False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.

Genotype imputation is widely used in genetic studies to boost the power of GWAS, to combine multiple studies for meta-analysis and to perform fine mapping. With advances of imputation tools and large reference panels, genotype imputation has become mature and accurate. However, the uncertain nature of imputed genotypes can cause bias in the downstream analysis. Many studies have compared the performance of popular imputation approaches, but few investigated bias characteristics of downstream association analyses. Herein, we showed that the imputation accuracy is diminished if the real genotypes contain minor alleles. Although these genotypes are less common, which is particularly true for loci with low minor allele frequency, a large discordance between imputed and observed genotypes significantly inflated the association results, especially in data with a large portion of uncertain SNPs. The significant discordance of P-values happened as the P-value approached 0 or the imputation quality was poor. Although elimination of poorly imputed SNPs can remove false positive (FP) SNPs, it sacrificed, sometimes, more than 80% true positive (TP) SNPs. For top ranked SNPs, removing variants with moderate imputation quality cannot reduce the proportion of FP SNPs, and increasing sample size in reference panels did not greatly benefit the results as well. Additionally, samples with a balanced ratio between cases and controls can dramatically improve the number of TP SNPs observed in the imputation based GWAS. These results raise concerns about results from analysis of association studies when rare variants are studied, particularly when case-control studies are unbalanced.

Genetic variants in the calcium signaling pathway genes are associated with cutaneous melanoma-specific survival.

Remodeling or deregulation of the calcium signaling pathway is a relevant hallmark of cancer including cutaneous melanoma (CM). In this study, using data from a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center, we assessed the role of 41,377 common single-nucleotide polymorphisms (SNPs) of 167 calcium signaling pathway genes in CM survival. We used another GWAS from Harvard University as the validation dataset. In the single-locus analysis, 1830 SNPs were found to be significantly associated with CM-specific survival (CMSS; P ≤ 0.050 and false-positive report probability ≤ 0.2), of which 9 SNPs were validated in the Harvard study (P ≤ 0.050). Among these, three independent SNPs (i.e. PDE1A rs6750552 T>C, ITPR1 rs6785564 A>G and RYR3 rs2596191 C>A) had a predictive role in CMSS, with a meta-analysis-derived hazards ratio of 1.52 (95% confidence interval = 1.19-1.94, P = 7.21 × 10-4), 0.49 (0.33-0.73, 3.94 × 10-4) and 0.67 (0.53-0.86, 0.0017), respectively. Patients with an increasing number of protective genotypes had remarkably improved CMSS. Additional expression quantitative trait loci analysis showed that these genotypes were also significantly associated with mRNA expression levels of the genes. Taken together, these results may help us to identify prospective biomarkers in the calcium signaling pathway for CM prognosis.

Genetic variants in ELOVL2 and HSD17B12 predict melanoma-specific survival.

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single-nucleotide polymorphisms (SNPs) in 149 genes of the fatty-acid synthesis pathway with cutaneous melanoma disease-specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses' Health and Health Professionals Follow-up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51-0.84 and p = 8.34 × 10-4 ) and 2.29 (1.55-3.39 and p = 3.61 × 10-5 ), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies.

Genetic variants in RORA and DNMT1 associated with cutaneous melanoma survival.

Cutaneous melanoma (CM) is considered as a steroid hormone-related malignancy. However, few studies have evaluated the roles of genetic variants encoding steroid hormone receptor genes and their related regulators (SHR-related genes) in CM-specific survival (CMSS). Here, we performed a pathway-based analysis to evaluate genetic variants of 191 SHR-related genes in 858 CMSS patients using a dataset from a genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC), and then validated the results in an additional dataset of 409 patients from the Harvard GWAS. Using multivariate Cox proportional hazards regression analysis, we identified three-independent SNPs (RORA rs782917 G > A, RORA rs17204952 C > T and DNMT1 rs7253062 G > A) as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) and 95% confidence interval of 1.62 (1.25-2.09), 1.60 (1.20-2.13) and 1.52 (1.20-1.94), respectively. Combined analysis of risk genotypes of these three SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (ptrend < 0.001); however, no improvement in the prediction model was observed (area under the curve [AUC] = 79.6-80.8%, p = 0.656), when these risk genotypes were added to the model containing clinical variables. Our findings suggest that genetic variants of RORA and DNMT1 may be promising biomarkers for CMSS, but these results needed to be validated in future larger studies.

