Rhabdomyosarcoma (RMS) is a childhood cancer originating from skeletal muscle, and patient survival is poor in the presence of metastatic disease. Few determinants that regulate metastasis development have been identified. The receptor tyrosine kinase FGFR4 is highly expressed in RMS tissue, suggesting a role in tumorigenesis, although its functional importance has not been defined. Here, we report the identification of mutations in FGFR4 in human RMS tumors that lead to its activation and present evidence that it functions as an oncogene in RMS. Higher FGFR4 expression in RMS tumors was associated with advanced-stage cancer and poor survival, while FGFR4 knockdown in a human RMS cell line reduced tumor growth and experimental lung metastases when the cells were transplanted into mice. Moreover, 6 FGFR4 tyrosine kinase domain mutations were found among 7 of 94 (7.5%) primary human RMS tumors. The mutants K535 and E550 increased autophosphorylation, Stat3 signaling, tumor proliferation, and metastatic potential when expressed in a murine RMS cell line. These mutants also transformed NIH 3T3 cells and led to an enhanced metastatic phenotype. Finally, murine RMS cell lines expressing the K535 and E550 FGFR4 mutants were substantially more susceptible to apoptosis in the presence of a pharmacologic FGFR inhibitor than the control cell lines expressing the empty vector or wild-type FGFR4. Together, our results demonstrate that mutationally activated FGFR4 acts as an oncogene, and these are what we believe to be the first known mutations in a receptor tyrosine kinase in RMS. These findings support the potential therapeutic targeting of FGFR4 in RMS.
The 13th International Meeting on Human Genome Variation and Complex Genome Analysis (HGV2012: Shanghai, China, 6th 8th September 2012) was a stimulating workshop where researchers from academia and industry explored the latest progress, challenges, and opportunities in genome variation research. Key themes included advancements in next-generation sequencing (NGS) technology, investigation of common and rare diseases, employing NGS in the clinic, utilizing large datasets that leverage biobanks and population-specific cohorts, and exploration of genomic features.
variation; SNP; GWAS; next generation sequencing; NGS; inherited disease
In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving towards more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
big data; clinical trials; cohort studies; epidemiology; genomics; medicine; public health; technologies; training; translational research
BACKGROUND & AIMS
Heritable factors contribute to the development of colorectal cancer. Identifying the genetic loci associated with colorectal tumor formation could elucidate the mechanisms of pathogenesis.
We conducted a genome-wide association study that included 14 studies, 12,696 cases of colorectal tumors (11,870 cancer, 826 adenoma), and 15,113 controls of European descent. The 10 most statistically significant, previously unreported findings were followed up in 6 studies; these included 3056 colorectal tumor cases (2098 cancer, 958 adenoma) and 6658 controls of European and Asian descent.
Based on the combined analysis, we identified a locus that reached the conventional genome-wide significance level at less than 5.0 × 10−8: an intergenic region on chromosome 2q32.3, close to nucleic acid binding protein 1 (most significant single nucleotide polymorphism: rs11903757; odds ratio [OR], 1.15 per risk allele; P = 3.7 × 10−8). We also found evidence for 3 additional loci with P values less than 5.0 × 10−7: a locus within the laminin gamma 1 gene on chromosome 1q25.3 (rs10911251; OR, 1.10 per risk allele; P = 9.5 × 10−8), a locus within the cyclin D2 gene on chromosome 12p13.32 (rs3217810 per risk allele; OR, 0.84; P = 5.9 × 10−8), and a locus in the T-box 3 gene on chromosome 12q24.21 (rs59336; OR, 0.91 per risk allele; P = 3.7 × 10−7).
In a large genome-wide association study, we associated polymorphisms close to nucleic acid binding protein 1 (which encodes a DNA-binding protein involved in DNA repair) with colorectal tumor risk. We also provided evidence for an association between colorectal tumor risk and polymorphisms in laminin gamma 1 (this is the second gene in the laminin family to be associated with colorectal cancers), cyclin D2 (which encodes for cyclin D2), and T-box 3 (which encodes a T-box transcription factor and is a target of Wnt signaling to β-catenin). The roles of these genes and their products in cancer pathogenesis warrant further investigation.
