Blood Calcium, Genetic Risk, and Risk of Incident Kidney Stone: A Population-Based Cohort Study.

OBJECTIVE: To investigate the association between blood calcium concentration and incident kidney stone as well as to assess the role played by genetic susceptibility. METHODS: We performed a population-based cohort study based on participants from the UK Biobank. A multivariable Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% CIs of incident kidney stone for blood calcium level and polygenic risk score (PRS). In addition, the potential interaction was explored. The study was conducted from January 28, 2023, through June 4, 2023. RESULTS: During the follow-up of 423,301 participants with a total of 5,490,332 person-years (median follow-up of 13.4 years), 4502 cases of kidney stone were recorded. Compared with the low blood calcium concentration group (first tertile), individuals in the high (third tertile) and moderate (second tertile) concentration groups had higher risks of kidney stone with HRs of 1.24 (95% CI, 1.15 to 1.33) and 1.11 (1.04 to 1.20), respectively. The PRS for kidney stone contained 40 independent single-nucleotide polymorphisms and was used to assign individuals to 3 groups according to the quintile. Participants with high (Q5) and moderate (Q2 to Q4) genetic risks had increased risks of kidney stone compared with low (Q1) genetic risk with HRs of 1.70 (1.53 to 1.89) and 1.31 (1.20 to 1.44), respectively. There was a joint cumulative risk of incident kidney stone between blood calcium concentration and genetic susceptibility. CONCLUSIONS: Blood calcium concentration and PRS are significantly associated with incident kidney stone risk. Excessive blood calcium concentration might bring additional stone risk in populations at high genetic risk. A nonlinear correlation between blood calcium concentration and kidney stone risk was indicated.

Associations of prior pulmonary tuberculosis with the incident COPD: a prospective cohort study.

BACKGROUND: Prior pulmonary tuberculosis (PTB) might be associated with the development of chronic obstructive pulmonary disease (COPD). However, the impact of prior PTB on the risk of incident COPD has not been studied in a large prospective cohort study of the European population. OBJECTIVES: This study aimed to investigate the association of prior PTB with the risk of COPD. DESIGN: Prospective cohort study. METHODS: A multivariable Cox proportional model was used to estimate the hazard ratio (HR) and 95% confidence interval (95% CI) for the association of prior PTB with COPD. Subgroup analyses were further conducted among individuals stratified by age, sex, body mass index, smoking status, drinking status, physical activity, and polygenic risk score (PRS). RESULTS: The study involved a total of 216,130 participants, with a median follow-up period of 12.6 years and 2788 incident cases of COPD. Individuals with a prior history of PTB at baseline had an 87% higher risk of developing incident COPD compared to those without such history [adjusted hazard ratio (aHR) = 1.87; 95% confidence interval (CI): 1.26-2.77; p = 0.002]. Subgroup analysis revealed that individuals having prior PTB history presented a higher risk of incident COPD among individuals who were aged from 50 to 59 years with aHR of 2.47 (1.02-5.95, p = 0.044), older than 59 years with aHR of 1.81 (1.16-2.81, p = 0.008), male with aHR of 2.37 (1.47-3.83, p < 0.001), obesity with aHR of 3.35 (2.16-5.82, p < 0.001), previous smoking with aHR of 2.27 (1.39-3.72, p < 0.001), current drinking with aHR of 1.98 (1.47-3.83, p < 0.001), low physical activity with aHR of 2.62 (1.30-5.26, p = 0.007), and low PRS with aHR of 3.24 (1.61-6.53, p < 0.001), as well as high PRS with aHR of 2.43 (1.15-5.14, p = 0.019). CONCLUSION: A history of PTB is an important independent risk factor for COPD. Clinical staff should be aware of this risk factor in patients with prior PTB, particularly in countries or regions with high burdens of PTB.

Associations of prior pulmonary tuberculosis with the incident COPDPrior pulmonary tuberculosis (PTB) indicates that an individual has a history of PTB. The impact of prior PTB on the risk of incident chronic obstructive pulmonary disease (COPD) has not been studied in a large prospective cohort study of European population. Here, we investigated the association between prior PTB and risk of COPD in 216,130 participants from the UK biobank (a large biomedical database). After a median follow up of more than 12 years, 2,788 incident COPD cases were recorded. Individuals with prior PTB at baseline had an 87% higher risk of developing incident COPD compared to those without history of PTB. Specifically, individuals with prior PTB presented with a higher risk of incident COPD among those who were older than 50 years, male, obese, had a previous history of smoking, are currently drinking, have low physical activity, and have a low and high genetic predicted lung function. This study suggested prior PTB as an important and independent risk factor for COPD. Clinical staff should be aware of this risk factor in patients with prior PTB, particularly in countries or regions with high burdens of PTB.

Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture.