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival.

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (Ptrend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS.

Genetic variants in the integrin signaling pathway genes predict cutaneous melanoma survival.

To identify genetic variants involved in prognosis of cutaneous melanoma (CM), we investigated associations of single nucleotide polymorphisms (SNPs) of genes in the integrin signaling pathway with CM survival by re-analyzing a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center (MDACC) and then validated significant SNPs in another GWAS from Harvard University. In the MDACC study, 1,148 SNPs were significantly associated with CM-specific survival (CMSS) (p ≤ 0.050 and false-positive report probability ≤ 0.20), and nine SNPs were validated in the Harvard study (p ≤ 0.050). Among these, three independent SNPs (i.e., DOCK1 rs11018104 T > A, rs35748949 C > T and PAK2 rs1718404 C > T) showed a predictive role in CMSS, with an effect-allele attributed adjusted hazards ratio [adjHR of 1.50 (95% confidence interval (CI) = 1.18-1.90, p = 7.46E-04), 1.53 (1.18-1.97, 1.18E-03) and 0.58 (0.45-0.76, 5.60E-05), respectively]. Haplotype analysis revealed that a haplotype carrying two risk alleles A-T in DOCK1 was associated with the poorest survival in both MDACC (adjHR = 1.73, 95% CI = 1.19-2.50, p = 0.004) and Harvard (adjHR = 1.95, 95% CI = 1.14-3.33, p = 0.010) studies. In addition, patients with an increasing number of unfavorable genotypes (NUGs) for these three SNPs had a poorer survival. Incorporating NUGs with clinical variables showed a significantly improved ability to classify CMSS (AUC increased from 86.8% to 88.6%, p = 0.031). Genetic variants in the integrin signaling pathway may independently or jointly modulate the survival of CM patients. Further large, prospective studies are needed to validate these findings.

Genetic variants in the genes encoding rho GTPases and related regulators predict cutaneous melanoma-specific survival.

Rho GTPases control cell division, motility, adhesion, vesicular trafficking and phagocytosis, which may affect progression and/or prognosis of cancers. Here, we investigated associations between genetic variants of Rho GTPases-related genes and cutaneous melanoma-specific survival (CMSS) by re-analyzing a published melanoma genome-wide association study (GWAS) and validating the results in another melanoma GWAS. In the single-locus analysis of 36,018 SNPs in 129 Rho-related genes, 427 SNPs were significantly associated with CMSS (p < 0.050 and false-positive report probability <0.2) in the discovery dataset, and five SNPs were replicated in the validation dataset. Among these, four SNPs (i.e., RHOU rs10916352 G > C, ARHGAP22 rs3851552 T > C, ARHGAP44 rs72635537 C > T and ARHGEF10 rs7826362 A > T) were independently predictive of CMSS (a meta-analysis derived p = 9.04 × 10-4 , 9.58 × 10-4 , 1.21 × 10-4 and 8.47 × 10-4 , respectively). Additionally, patients with an increasing number of unfavorable genotypes (NUGs) of these loci had markedly reduced CMSS in both discovery dataset and validation dataset (ptrend =1.47 × 10-7 and 3.12 × 10-5 ). The model including the NUGs and clinical variables demonstrated a significant improvement in predicting the five-year CMSS. Moreover, rs10916352C and rs3851552C alleles were significantly associated with an increased mRNA expression levels of RHOU (p = 1.8 × 10-6 ) and ARHGAP22 (p = 5.0 × 10-6 ), respectively. These results may provide promising prognostic biomarkers for CM personalized management and treatment.

Genetic variants in the PIWI-piRNA pathway gene DCP1A predict melanoma disease-specific survival.

