A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.

BACKGROUND: Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS: We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION: In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Disparity analysis and prognostic value of pretreatment whole blood Epstein-Barr virus DNA load and Epstein-Barr encoding region status in lymphomas: A retrospective multicenter study in Huaihai Lymphoma Working Group.

Elevated Epstein-Barr virus (EBV) DNA load is common in lymphomas. However, it remains unclear whether the disparity in viral load and its prognostic value in lymphomas are correlated with Epstein-Barr encoding region (EBER) status. In this retrospective multicenter study, we collected the data of pretreatment whole blood EBV DNA (pre-EBV DNA) and EBER status and evaluated their disparity and prognostic values in lymphomas. A total of 454 lymphoma patients from December 2014 to August 2020 were retrospectively retrieved. Mann-Whitney U test, Kruskal-Wallis test and Bonferroni's adjustment were used to explore the disparity of EBV DNA and EBER status in lymphomas. Time-dependent receiver operating characteristic analysis and MaxStat analysis were used to determine optimal cutoff points of pre-EBV DNA load. Univariable and multivariable Cox proportional hazards models were established for the estimation of prognostic factors. The positive rate of EBV DNA in natural killer T-cell lymphoma (NKTL) patients was higher than that in diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and Hodgkin lymphoma (HL) patients, and the median positive pre-EBV copy number of NKTL was also higher than that of FL and DLBCL. EBV DNA could clearly distinguish the prognosis of DLBCL, NKTL, HL and peripheral T-cell lymphoma, and the integration of EBER status and EBV DNA could differentiate the prognosis of HL patients. Multivariable results revealed that pre-EBV DNA load had an effect on the prognosis of NKTL, FL and DLBCL. The status of pre-EBV DNA and EBER were disparate. Whole blood pre-EBV DNA predicted the prognosis of lymphomas, and the combination of EBV and EBER status could differentiate the prognosis of HL.

Relationship between birth weight and chronic kidney disease: evidence from systematics review and two-sample Mendelian randomization analysis.

Observational studies showed an inverse association between birth weight and chronic kidney disease (CKD) in adulthood existed. However, whether such an association is causal remains fully elusive. Moreover, none of prior studies distinguished the direct fetal effect from the indirect maternal effect. Herein, we aimed to investigate the causal relationship between birth weight and CKD and to understand the relative fetal and maternal contributions. Meta-analysis (n = ~22 million) showed that low birth weight led to ~83% (95% confidence interval [CI] 37-146%) higher risk of CKD in late life. With summary statistics from large scale GWASs (n = ~300 000 for birth weight and ~481 000 for CKD), linkage disequilibrium score regression demonstrated birth weight had a negative maternal, but not fetal, genetic correlation with CKD and several other kidney-function related phenotypes. Furthermore, with multiple instruments of birth weight, Mendelian randomization showed there existed a negative fetal casual association (OR = 1.10, 95% CI 1.01-1.16) between birth weight and CKD; a negative but non-significant maternal casual association (OR = 1.09, 95% CI 0.98-1.21) was also identified. Those associations were robust against various sensitivity analyses. However, no maternal/fetal casual effects of birth weight were significant for other kidney-function related phenotypes. Overall, our study confirmed the inverse association between birth weight and CKD observed in prior studies, and further revealed the shared maternal genetic foundation between low birth weight and CKD, and the direct fetal and indirect maternal causal effects of birth weight may commonly drive this negative relationship.

Value of the prognostic nutritional index (PNI) in patients with newly diagnosed, CD5-positive diffuse large B-cell lymphoma: A multicenter retrospective study of the Huaihai Lymphoma Working Group.

