Standard genotyping overestimates transmission of Mycobacterium tuberculosis among immigrants in a low incidence country.

Immigrants from high tuberculosis (TB) incidence regions are a risk group for TB in low-incidence countries such as Switzerland. In a previous analysis of a nationwide collection of 520 Mycobacterium tuberculosis isolates from 2000-2008, we identified 35 clusters comprising 90 patients based on standard genotyping (24-loci MIRU-VNTR and spoligotyping). Here, we used whole genome sequencing (WGS) to revisit these transmission clusters. Genome-based transmission clusters were defined as isolate pairs separated by ≤12 single nucleotide polymorphisms (SNPs). WGS confirmed 17/35 (49%) MIRU-VNTR clusters; the other 18 clusters contained pairs separated by >12 SNPs. Most transmission clusters (3/4) of Swiss-born patients were confirmed by WGS, as opposed to 25% (4/16) of clusters involving only foreign-born patients. The overall clustering proportion using standard genotyping was 17% (90 patients, 95% confidence interval [CI]: 14-21%), but only 8% (43 patients, 95% CI: 6-11%) using WGS. The clustering proportion was 17% (67/401, 95% CI: 13-21%) using standard genotyping and 7% (26/401, 95% CI: 4-9%) using WGS among foreign-born patients, and 19% (23/119, 95% CI: 13-28%) and 14% (17/119, 95% CI: 9-22%), respectively, among Swiss-born patients. Using weighted logistic regression, we found weak evidence for an association between birth origin and transmission (aOR 2.2, 95% CI: 0.9-5.5, comparing Swiss-born patients to others). In conclusion, standard genotyping overestimated recent TB transmission in Switzerland when compared to WGS, particularly among immigrants from high TB incidence regions, where genetically closely related strains often predominate. We recommend the use of WGS to identify transmission clusters in low TB incidence settings.

VERIFICATION OF DNA PREDICTED PROTEIN SEQUENCES BY ENZYME HYDROLYSIS AND MASS SPECTROMETRIC ANALYSIS

The focus of this thesis lies in the development of a sensitive method for the analysis of protein primary structure which can be easily used to confirm the DNA sequence of a protein's gene and determine the modifications which are made after translation. This technique involves the use of dipeptidyl aminopeptidase (DAP) and dipeptidyl carboxypeptidase (DCP) to hydrolyze the protein and the mass spectrometric analysis of the dipeptide products.^ Dipeptidyl carboxypeptidase was purified from human lung tissue and characterized with respect to its proteolytic activity. The results showed that the enzyme has a relatively unrestricted specificity, making it useful for the analysis of the C-terminal of proteins. Most of the dipeptide products were identified using gas chromatography/mass spectrometry (GC/MS). In order to analyze the peptides not hydrolyzed by DCP and DAP, as well as the dipeptides not identified by GC/MS, a FAB ion source was installed on a quadrupole mass spectrometer and its performance evaluated with a variety of compounds.^ Using these techniques, the sequences of the N-terminal and C-terminal regions and seven fragments of bacteriophage P22 tail protein have been verified. All of the dipeptides identified in these analysis were in the same DNA reading frame, thus ruling out the possibility of a single base being inserted or deleted from the DNA sequence. The verification of small sequences throughout the protein sequence also indicates that no large portions of the protein have been removed after translation. ^

