Shared genetic architecture between autoimmune disorders and B-cell acute lymphoblastic leukemia: insights from large-scale genome-wide cross-trait analysis.

BACKGROUND: To study the shared genetic structure between autoimmune diseases and B-cell acute lymphoblastic leukemia (B-ALL) and identify the shared risk loci and genes and genetic mechanisms involved. METHODS: Based on large-scale genome-wide association study (GWAS) summary-level data sets, we observed genetic overlaps between autoimmune diseases and B-ALL, and cross-trait pleiotropic analysis was performed to detect shared pleiotropic loci and genes. A series of functional annotation and tissue-specific analysis were performed to determine the influence of pleiotropic genes. The heritability enrichment analysis was used to detect crucial immune cells and tissues. Finally, bidirectional Mendelian randomization (MR) methods were utilized to investigate the casual associations. RESULTS: Our research highlighted shared genetic mechanisms between seven autoimmune disorders and B-ALL. A total of 73 pleiotropic loci were identified at the genome-wide significance level (P < 5 × 10-8), 16 of which had strong evidence of colocalization. We demonstrated that several loci have been previously reported (e.g., 17q21) and discovered some novel loci (e.g., 10p12, 5p13). Further gene-level identified 194 unique pleiotropic genes, for example IKZF1, GATA3, IKZF3, GSDMB, and ORMDL3. Pathway analysis determined the key role of cellular response to cytokine stimulus, B cell activation, and JAK-STAT signaling pathways. SNP-level and gene-level tissue enrichment suggested that crucial role pleiotropic mechanisms involved in the spleen, whole blood, and EBV-transformed lymphocytes. Also, hyprcoloc and stratified LD score regression analyses revealed that B cells at different developmental stages may be involved in mechanisms shared between two different diseases. Finally, two-sample MR analysis determined causal effects of asthma and rheumatoid arthritis on B-ALL. CONCLUSIONS: Our research proved shared genetic architecture between autoimmune disorders and B-ALL and shed light on the potential mechanism that might involve in.

Predicting adverse pregnancy outcomes of pregnant mothers with syphilis based on a logistic regression model: a retrospective study.

Objective: Maternal syphilis could cause serious consequences. The aim of this study was to identify risk factors for maternal syphilis in order to predict an individual's risk of developing adverse pregnancy outcomes (APOs). Methods: A retrospective study was conducted on 768 pregnant women with syphilis. A questionnaire was completed and data analyzed. The data was divided into a training set and a testing set. Using logistic regression to establish predictive models in the training set, and its predictive performance was evaluated in the testing set. The probability of APOs occurrence is presented through a nomogram. Results: Compared with the APOs group, pregnant women in the non-APOs group participated in a longer treatment course. Course, time of the first antenatal care, gestation week at syphilis diagnosis, and gestation age at delivery in weeks were independent predictors of APOs, and they were used to establish the nomogram. Conclusions: Our study investigated the impact of various characteristics of syphilis pregnant women on pregnancy outcomes and established a prediction model of APOs in Suzhou. The incidence of APOs can be reduced by controlling for these risk factors.

Air pollution, genetic factors and the risk of osteoporosis: A prospective study in the UK biobank.

Purpose: To reveal relationship between air pollution exposure and osteoporosis (OP) risk. Methods: Based on large-scale data from the UK Biobank, we evaluated the relationship between OP risk and several air pollutants. Then air pollution scores (APS) were constructed to assess the combined effects of multiple air pollutants on OP risk. Finally, we constructed a genetic risk score (GRS) based on a large genome-wide association study of femoral neck bone mineral density and assessed whether single or combined exposure to air pollutants modifies the effect of genetic risk on OP and fracture risk. Results: PM2.5, NO2, NOx, and APS were significantly associated with an increased risk of OP/fracture. OP and fracture risk raised with increasing concentrations of air pollutants: compared to the lowest APS quintile group, subjects in the highest quintile group had a hazard ratio (HR) (95% CI) estimated at 1.140 (1.072-1.213) for OP and 1.080 (1.026-1.136) for fracture. Moreover, participants with low GRS and the highest air pollutant concentration had the highest risk of OP, the HRs (95% CI) of OP were 1.706 (1.483-1.964), 1.658 (1.434-1.916), 1.696 (1.478-1.947), 1.740 (1.506-2.001) and 1.659 (1.442-1.908), respectively, for PM2.5, PM10, PM2.5-10, NO2, and NOx. Similar results were also observed for fractures. Finally, we assessed the joint effect of APS and GRS on the risk of OP. Participants with higher APS and lower GRS had a higher risk of developing OP. Similar results were observed in the joint effect of GRS and APS on fracture. Conclusions: We found that exposure to air pollution, individually or jointly, could improve the risk of developing OP and fractures, and increased the risk by interacting with genetic factors.

