We propose a two-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma.
First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N=281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N=74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are re-estimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis.
Using the best fitting segregation models in model-based multipoint linkage analysis, we identified two separate peaks on chromosome 17; the first agreed with a region identified by Shete et al. who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD).
Our approach has the advantage of not requiring markers to be in linkage equilibrium unless the minor allele frequency is small (markers which tend to be uninformative for linkage), and of using more of the available information for LOD-based linkage analysis.
Glioma; model-based linkage; segregation; age of onset; prevalence constraint
Peutz-Jeghers syndrome (PJS) is characterized by intestinal polyposis, mucocutaneous pigmentation and an increased cancer risk, usually caused by mutations of the STK11 gene. This study collected epidemiological, clinical and genetic data from all Uruguayan PJS patients.
Clinical data were obtained from public and private medical centers and updated annually. Sequencing of the STK11 gene in one member of each family was performed.
Results and discussion
25 cases in 11 unrelated families were registered (15 males, 10 females). The average age of diagnosis and death was 18 and 41 years respectively. All patients had characteristic PJS pigmentation and gastrointestinal polyps. 72% required urgent surgery due to intestinal obstruction. 3 families had multiple cases of seizure disorder, representing 20% of cases. 28% developed cancer and two patients had more than one cancer. An STK11 mutation was found in 8 of the 9 families analyzed. A unique M136K missense mutation was noted in one family. Comparing annual live births and PJS birth records from 1970 to 2009 yielded an incidence of 1 in 155,000.
The Uruguayan Registry for Peutz-Jeghers patients showed a high chance of emergent surgery, epilepsy, cancer and shortened life expectancy. The M136K missense mutation is a newly reported STK 11 mutation.
p21 and p27, members of the kinase inhibitor protein (KIP) family, bind to cyclin-CDK complexes to inhibit their catalytic activity and induce cell cycle arrest. The purpose of our study was to identify whether the p21 (C-to-A), and p27 (T-to-G) polymorphisms were associated with age at diagnosis of pancreatic cancer, either independently or jointly. 205 patients with a diagnosis of pancreatic cancer were genotyped for the p21 and p27 polymorphisms. We found patients with the p21 variant genotype (CA/AA) had an earlier age at diagnosis than those with the wild-type genotype (CC) (Log-rank, P=0.001; HR=1.89; 95%CI, 1.28–2.78). The p21 and p27 polymorphisms combined had a joint effect on age-associated risk for early diagnosis of pancreatic cancer (Log-rank, P=0.004; HR=2.91; 95%CI, 1.49–5.67). Our findings suggest that the p21 polymorphism independently and p21 and p27 polymorphisms jointly contribute to a significantly earlier age at diagnosis of pancreatic cancer.
p21; p27; polymorphisms; pancreatic cancer; age of diagnosis
Three genome-wide association studies identified a region on chromosome 15q25.1 associated with lung cancer and measures of nicotine addiction. This region includes nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5. These studies were conducted in European or European American populations and do not provide risk estimates for African Americans. The goal of this study was to determine whether recently identified genetic variation in 3 SNPs (rs1051730, rs931794, rs8034191) on chromosome 15q25.1 contributes to risk of lung cancer in African Americans.
Data were derived from three case-control studies. Participants included 1058 population-based non-small cell lung cancer cases selected from the Detroit area SEER registry and 1314 controls matched within study by age, race, and sex. Thirty-nine percent of participants were African American.
Risk associated with rs1051730 (odds ratio 1.59; 95% confidence interval 1.16–2.19) and rs931794 (odds ratio 1.39; 95% confidence interval 1.09–1.78) increased in ever smoking African Americans adjusting for cigarettes smoked per day. Among white cases, the number of cigarettes smoked varied by genotype at all three SNPs, and when smoking quantity was included in the models, risk was not significantly associated with any of the three SNPs.
These findings suggest that SNPs in the CHRNA3 and CHRNA5 region contribute to lung cancer risk, and while variant alleles are less frequent in African Americans, risk in this group may be greater than in whites and less likely to reflect an indirect effect on lung cancer risk through nicotine dependence.