Evaluation of candidate genes for susceptibility to spina bifida

Neural tube defects (NTDs) are the most common severely disabling birth defects in the United States, with a frequency of approximately 1–2 of every 1,000 births. This text includes the identification and evaluation of candidate susceptibility genes that confer risk for the development of neural tube defects (NTDs). The project focused on isolated meningomyelocele, also termed spina bifida (SB). ^ Spina bifida is a complex disease with multifactorial inheritance, therefore the subject population (consisting of North American Caucasians and Hispanics of Mexicali-American descent) was composed of 459 simplex SB families who were tested for genetic associations utilizing the transmission disequilibrium test (TDT), a nonparametric linkage technique. Three categories of candidate genes were studied, including (1) human equivalents of genes determined in mouse models to cause NTDs, (2) HOX and PAX genes, and (3) the MTHFR gene involved in the metabolic pathway of folate. ^ The C677T variant of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene was the first mutation in this gene to be implicated as a risk factor for NTDs. Our evaluation of the MTHFR gene provides evidence that maternal C677T homozygosity is a risk factor for upper level spina bifida defects in Hispanics [OR = 2.3, P = 0.02]. This observed risk factor is of great importance due to the high prevalence of this homozygous genotype in the Hispanic population. Additionally, maternal C677T/A1298C compound heterozygosity is a risk factor for upper level spina bifida defects in non-Hispanic whites [OR = 3.6, P = 0.03]. ^ For TDT analysis, our total population of 1128 subjects were genotyped for 54 markers from within and/or flanking the 20 candidate genes/gene regions of interest. Significant TDT findings were obtained for 3 of the 54 analyzed markers: d20s101 flanking the PAX1 gene (P = 0.019), d1s228 within the PAX7 gene (P = 0.011), and d2s110 within the PAX8 gene (P = 0.013). These results were followed-up by testing the genes directly for mutations utilizing single-strand conformational analysis (SSCA) and direct sequencing. Multiple variations were detected in each of these PAX genes; however, these variations were not passed from parent to child in phase with the positively transmitted alleles. Therefore, these variations do not contribute to the susceptibility of spina bifida, but rather are previously unreported single nucleotide polymorphisms. ^

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. ^

Physiogenomic Analysis of Weight Loss Induced by Dietary Carbohydrate Restriction

ABSTRACT : BACKGROUND : Diets that restrict carbohydrate (CHO) have proven to be a successful dietary treatment of obesity for many people, but the degree of weight loss varies across individuals. The extent to which genetic factors associate with the magnitude of weight loss induced by CHO restriction is unknown. We examined associations among polymorphisms in candidate genes and weight loss in order to understand the physiological factors influencing body weight responses to CHO restriction. METHODS : We screened for genetic associations with weight loss in 86 healthy adults who were instructed to restrict CHO to a level that induced a small level of ketosis (CHO ~10% of total energy). A total of 27 single nucleotide polymorphisms (SNPs) were selected from 15 candidate genes involved in fat digestion/metabolism, intracellular glucose metabolism, lipoprotein remodeling, and appetite regulation. Multiple linear regression was used to rank the SNPs according to probability of association, and the most significant associations were analyzed in greater detail. RESULTS : Mean weight loss was 6.4 kg. SNPs in the gastric lipase (LIPF), hepatic glycogen synthase (GYS2), cholesteryl ester transfer protein (CETP) and galanin (GAL) genes were significantly associated with weight loss. CONCLUSION : A strong association between weight loss induced by dietary CHO restriction and variability in genes regulating fat digestion, hepatic glucose metabolism, intravascular lipoprotein remodeling, and appetite were detected. These discoveries could provide clues to important physiologic adaptations underlying the body mass response to CHO restriction.