The Piwi-piRNA pathway is important for germ cell maintenance, genome integrity, DNA methylation and retrotransposon control and thus may be involved in cancer development. In this study, we comprehensively analyzed prognostic roles of 3,116 common SNPs in PIWI-piRNA pathway genes in melanoma disease-specific survival. A published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used to identify associated SNPs, which were later validated by another GWAS from the Harvard Nurses' Health Study and Health Professionals Follow-up Study. After multiple testing correction, we found that there were 27 common SNPs in two genes (PIWIL4 and DCP1A) with false discovery rate < 0.2 in the discovery dataset. Three tagSNPs (i.e., rs7933369 and rs508485 in PIWIL4; rs11551405 in DCP1A) were replicated. The rs11551405 A allele, located at the 3' UTR microRNA binding site of DCP1A, was associated with an increased risk of melanoma disease-specific death in both discovery dataset [adjusted Hazards ratio (HR) = 1.66, 95% confidence interval (CI) = 1.21-2.27, p =1.50 × 10-3 ] and validation dataset (HR = 1.55, 95% CI = 1.03-2.34, p = 0.038), compared with the C allele, and their meta-analysis showed an HR of 1.62 (95% CI, 1.26-2.08, p =1.55 × 10-4 ). Using RNA-seq data from the 1000 Genomes Project, we found that DCP1A mRNA expression levels increased significantly with the A allele number of rs11551405. Additional large, prospective studies are needed to validate these findings.

Genetic variants in Hippo pathway genes YAP1, TEAD1 and TEAD4 are associated with melanoma-specific survival.

Cutaneous melanoma (CM) is the most lethal form of skin cancers. The Hippo pathway controls cell migration, development and sizes of the organs in diverse species, and deregulation of this pathway may affect CM progression and prognosis. Therefore, we hypothesized that genetic variants of Hippo pathway genes might predict survival of CM patients. We used the genotyping data of 1,115 common single nucleotide polymorphisms (SNPs) in the 12 pathway core genes (i.e., MST1, MST2, SAV1, LATS1, LATS2, MOB1A, MOB1B, YAP1, TEAD1, TEAD2, TEAD3 and TEAD4) from the dataset of our previously published CM genome-wide association study and comprehensively analyzed their associations with CM-specific survival (CSS) in 858 CM patients by using the Kaplan-Meier analyses and Cox proportional hazards regression models. We found a predictive role of YAP1 rs11225163 CC, TEAD1 rs7944031 AG+GG and TEAD4 rs1990330 CA+AA in the prognosis of CM. In addition, patients with an increasing number of unfavorable genotypes (NUG) had a markedly increased risk of death. After incorporating NUG in the model with clinical variables, the new model showed a significantly improved discriminatory ability to classify CSS (AUC increased from 82.03% to 84.56%). Our findings suggest that genetic variants of Hippo pathway genes, particularly YAP1 rs11225163, TEAD1 rs7944031 and TEAD4 rs1990330, may independently or jointly modulate survival of CM patients. Additional large, prospective studies are needed to validate these findings.

Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls.

Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p21-6p22 and 15q25.1 associated with lung cancer risk. Several other genetic regions including variants of CHEK2 (22q12), TP53BP1 (15q15) and RAD52 (12p13) have been demonstrated to influence lung cancer risk in candidate- or pathway-based analyses. To identify novel risk variants for lung cancer, we performed a meta-analysis of 16 GWASs, totaling 14 900 cases and 29 485 controls of European descent. Our data provided increased support for previously identified risk loci at 5p15 (P = 7.2 × 10(-16)), 6p21 (P = 2.3 × 10(-14)) and 15q25 (P = 2.2 × 10(-63)). Furthermore, we demonstrated histology-specific effects for 5p15, 6p21 and 12p13 loci but not for the 15q25 region. Subgroup analysis also identified a novel disease locus for squamous cell carcinoma at 9p21 (CDKN2A/p16(INK4A)/p14(ARF)/CDKN2B/p15(INK4B)/ANRIL; rs1333040, P = 3.0 × 10(-7)) which was replicated in a series of 5415 Han Chinese (P = 0.03; combined analysis, P = 2.3 × 10(-8)). This large analysis provides additional evidence for the role of inherited genetic susceptibility to lung cancer and insight into biological differences in the development of the different histological types of lung cancer.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians.

INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

A multi-omics study links TNS3 and SEPT7 to long-term former smoking NSCLC survival.