Non-small cell lung cancer; Smoking; SNPs
The risk of glioma has consistently been shown to be increased two-fold in relatives of patients with primary brain tumors (PBT). A recent genome-wide linkage study of glioma families provided evidence for a disease locus on 17q12-21.32, with the possibility of four additional risk loci at 6p22.3, 12p13.33-12.1, 17q22-23.2, and 18q23.
To identify the underlying genetic variants responsible for the linkage signals, we compared the genotype frequencies of 5,122 SNPs mapping to these five regions in 88 glioma cases with and 1,100 cases without a family history of PBT (discovery study). An additional series of 84 familial and 903 non-familial cases were used to replicate associations.
In the discovery study, 12 SNPs showed significant associations with family history of PBT (P < 0.001). In the replication study, two of the 12 SNPs were confirmed: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.031) and 17q12-21.32 SPOP rs650461 (P = 0.025). In the combined analysis of discovery and replication studies, the strongest associations were attained at four SNPs: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.0001), SOX5 rs7305773 (P = 0.0001) and STKY1 rs2418087 (P = 0.0003), and 17q12-21.32 SPOP rs6504618 (P = 0.0006). Further, a significant gene-dosage effect was found for increased risk of family history of PBT with these four SNPs in the combined data set (Ptrend < 1.0 ×10−8).
The results support the linkage finding that some loci in the 12p13.33-12.1 and 17q12-q21.32 may contribute to gliomagenesis and suggest potential target genes underscoring linkage signals.
Association; Polymorphisms; Glioma; Family history of primary brain tumor; Linkage analysis
Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.
We aimed at extending the natural and orthogonal interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data.
The NOIA statistical models are developed for the additive, dominant, recessive genetic models, and a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data.
Our simulations showed that power for testing associations while allowing for interaction using the statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to the lung cancer data, much smaller P-values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested.
The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.
Statistical power; Genetic association studies; Case-control association analysis; Gene-environment interaction; Environmental risk factor; Association mapping; Orthogonal modeling
Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P-values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P-value for the most significant SNP rs13383928 was 2.17×10–6, which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54, located on 7q32.1 (P = 8.90×10–5). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk.
Abbreviations:CIconfidence intervalEenvironmentFDRfalse discovery rateGgeneGSEAgene-set-enrichment analysisGWASgenome-wide association studiesi-GSEAimproved gene-set-enrichment analysis approachORodds ratioSNPsingle nucleotide polymorphism
In this study, we directly sequenced the Melanocortin 1 Receptor (MC1R) gene in 2,212 individuals to detect all variants and assessed their associations with cutaneous melanoma (CM) risk in a hospital-based study of 1,106 CM patients and 1,106 control subjects. Of 61 MC1R variants identified, 16 rare variants have not been previously reported by others; three MC1R variants were associated with a significant CM risk [c.451C>T (OR = 1.78, 95% CI = 1.44–2.20), c.478C>T (OR = 1.31, 95% CI = 1.05–1.63), and c.880G>C (OR = 1.69, 95% CI = 1.15–2.48)]; and two with borderline CM risk [c.942A>G (OR =1.23, 95% CI =1.00–1.51, and c.274G>A (OR = 1.23,95% CI = 0.99–1.53)] under a dominant model. When combined these five MC1R variants for cumulative effect analysis, we found that subjects with an increased number of variant genotypes from any of these five variants had significantly increased risk of CM with ORs of 1.68 (95% CI = 1.39–2.04), 1.61 (95% CI = 1.27–2.04), and 2.64 (95% CI = 1.72–4.05) for one, two, and three or more variant genotypes, respectively (trend test: P <0.001). Further haplotype and diplotype analyses based on the above-mentioned five SNPs suggested that the c.451T allele contributed to the high risk of CM and that the five variants may have joint effects on the risk of CM. Additional analysis suggests that the three most significant SNPs may be the molecular mechanisms underlying the known risk factors of the colors of the eyes, skin and hair in this study population. In conclusion, our study provided confirmatory evidence that both common and rare variants in the MC1R coding region may be biomarkers for susceptibility to CM in US populations.