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. ^

A coalescent analysis for modeling the mutation process in colorectal cancer

Colorectal cancer is the forth most common diagnosed cancer in the United States. Every year about a hundred forty-seven thousand people will be diagnosed with colorectal cancer and fifty-six thousand people lose their lives due to this disease. Most of the hereditary nonpolyposis colorectal cancer (HNPCC) and 12% of the sporadic colorectal cancer show microsatellite instability. Colorectal cancer is a multistep progressive disease. It starts from a mutation in a normal colorectal cell and grows into a clone of cells that further accumulates mutations and finally develops into a malignant tumor. In terms of molecular evolution, the process of colorectal tumor progression represents the acquisition of sequential mutations. ^ Clinical studies use biomarkers such as microsatellite or single nucleotide polymorphisms (SNPs) to study mutation frequencies in colorectal cancer. Microsatellite data obtained from single genome equivalent PCR or small pool PCR can be used to infer tumor progression. Since tumor progression is similar to population evolution, we used an approach known as coalescent, which is well established in population genetics, to analyze this type of data. Coalescent theory has been known to infer the sample's evolutionary path through the analysis of microsatellite data. ^ The simulation results indicate that the constant population size pattern and the rapid tumor growth pattern have different genetic polymorphic patterns. The simulation results were compared with experimental data collected from HNPCC patients. The preliminary result shows the mutation rate in 6 HNPCC patients range from 0.001 to 0.01. The patients' polymorphic patterns are similar to the constant population size pattern which implies the tumor progression is through multilineage persistence instead of clonal sequential evolution. The results should be further verified using a larger dataset. ^

Diabetes genes and risk of prostate cancer in the Atherosclerosis Risk in Communities study

Prostate cancer (PrCa) is a leading cause of morbidity and mortality, yet the etiology remains uncertain. Meta-analyses show that PrCa risk is reduced by 16% in men with type 2 diabetes (T2D), but the mechanism is unknown. Recent genome-wide association studies and meta-analyses have found single nucleotide polymorphisms (SNPs) that consistently predict T2D risk. We evaluated associations of incident PrCa with 14 T2D SNPs in the Atherosclerosis Risk in Communities (ARIC) study. From 1987-2000, there were 397 incident PrCa cases ascertained from state or local cancer registries among 6,642 men (1,560 blacks and 5,082 whites) aged 45-64 years at baseline. Genotypes were determined by TaqMan assay. Cox proportional hazards models were used to assess the association between PrCa and increasing number of T2D risk-raising alleles for individual SNPs and for genetic risk scores (GRS) comprised of the number of T2D risk-raising alleles across SNPs. Two-way gene-gene interactions were evaluated with likelihood ratio tests. Using additive genetic models, the T2D risk-raising allele was associated with significantly reduced risk of PrCa for IGF2BP2 rs4402960 (hazard ratio [HR]=0.79; P=0.07 among blacks only), SLC2A2 rs5400 (race-adjusted HR=0.85; P=0.05) and UCP2 rs660339 (race-adjusted HR=0.84; P=0.02), but significantly increased risk of PrCa for CAPN10 rs3792267 (race-adjusted HR=1.20; P=0.05). No other SNPs were associated with PrCa using an additive genetic model. However, at least one copy of the T2D risk-raising allele for TCF7L2 rs7903146 was associated with reduced PrCa risk using a dominant genetic model (race-adjusted HR=0.79; P=0.03). These results imply that the T2D-PrCa association may be partly due to shared genetic variation, but these results should be verified since multiple tests were performed. When the combined, additive effects of these SNPs were tested using a GRS, there was nearly a 10% reduction in risk of PrCa per T2D risk-raising allele (race-adjusted HR=0.92; P=0.02). SNPs in IGF2BP2, KCNJ11 and SLC2A2 were also involved in multiple synergistic gene-gene interactions on a multiplicative scale. In conclusion, it appears that the T2D-PrCa association may be due, in part, to common genetic variation. Further knowledge of T2D gene-PrCa mechanisms may improve understanding of PrCa etiology and may inform PrCa prevention and treatment.^

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.^

The effect of genotype-by-environment interaction on longitudinal triglyceride profiles in the Bogalusa Heart Study

