Cases (n= 20) and controls (n= 53) were recruited from the Childhood Cancer Epidemiology and Prevention Center, in Houston, TX. DNA samples were genotyped using the Illumina Human 1M Quad SNP chip. Ten pathways were identified from logistic regression used to identify the marginal effect of each SNP on CM risk. The minP test was conducted to identify associations between SNPs categorized to genes and CM risk. Pathways were assessed to determine if there was a significant enrichment of genes in the pathway compared to all other pathways. There were 78 genes that reached the threshold of min P ≤0.05 in 948 genes. The Neurotoxicity pathway was the most significant pathway after adjusting for multiple comparisons (q=0.040 and q=0.005, using Fisher's exact test and a test of proportions, respectively). Most genes within the Neurotoxicity pathway that reached a threshold of minP ≤0.05 were known to have an apoptosis function, possibly inducing neuronal apoptosis in the dentatothalamocortical pathway, and may be important in CM etiology in this population. This is the first study to assess the potential role of genetic risk factors on CM. As an exploratory study, these results should be replicated in a larger sample. ^

Identification of genes associated with quantitative traits involved in cardiovascular disease and lipoprotein metabolism

Cardiovascular disease (CVD) is a threat to public health. It has been reported to be the leading cause of death in United States. The invention of next generation sequencing (NGS) technology has revolutionized the biomedical research. To investigate NGS data of CVD related quantitative traits would contribute to address the unknown etiology and disease mechanism of CVD. NHLBI's Exome Sequencing Project (ESP) contains CVD related phenotypes and their associated NGS exomes sequence data. Initially, a subset of next generation sequencing data consisting of 13 CVD-related quantitative traits was investigated. Only 6 traits, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, platelet counts, waist circumference, and weight, were analyzed by functional linear model (FLM) and 7 currently existing methods. FLM outperformed all currently existing methods by identifying the highest number of significant genes and had identified 96, 139, 756, 1162, 1106, and 298 genes associated with SBP, DBP, Height, Platelet, Waist, and Weight respectively. ^