melanocortin 1 receptor gene; direct sequencing; interaction; melanoma; case-control
To mine possibly hidden causal single nucleotide polymorphisms (SNPs) in the etiology of melanoma, we investigated the association of SNPs in 76 M/G1 transition genes with melanoma risk using our published genome-wide association study (GWAS) dataset with 1804 melanoma cases and 1,026 cancer-free controls. We found multiple SNPs with P < 0.01 and performed validation studies for 18 putative functional SNPs in PSMB9 in other two GWAS datasets. Two SNPs (rs1351383 and rs2127675) were associated with melanoma risk in the GenoMEL dataset (P = 0.013 and 0.004, respectively), but failed validation in the Australia dataset. Genotype-phenotype analysis revealed these two SNPs were significantly correlated with mRNA expression levels of PSMB9. Further experiments revealed that the promoter SNP rs2071480, which is in high LD with rs1351383 and rs2127675, involved in influencing transcription factor binding and gene expression. Taken together, our data suggested that functional variants in PSMB9 may contribute to melanoma susceptibility.
GWAS; Cell cycle; PSMB9; Polymorphism; melanoma
Using the Immunochip custom single nucleotide polymorphism (SNP) array, designed for dense genotyping of 186 genome wide association study (GWAS) confirmed loci we analysed 11,475 rheumatoid arthritis cases of European ancestry and 15,870 controls for 129,464 markers. The data were combined in meta-analysis with GWAS data from additional independent cases (n=2,363) and controls (n=17,872). We identified fourteen novel loci; nine were associated with rheumatoid arthritis overall and 5 specifically in anti-citrillunated peptide antibody positive disease, bringing the number of confirmed European ancestry rheumatoid arthritis loci to 46. We refined the peak of association to a single gene for 19 loci, identified secondary independent effects at six loci and association to low frequency variants (minor allele frequency <0.05) at 4 loci. Bioinformatic analysis of the data generated strong hypotheses for the causal SNP at seven loci. This study illustrates the advantages of dense SNP mapping analysis to inform subsequent functional investigations.
Evolutionary aspects of the genetic architecture of common human diseases remain enigmatic. The results of more than 200 genome-wide association studies published to date were compiled in a catalog (http://www.genome.gov/26525384/). We used cataloged data to determine whether derived (mutant) alleles are associated with higher risk of human disease more frequently than ancestral alleles. We placed all allelic variants into ten categories of population frequency (0%–100%) in 10% increments. We then analyzed the relationship between allelic frequency, evolutionary status of the polymorphic site (ancestral versus derived), and disease risk status (risk versus protection). Given the same population frequency, derived alleles are more likely to be risk associated than ancestral alleles, as are rarer alleles. The common interpretation of this association is that negative selection prevents fixation of the risk variants. However, disease stratification as early or late onset suggests that weak selection against risk-associated alleles is unlikely a major factor shaping genetic architecture of common diseases. Our results clearly suggest that the duration of existence of an allele in a population is more important. Alleles existing longer tend to show weaker linkage disequilibrium with neighboring alleles, including the causal alleles, and are less likely to tag a SNP-disease association.
Genome-wide association studies; ancestral allele; derived allele; minor allele frequency
Genome-wide association studies have identified variants on chromosome 15q25.1 that increase the risks of both lung cancer and nicotine dependence and associated smoking behavior. However, there remains debate as to whether the association with lung cancer is direct or is mediated by pathways related to smoking behavior. Here, the authors apply a novel method for mediation analysis, allowing for gene-environment interaction, to a lung cancer case-control study (1992–2004) conducted at Massachusetts General Hospital using 2 single nucleotide polymorphisms, rs8034191 and rs1051730, on 15q25.1. The results are validated using data from 3 other lung cancer studies. Tests for additive interaction (P = 2 × 10−10 and P = 1 × 10−9) and multiplicative interaction (P = 0.01 and P = 0.01) were significant. Pooled analyses yielded a direct-effect odds ratio of 1.26 (95% confidence interval (CI): 1.19, 1.33; P = 2 × 10−15) for rs8034191 and an indirect-effect odds ratio of 1.01 (95% CI: 1.00, 1.01; P = 0.09); the proportion of increased risk mediated by smoking was 3.2%. For rs1051730, direct- and indirect-effect odds ratios were 1.26 (95% CI: 1.19, 1.33; P = 1 × 10−15) and 1.00 (95% CI: 0.99, 1.01; P = 0.22), respectively, with a proportion mediated of 2.3%. Adjustment for measurement error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to 12.5% and 9.2%, respectively. These analyses indicate that the association of the variants with lung cancer operates primarily through other pathways.
