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Multi-ancestry genome-wide association analyses identify 124 risk loci for rheumatoid arthritis, of which 34 are novel. A polygenic risk score based on multi-ancestry data showed comparable performance between populations of European and East Asian ancestries.Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 x 10(-8)), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.

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.

Background Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors. Methods In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors. Results We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models. Conclusions Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models.

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs). Specifically, we computationally inferred mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.Author SummaryIn the past decade, many studies have identified genetic variants associated with gene expression level (eQTLs) in different phenotypes, including tissues and diseases. Gene expression is the result of cooperation between transcriptional regulation, such as transcriptional activity, and post-transcriptional regulation, such as mRNA stability. Here, we present a computational framework that take advantage of recently developed methods to estimate mRNA stability from RNA-Seq, which is widely used to estimate gene expression, and then to identify genetic variants associated with mRNA stability (stQTLs) in lung tissue. Compared to eQTLs, we found that genetic variants that affects mRNA stability are more significantly located in the CDS and 3’UTR regions, which are known to interact with RNA-binding proteins (RBPs) or microRNAs to regulate stability. In addition, stQTLs are significantly more likely to overlap the binding sites of RBPs. We show that the six RBPs that most significantly bind to stQTLs are all known to regulate mRNA stability. This pipeline of simultaneously identifying eQTLs and stQTLs using only RNA-Seq data can provide higher resolution than traditional eQTLs study to better understand the molecular mechanisms of genetic variants on the regulation of gene expression.

We hypothesized that a joint analysis of cancer risk-associated single-nucleotide polymorphism (SNP) and somatic mutations in tumor samples can predict functional and potentially causal SNPs from GWASs. We used mutations reported in the Catalog of Somatic Mutations in Cancer (COSMIC). Confirmed somatic mutations were subdivided into two groups: (1) mutations reported as SNPs, which we call mutational/SNPs and (2) somatic mutations that are not reported as SNPs, which we call mutational/noSNPs. It is generally accepted that the number of times a somatic mutation is reported in COSMIC correlates with its selective advantage to tumors, with more frequently reported mutations being more functional and providing a stronger selective advantage to the tumor cell. We found that mutations reported ≥10 times in COSMIC—frequent mutational/SNPs (fmSNPs) are likely to be functional. We identified 12 cancer risk-associated SNPs reported in the Catalog of published GWASs at least 10 times as confirmed somatic mutations and therefore deemed to be functional. Additionally, we have identified 42 SNPs that are tightly linked (R2 ≥ 0.8) to SNPs reported in the Catalog of published GWASs as cancer risk associated and that are also reported as fmSNPs. As a result, 54 candidate functional/potentially causal cancer risk associated SNPs were identified. We found that fmSNPs are more likely to be located in evolutionarily conserved regions compared with cancer risk associated SNPs that are not fmSNPs. We also found that fmSNPs also underwent positive selection, which can explain why they exist as population polymorphisms.

Extensive studies have been conducted on the analysis of genome function, especially on the expression quantitative trait loci (eQTL). These studies offered promising results for characterization of the functional sequencing variation and understanding of the basic processes of gene regulation. Parent of origin effect (POE) is an important epigenetic phenomenon describing that the expression of certain genes depends on their allelic parent-of-origin and it is known to play important roles in human complex diseases. However, traditional eQTL mapping approaches do not allow for the detection of imprinting, or they focus on modeling the additive genetic effect thereby ignoring the estimation of the dominance genetic effect. In this study, we proposed a statistical framework to test the additive and dominance genetic effects of the candidate eQTLs along with detection of the POE with a functional model and an orthogonal model for RNA-seq data. We demonstrated the desirable power and preserved Type I errors of the methods in most scenarios, especially the orthogonal model with un-biased estimation of the genetic effects and over-dispersion of the RNA-seq data. The application to a HapMap project trio dataset validated existing imprinting genes and discovered two novel imprinting genes with potential dominance genetic effect and RB1 and IGF1R genes. This study provides new insights into the next generation statistical modeling of eQTL mapping for better understanding of the genetic architecture underlying the mechanisms of gene expression regulation.