Colon Cancer; Genetics; Risk Factors; SNP
Solvent exposure has been inconsistently linked to the risk for non-Hodgkin lymphoma (NHL). The aim of this study was to determine whether the association is modified by genetic variation in immune genes. A population-based case–control study involving 601 incident cases of NHL and 717 controls was carried out in 1996–2000 among women from Connecticut. Thirty single nucleotide polymorphisms in 17 immune genes were examined in relation to the associations between exposure to various solvents and the risk for NHL. The study found that polymorphism in interleukin 10 (IL10; rs1800890) modified the association between occupational exposure to organic solvents and the risk for diffuse large B-cell lymphoma (Pfor interaction=0.0058). The results remained statistically significant after adjustment for false discovery rate. Compared with women who were never occupationally exposed to any organic solvents, women who were exposed to organic solvents at least once had a significantly increased risk for diffuse large B-cell lymphoma if they carried the IL10 (rs1800890) TT genotype (odds ratio=3.31, 95% confidence interval: 1.80–6.08), but not if they carried the AT/AA genotype (odds ratio=1.14, 95% confidence interval: 0.72–1.79). No significant interactions were observed for other immune gene single nucleotide polymorphisms and various solvents in relation to NHL overall and its major subtypes. The study provided preliminary evidence supporting a role of immune gene variations in modifying the association between occupational solvent exposure and the risk for NHL.
immune genes; non-Hodgkin lymphoma; occupational exposure; single nucleotide polymorphism; solvents
The Centre for Applied Genomics of the Hospital for Sick Children and the University of Toronto hosted the 10th Human Genome Variation (HGV) Meeting in Toronto, Canada, in October 2008, welcoming about 240 registrants from 34 countries. During the 3 days of plenary workshops, keynote address, and poster sessions, a strong cross-disciplinary trend was evident, integrating expertise from technology and computation, through biology and medicine, to ethics and law. Single nucleotide polymorphisms (SNPs) as well as the larger copy number variants (CNVs) are recognized by ever-improving array and next-generation sequencing technologies, and the data are being incorporated into studies that are increasingly genome-wide as well as global in scope. A greater challenge is to convert data to information, through databases, and to use the information for greater understanding of human variation. In the wake of publications of the first individual genome sequences, an inaugural public forum provided the opportunity to debate whether we are ready for personalized medicine through direct-to-consumer testing. The HGV meetings foster collaboration, and fruits of the interactions from 2008 are anticipated for the 11th annual meeting in September 2009.
SNP; CNV; GWAS; personalized medicine
The 11th International Meeting on Human Genome Variation and Complex Genome Analysis (HGV2009: Tallinn, Estonia, 11th–13th September 2009) provided a stimulating workshop environment where diverse academics and industry representatives explored the latest progress, challenges, and opportunities in relating genome variation to evolution, technology, health, and disease. Key themes included Genome-Wide Association Studies (GWAS), progress beyond GWAS, sequencing developments, and bioinformatics approaches to large-scale datasets.
HGV2009; SNP; variation; GWAS; CNV
The 12th International Meeting on Human Genome Variation and Complex Genome Analysis (HGV2011: Berkeley, California, USA, 8th–10th September 2011) was a stimulating workshop where researchers from academia and industry explored the latest progress, challenges, and opportunities in genome variation research. Key themes included progress beyond GWAS, variation in human populations, use of sequence data in medical settings, large-scale sequencing data analysis, and bioinformatics approaches to large datasets.
human variation; GWAS; SNP; medical genomics
Epidemiologic studies have shown consistent associations between obesity and increased thyroid cancer risk, but, to date, no studies have investigated the relationship between thyroid cancer risk and obesity-related single nucleotide polymorphisms (SNPs).