Triglyceride levels are a component of plasma lipids that are thought to be an important risk factor for coronary heart disease and are influenced by genetic and environmental factors, such as single nucleotide polymorphisms (SNPs), alcohol intake, and smoking. This study used longitudinal data from the Bogalusa Heart Study, a biracial community-based survey of cardiovascular disease risk factors. A sample of 1191 individuals, 4 to 38 years of age, was measured multiple times from 1973 to 2000. The study sample consisted of 730 white and 461 African American participants. Individual growth models were developed in order to assess gene-environment interactions affecting plasma triglycerides over time. After testing for inclusion of significant covariates and interactions, final models, each accounting for the effects of a different SNP, were assessed for fit and normality. After adjustment for all other covariates and interactions, LIPC -514C/T was found to interact with age3, age2, and age and a non-significant interaction of CETP -971G/A genotype with smoking status was found (p = 0.0812). Ever-smokers had higher triglyceride levels than never smokers, but persons heterozygous at this locus, about half of both races, had higher triglyceride levels after smoking cessation compared to current smokers. Since tobacco products increase free fatty acids circulating in the bloodstream, smoking cessation programs have the potential to ultimately reduce triglyceride levels for many persons. However, due to the effect of smoking cessation on the triglyceride levels of CETP -971G/A heterozygotes, the need for smoking prevention programs is also demonstrated. Both smoking cessation and prevention programs would have a great public health impact on minimizing triglyceride levels and ultimately reducing heart disease. ^

Genome-wide gene-gene interaction analysis for cardiovascular disease

Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5×10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.^

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

Genome-wide association study of LDL-cholesterol over ApoB ratio

Genome-Wide Association Study analytical (GWAS) methods were applied in a large biracial sample of individuals to investigate variation across the genome for its association with a surrogate low-density lipoprotein (LDL) particle size phenotype, the ratio of LDL-cholesterol level over ApoB level. Genotyping was performed on the Affymetrix 6.0 GeneChip with approximately one million single nucleotide polymorphisms (SNPs). The ratio of LDL cholesterol to ApoB was calculated, and association tests used multivariable linear regression analysis with an additive genetic model after adjustment for the covariates sex, age and BMI. Association tests were performed separately in African Americans and Caucasians. There were 9,562 qualified individuals in the Caucasian group and 3,015 qualified individuals in the African American group. Overall, in Caucasians two statistically significant loci were identified as being associated with the ratio of LDL-cholesterol over ApoB: rs10488699 (p<5 x10-8, 11q23.3 near BUD13) and the SNP rs964184 (p<5 x10-8 11q23.3 near ZNF259). We also found rs12286037 ((p<4x10-7) (11q23.3) near APOA5/A4/C3/A1 with suggestive associate in the Caucasian sample. In exploratory analyses, a difference in the pattern of association between individuals taking and not taking LDL-cholesterol lowering medications was observed. Individuals who were not taking medications had smaller p-value than those taking medication. In the African-American group, there were no significant (p<5x10-8) or suggestive associations (p<4x10-7) with the ratio of LDL-cholesterol over ApoB after adjusting for age, BMI, and sex and comparing individuals with and without LDL-cholesterol lowering medication. Conclusions: There were significant and suggestive associations between SNP genotype and the ratio of LDL-cholesterol to ApoB in Caucasians, but these associations may be modified by medication treatment.^

Genetic variants in the mammalian target of rapamycin (mTOR) signaling pathway as predictors of survival and clinical response in women with ovarian cancer