Background: Lung cancer kills more people than any other cancer in the United States. In addition to environmental factors, lung cancer has genetic risk factors as well, though the genetic etiology is still not well understood. We have performed whole exome sequencing on 262 individuals from 28 extended families with a family history of lung cancer. Methods: Parametric genetic linkage analysis was performed on these samples using two distinct analyses—the lung cancer only (LCO) analysis, where only patients with lung cancer were coded as affected, and the all aggregated cancers (AAC) analysis, where other cancers seen in the pedigree were coded as affected. Results: The AAC analysis yielded a genome-wide significant result at rs61943670 in POLR3B at 12q23.3. POLR3B has been implicated somatically in lung cancer, but this germline finding is novel and is a significant expression quantitative trait locus in lung tissue. Interesting genome-wide suggestive haplotypes were also found within individual families, particularly near SSPO at 7p36.1 in one family and a large linked haplotype spanning 4q21.3-28.3 in a different family. The 4q haplotype contains potential causal rare variants in DSPP at 4q22.1 and PTPN13 at 4q21.3. Conclusions: Regions on 12q, 7p, and 4q are linked to increased cancer risk in highly aggregated lung cancer families, 12q across families and 7p and 4q within a single family. POLR3B, SSPO, DSPP, and PTPN13 are currently the best candidate genes. Impact: Functional work on these genes is planned for future studies and if confirmed would lead to potential biomarkers for risk in cancer.

Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene–sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason–a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

BackgroundOver the relatively short history of Genome Wide Association Studies (GWASs), hundreds of GWASs have been published and thousands of disease risk-associated SNPs have been identified. Summary statistics from the conducted GWASs are often available and can be used to identify SNP features associated with the level of GWAS statistical significance. Those features could be used to select SNPs from gray zones (SNPs that are nominally significant but do not reach the genome-wide level of significance) for targeted analyses.MethodsWe used summary statistics from recently published breast and lung cancer and scleroderma GWASs to explore the association between the level of the GWAS statistical significance and the expression quantitative trait loci (eQTL) status of the SNP. Data from the Genotype-Tissue Expression Project (GTEx) were used to identify eQTL SNPs.ResultsWe found that SNPs reported as eQTLs were more significant in GWAS (higher -log10p) regardless of the tissue specificity of the eQTL. Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant in the GWAS compared to those reported as eQTL in only one tissue type. eQTL density in the ±5 kb adjacent region of a given SNP was also positively associated with the level of GWAS statistical significance regardless of the eQTL status of the SNP. We found that SNPs located in the regions of high eQTL density were more likely to be located in regulatory elements (transcription factor or miRNA binding sites).When SNPs were stratified by the level of statistical significance, the proportion of eQTLs was positively associated with the mean level of statistical significance in the group. The association curve reaches a plateau around -log10p ≈ 5. The observed associations suggest that quasi-significant SNPs (10− 5 p − 8) and SNPs at the genome wide level of statistical significance (p − 8) may have a similar proportions of risk associated SNPs.ConclusionsThe results of this study indicate that the SNP’s eQTL status, as well as eQTL density in the adjacent region are positively associated with the level of statistical significance of the SNP in GWAS.

Background: Identifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European-descent individuals. Yet population-specific genetic variation can occur, and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans. Methods: We conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status. Results: We identified three novel genomic regions significantly associated (FDR-corrected P Conclusions: We identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans. Impact: Our findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

Background Usually, genes with a higher-than-expected number of somatic mutations in tumor samples are assumed to be cancer related. We identified genes with a fewer-than-expected number of somatic mutations - “untouchable genes”. Methods To predict the expected number of somatic mutations, we used a linear regression model with the number of mutations in the gene as an outcome, and gene characteristics, including gene size, nucleotide composition, level of evolutionary conservation, expression level and others, as predictors. Analysis of residuals from the regression model was used to compare the observed and predicted number of mutations. Results We have identified 19 genes with a less-than-expected number of loss-off-function (nonsense, frameshift or pathogenic missense) mutations – i.e., untouchable genes. The number of silent or neutral missense mutations in untouchable genes was equal or higher than the expected number. Many mucins, including MUC16, MUC17, MUC6, MUC5AC, MUC5B, and MUC12, are untouchable. We hypothesized that untouchable mucins help tumor cells to avoid immune response by providing a protective coat that prevents direct contact between effector immune cells, e.g., cytotoxic T-cells, and tumor cells. Survival analysis of available TCGA data demonstrated that overall survival of patients with low (below the median) expression of untouchable mucins was better compared to patients with high expression of untouchable mucins. Aside from mucins, we have identified a number of other untouchable genes. Conclusions Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.

Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

Germline genetic variants are involved in lung cancer (LC) susceptibility. Previous genome-wide association studies (GWAS) have implicated genes involved in smoking propensity and DNA repair but further work is required to identify additional LC susceptibility variants and to investigate LC disease development dynamics.We have undertaken a family history-based genome-wide association (GWAx) study of LC, analysing 48,843 European cases with a parent/sibling with LC compared to 195,387 controls from the UK Biobank. This was meta-analysed with previously described LC GWAS results. We performed Polygenic Risk Scores (PRS) analyses and further evaluated the PRS influence on the somatic environment in exome (N=736) and genome sequencing (N=61) profiled cohorts.Eight novel loci were identified including DNA repair genes (CHEK1, MDM4), metabolic genes (CYP1A1) and variants that were also associated with smoking propensity, such as both subunits of the neuronal α4β2 nicotinic acetylcholine receptor (CHRNA4 and CHRNB2). PRS analysis demonstrated that variants related to eQTLs and/or smoking propensity are enriched for susceptibility variants, including variants below genome-wide significant threshold. PRS of LC variants related to smoking propensity were associated with somatic mutation burden in two case cohorts, with individuals with higher polygenic genetic risk having increased numbers of somatic mutations in their lung tumours.This study has expanded the number of susceptibility loci linked with LC and provided insights into the molecular mechanisms by which these susceptibility variants contribute to the development of lung cancer.

Although lung cancer is known to be caused by environmental factors, it has also been shown to have genetic components, and the genetic etiology of lung cancer remains understudied. We previously identified a lung cancer risk locus on 6q23-25 using microsatellite data in families with a history of lung cancer. To further elucidate that signal, we performed targeted sequencing on nine of our most strongly linked families. Two-point linkage analysis of the sequencing data revealed that the signal was heterogeneous and that different families likely had different risk variants. Three specific haplotypes were shared by some of the families: 6q25.3-26 in families 42 and 44, 6q25.2-25.3 in families 47 and 59, and 6q24.2-25.1 in families 30, 33, and 35. Region-based logarithm of the odds scores and expression data identified the likely candidate genes for each haplotype overlap: ARID1B at 6q25.3, MAP3K4 at 6q26, and UTRN (6q24.1) and PHACTR2 (6q24.2). Further annotation was used to zero in on potential risk variants in those genes. All four genes are good candidate genes for lung cancer risk, having been linked to either lung cancer specifically or other cancers. However, this is the first time any of these genes has been implicated in germline risk. Functional analysis of these four genes is planned for future work. Significance: This study identifies four genes associated with lung cancer risk, which could help guide future lung cancer prevention and treatment approaches.

Background Observational studies indicate that periodontal disease may increase the risk of colorectal, lung, and pancreatic cancers. Using a 2-sample Mendelian randomization (MR) analysis, we assessed whether a genetic predisposition index for periodontal disease was associated with colorectal, lung, or pancreatic cancer risks. Methods Our primary instrument included single nucleotide polymorphisms with strong genome-wide association study evidence for associations with chronic, aggressive, and/or severe periodontal disease (rs729876, rs1537415, rs2738058, rs12461706, rs16870060, rs2521634, rs3826782, and rs7762544). We used summary-level genetic data for colorectal cancer (n = 58 131 cases; Genetics and Epidemiology of Colorectal Cancer Consortium, Colon Cancer Family Registry, and Colorectal Transdisciplinary Study), lung cancer (n = 18 082 cases; International Lung Cancer Consortium), and pancreatic cancer (n = 9254 cases; Pancreatic Cancer Consortia). Four MR approaches were employed for this analysis: random-effects inverse-variance weighted (primary analyses), Mendelian Randomization-Pleiotropy RESidual Sum and Outlier, simple median, and weighted median. We conducted secondary analyses to determine if associations varied by cancer subtype (colorectal cancer location, lung cancer histology), sex (colorectal and pancreatic cancers), or smoking history (lung and pancreatic cancer). All statistical tests were 2-sided. Results The genetic predisposition index for chronic or aggressive periodontitis was statistically significantly associated with a 3% increased risk of colorectal cancer (per unit increase in genetic index of periodontal disease; P = .03), 3% increased risk of colon cancer (P = .02), 4% increased risk of proximal colon cancer (P = .01), and 3% increased risk of colorectal cancer among females (P = .04); however, it was not statistically significantly associated with the risk of lung cancer or pancreatic cancer, overall or within most subgroups. Conclusions Genetic predisposition to periodontitis may be associated with colorectal cancer risk. Further research should determine whether increased periodontitis prevention and increased cancer surveillance of patients with periodontitis is warranted.