Body surface area is a potential obesity index: Its genetic determination and its causality for later-life diseases.

OBJECTIVE: This study aimed to identify novel genetic factors that contribute to body surface area (BSA) and explore its relationship with complex traits and diseases. METHODS: Based on more than 330,000 European individuals in the UK Biobank, the first large-scale genome-wide association study for BSA was performed. Comprehensive genetic analysis and enrichment analysis were then performed to explore the biological function of the identified loci. The genetic correlations and causal associations between BSA and other anthropometry parameters, early growth indices, and later-life diseases, respectively, were assessed by complex genetic approaches. RESULTS: Genome-wide association study analysis identified a total of 456 conditionally independent single-nucleotide polymorphism mapping genes with known functions in the regulation of adipogenesis and metabolism and enriched in adipogenesis-related pathways. BSA was highly genetically correlated with obesity phenotypes, and all the studied anthropometry parameters from the UK Biobank were significantly positively associated with BSA. BSA was phenotypically associated with 13 chronic diseases and genetically associated with 6 diseases. Mendelian randomization analyses showed that BSA has a causal effect in increasing the risk of some diseases. CONCLUSIONS: These findings increase understanding of genetic determinants for BSA and its relationship with complex traits and diseases, and BSA could be regarded as a potential obesity trait.

Causal effects and immune cell mediators between prescription analgesic use and risk of infectious diseases: a Mendelian randomization study.

Introduction: Clinical observations have found that prolonged use of analgesics increases the incidence of infection. However, the direct causal relationship between prescription analgesic use (PAU) and risk of infection (ROI) remains unclear. Methods: This study used Mendelian randomization (MR) design to estimate the causal effect of PAU on ROI, as well as their mediating factors. Genetic data on prescription analgesics use and immune cells were obtained from published GWAS. Additionally, data on ROI were extracted from the FinnGen database. Two-sample MR analysis and multivariate MR (MVMR) analysis were performed using inverse variance weighting (IVW) to ascertain the causal association between PAU and ROI. Finally, 731 immune cell phenotypes were analyzed for their mediating role between analgesics and infection. Results: Using two-sample MR, IVW modeling showed that genetically predicted opioid use was associated with increased risk of pulmonary infection (PI) (OR = 1.13, 95% CI: 1.05-1.21, p< 0.001) and upper respiratory infection (URI) (OR = 1.18, 95% CI: 1.08-1.30, p< 0.001); non-steroidal anti-inflammatory drugs (NSAIDs) were related to increased risk of skin and subcutaneous tissue infection (OR = 1.21, 95% CI: 1.05-1.39, p = 0.007), and antimigraine preparations were linked to a reduced risk of virus hepatitis (OR = 0.79, 95% CI: 0.69-0.91, p< 0.001). In MVMR, the association of opioids with URI and PI remained after accounting for cancer conditions. Even with a stricter threshold (p< 0.05/30), we found a significant causal association between opioids and respiratory infections (URI/PI). Finally, mediation analyses found that analgesics influence the ROI through different phenotypes of immune cells as mediators. Conclusion: This MR study provides new genetic evidence for the causal relationship between PAU and ROI, and the mediating role of immune cells was demonstrated.

The immune factors have complex causal regulation effects on bone mineral density.

