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One of the central activities of software product line development is variability modeling for a product family. Because variability models are needed at various stages of software product line development, determining whether a variability model has been modeled correctly is an essential activity for successful software product line development. Existing studies proposed various methods for analysis of various aspects of correctness of a variability model. In particular, analyzing whether a variability model is consistent or not is considered the most important analysis perspective since it is impossible to configure products from such a model. There are few studies in the software product line field that locate causes of inconsistency in a variability model. Furthermore, these existing methods cannot locate the exact causes of inconsistency due to the fact that the feature model they are based on allows ambiguity in its parent–child relationship or due to the fact that they are designed to produce explanations rather than locations of causes, resulting in producing long and complex explanations as the size of the feature model increases. In this work, we propose a method that determines whether or not a variability model has an inconsistency and identifies the exact locations of its causes if it has an inconsistency. To evaluate the proposed method, we developed a tool that automatically performs all the steps of the method and used it to conduct experiments with 49 models, including real-world variability models. As a result, the proposed method accurately identified all models with an inconsistency and located all causes of inconsistency in them.

Christopher Amos and colleagues perform genome-wide association analysis for lung cancer using cohorts genotyped on the OncoArray and combing these with existing data. They identify 18 loci, 10 of which are new, finding heterogeneity across the different lung cancer subtypes, and explore candidate genes through eQTL analysis in lung tissue. Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2 , SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2 , and the telomere-related genes OFBC1 and RTEL1 . Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer.

Smoking may be associated with an increased risk of lung metastasis in cancers of non-lung origin. We leverage survey and electronic health record data from the diverse All of Us Research Program (AoURP) database to investigate whether smoking and smoking-related behaviors increase the risk of lung metastasis in non-lung primary cancers. The results suggest that cigarette use, measured by four continuous variables, does not increase the risk of lung metastasis in seven common cancer types but demonstrates a small significant effect in a cohort including all types of cancer in the database in both univariable and multivariable analyses. An increased odds ratio of electronic smoke use in patients with lung metastasis was seen in multivariable analyses of the all cancer (OR = 1.29, 95% CI = 1.04–1.59, P  = 0.02) and liver cancer (OR = 1.57, 95% CI = 1.06–2.28, P  = 0.02) groups. After adjusting for estimated cigarette pack years in the multivariable model, the result remained significant for liver cancer (OR = 1.60, 95% CI = 1.02–2.47, P  = 0.04) but not the all cancer cohort. These results warrant further inquiry and suggest that smoking and e-cigarettes may be associated with lung metastasis risk in patients with non-lung tumors.

Richard Houlston, Maria Teresa Landi and colleagues report the identification of large-effect associations for squamous lung cancer with rare variants in BRCA2 and CHEK2 . We conducted imputation to the 1000 Genomes Project of four genome-wide association studies of lung cancer in populations of European ancestry (11,348 cases and 15,861 controls) and genotyped an additional 10,246 cases and 38,295 controls for follow-up. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants BRCA2 p.Lys3326X (rs11571833, odds ratio (OR) = 2.47, P = 4.74 × 10 −20 ) and CHEK2 p.Ile157Thr (rs17879961, OR = 0.38, P = 1.27 × 10 −13 ). We also showed an association between common variation at 3q28 ( TP63 , rs13314271, OR = 1.13, P = 7.22 × 10 −10 ) and lung adenocarcinoma that had been previously reported only in Asians. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants with substantive effects on cancer risk from preexisting genome-wide association study data.

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 cross-ancestry genome-wide association meta-analysis of lung cancer including 61,047 cases and 947,237 controls identifies five new cross-ancestry susceptibility loci and highlights ancestry-specific effects of common and rare variants on lung cancer risk.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects ( n =2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort ( n =3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC ( P combined <5 × 10 −8 ) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine–cytokine pathways, for which relevant therapies exist. Primary biliary cirrhosis is an autoimmune liver disease with poor therapeutic options. Here Cordell et al . a perform meta-analysis of European genome-wide association studies identifying six novel risk loci and a number of potential therapeutic pathways.

This study examined individual-level determinants of self-reported changes in healthy (diet and physical activity) and addictive (alcohol use, smoking, and vaping) lifestyle behaviors during the initial COVID-19 lockdown period in the USA. A national online survey was administered between May and June 2020 that targeted a representative U.S. sample and yielded data from 1276 respondents, including 58% male and 50% racial/ethnic minorities. We used univariate and multivariable linear regression models to examine the associations of sociodemographic, mental health, and behavioral determinants with self-reported changes in lifestyle behaviors. Some study participants reported increases in healthy lifestyle behaviors since the pandemic (i.e., 36% increased healthy eating behaviors, and 33% increased physical activity). However, they also reported increases in addictive lifestyle behaviors including alcohol use (40%), tobacco use (41%), and vaping (46%). With regard to individual-level determinants, individuals who reported adhering to social distancing guidelines were also more likely to report increases in healthy lifestyle behaviors (β = 0.12, 95% CI 0.04 to 0.21). Conversely, women (β = −0.37, 95% CI −0.62 to −0.12), and unemployed individuals (β = −0.33, 95% CI −0.64 to −0.02) were less likely to report increases in healthy lifestyle behaviors. In addition, individuals reporting anxiety were more likely to report increases in addictive behaviors (β = 0.26, 95% CI 0.09 to 0.43). Taken together, these findings suggest that women and unemployed individuals may benefit from interventions targeting diet and physical activity, and that individuals reporting anxiety may benefit from interventions targeting smoking and alcohol cessation to address lifestyle changes during the pandemic.

The COVID-19 pandemic has produced broad clinical manifestations, from asymptomatic infection to hospitalization and death. Despite progress from genomic and clinical epidemiology research, risk factors for developing severe COVID-19 are incompletely understood and identification of modifiable risk factors is desperately needed. We conducted linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlation between COVID-19 severity and various polygenic phenotypes. To attenuate the genetic contribution of smoking and BMI, we further conducted sensitivity analyses by pruning genomic regions associated with smoking/BMI and repeating LDSR analyses. We identified robust positive associations between the genetic architecture of severe COVID-19 and both BMI and smoking. We observed strong positive genetic correlation (rg) with diabetes (rg = 0.25) and shortness of breath walking on level ground (rg = 0.28) and novel protective associations with vitamin E (rg = − 0.53), calcium (rg = − 0.33), retinol (rg = − 0.59), Apolipoprotein A (rg = − 0.13), and HDL (rg = − 0.17), but no association with vitamin D (rg = − 0.02). Removing genomic regions associated with smoking and BMI generally attenuated the associations, but the associations with nutrient biomarkers persisted. This study provides a comprehensive assessment of the shared genetic architecture of COVID-19 severity and numerous clinical/physiologic parameters. Associations with blood and plasma-derived traits identified biomarkers for Mendelian randomization studies to explore causality and nominates therapeutic targets for clinical evaluation.

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis -regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function. Multiple genetic loci are associated with lung cancer risk, but the underlying genetic mechanisms remain poorly understood. Here, the authors perform single-cell RNA-seq and ATAC-seq analyses of lung cells from ever- and never-smokers; they report candidate cis-regulatory elements that colocalise with candidate causal variants in lung cancer risk loci and potential susceptibility genes.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer ( r g  = 0.57, p  = 4.6 × 10 −8 ), breast and ovarian cancer ( r g  = 0.24, p  = 7 × 10 −5 ), breast and lung cancer ( r g  = 0.18, p  =1.5 × 10 −6 ) and breast and colorectal cancer ( r g  = 0.15, p  = 1.1 × 10 −4 ). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis. Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.