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.

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.

Squamous cell carcinomas (SqCC) of the aerodigestive tract have similar etiological risk factors. Although genetic risk variants for individual cancers have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. To identify novel and pleotropic SqCC risk variants, we performed a meta-analysis of GWAS data on lung SqCC (LuSqCC), oro/pharyngeal SqCC (OSqCC), laryngeal SqCC (LaSqCC) and esophageal SqCC (ESqCC) cancers, totaling 13,887 cases and 61,961 controls of European ancestry. We identified one novel genome-wide significant (Pmeta, Author summary Squamous cell carcinomas are specific type of cancer that can arise in multiple organs of the aerodigestive tract including the lung, oral cavity, oropharynx, larynx and esophagus. Previous studies have shown that aerodigestive squamous cell carcinomas share common environmental risk factors (tobacco smoking and alcohol intake). Here, we investigate genetic factors involved in the risk of aerodigestive squamous cell carcinomas as a group in a large genetic association study involving 13,887 cancer cases and 61,961 controls. We identified one genome-wide significant region within 2q33.1 and 3 other suggestive regions at 1q32.1, 5q31.2 and 19p13.11. Gene-based analyses also identify a list of SqCC-related genes that are involved in DNA damage response and epigenetic regulation. Our results suggest some overlap in the genetic factors influencing the risk of aerodigestive squamous cell carcinomas in European populations and highlights the importance of cross-cancer studies.

Background & aims: Genome-wide association studies in primary biliary cholangitis (PBC) have failed to find X chromosome (chrX) variants associated with the disease. Here, we specifically explore the chrX contribution to PBC, a sexually dimorphic complex autoimmune disease. Methods: We performed a chrX-wide association study, including genotype data from 5 genome-wide association studies (from Italy, United Kingdom, Canada, China, and Japan; 5244 case patients and 11,875 control individuals). Results: Single-marker association analyses found approximately 100 loci displaying P < 5 × 10-4, with the most significant being a signal within the OTUD5 gene (rs3027490; P = 4.80 × 10-6; odds ratio [OR], 1.39; 95% confidence interval [CI], 1.028-1.88; Japanese cohort). Although the transethnic meta-analysis evidenced only a suggestive signal (rs2239452, mapping within the PIM2 gene; OR, 1.17; 95% CI, 1.09-1.26; P = 9.93 × 10-8), the population-specific meta-analysis showed a genome-wide significant locus in East Asian individuals pointing to the same region (rs7059064, mapping within the GRIPAP1 gene; P = 6.2 × 10-9; OR, 1.33; 95% CI, 1.21-1.46). Indeed, rs7059064 tags a unique linkage disequilibrium block including 7 genes: TIMM17B, PQBP1, PIM2, SLC35A2, OTUD5, KCND1, and GRIPAP1, as well as a superenhancer (GH0XJ048933 within OTUD5) targeting all these genes. GH0XJ048933 is also predicted to target FOXP3, the main T-regulatory cell lineage specification factor. Consistently, OTUD5 and FOXP3 RNA levels were up-regulated in PBC case patients (1.75- and 1.64-fold, respectively). Conclusions: This work represents the first comprehensive study, to our knowledge, of the chrX contribution to the genetics of an autoimmune liver disease and shows a novel PBC-related genome-wide significant locus.