Kidney stone disease (KSD) is a complex disorder with high heritability and prevalence. We performed a large genome-wide association study (GWAS) meta-analysis for KSD to date, including 720,199 individuals with 17,969 cases in European population. We identified 44 susceptibility loci, including 28 novel loci. Cell type-specific analysis pinpointed the proximal tubule as the most relevant cells where susceptibility variants might act through a tissue-specific fashion. By integrating kidney-specific omics data, we prioritized 223 genes which strengthened the importance of ion homeostasis, including calcium and magnesium in stone formation, and suggested potential target drugs for the treatment. The genitourinary and digestive diseases showed stronger genetic correlations with KSD. In this study, we generate an atlas of candidate genes, tissue and cell types involved in the formation of KSD. In addition, we provide potential drug targets for KSD treatment and insights into shared regulation with other diseases.

How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis.

Background: A greatly growing body of literature has revealed the mediating role of DNA methylation in the influence path from childhood maltreatment to psychiatric disorders such as post-traumatic stress disorder (PTSD) in adult. However, the statistical method is challenging and powerful mediation analyses regarding this issue are lacking. Methods: To study how the maltreatment in childhood alters long-lasting DNA methylation changes which further affect PTSD in adult, we here carried out a gene-based mediation analysis from a perspective of composite null hypothesis in the Grady Trauma Project (352 participants and 16,565 genes) with childhood maltreatment as exposure, multiple DNA methylation sites as mediators, and PTSD or its relevant scores as outcome. We effectively addressed the challenging issue of gene-based mediation analysis by taking its composite null hypothesis testing nature into consideration and fitting a weighted test statistic. Results: We discovered that childhood maltreatment could substantially affected PTSD or PTSD-related scores, and that childhood maltreatment was associated with DNA methylation which further had significant roles in PTSD and these scores. Furthermore, using the proposed mediation method, we identified multiple genes within which DNA methylation sites exhibited mediating roles in the influence path from childhood maltreatment to PTSD-relevant scores in adult, with 13 for Beck Depression Inventory and 6 for modified PTSD Symptom Scale, respectively. Conclusion: Our results have the potential to confer meaningful insights into the biological mechanism for the impact of early adverse experience on adult diseases; and our proposed mediation methods can be applied to other similar analysis settings.

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.

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing.

BACKGROUND: Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. METHODS: By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. RESULTS: Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. CONCLUSION: Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

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/ .

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.

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.

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.

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.

Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.

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.

Are blood lipids risk factors for fracture? Integrative evidence from instrumental variable causal inference and mediation analysis using genetic data.

BACKGROUND: The relationship between lipids and the risk of fracture is currently controversial and whether such association is causal remains elusive. METHODS: We performed two-sample inverse variance weighted (IVW) Mendelian randomization (MR) analyses to evaluate causal effects of four lipids (i.e. high-density lipoprotein cholesterol [HDL], low-density lipoprotein cholesterol [LDL], total cholesterol [TC] and triglyceride [TG]) on fracture or bone mineral density (BMD) with summary statistics from large scale genome-wide association studies (up to ~190,000 for lipids, ~66,628 for BMD and ~53,000 for fracture). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. Multivariable analyses were implemented to investigate whether other lipids (i.e. LDL and TG) may confound the causal effect of HDL on fracture and mediation analyses were conducted to assess indirect effects of lipids on fracture mediated by BMD. RESULTS: The IVW MR showed there existed a statistically significant association between HDL and fracture, with the odd ratio (OR) per standard deviation change of HDL on fracture being 1.12 (95% CI: 1.02-1.22, p = 1.20E-02). HDL was also detected to be causally associated with BMD (beta = -0.116; 95% CI: -0.182 ~ -0.050, p = 5.47E-04). These associations were further confirmed by the weighted median and maximum likelihood methods, with the MR-Egger regression removing the possibility of pleiotropy and the multivariable analysis excluding the confounding effect of other lipids on HDL. Negative associations of HDL with BMD among the elderly and with BMD at the lumbar spine were also discovered. However, no causal associations were detected between other lipids (OR = 0.87, 95% CI: 0.74-1.03, p = .107 for LDL; OR = 1.03; 95% CI: 0.88-1.21, p = .696 for TC and OR = 1.04; 95% CI: 0.90-1.20, p = .610 for TG) and fracture; whereas TG was positively associated BMD (beta = 0.184; 95% CI: 0.048-0.319, p = 7.93E-03). Finally, the mediation effect of BMD was estimated to be -0.116 (95% CI: -0.182 to -0.05, p = 5.47E-04) for HDL or 0.184 (95% CI: 0.048-0.319, p = 7.93E-03) for TG, implying HDL and TG could be indirectly associated with fracture risk via the pathway of BMD. CONCLUSION: Our study is supportive of the causal relationship between HDL and fracture but offers little direct evidence for causal associations between other lipids and fracture, and further reveals HDL and TG may have an indirect influence on fracture mediated by BMD.