Recent evidence has gradually recognized that the immune and skeletal systems are two closely correlated systems, but the specific immune factors on bone mineral density (BMD) are largely unknown. Based on the summary-level data of genome-wide association studies (GWASs), we performed a series of analyses including two-sample Mendelian randomization (MR) analysis to test potential causal links between 731 immune traits [including median fluorescence intensities (MFIs), absolute cell (AC) counts, relative cell (RC) counts, and morphological parameters (MP)] and BMD. After false discovery rate (FDR) correction, 9 MFI-BMD, 16 AC-BMD, 22 RC-BMD, and 5 MP-BMD pairs reached the level of significance (FDR-adjusted p< 0.05). For MFI traits, the T- and B-cell panels had the largest number of significant immune trait pairs than other panels. CD40, as a molecule expressed by four subsets of monocytes, was highlighted due to its consistently positive correlation with BMD at four sites. For both AC and RC traits, immune traits from the T-cell panel were also highlighted, with CD39-positive T-cell subsets being the most frequently observed feature. For MP traits, the most significant association immune trait with BMD was SSC-A on CD14+ monocyte. Sensitivity analyses suggested that the identified immune factors were robust to pleiotropy. Multivariable MR analysis confirmed the independent causal effect of several immune traits on BMD. Mediation analyses showed that CD40 on monocytes could mediate multiple immune traits, especially the suggestive associations of CD27 on several memory B cells with BMD mediated by CD40 on CD14+ CD16- monocyte. Our study represents the first comprehensive evaluation of the causal effects of immune traits on the risk of osteoporosis. The findings highlighted the complex and important role of immune-derived factors in the pathogenesis of osteoporosis.

Systematic evaluation for the causal effects of blood metabolites on osteoporosis: Genetic risk score and Mendelian randomization.

Purpose: Osteoporosis is associated with metabolic alterations, but the causal roles of serum metabolites on osteoporosis have not been identified. Methods: Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) for 308 of 486 human serum metabolites and evaluated the effect of each GRS on 2 major osteoporosis phenotypes, i.e., estimated bone miner density (eBMD) and fracture, respectively. Then, two-sample Mendelian Randomization (MR) was performed to validate the casual metabolites on osteoporosis. Multivariable MR analysis tested whether the effects of metabolites on osteoporosis are independent of possible confounders. Finally, we conducted metabolic pathway analysis for the metabolites involved in bone metabolism. Results: We identified causal effects of 18 metabolites on eBMD and 1 metabolite on fracture with the GRS method after adjusting for multiple tests. Then, 9 of them were further validated with MR as replication, where comprehensive sensitive analyses proved robust of the causal associations. Although not identified in GRS, 3 metabolites were associated with at least three osteoporosis traits in MR results. Multivariable MR analysis determined the independent causal effect of several metabolites on osteoporosis. Besides, 23 bone metabolic pathways were detected, such as valine, leucine, isoleucine biosynthesis (p = 0.053), and Aminoacyl-tRNA biosynthesis (p = 0.076), and D-glutamine and D-glutamate metabolism (p = 0.004). Conclusions: The systematic causal analyses strongly suggested that blood metabolites have causal effects on osteoporosis risk.

Genetic Risk for Osteoporosis and the Benefit of Adherence to Healthy Lifestyles.

Objectives: We aimed to explore how healthy lifestyles and genetic factors influence the risk of Osteoporosis (OP). Methods: In this prospective cohort study, we first performed a genome-wide association study (GWAS) of estimated bone mineral density (eBMD) and constructed the genetic risk score (GRS) based on the effect of single nucleotide polymorphism (SNP) on eBMD. We then assessed the effect of three-level GRS and adherence to healthy lifestyles on the risk of OP and fracture, respectively. Finally, we assessed the joint effects of GRS and lifestyle on the OP and fracture risk. Results: People with higher GRS have a lower risk of OP and fracture. Negative associations were detected between healthy lifestyle factors and the risk of OP and fracture. Compare with the group with high GRS and favorable lifestyles, the group with low GRS and unfavorable lifestyles had a high Hazard Ratio (HR). Conclusion: The findings suggest that adherence to healthy lifestyles can reduce the risk of OP and fracture in people with different genetic risks.

Systematic Evaluation of Rheumatoid Arthritis Risk by Integrating Lifestyle Factors and Genetic Risk Scores.

