Analysis of Variation in Clubfoot Candidate Genes

Isolated clubfoot, a common birth defect occurring in more than 135,000 livebirths worldwide each year, is associated with significant health care and financial burdens. Clubfoot is defined by forefoot adduction, hindfoot varus, midfoot cavus and hindfoot equinus. Isolated clubfoot, which is the focus of these studies, is distinct from syndromic clubfoot because there are no other associated malformations. Population, family, twin and segregation analysis studies provide evidence that genetic and environmental factors play an etiologic role in isolated clubfoot. The studies described in this thesis were performed to define the role of genetic variation in isolated clubfoot. Interrogation of a deletion region associated with syndromic clubfoot, suggested that CASP8 and CASP10, two apoptotic genes, play a role in isolated clubfoot. To explore the role of apoptotic genes in clubfoot, SNPs spanning genes involved in the apoptotic pathway in the six chromosomal deletion regions, and limb patterning genes, HOXD and HOXA, were interrogated. SNPs in mitochondrial mediated apoptotic genes and several SNPs in HOXA and HOXD genes were modestly associated with clubfoot with the most significant SNP, rs3801776, located in the basal promoter of HOXA9. Several significant associations were found with SNPs in NFAT2 and TNIP2. Significant gene interactions were detected between SNPs in HOX and apoptotic genes. These findings suggest a model for clubfoot in which variation in one gene is not sufficient to cause the malformation but requires variation several genes to perturb protein expression sufficiently to alter muscle and foot development. These results significantly impact our knowledge base by delineating underlying mechanisms causing clubfoot.

GSTM1 and GSTT1 null polymorphisms and risk of salivary gland carcinoma.

Glutathione S-transferase (GST) genes detoxify and metabolize carcinogens, including oxygen free radicals which may contribute to salivary gland carcinogenesis. This cancer center-based case-control association study included 166 patients with incident salivary gland carcinoma (SGC) and 511 cancer-free controls. We performed multiplex polymerase chain reaction-based polymorphism genotyping assays for GSTM1 and GSTT1 null genotypes. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated with multivariable logistic regression analyses adjusted for age, sex, ethnicity, tobacco use, family history of cancer, alcohol use and radiation exposure. In our results, 27.7% of the SGC cases and 20.6% of the controls were null for the GSTT1 (P = 0.054), and 53.0% of the SGC cases and 50.9% of the controls were null for the GSTM1 (P = 0.633). The results of the adjusted multivariale regression analysis suggested that having GSTT1 null genotype was associated with a significantly increased risk for SGC (odds ratio 1.5, 95% confidence interval 1.0-2.3). Additionally, 13.9% of the SGC cases but only 8.4% of the controls were null for both genes and the results of the adjusted multivariable regression analysis suggested that having both null genotypes was significantly associated with an approximately 2-fold increased risk for SGC (odds ratio 1.9, 95% confidence interval 1.0-3.5). The presence of GSTT1 null genotype and the simultaneous presence of GSTM1 and GSTT1 null genotypes appear associated with significantly increased SGC risk. These findings warrant further study with larger sample sizes.

Molecular epidemiology of vancomycin-resistant Enterococcus faecium: a prospective, multicenter study in South American hospitals.

Enterococcus faecium has emerged as an important nosocomial pathogen worldwide, and this trend has been associated with the dissemination of a genetic lineage designated clonal cluster 17 (CC17). Enterococcal isolates were collected prospectively (2006 to 2008) from 32 hospitals in Colombia, Ecuador, Perú, and Venezuela and subjected to antimicrobial susceptibility testing. Genotyping was performed with all vancomycin-resistant E. faecium (VREfm) isolates by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing. All VREfm isolates were evaluated for the presence of 16 putative virulence genes (14 fms genes, the esp gene of E. faecium [espEfm], and the hyl gene of E. faecium [hylEfm]) and plasmids carrying the fms20-fms21 (pilA), hylEfm, and vanA genes. Of 723 enterococcal isolates recovered, E. faecalis was the most common (78%). Vancomycin resistance was detected in 6% of the isolates (74% of which were E. faecium). Eleven distinct PFGE types were found among the VREfm isolates, with most belonging to sequence types 412 and 18. The ebpAEfm-ebpBEfm-ebpCEfm (pilB) and fms11-fms19-fms16 clusters were detected in all VREfm isolates from the region, whereas espEfm and hylEfm were detected in 69% and 23% of the isolates, respectively. The fms20-fms21 (pilA) cluster, which encodes a putative pilus-like protein, was found on plasmids from almost all VREfm isolates and was sometimes found to coexist with hylEfm and the vanA gene cluster. The population genetics of VREfm in South America appear to resemble those of such strains in the United States in the early years of the CC17 epidemic. The overwhelming presence of plasmids encoding putative virulence factors and vanA genes suggests that E. faecium from the CC17 genogroup may disseminate in the region in the coming years.

