HLA-DPB1 and DPB2 are genetic loci for systemic sclerosis: a genome-wide association study in Koreans with replication in North Americans.

OBJECTIVE: To identify systemic sclerosis (SSc) susceptibility loci via a genome-wide association study. METHODS: A genome-wide association study was performed in 137 patients with SSc and 564 controls from Korea using the Affymetrix Human SNP Array 5.0. After fine-mapping studies, the results were replicated in 1,107 SSc patients and 2,747 controls from a US Caucasian population. RESULTS: The single-nucleotide polymorphisms (SNPs) (rs3128930, rs7763822, rs7764491, rs3117230, and rs3128965) of HLA-DPB1 and DPB2 on chromosome 6 formed a distinctive peak with log P values for association with SSc susceptibility (P=8.16x10(-13)). Subtyping analysis of HLA-DPB1 showed that DPB1*1301 (P=7.61x10(-8)) and DPB1*0901 (P=2.55x10(-5)) were the subtypes most susceptible to SSc in Korean subjects. In US Caucasians, 2 pairs of SNPs, rs7763822/rs7764491 and rs3117230/rs3128965, showed strong association with SSc patients who had either circulating anti-DNA topoisomerase I (P=7.58x10(-17)/4.84x10(-16)) or anticentromere autoantibodies (P=1.12x10(-3)/3.2x10(-5)), respectively. CONCLUSION: The results of our genome-wide association study in Korean subjects indicate that the region of HLA-DPB1 and DPB2 contains the loci most susceptible to SSc in a Korean population. The confirmatory studies in US Caucasians indicate that specific SNPs of HLA-DPB1 and/or DPB2 are strongly associated with US Caucasian patients with SSc who are positive for anti-DNA topoisomerase I or anticentromere autoantibodies.

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.^

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.^

Network-based genome-wide association study of Crohn's disease

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. Many recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. The current study incorporated gene network information into gene-based analysis of GWAS data for Crohn's disease (CD). The purpose was to develop statistical models to boost the power of identifying disease-associated genes and gene subnetworks by maximizing the use of existing biological knowledge from multiple sources. The results revealed that Markov random field (MRF) based mixture model incorporating direct neighborhood information from a single gene network is not efficient in identifying CD-related genes based on the GWAS data. The incorporation of solely direct neighborhood information might lead to the low efficiency of these models. Alternative MRF models looking beyond direct neighboring information are necessary to be developed in the future for the purpose of this study.^

Pathway analysis of genome-wide association study data in primary biliary cirrhosisa

Genome-wide association studies (GWAS) have successfully identified several genetic loci associated with inherited predisposition to primary biliary cirrhosis (PBC), the most common autoimmune disease of the liver. Pathway-based tests constitute a novel paradigm for GWAS analysis. By evaluating genetic variation across a biological pathway (gene set), these tests have the potential to determine the collective impact of variants with subtle effects that are individually too weak to be detected in traditional single variant GWAS analysis. To identify biological pathways associated with the risk of development of PBC, GWAS of PBC from Italy (449 cases and 940 controls) and Canada (530 cases and 398 controls) were independently analyzed. The linear combination test (LCT), a recently developed pathway-level statistical method was used for this analysis. For additional validation, pathways that were replicated at the P <0.05 level of significance in both GWAS on LCT analysis were also tested for association with PBC in each dataset using two complementary GWAS pathway approaches. The complementary approaches included a modification of the gene set enrichment analysis algorithm (i-GSEA4GWAS) and Fisher's exact test for pathway enrichment ratios. Twenty-five pathways were associated with PBC risk on LCT analysis in the Italian dataset at P<0.05, of which eight had an FDR<0.25. The top pathway in the Italian dataset was the TNF/stress related signaling pathway (p=7.38×10 -4, FDR=0.18). Twenty-six pathways were associated with PBC at the P<0.05 level using the LCT in the Canadian dataset with the regulation and function of ChREBP in liver pathway (p=5.68×10-4, FDR=0.285) emerging as the most significant pathway. Two pathways, phosphatidylinositol signaling system (Italian: p=0.016, FDR=0.436; Canadian: p=0.034, FDR=0.693) and hedgehog signaling (Italian: p=0.044, FDR=0.636; Canadian: p=0.041, FDR=0.693), were replicated at LCT P<0.05 in both datasets. Statistically significant association of both pathways with PBC genetic susceptibility was confirmed in the Italian dataset on i-GSEA4GWAS. Results for the phosphatidylinositol signaling system were also significant in both datasets on applying Fisher's exact test for pathway enrichment ratios. This study identified a combination of known and novel pathway-level associations with PBC risk. If functionally validated, the findings may yield fresh insights into the etiology of this complex autoimmune disease with possible preventive and therapeutic application.^

Genetic underpinnings of variation in brain ventricular volume: A genome-wide association study