gene-environment interaction; lung neoplasms; mediation; pathway analysis; smoking
The common disease/common variant hypothesis has been popular for describing the genetic architecture of common human diseases for several years. According to the originally stated hypothesis, one or a few common genetic variants with a relatively large effect size control the risk of common diseases. A growing body of evidence, however, suggests that rare single-nucleotide polymorphisms (SNPs), i.e., those with a minor allele frequency of less than 5%, are also an important component of the genetic architecture of common human diseases. In this study, we analyzed the relevance of rare SNPs to the risk of common disease from an evolutionary perspective and found that rare SNPs are more likely than common SNPs to be functional and tend to have a stronger effect size than do common SNPs. This observation, plus the fact that most of the SNPs in the human genome are rare, suggests that rare SNPs are a crucial element of the genetic architecture of common human diseases. We propose that the next generation of genomic studies should focus on analyzing rare SNPs. Further, targeting patients with a family history of the disease, an extreme phenotype, or early disease onset may facilitate the detection of risk-associated rare SNPs.
Single Nucleotide Polymorphisms (SNPs); Genome Wide Association Studies (GWAS); Minor Allele Frequency (MAF); negative selection
We explored the contribution of nitrosamine metabolism to lung cancer in a pilot investigation of genetic variation in CYP2B6, a high-affinity enzymatic activator of tobacco-specific nitrosamines with a negligible role in nicotine metabolism. Previously we found that variation in CYP2A6 and CHRNA5-CHRNA3-CHRNB4 combined to increase lung cancer risk in a case-control study in European American ever-smokers (n = 860). However, these genes are involved in the pharmacology of both nicotine, through which they alter smoking behaviours, and carcinogenic nitrosamines. Herein, we separated participants by CYP2B6 genotype into a high- vs. low-risk group (*1/*1 + *1/*6 vs. *6/*6). Odds ratios estimated through logistic regression modeling were 1.25 (95% CI 0.68–2.30), 1.27 (95% CI 0.89–1.79) and 1.56 (95% CI 1.04–2.31) for CYP2B6, CYP2A6 and CHRNA5-CHRNA3-CHRNB4, respectively, with negligible differences when all genes were evaluated concurrently. Modeling the combined impact of high-risk genotypes yielded odds ratios that rose from 2.05 (95% CI 0.39–10.9) to 2.43 (95% CI 0.47–12.7) to 3.94 (95% CI 0.72–21.5) for those with 1, 2 and 3 vs. 0 high-risk genotypes, respectively. Findings from this pilot point to genetic variation in CYP2B6 as a lung cancer risk factor supporting a role for nitrosamine metabolic activation in the molecular mechanism of lung carcinogenesis.
CYP2B6; CYP2A6; CHRNA5-CHRNA3-CHRNB4; tobacco specific nitrosamines; lung cancer risk; genetic variation
DNA repair pathway genes play an important role in maintaining genomic integrity and protecting against cancer development. This study aimed to identify novel SNPs in the DNA repair–related genes associated with melanoma risk from a genome-wide association study (GWAS).
A total of 8,422 SNPs from the 165 DNA repair–related genes were extracted from a GWAS of melanoma risk, including 494 cases and 5,628 controls from the Nurses’ Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). We further replicated the top SNPs in a GWAS of melanoma risk from the MD Anderson Cancer Center (1,804 cases and 1,026 controls).
A total of 3 SNPs with P value < 0.001 were selected for in silico replication. One SNP was replicated: rs3902093 [A] in EXO1 promoter region (Pdiscovery = 6.6×10-4, Preplication = 0.039, Pjoint = 2.5×10-4; ORjoint = 0.80, 95% CI: 0.71, 0.90). This SNP was associated with the expression of the EXO1; carriers of the A allele showed lower expression (P = 0.002).
Our study found that a promoter region SNP in the editing and processing nucleases gene EXO1 was associated with decreased expression of EXO1 and decreased melanoma risk. Further studies are warranted to validate this association and to investigate the potential mechanisms.