Association of retinoic acid receptor genes with meningomyelocele.

BACKGROUND: Neural tube defects (NTDs) occur in as many as 0.5-2 per 1000 live births in the United States. One of the most common and severe neural tube defects is meningomyelocele (MM) resulting from failed closure of the caudal end of the neural tube. MM has been induced by retinoic acid teratogenicity in rodent models. We hypothesized that genetic variants influencing retinoic acid (RA) induction via retinoic acid receptors (RARs) may be associated with risk for MM. METHODS: We analyzed 47 single nucleotide polymorphisms (SNPs) that span across the three retinoic acid receptor genes using the SNPlex genotyping platform. Our cohort consisted of 610 MM families. RESULTS: One variant in the RARA gene (rs12051734), three variants in the RARB gene (rs6799734, rs12630816, rs17016462), and a single variant in the RARG gene (rs3741434) were found to be statistically significant at p < 0.05. CONCLUSION: RAR genes were associated with risk for MM. For all associated SNPs, the rare allele conferred a protective effect for MM susceptibility.

Association of folate receptor (FOLR1, FOLR2, FOLR3) and reduced folate carrier (SLC19A1) genes with meningomyelocele.

BACKGROUND: Meningomyelocele (MM) results from lack of closure of the neural tube during embryologic development. Periconceptional folic acid supplementation is a modifier of MM risk in humans, leading toan interest in the folate transport genes as potential candidates for association to MM. METHODS: This study used the SNPlex Genotyping (ABI, Foster City, CA) platform to genotype 20 single polymorphic variants across the folate receptor genes (FOLR1, FOLR2, FOLR3) and the folate carrier gene (SLC19A1) to assess their association to MM. The study population included 329 trio and 281 duo families. Only cases with MM were included. Genetic association was assessed using the transmission disequilibrium test in PLINK. RESULTS: A variant in the FOLR2 gene (rs13908), three linked variants in the FOLR3 gene (rs7925545, rs7926875, rs7926987), and two variants in the SLC19A1 gene (rs1888530 and rs3788200) were statistically significant for association to MM in our population. CONCLUSION: This study involved the analyses of selected single nucleotide polymorphisms across the folate receptor genes and the folate carrier gene in a large population sample. It provided evidence that the rare alleles of specific single nucleotide polymorphisms within these genes appear to be statistically significant for association to MM in the patient population that was tested.

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.

Genes in glucose metabolism and association with spina bifida.

The authors test single nucleotide polymorphisms (SNPs) in coding sequences of 12 candidate genes involved in glucose metabolism and obesity for associations with spina bifida. Genotyping was performed on 507 children with spina bifida and their parents plus anonymous control DNAs from Hispanic and Caucasian individuals. The transmission disequilibrium test was performed to test for genetic associations between transmission of alleles and spina bifida in the offspring (P < .05). A statistically significant association between Lys481 of HK1 (G allele), Arg109Lys of LEPR (G allele), and Pro196 of GLUT1 (A allele) was found ( P = .019, .039, and .040, respectively). Three SNPs on 3 genes involved with glucose metabolism and obesity may be associated with increased susceptibility to spina bifida.

Prognostic significance of xCT polymorphisms and expression in patients with advanced pancreatic cancer treated with chemotherapy