Background: Effective identification of high-risk rheumatoid arthritis (RA) individuals is still a challenge. Whether the combined effects of multiple previously reported genetic loci together with lifestyle factors can improve the prediction of RA risk remains unclear. Methods: Based on previously reported results and a large-scale Biobank dataset, we constructed a polygenic risk score (PRS) for RA to evaluate the combined effects of the previously identified genetic loci in both case-control and prospective cohorts. We then evaluated the relationships between several lifestyles and RA risk and determined healthy lifestyles. Then, the joint effects of healthy lifestyles and genetic risk on RA risk were evaluated. Results: We found a positive association between PRS and RA risk (OR = 1.407, 95% confidence interval (CI) = 1.354~1.463; HR = 1.316, 95% CI = 1.257~1.377). Compared with the low genetic risk group, the group with intermediate or high genetic risk had a higher risk (OR = 1.347, 95% CI = 1.213~1.496; HR = 1.246, 95% CI = 1.108~1.400) (OR = 2.169, 95% CI = 1.946~2.417; HR = 1.762, 95% CI = 1.557~1.995). After adjusting for covariates, we found protective effects of three lifestyles (no current smoking, regular physical activity, and moderate body mass index) on RA risk and defined them as healthy lifestyles. Compared with the individuals with low genetic risks and favorable lifestyles, those with high genetic risks and unfavorable lifestyles had as high as OR of 4.637 (95%CI = 3.767~5.708) and HR of 3.532 (95%CI = 2.799~4.458). Conclusions: In conclusion, the integration of PRS and lifestyles can improve the prediction of RA risk. High RA risk can be alleviated by adopting healthy lifestyles but aggravated by adopting unfavorable lifestyles.

Identification of causal metabolites related to multiple autoimmune diseases.

Observational studies provide evidence that metabolites may be involved in the development of autoimmune diseases (ADs), but whether it is causal is still unknown. Based on the large-scale genome-wide association studies (GWAS) summary statistics, we performed two-sample Mendelian randomization (MR) to evaluate the causal associations between human blood metabolites and multiple ADs, which were inflammatory bowel disease (IBD), ulcerative colitis (UC), crohns disease (CD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), multiple sclerosis (MS), primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC). After Bonferroni adjustment, we identified 6 causal features of metabolites, i.e., glycerol 2-phosphate for T1D, hexadecanedioate, phenylacetylglutamine and laurylcarnitine for RA, glycine and arachidonate (20:4n6) for CD. Comprehensive sensitive analysis was further performed to validate the robustness of associations. We also observed some overlaps of metabolites among different ADs, implying similar or shared underlying mechanisms in such pathogenic processes. Multivariable MR analysis was then conducted to avoid potential pleiotropic effect of other complex traits. After controlling for several common traits, multivariable MR analysis ruled out most of potential pleiotropic effects and validated independence of identified metabolites. Finally, metabolic pathway analysis was performed based on suggestive metabolites for each AD respectively and a total of seven metabolic pathways were identified. In conclusion, this study provided novel insights into investigating causal role of blood metabolites in development of multiple ADs through a comprehensive genetic pathway.

Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model's original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.

Novel Autophagy-Related Gene Signature Investigation for Patients With Oral Squamous Cell Carcinoma.

The correlation between autophagy defects and oral squamous cell carcinoma (OSCC) has been previously studied, but only based on a limited number of autophagy-related genes in cell lines or animal models. The aim of the present study was to analyze differentially expressed autophagy-related genes through The Cancer Genome Atlas (TCGA) database to explore enriched pathways and potential biological function. Based on TCGA database, a signature composed of four autophagy-related genes (CDKN2A, NKX2-3, NRG3, and FADD) was established by using multivariate Cox regression models and two Gene Expression Omnibus datasets were applied for external validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to study the function of autophagy-related genes and their pathways. The most significant GO and KEGG pathways were enriched in several key pathways that were related to the progression of autophagy and OSCC. Furthermore, a prognostic risk score was constructed based on the four genes; patients were then divided into two groups (i.e., high risk and low risk) in terms of the median of risk score. Prognosis of the two groups and results showed that patients at the low-risk group had a much better prognosis than those at the high-risk group, regardless of whether they were in the training datasets or validation datasets. Multivariate Cox regression results indicated that the risk score of the autophagy-related gene signatures could greatly predict the prognosis of patients after controlling for several clinical covariates. The findings of the present study revealed that autophagy-related gene signatures play an important role in OSCC and are potential prognostic biomarkers and therapeutic targets.