Susceptibility to primary biliary cirrhosis (PBC) is strongly associated with HLA region polymorphisms. To determine if associations can be explained by classical HLA determinants we studied Italian 676 cases and 1440 controls with genotyped with dense single nucleotide polymorphisms (SNPs) for which classical HLA alleles and amino acids were imputed. Although previous genome-wide association studies and our results show stronger SNP associations near DQB1, we demonstrate that the HLA signals can be attributed to classical DRB1 and DPB1 genes. Strong support for the predominant role of DRB1 is provided by our conditional analyses. We also demonstrate an independent association of DPB1. Specific HLA-DRB1 genes (*08, *11 and *14) account for most of the DRB1 association signal. Consistent with previous studies, DRB1*08 (p = 1.59 × 10−11) was the strongest predisposing allele where as DRB1*11 (p = 1.42 × 10−10) was protective. Additionally DRB1*14 and the DPB1 association (DPB1*03:01) (p = 9.18 × 10−7) were predisposing risk alleles. No signal was observed in the HLA class 1 or class 3 regions. These findings better define the association of PBC with HLA and specifically support the role of classical HLA-DRB1 and DPB1 genes and alleles in susceptibility to PBC.
genetic risk; risk allele; imputation; antigen binding pocket; autoimmune disease
Variance components (VC) and the Bayesian Markov chain Monte Carlo (MCMC) analysis are two of the widely used linkage analysis approaches to mapping genes for complex quantitative traits. Both approaches can handle extended pedigrees and multiple markers and do not require a prespecified genetic model. In this study, we used simulated data to compare the performance of these two approaches with the traditional parametric linkage analysis. Using simulated data sets without linkage between a quantitative trait and the markers, we estimated a critical value for various test scores used in VC or MCMC and the location (LOC) score at a fixed level of significance (5%). These critical values were then used to determine the power for the three methods for simulated data sets with linkage. We found that both the VC and MCMC approaches worked well, compared with the LOC score, when there was only one gene underlying the quantitative trait; however, VC had higher power than the other methods in a simulation study of a complex phenotype influenced by more than one gene. We also compared two implementations of MCMC analysis, finding interpretation of results using the log of placement score was more accurate for linkage inference than the Bayes factor but required much more intensive simulation studies.
Variance components; Linkage analysis; Location score; Multipoint analysis; Model-free methods; Markov chain Monte Carlo; Statistical power
The detection of tumor suppressor gene promoter methylation in sputum-derived exfoliated cells predicts early lung cancer. Here we identified genetic determinants for this epigenetic process and examined their biological effects on gene regulation. A two-stage approach involving discovery and replication was employed to assess the association between promoter hypermethylation of a 12-gene panel and common variation in 40 genes involved in carcinogen metabolism, regulation of methylation, and DNA damage response in members of the Lovelace Smokers Cohort (n=1434). Molecular validation of three identified variants was conducted using primary bronchial epithelial cells. Association of study-wide significance (P<8.2×10−5) was identified for rs1641511, rs3730859, and rs1883264 in TP53, LIG1, and BIK, respectively. These SNPs were significantly associated with altered expression of the corresponding genes in primary bronchial epithelial cells. In addition, rs3730859 in LIG1 was also moderately associated with increased risk for lung cancer among Caucasian smokers. Together, our findings suggest that genetic variation in DNA replication and apoptosis pathways impacts the propensity for gene promoter hypermethylation in the aerodigestive tract of smokers. The incorporation of genetic biomarkers for gene promoter hypermethylation with clinical and somatic markers may improve risk assessment models for lung cancer.
DNA damage response; promoter hypermethylation; single nucleotide polymorphism; sputum; smoker
Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
gene-gene interactions; gene-environment interactions; rare variants; next generation sequencing; complex phenotypes; simulations; computational resources
Recent data showed that melanoma was more common among patients with Parkinson’s disease (PD) than individuals without PD and vice versa. It has been hypothesized that these two diseases may share common genetic and environmental risk factors.
We evaluated the association between single-nucleotide polymorphisms (SNPs) selected based on recent genome-wide association studies (GWAS) on PD risk and the risk of melanoma using 2,297 melanoma cases and 6,651 controls.
The PD SNP rs156429 in the chromosome 7p15 region was nominally associated with melanoma risk with p-value of 0.04, which was not significant after the Bonferroni correction for multiple comparisons. No association was observed between the remaining 31 PD SNPs and the risk of melanoma. The genetic score based on the number of PD risk allele was not associated with melanoma risk (odds ratio for the highest genetic score quartile (30–35) vs. the lowest (15–20), 1.13, 95% confidence interval, 0.47–2.70).