The plasma membrane xc- cystine/glutamate transporter mediates cellular uptake of cystine in exchange for intracellular glutamate and is highly expressed by pancreatic cancer cells. The xCT gene, encoding the cystine-specific xCT protein subunit of xc-, is important in regulating intracellular glutathione (GSH) levels, critical for cancer cell protection against oxidative stress, tumor growth and resistance to chemotherapeutic agents including platinum. We examined 4 single nucleotide polymorphisms (SNPs) of the xCT gene in 269 advanced pancreatic cancer patients who received first line gemcitabine with or without cisplatin or oxaliplatin. Genotyping was performed using Taqman real-time PCR assays. A statistically significant correlation was noted between the 3' untranslated region (UTR) xCT SNP rs7674870 and overall survival (OS): Median survival time (MST) was 10.9 and 13.6 months, respectively, for the TT and TC/CC genotypes (p = 0.027). Stratified analysis showed the genotype effect was significant in patients receiving gemcitabine in combination with platinum therapy (n = 145): MST was 10.5 versus 14.1 months for the TT and TC/CC genotypes, respectively (p = 0.013). The 3' UTR xCT SNP rs7674870 may correlate with OS in pancreatic cancer patients receiving gemcitabine and platinum combination therapy. Paraffin-embedded core and surgical biopsy tumor specimens from 98 patients with metastatic pancreatic adenocarcinoma were analyzed by immunohistochemistry using an xCT specific antibody. xCT protein IHC expression scores were analyzed in relation to overall survival in 86 patients and genotype in 12 patients and no statistically significant association was found between the level of xCT IHC expression score and overall survival (p = 0.514). When xCT expression was analyzed in terms of treatment response, no statistically significant associations could be determined (p = 0.908). These data suggest that polymorphic variants of xCT may have predictive value, and that the xc- transporter may represent an important target for therapy in pancreatic cancer.

Cost and power analysis of linkage disequilibrium methods for locating genes influencing quantitative traits in humans

Linkage disequilibrium methods can be used to find genes influencing quantitative trait variation in humans. Linkage disequilibrium methods can require smaller sample sizes than linkage equilibrium methods, such as the variance component approach to find loci with a specific effect size. The increase in power is at the expense of requiring more markers to be typed to scan the entire genome. This thesis compares different linkage disequilibrium methods to determine which factors influence the power to detect disequilibrium. The costs of disequilibrium and equilibrium tests were compared to determine whether the savings in phenotyping costs when using disequilibrium methods outweigh the additional genotyping costs.^ Nine linkage disequilibrium tests were examined by simulation. Five tests involve selecting isolated unrelated individuals while four involved the selection of parent child trios (TDT). All nine tests were found to be able to identify disequilibrium with the correct significance level in Hardy-Weinberg populations. Increasing linked genetic variance and trait allele frequency were found to increase the power to detect disequilibrium, while increasing the number of generations and distance between marker and trait loci decreased the power to detect disequilibrium. Discordant sampling was used for several of the tests. It was found that the more stringent the sampling, the greater the power to detect disequilibrium in a sample of given size. The power to detect disequilibrium was not affected by the presence of polygenic effects.^ When the trait locus had more than two trait alleles, the power of the tests maximized to less than one. For the simulation methods used here, when there were more than two-trait alleles there was a probability equal to 1-heterozygosity of the marker locus that both trait alleles were in disequilibrium with the same marker allele, resulting in the marker being uninformative for disequilibrium.^ The five tests using isolated unrelated individuals were found to have excess error rates when there was disequilibrium due to population admixture. Increased error rates also resulted from increased unlinked major gene effects, discordant trait allele frequency, and increased disequilibrium. Polygenic effects did not affect the error rates. The TDT, Transmission Disequilibrium Test, based tests were not liable to any increase in error rates.^ For all sample ascertainment costs, for recent mutations ($<$100 generations) linkage disequilibrium tests were less expensive than the variance component test to carry out. Candidate gene scans saved even more money. The use of recently admixed populations also decreased the cost of performing a linkage disequilibrium test. ^

Theoretical and experimental studies of linkage disequilibrium in human populations

Linkage disequilibrium (LD) is defined as the nonrandom association of alleles at two or more loci in a population and may be a useful tool in a diverse array of applications including disease gene mapping, elucidating the demographic history of populations, and testing hypotheses of human evolution. However, the successful application of LD-based approaches to pertinent genetic questions is hampered by a lack of understanding about the forces that mediate the genome-wide distribution of LD within and between human populations. Delineating the genomic patterns of LD is a complex task that will require interdisciplinary research that transcends traditional scientific boundaries. The research presented in this dissertation is predicated upon the need for interdisciplinary studies and both theoretical and experimental projects were pursued. In the theoretical studies, I have investigated the effect of genotyping errors and SNP identification strategies on estimates of LD. The primary importance of these two chapters is that they provide important insights and guidance for the design of future empirical LD studies. Furthermore, I analyzed the allele frequency distribution of 26,530 single nucleotide polymorphisms (SNPs) in three populations and generated the first-generation natural selection map of the human genome, which will be an important resource for explaining and understanding genomic patterns of LD. Finally, in the experimental study, I describe a novel and simple, low-cost, and high-throughput SNP genotyping method. The theoretical analyses and experimental tools developed in this dissertation will facilitate a more complete understanding of patterns of LD in human populations. ^

