myogenin: Human candidate gene and knockout mouse effect

The myogenin gene encodes an evolutionarily conserved basic helix-loop-helix transcription factor that regulates the expression of skeletal muscle-specific genes and its homozygous deletion results in mice who die of respiratory failure at birth. The histology of skeletal muscle in the myogenin null mice is reminiscent of that found in some severe congenital myopathy patients, many of whom also die of respiratory complications and provides the rationale that an aberrant human myogenin (myf4) coding region could be associated with some congenital myopathy conditions.^ With PCR, we found similarly sized amplimers for the three exons of the myogenin gene in 37 patient and 40 control samples. In contrast to the GeneBank sequence for human myogenin, we report several differences in flanking and coding regions plus an additional 659 and 498 bps in the first and second introns, respectively, in all patients and controls. We also find a novel (CA)-dinucleotide repeat in the second intron. No causative mutations were detected in the myogenin coding regions of genomic DNA from patients with severe congenital myopathy.^ Severe congenital myopathies in humans are often associated with respiratory complications and pulmonary hypoplasia. We have employed the myogenin null mouse, which lacks normal development of skeletal muscle fibers as a genetically defined severe congenital myopathy mouse model to evaluate the effect of absent fetal breathing movement on pulmonary development.^ Significant differences are observed at embryonic days E14, E17 and E20 of lung:body weight, total DNA and histologically, suggesting that the myogenin null lungs are hypoplastic. RT-PCR, in-situ immunofluorescence and EM reveal pneumocyte type II differentiation in both null and wild lungs as early as E14. However, at E14, myogenin null lungs have decreased BrdU incorporation while E17 through term, augmented cell death is detected in the myogenin null lungs, not seen in wild littermates. Absent mechanical forces appear to impair normal growth, but not maturation, of the developing lungs in myogenin null mouse.^ These investigations provide the basis for delineating the DNA sequence of the myogenin gene and and highlight the importance of skeletal muscle development in utero for normal lung organogenesis. My observation of no mutations within the coding regions of the human myogenin gene in DNA from patients with severe congenital myopathy do not support any association with this condition. ^

Familial aggregation, candidate genes and genome scans: Analyzing the role of genetics in stroke

The genetic etiology of stroke likely reflects the influence of multiple loci with small effects, each modulating different pathophysiological processes. This research project utilized three analytical strategies to address the paucity of information related to the identification and characterization of genetic variation associated with stroke in the general population. ^ First, the general contribution of familial factors to stroke susceptibility was evaluated in a population-based sample of unrelated individuals. Increased risk of subclinical cerebral infarction was observed among individuals with a positive parental history of stroke. This association did not appear to be mediated by established stroke risk factors, specifically blood pressure levels or hypertension status. ^ The need to identify specific gene variation associated with stroke in the general population was addressed by evaluating seven candidate gene polymorphisms in a population-based sample of unrelated individuals. Three polymorphisms were significantly associated with increased subclinical cerebral infarction or incident clinical ischemic stroke risk. These relationships include the G-protein β3 subunit 825C/T polymorphism and clinical stroke in Whites, the lipoprotein lipase S/X447 polymorphism and subclinical and clinical stroke in men, and the angiotensin I-converting enzyme Ins/Del polymorphism and subclinical stroke in White men. These associations did not appear to be obfuscated by the stroke risk factors adjusted for in the analysis models specifically blood pressure levels or anti-hypertensive medication use. ^ The final research strategy considered, on a genome-wide scale, the idea that genetic variation may contribute to the occurrence of hypertension or stroke through a common etiologic pathway. Genomic regions were identified for which significant evidence of heterogeneity was observed among hypertensive sibpairs stratified by family history of stroke information. Regions identified on chromosome 15 in African Americans, and chromosome 13 in Whites and African Americans, suggest the presence of genes influencing hypertension and stroke susceptibility. ^ Insight into the role of genetics in stroke is useful for the potential early identification of individuals at increased risk for stroke and improved understanding of the etiology of the disease. The ultimate goal of these endeavors is to guide the development of therapeutic intervention and informed prevention to provide a lasting and positive impact on public health. ^

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.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Single nucleotide polymorphism (SNP) selection for genotype-phenotype association studies