We evaluated 575 tag SNPs in 23 obesity-related gene regions in a case-control study of 341 incident papillary thyroid cancer (PTC) cases and 444 controls of European ancestry. Logistic regression models, adjusted for attained age, year of birth, and sex were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) with SNP genotypes, coded as 0, 1, and 2 and modeled continuously to calculate P-trends.
Nine out of 10 top-ranking SNPs (Ptrend<0.01) were located in the FTO (fat mass and obesity associated) gene region, while the other was located in INSR (insulin receptor). None of the associations were significant after correcting for multiple testing.
Our data do not support an important role of obesity-related genetic polymorphisms in determining the risk of PTC.
Factors other than selected genetic polymorphisms may be responsible for the observed associations between obesity and increased PTC risk.
single nucleotide polymorphisms; case-control study; obesity; body mass index; thyroid neoplasms
There has been a long-standing controversy in epidemiology with regard to an appropriate risk scale for testing interactions between genes (G) and environmental exposure (E ). Although interaction tests based on the logistic model—which approximates the multiplicative risk for rare diseases—have been more widely applied because of its convenience in statistical modeling, interactions under additive risk models have been regarded as closer to true biologic interactions and more useful in intervention-related decision-making processes in public health. It has been well known that exploiting a natural assumption of G-E independence for the underlying population can dramatically increase statistical power for detecting multiplicative interactions in case-control studies. However, the implication of the independence assumption for tests for additive interaction has not been previously investigated. In this article, the authors develop a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study.
additive risk model; case-control studies; gene-environment independence; gene-environment interaction; multiplicative risk model
Pulmonary inflammation may contribute to lung cancer etiology. We conducted a broad evaluation of the association of single nucleotide polymorphisms (SNPs) in innate immunity and inflammation pathways with lung cancer risk, and conducted comparisons with a lung cancer genome wide association study (GWAS).
We included 378 lung cancer cases and 450 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. An Illumina GoldenGate oligonucleotide pool assay was used to genotype 1,429 SNPs. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for each SNP, and p-values for trend were calculated. For statistically significant SNPs (p-trend<0.05), we replicated our results with genotyped or imputed SNPs in the GWAS, and adjusted p-values for multiple testing.
In our PLCO analysis, we observed a significant association between 81 SNPs located in 44 genes and lung cancer (p-trend<0.05). Of these 81 SNPS, there was evidence for confirmation in the GWAS for 10 SNPs. However, after adjusting for multiple comparisons, the only SNP that remained significantly associated with lung cancer in the replication phase was rs4648127 (NFKB1; multiple testing adjusted p-trend=0.02). The CT/TT genotype of NFKB1 was associated with reduced odds of lung cancer in the PLCO study (OR=0.56; 95% CI 0.37–0.86) and the GWAS (OR=0.79; 95% CI 0.69–0.90).
We found a significant association between a variant in the NFKB1 gene and lung cancer risk. Our findings add to evidence implicating inflammation and immunity in lung cancer etiology.
lung cancer; genetics; inflammation; immunity; epidemiology
There is growing evidence linking genetic variations to non–Hodgkin lymphoma (NHL) etiology. To complement ongoing agnostic approaches for identifying susceptibility genes, we evaluated 488 candidate gene regions and their relation to risk for NHL and NHL subtypes.
We genotyped 6,679 tag single nucleotide polymorphisms (SNPs) in 947 cases and 826 population-based controls from a multicenter U.S. case–control study. Gene-level summary of associations were obtained by computing the minimum P value (“minP test”) on the basis of 10,000 permutations. We used logistic regression to evaluate the association between genotypes and haplotypes with NHL. For NHL subtypes, we conducted polytomous multivariate unconditional logistic regression (adjusted for sex, race, age). We calculated P-trends under the codominant model for each SNP.