The ventricular system is a critical component of the central nervous system (CNS) that is formed early in the developmental stages and remains functional through the lifetime. Changes in the ventricular system can be easily discerned via neuroimaging procedures and most of the time it reflects changes in the physiology of the CNS. In this study we attempted to identify specific genes associated with variation in ventricular volume in humans. Methods. We conducted a genome wide association (GWA) analysis of the volume of the lateral ventricles among 1605 individuals of European ancestry from two community based cohorts, the Genetics of Microangiopathic Brain Injury (GMBI; N=814) and Atherosclerosis Risk in Communities (ARIC; N=791). Significant findings from the analysis were tested for replication in both the cohorts and then meta-analyzed to get an estimate of overall significance. Results. In our GWA analyses, no single nucleotide polymorphism (SNP) reached a genome-wide significance of p<10−8. There were 25 SNPs in GMBI and 9 SNPs in ARIC that reached a threshold of p<10 −5. However, none of the top SNPs from each cohort were replicated in the other. In the meta-analysis, no SNP reached the genome-wide threshold of 5×10−8, but we identified five novel SNPs associated with variation in ventricular volume at the p<10 −5 level. Strongest association was for rs2112536 in an intergenic region on chromosome 5q33 (Pmeta= 8.46×10−7 ). The remaining four SNPs were located on chromosome 3q23 encompassing the gene for Calsyntenin-2 (CLSTN2). The SNPs with strongest association in this region were rs17338555 (Pmeta= 5.28×10 −6), rs9812091 (Pmeta= 5.89×10−6 ), rs9812283 (Pmeta= 5.97×10−6) and rs9833213 (Pmeta= 6.96×10−6). Conclusions. This GWA study of ventricular volumes in the community-based cohorts of European descent identifies potential locus on chromosomes 3 and 5. Further characterization of these loci may provide insights into pathophysiology of ventricular involvement in various neurological diseases.^

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.^

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^

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

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

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.

Immune and genetic risk factors in glioma

Despite extensive research, the etiology of adult glioma remains largely unknown. We sought to further explore the role of immune and genetic factors in glioma etiology using data from the Harris County Brain Tumor Study and the first U.S. genome-wide association study of glioma. First, using a case-control study design, we examined the association between adult glioma risk and surrogates of the timing and frequency of common early childhood infections, birth order and sibship size, respectively. We found that each one-unit increase in birth order was associated with a 12% decreased risk of glioma development in adulthood (OR=0.88, 95% CI=0.81-0.96); however, sibship size was not associated with adult glioma risk (OR=0.96, 95% CI=0.91-1.02). Second, we used a multi-strategic approach to explore the relationships between glioma risk, history of asthma/allergies, and 23 functional SNPs in 11 inflammation genes. We found three inflammation gene SNPs to be significantly associated with glioma risk (COX2/PTGS2 rs20417 [OR=1.41]; IL10 rs1800896 [OR=1.57]; and IL13 rs20541 [OR=0.39]). Joint effects analysis of the risk-conferring alleles of these three SNPs revealed a trend of increasing risk with increasing number of adverse alleles among those without asthma/allergies (p<0.0001). Finally, we conducted a case-only study to explore pairwise SNP-SNP interactions in immune-related pathways among a population of 1304 non-Hispanic white glioma cases. After correction for multiple comparisons, we found 279 significant SNP-SNP interactions among polymorphisms of immune-related genes, many of which have not been previously examined. Our results, cumulatively, suggest an important role for immune and genetic factors in glioma etiology and provide several new hypotheses for future studies.^

GENETIC PREDICTORS OF HYPERGLYCEMIA DUE TO HYDROCHLOROTHIAZIDE THERAPY

Response to pharmacological treatment is variable among individuals. Some patients respond favorably to a drug while others develop adverse reactions. Early investigations showed evidence of variation in genes that code for drug receptors, drug transporters, and drug metabolizing enzymes; and pharmacogenetics appeared as the science that studies the relationship between drug response and genetic variation. Thiazide diuretics are the recommended first-line monotherapy for hypertension (i.e. SBP>140 or DBP>90). Even so, diuretics are associated with adverse metabolic side effects, such as hyperglycemia, which increase the risk of developing type 2 diabetes. Published approaches testing variation in candidate genes (e.g. the renin-angiotensin-aldosteron system (RAAS) and salt–sensitivity genes) have met with only limited success. We conducted the first genome wide association study to identify genes influencing hyperglycemia as an adverse effect of thiazide diuretics in non-Hispanic White hypertensive patients participating in the Genetic Epidemiology of Responses to Antihypertensives (GERA) and Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) clinical trials. No SNP reached the a priori defined threshold of statistical significance (p<5x10-8). We detected 50 SNPs in 9 genomic regions with suggestive p-values (p<1x10-5). Two of them, rs6870564 (p-value=3.28 X 10-6) and rs7702121 (p-value=5.09 X 10-6), were located close to biologic candidate genes, MYO and MGAT1, and one SNP in a genomic region in chromosome 6, rs7762018 (p-value=4.59 X 10-6) has been previously related to Insulin-Dependent Diabetes Mellitus (IDDM8). I conclude that 1) there are unlikely to be common SNPs with large effects on the adverse metabolic effects to hydrochlorothiazide treatment and 2) larger sample sizes are needed for pharmacogenetic studies of inter-individual variation in response to commonly prescribed medication.