With hundreds of single nucleotide polymorphisms (SNPs) in a candidate gene and millions of SNPs across the genome, selecting an informative subset of SNPs to maximize the ability to detect genotype-phenotype association is of great interest and importance. In addition, with a large number of SNPs, analytic methods are needed that allow investigators to control the false positive rate resulting from large numbers of SNP genotype-phenotype analyses. This dissertation uses simulated data to explore methods for selecting SNPs for genotype-phenotype association studies. I examined the pattern of linkage disequilibrium (LD) across a candidate gene region and used this pattern to aid in localizing a disease-influencing mutation. The results indicate that the r2 measure of linkage disequilibrium is preferred over the common D′ measure for use in genotype-phenotype association studies. Using step-wise linear regression, the best predictor of the quantitative trait was not usually the single functional mutation. Rather it was a SNP that was in high linkage disequilibrium with the functional mutation. Next, I compared three strategies for selecting SNPs for application to phenotype association studies: based on measures of linkage disequilibrium, based on a measure of haplotype diversity, and random selection. The results demonstrate that SNPs selected based on maximum haplotype diversity are more informative and yield higher power than randomly selected SNPs or SNPs selected based on low pair-wise LD. The data also indicate that for genes with small contribution to the phenotype, it is more prudent for investigators to increase their sample size than to continuously increase the number of SNPs in order to improve statistical power. When typing large numbers of SNPs, researchers are faced with the challenge of utilizing an appropriate statistical method that controls the type I error rate while maintaining adequate power. We show that an empirical genotype based multi-locus global test that uses permutation testing to investigate the null distribution of the maximum test statistic maintains a desired overall type I error rate while not overly sacrificing statistical power. The results also show that when the penetrance model is simple the multi-locus global test does as well or better than the haplotype analysis. However, for more complex models, haplotype analyses offer advantages. The results of this dissertation will be of utility to human geneticists designing large-scale multi-locus genotype-phenotype association studies. ^

Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas

To identify genetic susceptibility loci for severe diabetic retinopathy, 286 Mexican-Americans with type 2 diabetes from Starr County, Texas completed detailed physical and ophthalmologic examinations including fundus photography for diabetic retinopathy grading. 103 individuals with moderate-to-severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy were defined as cases for this study. DNA samples extracted from study subjects were genotyped using the Affymetrix GeneChip® Human Mapping 100K Set, which includes 116,204 single nucleotide polymorphisms (SNPs) across the whole genome. Single-marker allelic tests and 2- to 8-SNP sliding-window Haplotype Trend Regression implemented in HelixTreeTM were first performed with these direct genotypes to identify genes/regions contributing to the risk of severe diabetic retinopathy. An additional 1,885,781 HapMap Phase II SNPs were imputed from the direct genotypes to expand the genomic coverage for a more detailed exploration of genetic susceptibility to diabetic retinopathy. The average estimated allelic dosage and imputed genotypes with the highest posterior probabilities were subsequently analyzed for associations using logistic regression and Fisher's Exact allelic tests, respectively. To move beyond these SNP-based approaches, 104,572 directly genotyped and 333,375 well-imputed SNPs were used to construct genetic distance matrices based on 262 retinopathy candidate genes and their 112 related biological pathways. Multivariate distance matrix regression was then used to test hypotheses with genes and pathways as the units of inference in the context of susceptibility to diabetic retinopathy. This study provides a framework for genome-wide association analyses, and implicated several genes involved in the regulation of oxidative stress, inflammatory processes, histidine metabolism, and pancreatic cancer pathways associated with severe diabetic retinopathy. Many of these loci have not previously been implicated in either diabetic retinopathy or diabetes. In summary, CDC73, IL12RB2, and SULF1 had the best evidence as candidates to influence diabetic retinopathy, possibly through novel biological mechanisms related to VEGF-mediated signaling pathway or inflammatory processes. While this study uncovered some genes for diabetic retinopathy, a comprehensive picture of the genetic architecture of diabetic retinopathy has not yet been achieved. Once fully understood, the genetics and biology of diabetic retinopathy will contribute to better strategies for diagnosis, treatment and prevention of this disease.^

Expression study of the atherosclerosis mouse aorta reveals significant disturbance of calcium signaling pathway