Background: Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes. Methods: Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer. Results: We observed several traits showing moderate single nucleotide polymorphism–based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors. Conclusions: This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci. Impact: These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.

Population stratification, or systematic differences in allele frequencies between subpopulations, can distort the results from genome-wide association studies (GWAS). While GWAS are usually conducted within continental (e.g. European) ancestry groups, sub-groups may have specific population histories that result in enrichment for risk or protective alleles for complex disease. In this analysis, we examined genetic risk for glioma in a US population with predominantly Ashkenazi Jewish (AJ) ancestry. Genotyping data were obtained from three prior glioma case-control studies. Best-guess assignment to one of seven subcontinental European ancestry groups was performed using AIPS (https://github.com/biomedicaldatascience/AIPS). Out of a total of 6,416 cases and 7,441 controls across all three studies, 202 cases (3% of all cases, 63% GBM) and 403 controls (5% of all controls) were assigned to the AJ ancestry group. Unconditional logistic regression was performed for by study (adjusted for age and first two principal components) and then studies were combined using fixed effects meta-analysis. No associations reached genome-wide significance (p< 5x10-8), including those detected in prior pan-European GWAS. A nominally significant association was detected on chromosome 17 in ASPA (rs9904040, MAF=20%, p=2.61x10-6, Odds ratio=2.22 [95% confidence interval=1.59–3.09]) which is in linkage equilibrium (r2=0.0013) with the previously detected glioma risk SNP in TP53 (MAF=0.3% in this analysis, p=0.8801) in the European population. In our prior pan-European meta-analysis (Melin et al., 2017, this SNP was non-significant (p=0.0620). This study failed to replicate many previously identified glioma risk alleles, likely due to diminished power due to small sample size. We did identify a novel risk allele on chromosome 17 in ASPA, which encodes for aspartoacylase (which catalyzes deacylation of N-acetyl_L-aspartic acid). Larger sample sizes identified via targeted recruitment are necessary in order to fully characterize genetic risk for glioma in this population, including assessment of rare (MAF< 5%) risk allele associations.

SummaryLung cancer is the leading cause of cancer death worldwide. Genome-wide association studies have revealed genetic risk factors, highlighting the role of smoking, family history, telomere regulation, and DNA damage-repair in lung cancer etiology. Many studies have focused on a single ethnic group to avoid confounding from variability in allele frequencies across populations; however, comprehensive multi-ethnic analyses may identify variants that are more likely to be causal. This large-scale, multi- ethnic meta-analyses identified 28 novel risk loci achieving genome-wide significance. Leading candidates were further studied using single-cell methods for evaluating DNA-damage. DNA-damage promoting activities were confirmed for selected genes by knockdown genes and overexpression studies.

Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we introduce dynamic analysis of APA from single-cell RNA-seq (scDaPars), a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using either 3′-end (10x Chromium) or full-length (Smart-seq2) scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

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.

To investigate whether the 21q21 region may contain a quantitative trait locus (QTL) with pleiotropic effect on % predicted FEV1 (forced expiratory volume in 1 second) and SPTQ (number of positive skin test responses to 11 allergens), in 295 EGEA families ascertained through asthmatic probands, we conducted a bivariate linkage analysis using two approaches:(1) a bivariate variance components (VC) analysis and (2) A combined principal components (CPC) analysis, with 13 microsatellites covering the whole chromosome 21. To identify the genetic variants associated with these traits, we performed family-based association analysis (FBAT) for the second principal component (PC2) using two microsatellites and 27 SNPs belonging to three candidate genes, located in the vicinity of the linkage peak. Univariate linkage analyses showed suggestive evidence of linkage to %FEV1 and SPTQ at two positions. Bivariate VC linkage analysis of these phenotypes led to an increase in linkage signals as compared to univariate analysis at the same positions. The peaks obtained by the CPC led to similar results as the full bivariate VC approach; evidence for linkage mainly coming from PC2. The strongest association signal, using single marker analysis for PC2, was obtained with D21S1252 (p=0.003 for global test; p=0.004 for allele 2 and p=0.001 for allele 11) and rs2834213 of IFNGR2 (p=0.003), these two loci being 3 Mb apart. Multi-marker analysis further strengthened this finding. These results indicate that at least two genetic factors may be involved in SPTQ and %FEV1 variability but further genotyping is needed to better understand these findings.

