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.

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

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

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.^

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.

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.^

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.

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

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

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. ^

Gene by smoking interaction in blood pressure

Hypertension (HT) is mediated by the interaction of many genetic and environmental factors. Previous genome-wide linkage analysis studies have found many loci that show linkage to HT or blood pressure (BP) regulation, but the results were generally inconsistent. Gene by environment interaction is among the reasons that potentially explain these inconsistencies between studies. Here we investigate influences of gene by smoking (GxS) interaction on HT and BP in European American (EA), African American (AA) and Mexican American (MA) families from the GENOA study. A variance component-based method was utilized to perform genome-wide linkage analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP), and HT status, as well as bivariate analysis for SBP and DBP for smokers, non-smokers, and combined groups. The most significant results were found for SBP in MA. The strongest signal was for chromosome 17q24 (LOD = 4.2), increased to (LOD = 4.7) in bivariate analysis but there was no evidence of GxS interaction at this locus (p = 0.48). Two signals were identified only in one group: on chromosome 15q26.2 (LOD = 3.37) in non-smokers and chromosome 7q21.11 (LOD = 1.4) in smokers, both of which had strong evidence for GxS interaction (p = 0.00039 and 0.009 respectively). There were also two other signals, one on chromosome 20q12 (LOD = 2.45) in smokers, which became much higher in the combined sample (LOD = 3.53), and one on chromosome 6p22.2 (LOD = 2.06) in non-smokers. Neither peak had very strong evidence for GxS interaction (p = 0.08 and 0.06 respectively). A fine mapping association study was performed using 200 SNPs in 30 genes located under the linkage signals on chromosomes 15 and 17. Under the chromosome 15 peak, the association analysis identified 6 SNPs accounting for a 7 mmHg increase in SBP in MA non-smokers. For the chromosome 17 linkage peak, the association analysis identified 3 SNPs accounting for a 6 mmHg increase in SBP in MA. However, none of these SNPs was significant after correcting for multiple testing, and accounting for them in the linkage analysis produced very small reductions in the linkage signal. ^ The linkage analysis of BP traits considering the smoking status produced very interesting signals for SBP in the MA population. The fine mapping association analysis gave some insight into the contribution of some SNPs to two of the identified signals, but since these SNPs did not remain significant after multiple testing correction and did not explain the linkage peaks, more work is needed to confirm these exploratory results and identify the culprit variations under these linkage peaks. ^

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.^