Atherosclerosis is a complex disease resulting from interactions of genetic and environmental risk factors leading to heart failure and stroke. Using an atherosclerotic mouse model (ldlr-/-, apobec1-/- designated as LDb), we performed microarray analysis to identify candidate genes and pathways, which are most perturbed in changes in the following risk factors: genetics (control C57BL/6 vs. LDb mice), shearstress (lesion-prone vs. lesion-resistant regions in LDb mice), diet (chow vs. high fat fed LDb mice) and age (2-month-old vs. 8-month old LDb mice). ^ Atherosclerotic lesion quantification and lipid profile studies were performed to assess the disease phenotype. A microarray study was performed on lesion-prone and lesion-resistant regions of each aorta. Briefly, 32 male C57BL/6 and LDb mice (n =16/each) were fed on either chow or high fat diet, sacrificed at 2- and 8-months old, and RNA isolated from the aortic lesion-prone and aortic lesion-resistant segments. Using 64 Affymetrix Murine 430 2.0 chips, we profiled differentially expressed genes with the cut off value of FDR ≤ 0.15 for t-test, and q <0.0001 for the ANOVA. The data were normalized using two normalization methods---invariant probe sets (Loess) and Quantile normalization, the statistical analysis was performed using t-tests and ANOVA, and pathway characterization was done using Pathway Express (Wayne State). The result identified the calcium signaling pathway as the most significant overrepresented pathway, followed by focal adhesion. In the calcium signaling pathway, 56 genes were found to be significantly differentially expressed out of 180 genes listed in the KEGG calcium signaling pathway. Nineteen of these genes were consistently identified by both statistical tests, 11 of which were unique to the test, and 26 were unique to the ANOVA test, using the cutoffs noted above. ^ In conclusion, this finding suggested that hypercholesterolemia drives the disease progression by altering the expression of calcium channels and regulators which subsequently results in cell differentiation, growth, adhesion, cytoskeletal change and death. Clinically, this pathway may serve as an important target for future therapeutic intervention, and thus the calcium signaling pathway may serve as an important target for future diagnostic and therapeutic intervention. ^

Formalizing a Conceptual Framework of Work Domain Knowledge

Background: The failure rate of health information systems is high, partially due to fragmented, incomplete, or incorrect identification and description of specific and critical domain requirements. In order to systematically transform the requirements of work into real information system, an explicit conceptual framework is essential to summarize the work requirements and guide system design. Recently, Butler, Zhang, and colleagues proposed a conceptual framework called Work Domain Ontology (WDO) to formally represent users’ work. This WDO approach has been successfully demonstrated in a real world design project on aircraft scheduling. However, as a top level conceptual framework, this WDO has not defined an explicit and well specified schema (WDOS) , and it does not have a generalizable and operationalized procedure that can be easily applied to develop WDO. Moreover, WDO has not been developed for any concrete healthcare domain. These limitations hinder the utility of WDO in real world information system in general and in health information system in particular. Objective: The objective of this research is to formalize the WDOS, operationalize a procedure to develop WDO, and evaluate WDO approach using Self-Nutrition Management (SNM) work domain. Method: Concept analysis was implemented to formalize WDOS. Focus group interview was conducted to capture concepts in SNM work domain. Ontology engineering methods were adopted to model SNM WDO. Part of the concepts under the primary goal “staying healthy” for SNM were selected and transformed into a semi-structured survey to evaluate the acceptance, explicitness, completeness, consistency, experience dependency of SNM WDO. Result: Four concepts, “goal, operation, object and constraint”, were identified and formally modeled in WDOS with definitions and attributes. 72 SNM WDO concepts under primary goal were selected and transformed into semi-structured survey questions. The evaluation indicated that the major concepts of SNM WDO were accepted by 41 overweight subjects. SNM WDO is generally independent of user domain experience but partially dependent on SNM application experience. 23 of 41 paired concepts had significant correlations. Two concepts were identified as ambiguous concepts. 8 extra concepts were recommended towards the completeness of SNM WDO. Conclusion: The preliminary WDOS is ready with an operationalized procedure. SNM WDO has been developed to guide future SNM application design. This research is an essential step towards Work-Centered Design (WCD).