BACKGROUND: CD5-positive diffuse large B-cell lymphoma (DLBCL) is a clinically rare subtype of DLBCL with aggressive clinical manifestations and a poor prognosis. It has been demonstrated that the prognostic nutritional index (PNI), an indicator of nutritional status and systemic inflammation, is a significant prognostic factor for several types of lymphoma. The objective of this multicenter retrospective study was to explore the prognostic value of the PNI in patients with CD5-positive DLBCL. METHODS: In total, 207 patients with CD5-positive DLBCL were recruited from 11 centers of the Huaihai Lymphoma Working Group. Maximally selected rank statistics analysis was used to identify optimal cutoff points for the PNI. A Cox proportional hazards model was used for univariable and multivariable analyses. Kaplan-Meier curves were used to calculate survival rates and draw survival curves, and the log-rank test was used to compare differences between groups. RESULTS: The median age at diagnosis was 61 years, and the 5-year overall survival rate was 47.5%. According to the maximally selected rank statistics analysis, a score of 49.7 was the optimal cutoff point for the PNI. Subgroup analysis showed that the PNI could re-stratify patients in BCL-2-negative, MYC-negative, high-intermediate-risk and high-risk International Prognostic Index, BCL-6-positive and BCL-6-negative, high Ki-67 score (≥0.9), Ann Arbor stage III/IV, Eastern Cooperative Oncology Group performance status ≥2, and germinal center B subgroups. Multivariable analysis revealed that PNI, age, Eastern Cooperative Oncology Group performance status, albumin level, and red blood cell count were independent prognostic factors for CD5-positive DLBCL. CONCLUSIONS: The PNI was a significant prognostic indicator for CD5-positive DLBCL and was able to re-stratify the prognosis for clinicopathologic subgroups of patients with CD5-positive DLBCL.

IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.

Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.

Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations.

Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (rm) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (Nmax = 254,373) and European (Nmax = 1,220,901) populations. Among these phenotypes, rm was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics.

BACKGROUND: Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. METHODS: We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. RESULTS: We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. CONCLUSIONS: Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies.

BACKGROUND: Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. METHODS: We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. RESULTS: Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. CONCLUSION: IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.

Evaluation of the causal effects of blood lipid levels on gout with summary level GWAS data: two-sample Mendelian randomization and mediation analysis.

Observational studies have identified gout patients are often comorbid with dyslipidemia. However, the relationship between dyslipidemia and gout is still unclear. We first performed Mendelian randomization (MR) to evaluate the causal effect of four lipid traits on gout and serum urate based on publicly available GWAS summary statistics (n ~100,000 for lipid, 69,374 for gout and 110,347 for serum urate). MR showed each standard deviation (SD) (~12.26 mg/dL) increase in HDL resulted in about 25% (95% CI 9.0%-38%, p = 3.31E-3) reduction of gout risk, with 0.09 mg/dL (95% CI: -0.12 to -0.05, p = 7.00E-04) decrease in serum urate, and each SD (~112.33 mg/dL) increase of TG was associated with 0.10 mg/dL (95% CI: 0.06-0.14, p = 9.87E-05) increase in serum urate. Those results were robust against various sensitive analyses. Additionally, independent effects of HDL and TG on gout/serum urate were confirmed with multivariable MR. Finally, mediation analysis demonstrated HDL or TG could also indirectly affect gout via the pathway of serum urate. In conclusion, our study confirmed the causal associations between HDL (and TG) and gout, and further revealed the effect of HDL or TG on gout could also be mediated via serum urate.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

BACKGROUND: It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. METHODS: We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. RESULTS: The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2 ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2 ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. CONCLUSIONS: Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Likelihood ratio tests in rare variant detection for continuous phenotypes.

It is believed that rare variants play an important role in human phenotypes; however, the detection of rare variants is extremely challenging due to their very low minor allele frequency. In this paper, the likelihood ratio test (LRT) and restricted likelihood ratio test (ReLRT) are proposed to test the association of rare variants based on the linear mixed effects model, where a group of rare variants are treated as random effects. Like the sequence kernel association test (SKAT), a state-of-the-art method for rare variant detection, LRT and ReLRT can effectively overcome the problem of directionality of effect inherent in the burden test in practice. By taking full advantage of the spectral decomposition, exact finite sample null distributions for LRT and ReLRT are obtained by simulation. We perform extensive numerical studies to evaluate the performance of LRT and ReLRT, and compare to the burden test, SKAT and SKAT-O. The simulations have shown that LRT and ReLRT can correctly control the type I error, and the controls are robust to the weights chosen and the number of rare variants under study. LRT and ReLRT behave similarly to the burden test when all the causal rare variants share the same direction of effect, and outperform SKAT across various situations. When both positive and negative effects exist, LRT and ReLRT suffer from few power reductions compared to the other two competing methods; under this case, an additional finding from our simulations is that SKAT-O is no longer the optimal test, and its power is even lower than that of SKAT. The exome sequencing SNP data from Genetic Analysis Workshop 17 were employed to illustrate the proposed methods, and interesting results are described.