Nonlinear tests for genetic studies of complex disease

Linkage and association studies are major analytical tools to search for susceptibility genes for complex diseases. With the availability of large collection of single nucleotide polymorphisms (SNPs) and the rapid progresses for high throughput genotyping technologies, together with the ambitious goals of the International HapMap Project, genetic markers covering the whole genome will be available for genome-wide linkage and association studies. In order not to inflate the type I error rate in performing genome-wide linkage and association studies, multiple adjustment for the significant level for each independent linkage and/or association test is required, and this has led to the suggestion of genome-wide significant cut-off as low as 5 × 10 −7. Almost no linkage and/or association study can meet such a stringent threshold by the standard statistical methods. Developing new statistics with high power is urgently needed to tackle this problem. This dissertation proposes and explores a class of novel test statistics that can be used in both population-based and family-based genetic data by employing a completely new strategy, which uses nonlinear transformation of the sample means to construct test statistics for linkage and association studies. Extensive simulation studies are used to illustrate the properties of the nonlinear test statistics. Power calculations are performed using both analytical and empirical methods. Finally, real data sets are analyzed with the nonlinear test statistics. Results show that the nonlinear test statistics have correct type I error rates, and most of the studied nonlinear test statistics have higher power than the standard chi-square test. This dissertation introduces a new idea to design novel test statistics with high power and might open new ways to mapping susceptibility genes for complex diseases. ^

Characterization of glucocorticoid receptor (NR3C1) gene polymorphisms and a family-based association study with hypertension

Hypertension is usually defined as having values of systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg. Hypertension is one of the main adverse effects of glucocorticoid on the cardiovascular system. Glucocorticoids are essential hormones, secreted from adrenal glands in circadian fashion. Glucocorticoid's effect on blood pressure is conveyed by the glucocorticoid receptor (NR3C1), an omnipresent nuclear transcription factor. Although polymorphisms in this gene have long been implicated to be a causal factor for cardiovascular diseases such as hypertension, no study has yet thoroughly interrogated the gene's polymorphisms for their effect on blood pressure levels. Therefore, I have first resequenced ∼30 kb of the gene, encompassing all exons, promoter regions, 5'/3' UTRs as well as at least 1.5 kb of the gene's flanking regions from 114 chromosome 5 monosomic cell lines, comprised of three major American ethnic groups—European American, African American and Mexican American. I observed 115 polymorphisms and 14 common molecularly phased haplotypes. A subset of markers was chosen for genotyping study populations of GENOA (Genetic Epidemiology Network of Atherosclerosis; 1022 non-Hispanic whites, 1228 African Americans and 954 Mexican Americans). Since these study populations include sibships, the family-based association test was performed on 4 blood pressure-related quantitative variables—pulse, systolic blood pressure, diastolic blood pressure and mean arterial pressure. Using these analyses, multiple correlated SNPs are significantly protective against high systolic blood pressure in non-Hispanic whites, which includes rsb198, a SNP formerly associated with beneficial body compositions. Haplotype association analysis also supports this finding and all p-values remained significant after permutation tests. I therefore conclude that multiple correlated SNPs on the gene may confer protection against high blood pressure in non-Hispanic whites. ^

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.^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Characterization of Staphylococcus aureus isolates associated with pediatric carriage in Houston, Texas

Infections caused by Methicillin-resistant Staphylococcus aureus (MRSA) have been of great concern in hospitals due the difficulty in treating virulent, antibiotic resistant microorganisms in sensitive populations including children, the elderly, and immunocomprimised individuals. Since the late 1990's, MRSA infections have become a problem in the general community, and the strains of S. aureus that cause infections in the community are known to be genetically different than the hospital acquired strains. Community-acquired strains tend to be more virulent, affecting even relatively healthy individuals, and disease presentation tends to be more diverse than diseases observed in patients suffering from hospital-acquired strains. From the year 2000 to the present, there has been a significant increase in community-acquired infections in children, a population already particularly sensitive to S. aureus infection. Genotyping the strains of CA-MRSA circulating in the pediatric population is an important step in developing better antibiotic treatment strategies. Additionally, determining the carriage status of individuals in this population and comparing these data with strain genotypes will also be valuable in establishing prevention and control practices. ^