Fourteen gene regions were associated with NHL (P < 0.01). The most significant SNP associated with NHL maps to the SYK gene (rs2991216, P-trend = 0.00005). The three most significant gene regions were on chromosome 6p21.3 (RING1/RXRB; AIF1; BAT4). Accordingly, SNPs in RING1/RXRB (rs2855429), AIF1 (rs2857597), and BAT4 (rs3115667) were associated with NHL (P-trends ≤ 0.0002) and both diffuse large B-cell and follicular lymphomas (P-trends < 0.05).
Our results suggest potential importance for SYK on chromosome 9 with NHL etiology. Our results further implicate 6p21.3 gene variants, supporting the need for full characterization of this chromosomal region in relation to lymphomagenesis.
Gene variants on chromosome 9 may represent a new region of interesting for NHL etiology. The independence of the reported variants in 6p21.3 from implicated variants (TNF/HLA) supports the need to confirm causal variants in this region
To test the hypothesis that genetic variations in DNA repair genes may modify the association between occupational exposure to solvents and the risk of non-Hodgkin lymphoma (NHL).
A population-based case-control study was conducted in Connecticut women including 518 histologically confirmed incident NHL cases and 597 controls. Unconditional logistic regression models were used to estimate odds ratios (OR) and effect modification from the 30 SNPs in 16 DNA repair genes of the association between solvent exposure and risk of NHL overall and subtypes.
SNPs in MGMT (rs12917) and NBS1 (rs1805794) significantly modified the association between exposure to chlorinated solvents and NHL risk (Pforinteraction = 0.0003 and 0.0048 respectively). After stratified by major NHL histological subtypes, MGMT (rs12917) modified the association between chlorinated solvents and risk of diffuse large B-cell lymphoma (Pforinteraction = 0.0027) and follicular lymphoma (Pforinteraction = 0.0024). A significant interaction was also observed between occupational exposure to benzene and BRCA2 (rs144848) for NHL overall (Pforinteraction = 0.0042).
Our study results suggest that genetic variations in DNA repair genes modify the association between occupational exposure to solvents and risk of NHL.
Non-Hodgkin Lymphoma; Occupational Exposure; Solvents; Single Nucleotide Polymorphism; DNA Repair Genes
Previous studies have examined the association between ABO blood group and ovarian cancer risk, with inconclusive results.
In 8 studies participating in the Ovarian Cancer Association Consortium (OCAC), we determined ABO blood groups and diplotypes by genotyping 3 SNPs in the ABO locus. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated in each study using logistic regression; individual study results were combined using random effects meta-analysis.
Compared to blood group O, the A blood group was associated with a modestly increased ovarian cancer risk: (OR: 1.09; 95% CI: 1.01–1.18; p=0.03). In diplotype analysis, the AO, but not the AA diplotype was associated with increased risk (AO: OR: 1.11; 95% CI: 1.01–1.22; p=0.03; AA: OR: 1.03; 95% CI: 0.87–1.21; p=0.76). Neither AB nor the B blood groups were associated with risk. Results were similar across ovarian cancer histologic subtypes.
Consistent with most previous reports, the A blood type was associated modestly with increased ovarian cancer risk in this large analysis of multiple studies of ovarian cancer. Future studies investigating potential biologic mechanisms are warranted.
ovarian cancer; ABO blood group; Ovarian Cancer Association Consortium (OCAC); genetic epidemiology
We report a new model to project the predictive performance of polygenic models based on the number and distribution of effect sizes for the underlying susceptibility alleles and the size of the training dataset. Using estimates of effect-size distribution and heritability derived from current studies, we project that while 45% of the variance of height has been attributed to common tagging Single Nucleotide Polymorphisms (SNP), a model trained on one million people may only explain 33.4% of variance of the trait. Current studies can identify 3.0%, 1.1%, and 7.0%, of the populations who are at two-fold or higher than average risk for Type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate the percentages to 18.8%, 6.1%, and 12.2%, respectively. The utility of future polygenic models will depend on achievable sample sizes, underlying genetic architecture and information on other risk-factors, including family history.