The genetic architecture of non-small cell lung cancer (NSCLC) is relevant to smoking status. However, the genetic contribution of long-term smoking cessation to the prognosis of NSCLC patients remains largely unknown. We conducted a genome-wide association study primarily on the prognosis of 1299 NSCLC patients of long-term former smokers from independent discovery (n = 566) and validation (n = 733) sets, and used in-silico function prediction and multi-omics analysis to identify single nucleotide polymorphisms (SNPs) on prognostics with NSCLC. We further detected SNPs with at least moderate association strength on survival within each group of never, short-term former, long-term former, and current smokers, and compared their genetic similarity at the SNP, gene, expression quantitative trait loci (eQTL), enhancer, and pathway levels. We identified two SNPs, rs34211819TNS3 at 7p12.3 (P = 3.90 × 10-9) and rs1143149SEPT7 at 7p14.2 (P = 9.75 × 10-9), were significantly associated with survival of NSCLC patients who were long-term former smokers. Both SNPs had significant interaction effects with years of smoking cessation (rs34211819TNS3: Pinteraction = 8.0 × 10-4; rs1143149SEPT7: Pinteraction = 0.003). In addition, in silico function prediction and multi-omics analysis provided evidence that these QTLs were associated with survival. Moreover, comparison analysis found higher genetic similarity between long-term former smokers and never-smokers, compared to short-term former smokers or current smokers. Pathway enrichment analysis indicated a unique pattern among long-term former smokers that was related to immune pathways. This study provides important insights into the genetic architecture associated with long-term former smoking NSCLC.

Rare deleterious germline variants and risk of lung cancer.

Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04-75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71-8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3' UTR (OR 4.33, 95%CI 2.03-9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73-11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33-5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.

A new efficient method to detect genetic interactions for lung cancer GWAS.

BACKGROUND: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. METHODS: To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. RESULTS: Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR 1.24, P = 3.15 × 10-15), as the top marker to predict age of lung cancer onset. CONCLUSIONS: From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

Protein-altering germline mutations implicate novel genes related to lung cancer development.

Few germline mutations are known to affect lung cancer risk. We performed analyses of rare variants from 39,146 individuals of European ancestry and investigated gene expression levels in 7,773 samples. We find a large-effect association with an ATM L2307F (rs56009889) mutation in adenocarcinoma for discovery (adjusted Odds Ratio = 8.82, P = 1.18 × 10-15) and replication (adjusted OR = 2.93, P = 2.22 × 10-3) that is more pronounced in females (adjusted OR = 6.81 and 3.19 and for discovery and replication). We observe an excess loss of heterozygosity in lung tumors among ATM L2307F allele carriers. L2307F is more frequent (4%) among Ashkenazi Jewish populations. We also observe an association in discovery (adjusted OR = 2.61, P = 7.98 × 10-22) and replication datasets (adjusted OR = 1.55, P = 0.06) with a loss-of-function mutation, Q4X (rs150665432) of an uncharacterized gene, KIAA0930. Our findings implicate germline genetic variants in ATM with lung cancer susceptibility and suggest KIAA0930 as a novel candidate gene for lung cancer risk.

Rare Variants in Known Susceptibility Loci and Their Contribution to Risk of Lung Cancer.

BACKGROUND: Genome-wide association studies are widely used to map genomic regions contributing to lung cancer (LC) susceptibility, but they typically do not identify the precise disease-causing genes/variants. To unveil the inherited genetic variants that cause LC, we performed focused exome-sequencing analyses on genes located in 121 genome-wide association study-identified loci previously implicated in the risk of LC, chronic obstructive pulmonary disease, pulmonary function level, and smoking behavior. METHODS: Germline DNA from 260 case patients with LC and 318 controls were sequenced by utilizing VCRome 2.1 exome capture. Filtering was based on enrichment of rare and potential deleterious variants in cases (risk alleles) or controls (protective alleles). Allelic association analyses of single-variant and gene-based burden tests of multiple variants were performed. Promising candidates were tested in two independent validation studies with a total of 1773 case patients and 1123 controls. RESULTS: We identified 48 rare variants with deleterious effects in the discovery analysis and validated 12 of the 43 candidates that were covered in the validation platforms. The top validated candidates included one well-established truncating variant, namely, BRCA2, DNA repair associated gene (BRCA2) K3326X (OR = 2.36, 95% confidence interval [CI]: 1.38-3.99), and three newly identified variations, namely, lymphotoxin beta gene (LTB) p.Leu87Phe (OR = 7.52, 95% CI: 1.01-16.56), prolyl 3-hydroxylase 2 gene (P3H2) p.Gln185His (OR = 5.39, 95% CI: 0.75-15.43), and dishevelled associated activator of morphogenesis 2 gene (DAAM2) p.Asp762Gly (OR = 0.25, 95% CI: 0.10-0.79). Burden tests revealed strong associations between zinc finger protein 93 gene (ZNF93), DAAM2, bromodomain containing 9 gene (BRD9), and the gene LTB and LC susceptibility. CONCLUSION: Our results extend the catalogue of regions associated with LC and highlight the importance of germline rare coding variants in LC susceptibility.