Genetic Predictors of Clinical Outcomes in Non-Small Cell Lung Cancer Patients

Lung cancer is the leading cause of cancer-related mortality in the US. Emerging evidence has shown that host genetic factors can interact with environmental exposures to influence patient susceptibility to the diseases as well as clinical outcomes, such as survival and recurrence. We aimed to identify genetic prognostic markers for non-small cell lung cancer (NSCLC), a major (85%) subtype of lung cancer, and also in other subgroups. With the fast evolution of genotyping technology, genetic association studies have went through candidate gene approach, to pathway-based approach, to the genome wide association study (GWAS). Even in the era of GWAS, pathway-based approach has its own advantages on studying cancer clinical outcomes: it is cost-effective, requiring a smaller sample size than GWAS easier to identify a validation population and explore gene-gene interactions. In the current study, we adopted pathway-based approach focusing on two critical pathways - miRNA and inflammation pathways. MicroRNAs (miRNA) post-transcriptionally regulate around 30% of human genes. Polymorphisms within miRNA processing pathways and binding sites may influence patients’ prognosis through altered gene regulation. Inflammation plays an important role in cancer initiation and progression, and also has shown to impact patients’ clinical outcomes. We first evaluated 240 single nucleotide polymorphisms (SNPs) in miRNA biogenesis genes and predicted binding sites in NSCLC patients to determine associations with clinical outcomes in early-stage (stage I and II) and late-stage (stage III and IV) lung cancer patients, respectively. First, in 535 early-stage patients, after correcting multiple comparisons, FZD4:rs713065 (hazard ratio [HR]:0.46, 95% confidence interval [CI]:0.32-0.65) showed a significant inverse association with survival in early stage surgery-only patients. SP1:rs17695156 (HR:2.22, 95% CI:1.44-3.41) and DROSHA:rs6886834 (HR:6.38, 95% CI:2.49-16.31) conferred increased risk of progression in the all patients and surgery-only populations, respectively. FAS:rs2234978 was significantly associated with improved survival in all patients (HR:0.59, 95% CI:0.44-0.77) and in the surgery plus chemotherapy populations (HR:0.19, 95% CI:0.07-0.46).. Functional genomics analysis demonstrated that this variant creates a miR-651 binding site resulting in altered miRNA regulation of FAS, providing biological plausibility for the observed association. We then analyzed these associations in 598 late-stage patients. After multiple comparison corrections, no SNPs remained significant in the late stage group, while the top SNP NAT1:rs15561 (HR=1.98, 96%CI=1.32-2.94) conferred a significantly increased risk of death in the chemotherapy subgroup. To test the hypothesis that genetic variants in the inflammation-related pathways may be associated with survival in NSCLC patients, we first conducted a three-stage study. In the discovery phase, we investigated a comprehensive panel of 11,930 inflammation-related SNPs in three independent lung cancer populations. A missense SNP (rs2071554) in HLA-DOB was significantly associated with poor survival in the discovery population (HR: 1.46, 95% CI: 1.02-2.09), internal validation population (HR: 1.51, 95% CI: 1.02-2.25), and external validation (HR: 1.52, 95% CI: 1.01-2.29) population. Rs2900420 in KLRK1 was significantly associated with a reduced risk for death in the discovery (HR: 0.76, 95% CI: 0.60-0.96) and internal validation (HR: 0.77, 95% CI: 0.61-0.99) populations, and the association reached borderline significance in the external validation population (HR: 0.80, 95% CI: 0.63-1.02). We also evaluated these inflammation-related SNPs in NSCLC patients in never smokers. Lung cancer in never smokers has been increasingly recognized as distinct disease from that in ever-smokers. A two-stage study was performed using a discovery population from MD Anderson (411 patients) and a validation population from Mayo Clinic (311 patients). Three SNPs (IL17RA:rs879576, BMP8A:rs698141, and STK:rs290229) that were significantly associated with survival were validated (pCD74:rs1056400 and CD38:rs10805347) were borderline significant (p=0.08) in the Mayo Clinic population. In the combined analysis, IL17RA:rs879576 resulted in a 40% reduction in the risk for death (p=4.1 × 10-5 [p=0.61, heterogeneity test]). We also validated a survival tree created in MD Anderson population in the Mayo Clinic population. In conclusion, our results provided strong evidence that genetic variations in specific pathways that examined (miRNA and inflammation pathways) influenced clinical outcomes in NSCLC patients, and with further functional studies, the novel loci have potential to be translated into clinical use.

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.