Development of a rapid bioassessment method and index of biotic integrity for estuaries located along the northwest Gulf of Mexico

Recently it has been proposed that the evaluation of effects of pollutants on aquatic organisms can provide an early warning system of potential environmental and human health risks (NRC 1991). Unfortunately there are few methods available to aquatic biologists to conduct assessments of the effects of pollutants on aquatic animal community health. The primary goal of this research was to develop and evaluate the feasibility of such a method. Specifically, the primary objective of this study was to develop a prototype rapid bioassessment technique similar to the Index of Biotic Integrity (IBI) for the upper Texas and Northwestern Gulf of Mexico coastal tributaries. The IBI consists of a series of "metrics" which describes specific attributes of the aquatic community. Each of these metrics are given a score which is then subtotaled to derive a total assessment of the "health" of the aquatic community. This IBI procedure may provide an additional assessment tool for professionals in water quality management.^ The experimental design consisted primarily of compiling previously collected data from monitoring conducted by the Texas Natural Resource Conservation Commission (TNRCC) at five bayous classified according to potential for anthropogenic impact and salinity regime. Standardized hydrological, chemical, and biological monitoring had been conducted in each of these watersheds. The identification and evaluation of candidate metrics for inclusion in the estuarine IBI was conducted through the use of correlation analysis, cluster analysis, stepwise and normal discriminant analysis, and evaluation of cumulative distribution frequencies. Scores of each included metric were determined based on exceedances of specific percentiles. Individual scores were summed and a total IBI score and rank for the community computed.^ Results of these analyses yielded the proposed metrics and rankings listed in this report. Based on the results of this study, incorporation of an estuarine IBI method as a water quality assessment tool is warranted. Adopted metrics were correlated to seasonal trends and less so to salinity gradients observed during the study (0-25 ppt). Further refinement of this method is needed using a larger more inclusive data set which includes additional habitat types, salinity ranges, and temporal variation. ^

Linkage and association of candidate obesity genes in Mexican-Americans from Starr County, Texas

Obesity and related chronic diseases represent a tremendous public health burden among Mexican Americans, a young and rapidly-expanding population. This study investigated the impact of variation within eight candidate obesity genes, which include leptin (LEP), leptin receptor (LEPR), neuropeptide Y (NPY), NPYY1 receptor (NPYY1), glucagon-like peptide-1 (GLP-1), GLP-1 receptor (GLP1R), beta-3 adrenergic receptor (β3AR), and uncoupling protein (UCP1), on variation in human obesity status and/or quantitative traits related to obesity in Mexican Americans from Starr County, Texas. The Trp64Arg polymorphism within β3AR was typed in 820 random individuals and 240 pedigrees (N = 2,044). The Arg allele frequency was significantly greater in obese versus non-obese individuals (0.20 versus 0. 15, respectively). In addition, within the random sample, the Arg allele was associated with significantly greater body weight (p = 0.031) and body mass index (BMI, p = 0.008) than the Trp allele. In the family sample, the Trp64Arg locus was also linked to percent fat (p = 0.045) but not to body weight or BMI. No linkage between obesity, diabetes, hypertension, or gallbladder disease and the Trp64Arg mutation was observed in families using affected sib pair linkage analysis or the transmission disequilibrium test. Microsatellite markers proximate to the remaining seven genes were typed in 302 individuals from 59 families. Sib pair linkage analysis provided evidence for linkage between obesity and NPY within affected sibling pairs (p = 0.042; n = 170 pairs). NPY was also linked to weight (p = 0.020), abdominal circumference (p = 0.031), hip circumference (p = 0.012), DBP (p ≤ 0.005), and a composite measure of body mass/fat (p ≤ 0.048) in all sibling pairs (n = 545 pairs). Additionally, LEP was linked to waist/hip ratio (p ≤ 0.009), total cholesterol (p ≤ 0.030), and HDL cholesterol (p ≤ 0.026), and LEPR was linked to fasting blood glucose (p ≤ 0.018) and DBP (p ≤ 0.003). Subsequent to the linkage analyses, the NPY gene was sequenced and eight variant sites identified. Two variant sites (-880I/D and 69I/D) were typed in a random sample of 914 individuals. The 880I/D variant was significantly associated with waist/hip ratio (p = 0.035) in the entire sample (N = 914) and with BMI (p = 0. 031), abdominal circumference (p = 0.044), and waist/hip ratio (p = 0.041) in a non-obese subsample (BW < 30 kg/m2, n = 594). The 69I/D variant was a rare mutation observed in only one pedigree and was not associated with obesity or body size/mass within this pedigree. Results of this study indicate that variation at or near β3AR, LEP, LEPR, and NPY may exert effects which increase obesity susceptibility and influence obesity-related measures in this population. ^

The localization and identification of candidate tumor suppressor genes involved in prostate cancer