This research was supported by funding from the National Institutes of Health (US NCI R25T CA112355 and R01 CA201358; PI: Witte) and the Canadian Institute for Health Research (Foundation grant FDN 167273, PI: Hung; Canada Research Chair, PI: Hung). The OncoArray project was supported by NIH U19 CA203654 (MPI: Hung, Amos, Brennan, Lin). The Boston Lung Cancer Study was funded by NIH (NCI) U01CA209414 (PI: Christiani). The lung eQTL study at Laval University was supported by the Fondation de l’Institut universitaire de cardiologie et de pneumologie de Québec and the Canadian Institutes of Health Research (MOP − 123369). Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. The EAGLE study was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS. The Multiethnic Cohort Study is supported by National Institutes of Health (CA164973). The CARET study was supported by the National Institutes of Health/National Cancer Institute: UM1 CA167462 (PI: Goodman), U01 CA6367307 (PIs: Omen, Goodman); R01 CA111703 (PI: Chen), 5R01 CA151989-01A1 (PI: Doherty) and U01 CA167462 (PI: Chen). M.B.S. was supported in part by a Cancer Center Support Grant (P30 CA076292) and by NIH P50 CA119997. R.M.M. is supported by a CRUK programme grant, the Integrative Cancer Epidemiology Programme (C18281/A19169), and by the National Institute for Health Research (NIHR) Bristol Biomedical Research Centre based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol., Kachuri, L., Johansson, M., Rashkin, S.R., Graff, R.E., Bossé, Y., Manem, V., Caporaso, N.E., Landi, M.T., Christiani, D.C., Vineis, P., Liu, G., Scelo, G., Zaridze, D., Shete, S.S., Albanes, D., Aldrich, M.C., Tardón, A., Rennert, G., Chen, C., Goodman, G.E., Doherty, J.A., Bickeböller, H., Field, J.K., Davies, M.P., Dawn Teare, M., Kiemeney, L.A., Bojesen, S.E., Haugen, A., Zienolddiny, S., Lam, S., Le Marchand, L., Cheng, I., Schabath, M.B., Duell, E.J., Andrew, A.S., Manjer, J., Lazarus, P., Arnold, S., McKay, J.D., Emami, N.C., Warkentin, M.T., Brhane, Y., Obeidat, M., Martin, R.M., Relton, C., Davey Smith, G., Haycock, P.C., Amos, C.I., Brennan, P., Witte, J.S., Hung, R.J.

The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN (...), Ji, X., Mukherjee, S., Landi, M.T., Bosse, Y., Joubert, P., Zhu, D., Gorlov, I., Xiao, X., Han, Y., Gorlova, O., Hung, R.J., Brhane, Y., Carreras-Torres, R., Christiani, D.C., Caporaso, N., Johansson, M., Liu, G., Bojesen, S.E., Le Marchand, L., Albanes, D., Bickeböller, H., Aldrich, M.C., Bush, W.S., Tardon, A., Rennert, G., Chen, C., Byun, J., Dragnev, K.H., Field, J.K., Kiemeney, L.F., Lazarus, P., Zienolddiny, S., Lam, S., Schabath, M.B., Andrew, A.S., Bertazzi, P.A., Pesatori, A.C., Diao, N., Su, L., Song, L., Zhang, R., Leighl, N., Johansen, J.S., Mellemgaard, A., Saliba, W., Haiman, C., Wilkens, L., Fernandez-Somoano, A., Fernandez-Tardon, G., Heijden, E.H.F.M., Kim, J.H., Davies, M.P.A., Marcus, M.W., Brunnström, H., Manjer, J., Melander, O., Muller, D.C., Overvad, K., Trichopoulou, A., Tumino, R., Goodman, G.E., Cox, A., Taylor, F., Woll, P., Wichmann, E., Muley, T., Risch, A., Rosenberger, A., Grankvist, K., Johansson, M., Shepherd, F., Tsao, M.-S., Arnold, S.M., Haura, E.B., Bolca, C., Holcatova, I., Janout, V., Kontic, M., Lissowska, J., Mukeria, A., Ognjanovic, S., Orlowski, T.M., Scelo, G., Swiatkowska, B., Zaridze, D., Bakke, P., Skaug, V., Butler, L.M., Offit, K., Srinivasan, P., Bandlamudi, C., Hellmann, M.D., Solit, D.B., Robson, M.E., Rudin, C.M., Stadler, Z.K., Taylor, B.S., Berger, M.F., Houlston, R., McLaughlin, J., Stevens, V., Nickle, D.C., Obeidat, M., Timens, W., Artigas, M.S., Shete, S., Brenner, H., Chanock, S., Brennan, P., McKay, J.D., Amos, C.I.