Imprinting detection by extending a regression-based QTL analysis method.

We present an extension of a regression-based quantitative-trait linkage analysis method to incorporate parent-of-origin effects. We separately regressed total, paternal, and maternal IBD sharing on traits' squared sums and differences. We also developed a test for imprinting that indicates whether there is any difference between the paternal and maternal regression coefficients. Since this method treats the identity-by-descent information as the dependent variable that is conditioned on the trait, it can be readily applied to data from complex ascertainment processes. We performed a simulation study to examine the performance of the method. We found that when using empirical critical values, the method shows identical or higher power compared to existing methods for evaluation of parent-of-origin effect in linkage analysis of quantitative traits. Missing parental genotypes increase the type I error rate of the linkage test and decrease the power of the imprinting test. When the major gene has a low heritability, the power of the method decreases considerably, but the statistical tests still perform well. We also applied a permutation algorithm, which ensures the appropriate type I error rate for the test for imprinting. The method was applied to a data from a study of 6 body size related measures and 23 loci on chromosome 7 for 255 nuclear families. Multipoint identities-by-descent (IBD) were obtained using a modification of the SIMWALK 2 program. A parent-of-origin effect consistent with maternal imprinting was suggested at 99.67-111.26 Mb for body mass index, bioelectrical impedance analysis, waist circumference, and leptin concentration.

Genetic variants of PDGF signaling pathway genes predict cutaneous melanoma survival.

To investigate whether genetic variants of platelet-derived growth factor (PDGF) signaling pathway genes are associated with survival of cutaneous melanoma (CM) patients, we assessed associations of single-nucleotide polymorphisms in PDGF pathway with melanoma-specific survival in 858 CM patients of M.D. Anderson Cancer Center (MDACC). Additional data of 409 cases from Harvard University were also included for further analysis. We identified 13 SNPs in four genes (COL6A3, NCK2, COL5A1 and PRKCD) with a nominal P < 0.05 and false discovery rate (FDR) < 0.2 in MDACC dataset. Based on linkage disequilibrium, functional prediction and minor allele frequency, a representative SNP in each gene was selected. In the meta-analysis using MDACC and Harvard datasets, there were two SNPs associated with poor survival of CM patients: rs6707820 C>T in NCK2 (HR = 1.87, 95% CI = 1.35-2.59, Pmeta= 1.53E-5); and rs2306574 T>C in PRKCD (HR = 1.73, 95% CI = 1.33-2.24, Pmeta= 4.56E-6). Moreover, CM patients in MDACC with combined risk genotypes of these two loci had markedly poorer survival (HR = 2.47, 95% CI = 1.58-3.84, P < 0.001). Genetic variants of rs6707820 C>T in NCK2 and rs2306574 T>C in PRKCD of the PDGF signaling pathway may be biomarkers for melanoma survival.

Genetic Variants in WNT2B and BTRC Predict Melanoma Survival.

Cutaneous melanoma (CM) is the most lethal skin cancer. The Wnt pathway has an impact on development, invasion, and metastasis of CM, thus likely affecting CM prognosis. Using data from a published genome-wide association study from The University of Texas MD Anderson Cancer Center, we assessed the associations of 19,830 common single-nucleotide polymorphisms (SNPs) in 151 Wnt pathway autosomal genes with CM-specific survival and then validated significant SNPs in another genome-wide association study from Harvard University. In the single-locus analysis, 1,855 SNPs were significantly associated with CM-specific survival at P < 0.05, of which 547 SNPs were still considered noteworthy after the correction by the false-positive report probability. In the replication, two SNPs remained significantly associated with CM-specific survival after multiple comparison correction. By performing functional prediction and stepwise selection, we identified two independent SNPs (i.e., WNT2B rs1175649 G>T and BTRC rs61873997 G>A) that showed a predictive role in CM-specific survival, with an effect-allele-attributed hazards ratio (adjusted hazards ratio) of 1.99 (95% confidence interval = 1.41-2.81, P = 8.10 × 10-5) and 0.61 (0.46-0.80, 3.12×10-4), respectively. Collectively, these variants in the Wnt pathway genes may be biomarkers for outcomes of patients with CM, if validated by larger studies.