Copy number variants (CNV) can be called from SNP-arrays; however, few studies have attempted to combine both CNV and SNP calls to test for association with complex diseases. Even when SNPs are located within CNVs, two separate association analyses are necessary, to compare the distribution of bi-allelic genotypes in cases and controls (referred to as SNP-only strategy) and the number of copies of a region (referred to as CNV-only strategy). However, when disease susceptibility is actually associated with allele specific copy-number states, the two strategies may not yield comparable results, raising a series of questions about the optimal analytical approach. We performed simulations of the performance of association testing under different scenarios that varied genotype frequencies and inheritance models. We show that the SNP-only strategy lacks power under most scenarios when the SNP is located within a CNV; frequently it is excluded from analysis as it does not pass quality control metrics either because of an increased rate of missing calls or a departure from fitness for Hardy-Weinberg proportion. The CNV-only strategy also lacks power because the association testing depends on the allele which copy number varies. The combined strategy performs well in most of the scenarios. Hence, we advocate the use of this combined strategy when testing for association with SNPs located within CNVs.
Recent studies have identified common genetic variants that are unequivocally associated with central adiposity, BMI, and/or fasting plasma glucose among individuals of European descent. Our objective was to evaluate these associations in a population of Asian-Indians. We examined 16 single-nucleotide polymorphisms (SNPs) from loci previously linked to waist circumference, BMI, or fasting glucose in 1,129 Asian-Indians from New Delhi and Trivandrum. Trained medical staff measured waist circumference, height, and weight. Fasting plasma glucose was measured from collected blood specimens. Genotype–phenotype associations were evaluated using linear regression, with adjustments for age, gender, religion, and study region. For gene–environment interaction tests, total physical activity (PA) during the past 7 days was assessed by the International Physical Activity Questionnaire (IPAQ). The T allele at the FTO rs3751812 locus was associated with increased waist circumference (per allele effect of +1.58 cm, Ptrend = 0.0015) after Bonferroni adjustment for multiple testing (Padj = 0.04). We also found a nominally statistically significant FTO–PA interaction (Pinteraction = 0.008). Among participants with <81 metabolic equivalent (MET)-h/wk of PA, the rs3751812 variant was associated with increased waist size (+2.68 cm; 95% confidence interval (CI) = 1.24, 4.12), but not among those with 212+ MET-h/wk (−1.79 cm; 95% CI = −4.17, 0.58). No other variant had statistically significant associations, although statistical power was modest. In conclusion, we confirmed that an FTO variant associated with central adiposity in European populations is associated with central adiposity among Asian-Indians and corroborated prior reports indicating that high PA attenuates FTO-related genetic susceptibility to adiposity.
We show how to use reports of cancer in family members to discover additional genetic associations or confirm previous findings in genome-wide association (GWA) studies conducted in case-control, cohort, or cross-sectional studies. Our novel family-history-based approach allows economical association studies for multiple cancers, without genotyping of relatives (as required in family studies), follow-up of participants (as required in cohort studies), or oversampling of specific cancer cases, (as required in case-control studies). We empirically evaluate the performance of the proposed family-history-based approach in studying associations with prostate and ovarian cancers, using data from GWA studies previously conducted within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. The family-history-based method may be particularly useful for investigating genetic susceptibility to rare diseases, for which accruing cases may be very difficult, by using disease information from non-genotyped relatives of participants in multiple case-control and cohort studies designed primarily for other purposes.
We examined host genetic factors to identify those more common in individuals whose human papillomavirus (HPV) infections were most likely to persist and progress to cervical intraepithelial neoplasia grade 3 (CIN3) and cancer.
We genotyped 92 single-nucleotide polymorphisms (SNPs) from 49 candidate immune response and DNA repair genes obtained from 469 women with CIN3 or cancer, 390 women with persistent HPV infections (median duration, 25 months), and 452 random control subjects from the 10,049-woman Guanacaste Costa Rica Natural History Study. We calculated odds ratios and 95% confidence intervals (CIs) for the association of SNP and haplotypes in women with CIN3 or cancer and HPV persistence, compared with random control subjects.