Meta-analysis of 36,760 cases and 375,188 controls identifies 54 loci associated with susceptibility to cutaneous melanoma. Further analysis combining nevus count and hair color GWAS results provide insights into the genetic architecture of melanoma.Most genetic susceptibility to cutaneous melanoma remains to be discovered. Meta-analysis genome-wide association study (GWAS) of 36,760 cases of melanoma (67% newly genotyped) and 375,188 controls identified 54 significant (P < 5 x 10(-8)) loci with 68 independent single nucleotide polymorphisms. Analysis of risk estimates across geographical regions and host factors suggests the acral melanoma subtype is uniquely unrelated to pigmentation. Combining this meta-analysis with GWAS of nevus count and hair color, and transcriptome association approaches, uncovered 31 potential secondary loci for a total of 85 cutaneous melanoma susceptibility loci. These findings provide insights into cutaneous melanoma genetic architecture, reinforcing the importance of nevogenesis, pigmentation and telomere maintenance, together with identifying potential new pathways for cutaneous melanoma pathogenesis.

Introduction: Transplant-associated thrombotic microangiopathy (TA-TMA) is an increasingly recognized hematologic complication after allogeneic hematopoietic cell transplantation (HCT). While few studies have reported germline association with rare variants in complement genes using targeted next generation sequencing (NGS) method, they were limited by small sample size (≤40 TMA cases) and lack of analysis of non-complement genes (PMIDs 26603840, 32131130). In the present study, we employed whole exome sequencing (WES) to assess rare variant contribution to the development of TMA in a hypothesis-driven pathway-specific approach. Methods: In the current case-control genetic association study conducted at Fred Hutchinson Cancer Research Center, we selected 100 patients with a diagnosis of TMA and pre-transplant DNA samples (case definition described previously in PMID 30940363, 33836868). We then performed incidence density sampling to randomly select 100 non-TMA controls after allogeneic HCT matching by age, sex, race, and year of HCT. WES (germline variant detection 40x) was conducted using Illumina NovaSeq. Sequence reads were mapped to hg38 reference genome followed by deduplication and base quality score recalibration. Joint-genotyping was performed to call single nucleotide polymorphism (SNPs) and insertion/deletion (indels) using the GATK v3.3 and Atlas2. Variants were filtered during quality control (QC) and variant quality score recalibration (VQSR) and annotated using ANNOVAR and Ensembl VEP. To optimize signal detection by reducing neutral background variation, we defined qualifying variants as those meeting all 3 criteria: 1) novel or rare variants with a minor allele frequency (MAF) We then focused on the exome profiles of 5 a priori selected genetic pathways: complement regulation (17 genes), VWF and coagulation (7 genes), VWF clearance (10 genes), ADAMTS13 mimics or interacting proteins (10 genes), and angiopoietin family and endothelial activation (7 genes). Pathway-based and gene-based collapsing association tests were performed using the Optimized Sequence Kernel Association (SKAT-O) test as an optimal test combining burden test and SKAT. Results: After joint variant calling, 91 TMA cases and 93 non-TMA controls passed all QC filters (Table 1). Among 1,485 variants detected in the 5 pathways after QC, 60 variants (73 total mutations) were considered as qualifying variants with MAF Conclusion: Contrary to the initial hypothesis, we did not observe pathogenic germline rare variants in the complement regulation pathway in patients with TA-TMA. Instead, we found a significant association in the VWF clearance pathway, particularly that of the LRP1 gene. In recent years, researchers have shown that VWF can bind to and activate complement proteins. Impaired VWF clearance could lead to the higher predisposition for complement activation observed in patients with TA-TMA. Future functional studies are needed to determine the impact of VWF clearance on the pathogenesis of the disease. Figure 1 Figure 1. Disclosures Sartain: Alexon Pharamaceuticals: Membership on an entity's Board of Directors or advisory committees. Lee: Incyte: Research Funding; Janssen: Other; Takeda: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Kadmon: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees; Syndax: Research Funding; AstraZeneca: Research Funding; Amgen: Research Funding.