Background. The mTOR pathway is commonly altered in human tumors and promotes cell survival and proliferation. Preliminary evidence suggests this pathway's involvement in chemoresistance to platinum and taxanes, first line therapy for epithelial ovarian cancer. A pathway-based approach was used to identify individual germline single nucleotide polymorphisms (SNPs) and cumulative effects of multiple genetic variants in mTOR pathway genes and their association with clinical outcome in women with ovarian cancer. ^ Methods. The case-series was restricted to 319 non-Hispanic white women with high grade ovarian cancer treated with surgery and platinum-based chemotherapy. 135 SNPs in 20 representative genes in the mTOR pathway were genotyped. Hazard ratios (HRs) for death and Odds ratios (ORs) for failure to respond to primary therapy were estimated for each SNP using the multivariate Cox proportional hazards model and multivariate logistic regression model, respectively, while adjusting for age, stage, histology and treatment sequence. A survival tree analysis of SNPs with a statistically significant association (p<0.05) was performed to identify higher order gene-gene interactions and their association with overall survival. ^ Results. There was no statistically significant difference in survival by tumor histology or treatment regimen. The median survival for the cohort was 48.3 months. Seven SNPs were significantly associated with decreased survival. Compared to those with no unfavorable genotypes, the HR for death increased significantly with the increasing number of unfavorable genotypes and women in the highest risk category had HR of 4.06 (95% CI 2.29–7.21). The survival tree analysis also identified patients with different survival patterns based on their genetic profiles. 13 SNPs on five different genes were found to be significantly associated with a treatment response, defined as no evidence of disease after completion of primary therapy. Rare homozygous genotype of SNP rs6973428 showed a 5.5-fold increased risk compared to the wild type carrying genotypes. In the cumulative effect analysis, the highest risk group (individuals with ≥8 unfavorable genotypes) was significantly less likely to respond to chemotherapy (OR=8.40, 95% CI 3.10–22.75) compared to the low risk group (≤4 unfavorable genotypes). ^ Conclusions. A pathway-based approach can demonstrate cumulative effects of multiple genetic variants on clinical response to chemotherapy and survival. Therapy targeting the mTOR pathway may modify outcome in select patients.^

Molecular epidemiological studies on genetic predispositions to squamous cell carcinoma of the head and neck

Two molecular epidemiological studies were conducted to examine associations between genetic variation and risk of squamous cell carcinoma of the head and neck (SCCHN). In the first study, we hypothesized that genetic variation in p53 response elements (REs) may play roles in the etiology of SCCHN. We selected and genotyped five polymorphic p53 REs as well as a most frequently studied p53 codon 72 (Arg72Pro, rs1042522) polymorphism in 1,100 non-Hispanic White SCCHN patients and 1,122 age-and sex-matched cancer-free controls recruited at The University of Texas M. D. Anderson Cancer Center. In multivariate logistic regression analysis with adjustment for age, sex, smoking and drinking status, marital status and education level, we observed that the EOMES rs3806624 CC genotype had a significant effect of protection against SCCHN risk (adjusted odds ratio= 0.79, 95% confidence interval =0.64–0.98), compared with the -838TT+CT genotypes. Moreover, a significantly increased risk associated with the combined genotypes of p53 codon 72CC and EOMES -838TT+CT was observed, especially in the subgroup of non-oropharyneal cancer patients. The values of false-positive report probability were also calculated for significant findings. In the second study, we assessed the association between SCCHN risk and four potential regulatory single nucleotide polymorphisms (SNPs) of DEC1 (deleted in esophageal cancer 1) gene, a candidate tumor suppressor gene for esophageal cancer. After adjustment for age, sex, and smoking and drinking status, the variant -606CC (i.e., -249CC) homozygotes had a significantly reduced SCCHN risk (adjusted odds ratio = 0.71, 95% confidence interval = 0.52–0.99), compared with the -606TT homozygotes. Stratification analyses showed that a reduced risk associated with the -606CC genotype was more pronounced in subgroups of non-smokers, non-drinkers, younger subjects (defined as ≤ 57 years), carriers of TP53 Arg/Arg (rs1042522) genotype, patients with oropharyngeal cancer or late-stage SCCHN. Further in silico analysis revealed that the -249 T-to-C change led to a gain of a transcription factor binding site. Additional functional analysis showed that the -249T-to-C change significantly enhanced transcriptional activity of the DEC1 promoter and the DNA-protein binding activity. We conclude that the DEC1 promoter -249 T>C (rs2012775) polymorphism is functional, modulating susceptibility to SCCHN among non-Hispanic Whites. Additional large-scale, preferably population-based studies are needed to validate our findings.^