A SNP in the Fanconi anemia complementation group A gene (FANCA) (G501S) was associated with increased risk of CIN3 or cancer. The AG and GG genotypes had a 1.3-fold (95% CI, 0.95–1.8-fold) and 1.7-fold (95% CI, 1.1–2.6-fold) increased risk for CIN3 or cancer, respectively (Ptrend = .008; referent, AA). The FANCA haplotype that included G501S also conferred increased risk of CIN3 or cancer, as did a different haplotype that included 2 other FANCA SNPs (G809A and T266A). A SNP in the innate immune gene IRF3 (S427T) was associated with increased risk for HPV persistence (Ptrend = .009).
Our results require replication but support the role of FANCA variants in cervical cancer susceptibility and of IRF3 in HPV persistence.
Relationships are unclear between polymorphisms in genes involved in metabolism and detoxification of various chemicals and papillary thyroid cancer (PTC) risk as well as their potential modification by alcohol or tobacco intake. We evaluated associations between 1647 tagging single nucleotide polymorphisms (SNPs) in 132 candidate genes/regions involved in metabolism of exogenous and endogenous compounds (Phase I/II, oxidative stress, and metal binding pathways) and PTC risk in 344 PTC cases and 452 controls. For 15 selected regions and their respective SNPs, we also assessed interaction with alcohol and tobacco use. Logistic regression models were used to evaluate the main effect of SNPs (Ptrend) and interaction with alcohol/tobacco intake. Gene- and pathway-level associations and interactions (Pgene interaction) were evaluated by combining Ptrend values using the adaptive rank-truncated product method. While we found associations between PTC risk and nine SNPs (Ptrend≤0.01) and seven genes/regions (Pregion<0.05), none remained significant after correction for the false discovery rate. We found a significant interaction between UGT2B7 and NAT1 genes and alcohol intake (Pgene interaction=0.01 and 0.02 respectively) and between the CYP26B1 gene and tobacco intake (Pgene interaction=0.02). Our results are suggestive of interaction between the genetic polymorphisms in several detoxification genes and alcohol or tobacco intake on risk of PTC. Larger studies with improved exposure assessment should address potential modification of PTC risk by alcohol and tobacco intake to confirm or refute our findings.
Both LMO2 mRNA and protein expression in diffuse large B-cell lymphoma (DLBCL) have been associated with superior survival; however, a role for germline genetic variation in LMO2 has not been previously reported. Immunohistochemistry (IHC) for LMO2 was conducted on tumor tissue from diagnostic biopsies, and 20 tag single nucleotide polymorphisms (SNPs) from LMO2 were genotyped from germline DNA. LMO2 IHC positivity was associated with superior survival (HR=0.55; 95% CI 0.31–0.97). Four LMO2 SNPs (rs10836127, rs941940, rs750781, rs1885524) were associated with survival after adjusting for LMO2 IHC and clinical factors (p<0.05), and one of these SNPs (rs941940) was also associated with IHC positivity (p=0.02). Compared to a model with clinical factors only (c-statistic=0.676), adding the 4 SNPs (c-statistic=0.751) or LMO2 IHC (c-statistic=0.691) increased the predictive ability of the model, while inclusion of all 3 factors (c-statistic=0.754) did not meaningfully add predictive ability above a model with clinical factors and the 4 SNPs. In conclusion, germline genetic variation in LMO2 was associated with DLBCL prognosis and provided slightly stronger predictive ability relative to LMO2 IHC status.
Diffuse large B-cell lymphoma; LMO2; prognosis; single nucleotide polymorphisms
Being overweight and obese increases oxidative stress in the body. To test the hypothesis that genetic variations in oxidative stress pathway genes modify the relationship between body mass index (BMI) and risk of non-Hodgkin lymphoma (NHL), we conducted a population-based case–control study in Connecticut women.