Maternal and fetal origins of offspring blood pressure: statistical analysis using genetic correlation and genetic risk score-based Mendelian randomization.

BACKGROUND: Epidemiological studies demonstrated that adverse in utero environment was associated with increased risk of offspring high blood pressure, by using birthweight as the proxy of maternal intrauterine exposure; however, the nature of such association remains less understood. METHODS: With maternal/fetal-specific summary statistics of birthweight (n = 297 356 for own birthweight and n = 210 248 for offspring birthweight) and summary statistics of blood pressure [i.e. systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP)] (n = 757 601), we evaluated the genetic correlation between fetal-specific birthweight and blood pressure using cross-trait linkage disequilibrium score regression, and next detected pleiotropic genes for them with a pleiotropy mapping method called mixture-adjusted intersect-union pleiotropy test. Furthermore, we conducted a genetic risk score (GRS)-based Mendelian randomization analysis in parent-offspring pairs (n = 6031) of the UK Biobank cohort, to assess the causal relation between maternal-specific GRS and blood pressure conditioning on fetal genotypes. RESULTS: We found fetal-specific birthweight had a negative genetic correlation with DBP (ρ^g = -0.174, P = 1.68 × 10-10), SBP (ρ^g = -0.198, P = 8.09 × 10-12), and PP (ρ^g = -0.152, P = 6.04 × 10-8), and detected 143, 137 and 135 pleiotropic genes shared between fetal-specific birthweight and PP, SBP and DBP, respectively. These genes often exhibited opposite genetic effects, and were more likely to be differentially expressed in pancreas, liver, heart, brain, whole blood and muscle skeletal tissues. A causal negative association of maternal-specific birthweight was identified with SBP (P = 2.20 × 10-2) and PP (P = 7.67 × 10-3) but not DBP (P = 0.396) in mother-offspring pairs, after accounting for the influence of fetal-specific GRS; and the two significant relations were robust against the horizontal pleiotropy of instruments and the confounding influence of gestational duration and preterm birth. However, these causal associations could not be detected in father-offspring pairs. CONCLUSIONS: This study revealed common genetic components underlying birthweight and blood pressure, and provided important insight into aetiology and early prevention of high blood pressure.

Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics.

Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.

Investigating the influence of breastfeeding on asthma in children under 12 years old in the UK Biobank.

Background: Childhood-onset asthma (COA) has become a major and growing problem worldwide and imposes a heavy socioeconomic burden on individuals and families; therefore, understanding the influence of early-life experiences such as breastfeeding on COA is of great importance for early prevention. Objectives: To investigate the impact of breastfeeding on asthma in children under 12 years of age and explore its role at two different stages of age in the UK Biobank cohort. Methods: A total of 7,157 COA cases and 158,253 controls were obtained, with information regarding breastfeeding, COA, and other important variables available through questionnaires. The relationship between breastfeeding and COA were examined with the logistic regression while adjusting for available covariates. In addition, a sibling analysis was performed on 398 pairs of siblings to explain unmeasured family factors, and a genetic risk score analysis was performed to control for genetic confounding impact. Finally, a power evaluation was conducted in the sibling data. Results: In the full cohort, it was identified that breastfeeding had a protective effect on COA (the adjusted odds ratio (OR)=0.875, 95% confidence intervals (CIs): 0.831~0.922; P=5.75×10-7). The impact was slightly pronounced in children aged 6-12 years (OR=0.852, 95%CIs: 0.794~0.914, P=7.41×10-6) compared to those aged under six years (OR=0.904, 95%CIs: 0.837~0.975, P=9.39×10-3), although such difference was not substantial (P=0.266). However, in the sibling cohort these protective effects were no longer significant largely due to inadequate samples as it was demonstrated that the power was only 23.8% for all children in the sibling cohort under our current setting. The protective effect of breastfeeding on COA was nearly unchanged after incorporating the genetic risk score into both the full and sibling cohorts. Conclusions: Our study offered supportive evidence for the protective effect of breastfeeding against asthma in children less than 12 years of age; however, sibling studies with larger samples were warranted to further validate the robustness our results against unmeasured family confounders. Our findings had the potential to encourage mothers to initiate and prolong breastfeeding.