Birth weight is positively associated with adult osteoporosis risk: observational and Mendelian randomization studies.

The relationship between birth weight and osteoporosis was inconsistent in previous observational studies. Therefore, we performed a systematic evaluation to determine the inconsistent relationship and further make causal inference based on the UK Biobank datasets (~500,000 individuals) and individual/summary-level genetic datasets. Observational analyses found consistent negative associations either between birth weight and estimated bone mineral density (eBMD) or between genetic risk score (GRS) of birth weight and eBMD in total subjects, and sex-stratified subgroups. Mediation analyses detected significant mediation effects of adult weight and height on associations between birth weight and eBMD. Birth weight was causally associated not only with three BMD phenotypes (eBMD, total body [TB]-BMD, and femoral neck [FN]-BMD) under two effect models (total and fetal effect), but also with the risk of fracture using different Mendelian randomization (MR) methods. Multivariable MR analyses detected the pleiotropic effects of some environmental factors (e.g., gestational duration, head circumference, hip circumference) on the associations between birth weight and BMD/fracture. Three BMD phenotypes (eBMD, TB-BMD, and FN-BMD) have significant mediation effects on the associations between birth weight and fracture by using a novel mediation MR analysis under the multivariable MR framework. This multistage systematic study found consistent causal associations between birth weight and osteoporosis risk, fetal origin of genetic effects underlying the associations, and several mediation factors on the detected associations. The results enhanced our understanding of the effects of fetal original phenotypes on outcomes in late adulthood and provided helpful clues for early prevention research on osteoporosis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Rheumatoid arthritis and osteoporosis: shared genetic effect, pleiotropy and causality.

Rheumatoid arthritis (RA) is associated with increased localized and generalized bone loss, but the complex genetic mechanism between them is still unknown. By leveraging large-scale genome-wide association studies summary statistics and individual-level datasets (i.e. UK Biobank), a series of genetic approaches were conducted. Linkage disequilibrium score regression reveals a shared genetic correlation between RA and estimated bone mineral density (eBMD) (rg = -0.059, P = 0.005). The PLACO analysis has identified 74 lead (8 novel) pleiotropic loci that could be mapped to 99 genes, the genetic functions of which reveal the possible mechanism underlying RA and osteoporosis. In European, genetic risk score (GRS) and comprehensive Mendelian randomization (MR) were utilized to evaluate the causal association between RA and osteoporosis in European and Asian. The increase in GRS of RA could lead to a decrease of eBMD (beta = -0.008, P = 3.77E-6) and a higher risk of facture [odds ratio (OR) = 1.012, P = 0.044]. MR analysis identified that genetically determined RA was causally associated with eBMD (beta = -0.021, P = 4.14E-05) and fracture risk (OR = 1.036, P = 0.004). Similar results were also observed in Asian that osteoporosis risk could be causally increased by RA (OR = 1.130, P = 1.04E-03) as well as antibodies against citrullinated proteins-positive RA (OR = 1.083, P = 0.015). Overall, our study reveals complex genetic mechanism between RA and osteoporosis and provides strong evidence for crucial role of RA in pathogenesis of osteoporosis.

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.

How Can Gene-Expression Information Improve Prognostic Prediction in TCGA Cancers: An Empirical Comparison Study on Regularization and Mixed Cox Models.

BACKGROUND: Previous cancer prognostic prediction models often consider only the most important transcriptomic expressions, and their power is limited. It is unknown whether prediction power can be further improved when additional transcriptomic information is incorporated. METHODS: To integrate transcriptomes, four models are compared based on 32 types of cancer in the Cancer Genome Atlas, including the general Cox model with only clinical covariates, the Cox model with a lasso penalty (coxlasso), the Cox model with an elastic net penalty (coxenet), and the mixed-effects Cox model (coxlmm). Furthermore, we partition the survival variance into the relative contribution of clinical and transcriptomic components within the framework of coxlmm. Finally, the influence of different numbers of genes was evaluated in the context of coxlmm. RESULTS: Compared with the clinical covariates-only Cox model, the average prediction gain was 2.4% for coxlasso, 4.2% for coxenet, and 7.2% for coxlmm across 16 low-censored cancers; a significant elevation of prediction power was observed for SARC, SKCM, LGG, PAAD, and HNSC. Similar findings were observed for all 32 cancers with the average prediction gain of 2.7, 3.8, and 5.8% for coxlasso, coxenet, and coxlmm. Coxlmm always had comparable or better prediction performance relative to coxlasso and coxenet with an average of 2.8% prediction improvement across the 16 low-censored cancers. In addition, it is shown that the predictive accuracy of coxlmm generally increases with the number of genes included. The survival variance partition analysis demonstrates that the transcriptomic contribution was higher for some cancers (e.g., LGG, CESC, PAAD, SKCM, and SARC) and lower for others (e.g., BRCA, COAD, KIRC, and STAD). CONCLUSION: This study demonstrates that the integration of transcriptomic information can substantially improve prognostic prediction accuracy, but the prediction performance is cancer-specific and varies across cancer types. It further reveals that gene expression exhibits distinct contributions to survival variation across cancers.