Individuals who were overweight/obese (BMI ≥ 25) were compared with normal and underweight individuals (BMI < 25), and their risk of NHL stratified assuming a dominant allele model for each oxidative stress pathway single-nucleotide polymorphism.
Polymorphisms in AKR1A1, AKR1C1, AKR1C3, CYBA, GPX1, MPO, NCF2, NCF4, NOS1, NOS2A NOS3, OGG1, ATG9B, SOD1, SOD2, SOD3,RAC1, and RAC2 genes after false discovery rate adjustment did not modify the association between BMI and risk of NHL overall and histologic subtypes.
The results suggest that common genetic variations in oxidative stress genes do not modify the relationship between BMI and risk of NHL.
Studies of BMI and oxidative stress independently may elevate NHL risk, but this study suggests no interaction of the two risk factors. Future studies with larger study populations may reveal interactions.
Selenium is an essential trace element and circulating selenium concentrations have been associated with a wide range of diseases. Candidate gene studies suggest that circulating selenium concentrations may be impacted by genetic variation; however, no study has comprehensively investigated this hypothesis. Therefore, we conducted a two-stage genome-wide association study to identify genetic variants associated with serum selenium concentrations in 1203 European descents from two cohorts: the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening and the Women’s Health Initiative (WHI). We tested association between 2,474,333 single nucleotide polymorphisms (SNPs) and serum selenium concentrations using linear regression models. In the first stage (PLCO) 41 SNPs clustered in 15 regions had p < 1 × 10−5. None of these 41 SNPs reached the significant threshold (p = 0.05/15 regions = 0.003) in the second stage (WHI). Three SNPs had p < 0.05 in the second stage (rs1395479 and rs1506807 in 4q34.3/AGA-NEIL3; and rs891684 in 17q24.3/SLC39A11) and had p between 2.62 × 10−7 and 4.04 × 10−7 in the combined analysis (PLCO + WHI). Additional studies are needed to replicate these findings. Identification of genetic variation that impacts selenium concentrations may contribute to a better understanding of which genes regulate circulating selenium concentrations.
selenium; serum; selenoprotein; genome-wide association study; AGA; NEIL3; SLC39A11
Genome-wide association studies (GWAS) of prostate cancer have identified single nucleotide polymorphism (SNP) markers in a region of chromosome 11q13.3 in men of European decent. A fine-mapping analysis with tag SNPs in the Cancer Genetic Markers of Susceptibility (CGEMS) study identified three independent loci, marked by rs10896438, rs12793759, and rs10896449. This study further annotates common and uncommon variation across this region.
A next generation resequence analysis of a 122.9kb region of 11q13.3 (68,642,755-68,765,690) was conducted in 78 unrelated individuals of European background, 1 CEPH trio, and 1 YRI trio.
In total, 644 polymorphic loci were identified by our sequence analysis. Of these, 166 variants – 118 SNPs and 48 insertion-deletion polymorphisms (indels) – were novel, namely not present in the 1000 Genomes or International HapMap Projects. We identified 22, 25, 6, and 4 variants strongly correlated (r2 ≥ 0.8) with rs10896438, rs10896449, rs12793759, and rs11228565, respectively. HapMap SNPs were in linkage disequilibrium (r2 ≥ 0.8) with 48%, 69%, 14%, and 60% of SNPs marking bins by rs10896438, rs10896449, rs12793759, and rs11228565, respectively.
Our next generation resequence analysis compliments publicly available datasets of European descent (HapMap, build 28 and 1000 Genome, Pilot 1, Oct 2010), underscoring the value of targeted resequence analysis prior to initiating functional studies based on public databases alone. Increasing the number of common variants enables investigators to better prioritize variants for functional studies designed to uncover the biological basis of the direct association(s) in the region.
Resequence; 11q13; prostate cancer; SNP