Frequent loss of heterozygosity (LOH) at specific chromosomal regions are highly associated with the inactivation of tumor suppressor genes (TSGs) (Weinberg, 1991; Bishop, 1989). Chromosome 8p is the most frequently reported site of LOH (∼60%) in prostate cancer (PC), suggesting that there may be inactivated TSG(s) involved in PC on chromosome 8p. (Bergerheim et. al., 1991; Kagan et. al., 1995). In order to identify the smallest common regions of frequent LOH (SCLs) on chromosome 8, we screened 52 PC patient/tumor samples with 39 polymorphic markers in successive screenings. In the course of refining the SCLs, we identified 3 tumors with >6 Mb homozygous deletions (HZDs) at 8p22 and 8p21, suggesting the presence of candidate TSGs at both loci. These HZDs spanned the two SCLs at 8p22 (46%) and 8p21 (45%). The SCLs were narrowed to 3.2 cM at 8p22 and less than 3 cM at 8p21. ^ In order to identify candidate TSGs within the SCLs on 8p, two approaches were used. In the candidate gene approach, thirty genes that mapped to the SCLs were evaluated for expression in normal prostate and in PC cell lines. One of the candidate genes, Clusterin, showed decreased expression in 4/7 (57%) prostate cancer cell lines by Northern blot analysis. Clusterin will be further examined as a candidate TSG. ^ The second approach involved utilizing subtractive hybridization and hybrid affinity capture to generate pools of expressed sequence tags (ESTs) enriched for genes that are downregulated or deleted in PC and that map to specific regions of interest. We took advantage of a prostate cancer cell line (PC3) with a known HZD of a candidate TSG, CTNNA1 on 5q31, to develop and validate a model system. We then developed subtracted libraries enriched for 8p22 and 8p21 ESTs by this method, using two cell lines, MDAPCa-2b and PC3. The ESTs were cloned, and 40 were sequenced and evaluated for expression in normal prostate and PC cell lines. Three ESTs from the subtracted libraries, C2, C17 and F12, showed decreased expression in 29–57% of the prostate tumor cell lines studied, and will be further examined as candidate TSGs. ^

Towards the identification of a tumor suppressor gene on chromosome 3p12: Physical mapping and candidate gene screening

Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney. Characterization of RCC tumors indicates that the most frequent genetic event associated with the initiation of tumor formation involves a loss of heterozygosity or cytogenetic aberration on the short arm of human chromosome 3. A tumor suppressor locus Nonpapillary Renal Carcinoma-1 (NRC-1, OMIM ID 604442) has been previously mapped to a 5–7 cM region on chromosome 3p12 and shown to induce rapid tumor cell death in vivo, as demonstrated by functional complementation experiments. ^ To identify the gene that accounts for the tumor suppressor activities of NRC-1, fine-scale physical mapping was conducted with a novel real-time quantitative PCR based method developed in this study. As a result, NRC-1 was mapped within a 4.6-Mb region defined by two unique sequences within UniGene clusters Hs.41407 and Hs.371835 (78,545Kb–83,172Kb in the NCBI build 31 physical map). The involvement of a putative tumor suppressor gene Robo1/Dutt1 was excluded as a candidate for NRC-1. Furthermore, a transcript map containing eleven candidate genes was established for the 4.6-Mb region. Analyses of gene expression patterns with real-time quantitative RT-PCR assays showed that one of the eleven candidate genes in the interval (TSGc28) is down-regulated in 15 out of 20 tumor samples compared with matched normal samples. Three exons of this gene have been identified by RACE experiments, although additional exon(s) seem to exist. Further gene characterization and functional studies are required to confirm the gene as a true tumor suppressor gene. ^ To study the cellular functions of NRC-1, gene expression profiles of three tumor suppressive microcell hybrids, each containing a functional copy of NRC-1, were compared with those of the corresponding parental tumor cell lines using 16K oligonucleotide microarrays. Differentially expressed genes were identified. Analyses based on the Gene Ontology showed that introduction of NRC-1 into tumor cell lines activates genes in multiple cellular pathways, including cell cycle, signal transduction, cytokines and stress response. NRC-1 is likely to induce cell growth arrest indirectly through WEE1. ^

Longitudinal and logistic analysis of endocrine hormone profiles, physical maturation, and candidate genes related to pubertal events in children