Mendelian randomization implies no direct causal association between leukocyte telomere length and amyotrophic lateral sclerosis.

We employed Mendelian randomization (MR) to evaluate the causal relationship between leukocyte telomere length (LTL) and amyotrophic lateral sclerosis (ALS) with summary statistics from genome-wide association studies (n = ~ 38,000 for LTL and ~ 81,000 for ALS in the European population; n = ~ 23,000 for LTL and ~ 4,100 for ALS in the Asian population). We further evaluated mediation roles of lipids in the pathway from LTL to ALS. The odds ratio per standard deviation decrease of LTL on ALS was 1.10 (95% CI 0.93-1.31, p = 0.274) in the European population and 0.75 (95% CI 0.53-1.07, p = 0.116) in the Asian population. This null association was also detected between LTL and frontotemporal dementia in the European population. However, we found that an indirect effect of LTL on ALS might be mediated by low density lipoprotein (LDL) or total cholesterol (TC) in the European population. These results were robust against extensive sensitivity analyses. Overall, our MR study did not support the direct causal association between LTL and the ALS risk in neither population, but provided suggestive evidence for the mediation role of LDL or TC on the influence of LTL and ALS in the European population.

Birth Weight and Stroke in Adult Life: Genetic Correlation and Causal Inference With Genome-Wide Association Data Sets.

OBJECTIVE: Prior studies have shown that there is an inverse association between birth weight and stroke in adulthood; however, whether such association is causal remains yet known and those studies cannot distinguish between the direct fetal effect and the indirect maternal effect. The aim of the study is to untangle such relationship using novel statistical genetic approaches. METHODS: We first utilized linkage disequilibrium score regression (LDSC) and Genetic analysis incorporating Pleiotropy and Annotation (GPA) to estimate the overall genetic correlation between birth weight and stroke. Then, with a set of valid birth-weight instruments which had adjusted fetal and maternal effects, we performed a two-sample Mendelian randomization (MR) to evaluate its causal effect on stroke based summary statistics from large scale genome-wide association study (GWAS) (n = 264,498 for birth weight and 446,696 for stroke). We further validated the MR results with extensive sensitivity analyses. RESULTS: Both LDSC and GPA demonstrated significant evidence of shared maternal genetic foundation between birth weight and stroke, with the genetic correlation estimated to -0.176. However, no fetal genetic correlation between birth weight and stroke was detected. Furthermore, the inverse variance weighted MR demonstrated the maternally causal effect of birth weight on stroke was 1.12 (95% confidence interval [CI] 1.00-1.27). The maternal ORs of birth weight on three subtypes of stroke including cardioembolic stroke (CES), large artery stroke (LAS) and small vessel stroke (SVS) were 1.16 (95% CI 0.93-1.43), 1.50 (95% CI 1.14-1.96) and 1.47 (95% CI 1.15-1.87), respectively. In contrast, no fetal causal associations were found between birth weight and stroke or the subtypes. Those results were robust against extensive sensitivity analyses, with Egger regression ruling out the possibility of pleiotropy and multivariable MR excluding the likelihood of confounding or mediation effects of other risk factors of stroke. CONCLUSION: This study provides empirically supportive evidence on the fetal developmental origins of stroke and its subtypes. However, further investigation is warranted to understand the pathophysiological role of low birth weight in developing stroke.

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.