A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

Genetic insights into the causal relationship between air pollutants and atrial fibrillation: a Mendelian randomization study

Previous observational studies reported associations between air pollutants and atrial fibrillation (AF), but their causal relationships remain unclear. We conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) summary data from the UK Biobank and IEU Open GWAS databases to investigate the genetic causality between air pollutants and AF. Air pollutants were subdivided into nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10), with single-nucleotide polymorphisms (SNPs) associated with each pollutant identified as instrumental variables (IVs). Subsequently, MR methods including MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode were applied to assess genetic causality, while pleiotropy, heterogeneity, and reliability were also evaluated. IVW findings indicate a consequential correlation between NO2 and increased AF risk, as evidenced by an odds ratio (OR 1.948 [95% confidence interval [CI] 1.011-3.756]; p = 0.046). Conversely, the causal effect of PM2.5 (OR 1.274 [95% CI 0.651-2.493; p = 0.480) and PM10 (OR 1.162 [95% CI 0.891-1.517]; p = 0.268) with AF was not statistically significant. The analysis revealed the absence of pleiotropy (p > 0.05). However, PM2.5 displayed significant heterogeneity (p = 0.2385), whereas NO2 (p = 0.5365) and PM10 (p = 0.7789) did not. This MR analysis suggested a causal effect of NO2 on AF, but not for PM2.5 or PM10.

Causal relationships between gut microbiota and male reproductive inflammation and infertility: Insights from Mendelian randomization

The study observed interactions between gut microbiota and male reproductive health, noting that the causal relationships were previously unclear. It aimed to explore the potential cause-and-effect relationship between gut bacteria and male reproductive problems such as inflammation, infertility, and sperm functionality, using a two-sample Mendelian randomization method to examine these connections. The analysis found that certain bacterial genera, such as Erysipelatoclostridium (0.71 [0.55-0.92]), Parasutterella (0.74 [0.57-0.96]), Ruminococcaceae UCG-009 (0.77 [0.60-0.98]), and Slackia (0.69 [0.49-0.96]), showed protective effects against prostatitis. In contrast, other genera like Faecalibacterium (1.59 [1.08-2.34]), Lachnospiraceae UCG004 (1.64 [1.15-2.34]), Odoribacter (1.68 [1.01-2.81]), Paraprevotella (1.28 [1.03-1.60]), and Sutterella (1.58 [1.13-2.19]) were detrimental. Additionally, causal relationships were identified between 2 genera and orchitis and epididymitis, 3 genera and male infertility, and 5 genera and abnormal spermatozoa. Further analysis of sperm-related proteins revealed causal associations between specific bacterial genera and proteins such as SPACA3, SPACA7, SPAG11A, SPAG11B, SPATA9, SPATA20, and ZPBP4. The results remained robust after sensitivity analysis and reverse Mendelian randomization analysis. The study concluded that specific bacterial genera have causal roles in reproductive inflammation, infertility, and sperm-associated proteins. This provides a novel strategy for the early diagnosis and identification of therapeutic targets in reproductive inflammation and infertility.

Causal effects of skin microbiota on intervertebral disk degeneration, low back pain and sciatica: a two-sample Mendelian randomization study

OBJECTIVE

The purpose of this study is to use two-sample Mendelian randomization (MR) to investigate the causal relationship between skin microbiota, especially Propionibacterium acnes, and intervertebral disc degeneration (IVDD), low back pain (LBP) and sciatica.

METHODS

We conducted a two-sample MR using the aggregated data from the whole genome-wide association studies (GWAS). 150 skin microbiota were derived from the GWAS catalog and IVDD, LBP and sciatica were obtained from the IEU Open GWAS project. Inverse-variance weighted (IVW) was the primary research method, with MR-Egger and Weighted median as supplementary methods. Perform sensitivity analysis and reverse MR analysis on all MR results and use multivariate MR to adjust for confounding factors.

RESULTS

MR revealed five skin microbiota associated with IVDD, four associated with LBP, and two with sciatica. Specifically, P.acnes in sebaceous skin environments were associated with reduced risk of IVDD; IVDD was found to increase the abundance of P.acnes in moist skin. Furthermore, ASV010 [Staphylococcus (unc.)] from dry skin was a risk factor for LBP and sciatica; ASV045 [Acinetobacter (unc.)] from dry skin and Genus Rothia from dry skin exhibited potential protective effects against LBP; ASV065 [Finegoldia (unc.)] from dry skin was a protective factor for IVDD and LBP. ASV054 [Enhydrobacter (unc.)] from moist skin, Genus Bacteroides from dry skin and Genus Kocuria from dry skin were identified as being associated with an increased risk of IVDD. Genus Streptococcus from moist skin was considered to be associated with an increased risk of sciatica.

CONCLUSIONS

This study identified a potential causal relationship between skin microbiota and IVDD, LBP, and sciatica. No evidence suggests skin-derived P.acnes is a risk factor for IVDD, LBP and sciatica. At the same time, IVDD can potentially cause an increase in P.acnes abundance, which supports the contamination theory.