Children who experience early pubertal development have an increased risk of developing cancer (breast, ovarian, and testicular), osteoporosis, insulin resistance, and obesity as adults. Early pubertal development has been associated with depression, aggressiveness, and increased sexual prowess. Possible explanations for the decline in age of pubertal onset include genetics, exposure to environmental toxins, better nutrition, and a reduction in childhood infections. In this study we (1) evaluated the association between 415 single nucleotide polymorphisms (SNPs) from hormonal pathways and early puberty, defined as menarche prior to age 12 in females and Tanner Stage 2 development prior to age 11 in males, and (2) measured endocrine hormone trajectories (estradiol, testosterone, and DHEAS) in relation to age, race, and Tanner Stage in a cohort of children from Project HeartBeat! At the end of the 4-year study, 193 females had onset of menarche and 121 males had pubertal staging at age 11. African American females had a younger mean age at menarche than Non-Hispanic White females. African American females and males had a lower mean age at each pubertal stage (1-5) than Non-Hispanic White females and males. African American females had higher mean BMI measures at each pubertal stage than Non-Hispanic White females. Of the 415 SNPs evaluated in females, 22 SNPs were associated with early menarche, when adjusted for race ( p<0.05), but none remained significant after adjusting for multiple testing by False Discovery Rate (p<0.00017). In males, 17 SNPs were associated with early pubertal development when adjusted for race (p<0.05), but none remained significant when adjusted for multiple testing (p<0.00017). ^ There were 4955 hormone measurements taken during the 4-year study period from 632 African American and Non-Hispanic White males and females. On average, African American females started and ended the pubertal process at a younger age than Non-Hispanic White females. The mean age of Tanner Stage 2 breast development in African American and Non-Hispanic White females was 9.7 (S.D.=0.8) and 10.2 (S.D.=1.1) years, respectively. There was a significant difference by race in mean age for each pubertal stage, except Tanner Stage 1 for pubic hair development. Both Estradiol and DHEAS levels in females varied significantly with age, but not by race. Estradiol and DHEAS levels increased from Tanner Stage 1 to Tanner Stage 5.^ African American males had a lower mean age at each Tanner Stage of development than Non-Hispanic White males. The mean age of Tanner Stage 2 genital development in African American and Non-Hispanic White males was 10.5 (S.D.=1.1) and 10.8 (S.D.=1.1) years, respectively, but this difference was not significant (p=0.11). Testosterone levels varied significantly with age and race. Non-Hispanic White males had higher levels of testosterone than African American males from Tanner Stage 1-4. Testosterone levels increased for both races from Tanner Stage 1 to Tanner Stage 5. Testosterone levels had the steepest increase from ages 11-15 for both races. DHEAS levels in males varied significantly with age, but not by race. DHEAS levels had the steepest increase from ages 14-17. ^ In conclusion, African American males and females experience pubertal onset at a younger age than Non-Hispanic White males and females, but in this study, we could not find a specific gene that explained the observed variation in age of pubertal onset. Future studies with larger study populations may provide a better understanding of the contribution of genes in early pubertal onset.^

FZD6, MATN2 AND SLC25A32, POSSIBLE CANDIDATE GENES IN NONSYNDROMIC CLEFT LIP AND PALATE

Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common birth defect with a multifactorial etiology. Despite decades of research, the genetic underpinnings of NSCLP still remain largely unexplained. A genome wide association study (GWAS) of a large NSCLP African American family with seven affected individuals across three generations found evidence for linkage at 8q21.3-24.12 (LOD = 2.98). This region contained three biologically relevant candidate genes: Frizzled-6 (FZD6) (LOD = 2.8), Matrilin-2 (MATN2) (LOD = 2.3), and Solute Carrier Family 25, Member 32 (SLC26A32) (LOD = 1.6). Sequencing of the coding regions and the 5’ and 3’ UTRs of these genes in two affected family members identified a rare intronic variant, rs138557689 (c.-153+432A>C), in FZD6. The rs138557689/C allele segregated with the NSCLP phenotype; in silico analysis predicted and EMSA analysis showed that the 138557689/C allele creates new DNA binding sites. FZD6 is part of the WNT pathway, which is involved in craniofacial development, including midface development and upper lip fusion. Our novel findings suggest that an alteration in FZD6 gene regulation may perturb this tightly controlled biological pathway and in turn contribute to the development of NSCLP in this family. Studies are underway to further define how the rs138557689/C variant affects expression of FZD6.