Quantitative trait loci (QTLs) are widely used as instruments to infer causal risk factors of diseases based on the idea of mendelian randomization. Plasma metabolites can serve as risk factors of cancer, and the heritability of many circulating metabolites was high. We conducted a metabolome-wide association study (MWAS) to systematically investigate the effects of genetic variants on metabolites and lung cancer based on published genome-wide association study (GWASs) and metabolic-QTL (mQTL) study. Then we confirmed the results by subsequent genetic and metabolic validations and inferred the causal relationship between identified metabolites and lung cancer through genetic variant(s). We firstly identified six polyunsaturated fatty acids (PUFAs) represented by rs174548-linked haplotype were significantly associated with lung cancer risk in a Chinese GWAS (2311 cases and 3077 controls). Rs174548 was further confirmed to be associated with lung cancer in 13 821 Europeans and 18 471 Asians (OR(meta) = 0.87, P(meta) = 1.76 × 10(–15)) and the effect was much stronger in females (P(interaction) = 6.00 × 10(–4)). We next validated rs174548-plasma PUFA association in 253 Chinese subjects (β = −0.57, P = 1.68 × 10(–3)). Rs174548 was also found associated with FADS1 (the major fatty acid desaturase of identified PUFAs) expression in liver tissues. Taken together, we found that rs174548 was associated with both PUFAs and lung cancer. Because rs174548 was the only mQTL variant of PUFAs reported by previous GWASs and explained a large proportion of heritability, we proposed that plasma PUFAs could be causally associated with lung cancer based on the idea of mendelian randomization. These findings provide a diet-related risk factor and may have important implications for prevention on lung cancer.

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 in 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

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 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. This article is protected by copyright. All rights reserved.

Objective To identify risk alleles relevant to the causal and biologic mechanisms of antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV). Methods A genome-wide association study and subsequent replication study were conducted in a total cohort of 1,986 cases of AAV (patients with granulomatosis with polyangiitis [Wegener's] [GPA] or microscopic polyangiitis [MPA]) and 4,723 healthy controls. Meta-analysis of these data sets and functional annotation of identified risk loci were performed, and candidate disease variants with unknown functional effects were investigated for their impact on gene expression and/or protein function. Results Among the genome-wide significant associations identified, the largest effect on risk of AAV came from the single-nucleotide polymorphism variants rs141530233 and rs1042169 at the HLA–DPB1 locus (odds ratio [OR] 2.99 and OR 2.82, respectively) which, together with a third variant, rs386699872, constitute a triallelic risk haplotype associated with reduced expression of the HLA–DPB1 gene and HLA–DP protein in B cells and monocytes and with increased frequency of complementary proteinase 3 (PR3)–reactive T cells relative to that in carriers of the protective haplotype. Significant associations were also observed at the SERPINA1 and PTPN22 loci, the peak signals arising from functionally relevant missense variants, and at PRTN3, in which the top-scoring variant correlated with increased PRTN3 expression in neutrophils. Effects of individual loci on AAV risk differed between patients with GPA and those with MPA or between patients with PR3-ANCAs and those with myeloperoxidase-ANCAs, but the collective population attributable fraction for these variants was substantive, at 77%. Conclusion This study reveals the association of susceptibility to GPA and MPA with functional gene variants that explain much of the genetic etiology of AAV, could influence and possibly be predictors of the clinical presentation, and appear to alter immune cell proteins and responses likely to be key factors in the pathogenesis of AAV.