Diabetes and osteoporosis: a two-sample mendelian randomization study

BACKGROUND

The effects on bone mineral density (BMD)/fracture between type 1 (T1D) and type 2 (T2D) diabetes are unknown. Therefore, we aimed to investigate the causal relationship between the two types of diabetes and BMD/fracture using a Mendelian randomization (MR) design.

METHODS

A two-sample MR study was conducted to examine the causal relationship between diabetes and BMD/fracture, with three phenotypes (T1D, T2D, and glycosylated hemoglobin [HbA1c]) of diabetes as exposures and five phenotypes (femoral neck BMD [FN-BMD], lumbar spine BMD [LS-BMD], heel-BMD, total body BMD [TB-BMD], and fracture) as outcomes, combining MR-Egger, weighted median, simple mode, and inverse variance weighted (IVW) sensitivity assessments. Additionally, horizontal pleiotropy was evaluated and corrected using the residual sum and outlier approaches.

RESULTS

The IVW method showed that genetically predicted T1D was negatively associated with TB-BMD (β = -0.018, 95% CI: -0.030, -0.006), while T2D was positively associated with FN-BMD (β = 0.033, 95% CI: 0.003, 0.062), heel-BMD (β = 0.018, 95% CI: 0.006, 0.031), and TB-BMD (β = 0.050, 95% CI: 0.022, 0.079). Further, HbA1c was not associated with the five outcomes (β ranged from - 0.012 to 0.075).

CONCLUSIONS

Our results showed that T1D and T2D have different effects on BMD at the genetic level. BMD decreased in patients with T1D and increased in those with T2D. These findings highlight the complex interplay between diabetes and bone health, suggesting potential age-specific effects and genetic influences. To better understand the mechanisms of bone metabolism in patients with diabetes, further longitudinal studies are required to explain BMD changes in different types of diabetes.

Osteoporosis and osteoarthritis: a bi-directional Mendelian randomization study

OBJECTIVE

To investigate the causal relationship between low bone mineral density (BMD) and osteoarthritis (OA) using Mendelian randomization (MR) design.

METHODS

Two-sample bi-directional MR analyses were performed using summary-level information on OA traits from UK Biobank and arcOGEN. Sensitivity analyses including MR-Egger, simple median, weighted median, MR pleiotropy residual sum, and outlier approaches were utilized in conjunction with inverse variance weighting (IVW). Gene ontology (GO) enrichment analyses and expression quantitative trait locus (eQTL) colocalization analyses were used to investigate the potential mechanism and shared genes between osteoporosis (OP) and OA.

RESULTS

The IVW method revealed that genetically predicted low femoral neck BMD was significantly linked with hip (β = 0.105, 95% CI: 0.023-0.188) and knee OA (β = 0.117, 95% CI: 0.049-0.184), but not with other site-specific OA. Genetically predicted low lumber spine BMD was significantly associated with OA at any sites (β = 0.048, 95% CI: 0.011-0.085), knee OA (β = 0.101, 95% CI: 0.045-0.156), and hip OA (β = 0.150, 95% CI: 0.077-0.224). Only hip OA was significantly linked with genetically predicted reduced total bone BMD (β = 0.092, 95% CI: 0.010-0.174). In the reverse MR analyses, no evidence for a causal effect of OA on BMD was found. GO enrichment analysis and eQTL analysis illustrated that DDN and SMAD-3 were the most prominent co-located genes.

CONCLUSIONS

These findings suggested that OP may be causally linked to an increased risk of OA, indicating that measures to raise BMD may be effective in preventing OA. More research is required to determine the underlying processes via which OP causes OA.

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

Exploration of Lung Cancer-Related Genetic Factors via Mendelian Randomization Method Based on Genomic and Transcriptomic Summarized Data

Lung carcinoma is one of the most deadly malignant tumors in mankind. With the rising incidence of lung cancer, searching for the high effective cures become more and more imperative. There has been sufficient research evidence that living habits and situations such as smoking and air pollution are associated with an increased risk of lung cancer. Simultaneously, the influence of individual genetic susceptibility on lung carcinoma morbidity has been confirmed, and a growing body of evidence has been accumulated on the relationship between various risk factors and the risk of different pathological types of lung cancer. Additionally, the analyses from many large-scale cancer registries have shown a degree of familial aggregation of lung cancer. To explore lung cancer-related genetic factors, Genome-Wide Association Studies (GWAS) have been used to identify several lung cancer susceptibility sites and have been widely validated. However, the biological mechanism behind the impact of these site mutations on lung cancer remains unclear. Therefore, this study applied the Summary data-based Mendelian Randomization (SMR) model through the integration of two GWAS datasets and four expression Quantitative Trait Loci (eQTL) datasets to identify susceptibility genes. Using this strategy, we found ten of Single Nucleotide Polymorphisms (SNPs) sites that affect the occurrence and development of lung tumors by regulating the expression of seven genes. Further analysis of the signaling pathway about these genes not only provides important clues to explain the pathogenesis of lung cancer but also has critical significance for the diagnosis and treatment of lung cancer.