The PD SNPs identified in published GWAS do not appear to play an important role in melanoma development.
The PD susceptibility loci discovered by GWAS contribute little to the observed epidemiological association between the PD and melanoma.
We performed a multistage genome-wide association study of melanoma. In a discovery cohort of 1804 melanoma cases and 1026 controls, we identified loci at chromosomes 15q13.1 (HERC2/OCA2 region) and 16q24.3 (MC1R) regions that reached genome-wide significance within this study and also found strong evidence for genetic effects on susceptibility to melanoma from markers on chromosome 9p21.3 in the p16/ARF region and on chromosome 1q21.3 (ARNT/LASS2/ANXA9 region). The most significant single-nucleotide polymorphisms (SNPs) in the 15q13.1 locus (rs1129038 and rs12913832) lie within a genomic region that has profound effects on eye and skin color; notably, 50% of variability in eye color is associated with variation in the SNP rs12913832. Because eye and skin colors vary across European populations, we further evaluated the associations of the significant SNPs after carefully adjusting for European substructure. We also evaluated the top 10 most significant SNPs by using data from three other genome-wide scans. Additional in silico data provided replication of the findings from the most significant region on chromosome 1q21.3 rs7412746 (P = 6 × 10−10). Together, these data identified several candidate genes for additional studies to identify causal variants predisposing to increased risk for developing melanoma.
Soft tissue sarcomas (STS) are heterogeneous mesenchymal tumors with diverse subtypes. STS can be classified into two main categories according to the type of genomic alteration: recurrent translocation driven STS, and non-recurrent translocations. However, little has known about acquired uniparental disomy in STS.
In this study, we analyzed SNP microarray data to determine the frequency and distribution patterns of acquired uniparental disomy (aUPD) in major soft tissue sarcoma (STS) subtypes using CNAG and R softwares.
We identified recurrent aUPD regions specific to alveolar rhabdomyosarcoma with the most frequent at 11p15.4, gastrointestinal stromal tumor at 1p36.11-p35.3, leiomyosarcoma at 17p13.3-p13.1, myxofibrosarcoma at 1p35.1-p34.2 and 16q23.3-q24.1, and pleomorphic liposarcoma at 13q13.2-q13.3 and 13q14.11-q14.2. In contrast, specific recurrent aUPD regions were not identified in dedifferentiated liposarcoma, Ewing sarcoma, myxoid/round cell liposarcoma, and synovial sarcoma. Strikingly total, centromeric and segmental aUPD regions are more frequent in STS that do not exhibit recurrent translocation events.
Our study yields a detailed map of aUPD across 9 diverse STS subtypes and suggests the potential location of several novel tumor suppressor genes and oncogenes.
Acquired uniparental disomy; Soft tissue sarcoma and whole-genome
Tumor size at diagnosis (TSD) indirectly reflects tumor growth rate. The relationship between TSD and smoking is poorly understood. The aim of the study was to determine the relationship between smoking and TSD. We reviewed 1712 newly diagnosed and previously untreated non-small cell lung cancer (NSCLC) patients’ electronic medical records and collected tumor characteristics. Demographic and epidemiologic characteristics were derived from questionnaires administered during personal interviews. Univariate and multivariate linear regression models were used to evaluate the relationship between TSD and smoking controlling for demographic and clinical factors. We also investigated the relationship between the rs1051730 SNP in an intron of the CHRNA3 gene (the polymorphism most significantly associated with lung cancer risk and smoking behavior) and TSD. We found a strong dose dependent relationship between TSD and smoking. Current smokers had largest and never smokers smallest TSD with former smokers having intermediate TSD. In the multivariate linear regression model, smoking status (never, former, and current), histological type (adenocarcinoma vs SqCC), and gender were significant predictors of TSD. Smoking duration and intensity may explain the gender effect in predicting TSD. We found that the variant allele of rs1051730 in CHRNA3 gene was associated with larger TSD of squamous cell carcinoma. In the multivariate linear regression model, both rs1051730 and smoking were significant predictors for the size of squamous carcinomas. We conclude that smoking is positively associated with lung tumor size at the moment of diagnosis.
Lung cancer; tumor size; epidemiologic characteristics; risk factors; CHRNA3