A Novel Prognostic Index Model for Adult Hemophagocytic Lymphohistiocytosis: A Multicenter Retrospective Analysis in China.

Hemophagocytic lymphohistiocytosis (HLH) is an immune disorder with rapid progression and poor survival. Individual treatment strategy is restricted, due to the absence of precise stratification criteria. In this multicenter retrospective study, we aimed to develop a feasible prognostic model for adult HLH in China. A total of 270 newly diagnosed patients of adult HLH were retrieved from the Huaihai Lymphoma Working Group (HHLWG), of whom 184 from 5 medical centers served as derivation cohort, and 86 cases from 3 other centers served as validation cohort. X-Tile program and Maxstat analysis were used to identify optimal cutoff points of continuous variables; univariate and multivariate Cox analyses were used for variable selection, and the Kaplan-Meier curve was used to analyze the value of variables on prognosis. The C-index, Brier Score, and calibration curve were used for model validation. Multivariate analysis showed that age, creatinine, albumin, platelet, lymphocyte ratio, and alanine aminotransferase were independent prognostic factors. By rounding up the hazard ratios from 6 significant variables, a maximum of 9 points was assigned. The final scoring model of HHLWG-HPI was identified with four risk groups: low risk (≤3 pts), low-intermediate risk (4 pts), high-intermediate risk (5-6 pts), and high risk (≥7 pts), with 5-year overall survival rates of 68.5%, 35.2%, 21.3%, and 10.8%, respectively. The C-indexes were 0.796 and 0.758 in the derivation and validation cohorts by using a bootstrap resampling program. In conclusion, the HHLWG-HPI model provides a feasible and accurate stratification system for individualized treatment strategy in adult HLH.

New novel non-MHC genes were identified for cervical cancer with an integrative analysis approach of transcriptome-wide association study.

Although genome-wide association studies (GWAS) have successfully identified multiple genetic variants associated with cervical cancer, the functional role of those variants is not well understood. To bridge such gap, we integrated the largest cervical cancer GWAS (N = 9,347) with gene expression measured in six human tissues to perform a multi-tissue transcriptome-wide association study (TWAS). We identified a total of 20 associated genes in the European population, especially four novel non-MHC genes (i.e. WDR19, RP11-384K6.2, RP11-384K6.6 and ITSN1). Further, we attempted to validate our results in another independent cervical cancer GWAS from the East Asian population (N = 3,314) and re-discovered four genes including WDR19, HLA-DOB, MICB and OR2B8P. In our subsequent co-expression analysis, we discovered SLAMF7 and LTA were co-expressed in TCGA tumor samples and showed both WDR19 and ITSN1 were enriched in "plasma membrane". Using the protein-protein interaction analysis we observed strong interactions between the proteins produced by genes that are associated with cervical cancer. Overall, our study identified multiple candidate genes, especially four non-MHC genes, which may be causally associated with the risk of cervical cancer. However, further investigations with larger sample size are warranted to validate our findings in diverse populations.

Evaluating Causal Relationship Between Metabolites and Six Cardiovascular Diseases Based on GWAS Summary Statistics.

Cardiovascular diseases (CVDs) remain the main cause of morbidity and mortality worldwide. The pathological mechanism and underlying biological processes of these diseases with metabolites remain unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of metabolites on these diseases by making full use of the latest GWAS summary statistics for 486 metabolites and six major CVDs. Extensive sensitivity analyses were implemented to validate our MR results. We also conducted linkage disequilibrium score regression (LDSC) and colocalization analysis to investigate whether MR findings were driven by genetic similarity or hybridization between LD and disease-associated gene loci. We identified a total of 310 suggestive associations across all metabolites and CVDs, and finally obtained four significant associations, including bradykinin, des-arg(9) (odds ratio [OR] = 1.160, 95% confidence intervals [CIs]: 1.080-1.246, false discovery rate [FDR] = 0.022) on ischemic stroke, N-acetylglycine (OR = 0.946, 95%CIs: 0.920-0.973, FDR = 0.023), X-09026 (OR = 0.845, 95%CIs: 0.779-0.916, FDR = 0.021) and X-14473 (OR = 0.938, 95%CIs = 0.907-0.971, FDR = 0.040) on hypertension. Sensitivity analyses showed that these causal associations were robust, the LDSC and colocalization analyses demonstrated that the identified associations were unlikely confused by LD. Moreover, we identified 15 important metabolic pathways might be involved in the pathogenesis of CVDs. Overall, our work identifies several metabolites that have a causal relationship with CVDs, and improves our understanding of the pathogenesis and treatment strategies for these diseases.

Type 2 Diabetes Mellitus and Amyotrophic Lateral Sclerosis: Genetic Overlap, Causality, and Mediation.

CONTEXT: Understanding phenotypic connection between type II diabetes (T2D) mellitus and amyotrophic lateral sclerosis (ALS) can offer valuable sight into shared disease etiology and have important implication in drug repositioning and therapeutic intervention. OBJECTIVE: This work aims to disentangle the nature of the inverse relationship between T2D mellitus and ALS. METHODS: Depending on summary statistics of T2D (n = 898 130) and ALS (n = 80 610), we estimated the genetic correlation between them and prioritized pleiotropic genes through a multiple-tissue expression quantitative trait loci-weighted integrative analysis and the conjunction conditional false discovery rate (ccFDR) method. We implemented mendelian randomization (MR) analyses to evaluate the causal relationship between the 2 diseases. A mediation analysis was performed to assess the mediating role of T2D in the pathway from T2D-related glycemic/anthropometric traits to ALS. RESULTS: We found supportive evidence of a common genetic foundation between T2D and ALS (rg = -0.223, P = .004) and identified 8 pleiotropic genes (ccFDR < 0.10). The MR analyses confirmed that T2D exhibited a neuroprotective effect on ALS, leading to an approximately 5% (95% CI, 0% ~ 9.6%, P = .038) reduction in disease risk. In contrast, no substantial evidence was observed that supported the causal influence of ALS on T2D. The mediation analysis revealed T2D can also serve as an active mediator for several glycemic/anthropometric traits, including high-density lipoprotein cholesterol, overweight, body mass index, obesity class 1, and obesity class 2, with the mediation effect estimated to be 0.024, -0.022, -0.041, -0.016, and -0.012, respectively. CONCLUSION: We provide new evidence supporting the observed inverse link between T2D and ALS, and revealed that a shared genetic component and causal association commonly drove such a relationship. We also demonstrate the mediating role of T2D standing in the pathway from T2D-related glycemic/anthropometric traits to ALS.

Assessing Causal Relationship Between Human Blood Metabolites and Five Neurodegenerative Diseases With GWAS Summary Statistics.

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs. Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer's Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson's disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways. Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as "urea cycle" (P = 0.036), "arginine biosynthesis" (P = 0.004) on AD and "phenylalanine, tyrosine and tryptophan biosynthesis" (P = 0.046) on ALS. Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

The Value of Prognostic Nutritional Index (PNI) on Newly Diagnosed Diffuse Large B-Cell Lymphoma Patients: A Multicenter Retrospective Study of HHLWG Based on Propensity Score Matched Analysis.

INTRODUCTION: Immunonutritional status is associated with the survival of DLBCL. This multicenter retrospective study aimed to explore the prognostic value of Prognostic Nutrition Index (PNI) in DLBCL patients by using propensity score matched analysis (PSM). METHODS: A total of 990 DLBCL cases were recruited from 5 centers of Huaihai Lymphoma Working Group (HHLWG). A 1:1 PSM analysis was performed using the nearest-neighbor method, with a caliper size of 0.02. Cox regression analysis was used to examine factors associated with survival. RESULTS: The median age at diagnosis was 62 years and 52.5% were males, with the 3-y overall survival of 65.1%. According to the MaxStat analysis, 44 was the optimal cut-off point of PNI. After PSM analysis, a total of 282 patients in PNI < 44 group could be propensity matched to PNI ≥ 44 patients, creating a group of 564 patients. Multivariable analysis revealed that PNI, age, central nervous system involvement and International Prognostic Index (IPI) were independent prognostic factors for DLBCL. Kaplan-Meier analysis indicated that patients with low PNI in Ann Arbor Stage (III/VI), ECOG (<2), IPI (LR+LIR), GCB, and BCL-2 negative groups had a poor prognosis. DISCUSSION: PNI could accurately stratify the prognosis of DLBCL after PSM analysis.