Heritability and GWAS Studies for Monocyte-Lymphocyte Ratio

Bidirectional two-sample Mendelian randomization study of differential white blood cell counts and schizophrenia

BACKGROUND

Schizophrenia and white blood cell counts (WBC) are both complex and polygenic traits. Previous evidence suggests that increased WBC are associated with higher all-cause mortality, and other studies have found elevated WBC in first-episode psychosis and chronic schizophrenia. However, these observational findings may be confounded by antipsychotic exposures and their effects on WBC. Mendelian randomization (MR) is a useful method for examining the directions of genetically-predicted relationships between schizophrenia and WBC.

METHODS

We performed a two-sample MR using summary statistics from genome-wide association studies (GWAS) conducted by the Psychiatric Genomics Consortium Schizophrenia Workgroup (N = 130,644) and the Blood Cell Consortium (N = 563,946). The MR methods included inverse variance weighted (IVW), MR Egger, weighted median, MR-PRESSO, contamination mixture, and a novel approach called mixture model reciprocal causal inference (MRCI). False discovery rate was employed to correct for multiple testing.

RESULTS

Multiple MR methods supported bidirectional genetically-predicted relationships between lymphocyte count and schizophrenia: IVW (b = 0.026; FDR p-value = 0.008), MR Egger (b = 0.026; FDR p-value = 0.008), weighted median (b = 0.013; FDR p-value = 0.049), and MR-PRESSO (b = 0.014; FDR p-value = 0.010) in the forward direction, and IVW (OR = 1.100; FDR p-value = 0.021), MR Egger (OR = 1.231; FDR p-value < 0.001), weighted median (OR = 1.136; FDR p-value = 0.006) and MRCI (OR = 1.260; FDR p-value = 0.026) in the reverse direction. MR Egger (OR = 1.171; FDR p-value < 0.001) and MRCI (OR = 1.154; FDR p-value = 0.026) both suggested genetically-predicted eosinophil count is associated with schizophrenia, but MR Egger (b = 0.060; FDR p-value = 0.010) and contamination mixture (b = -0.013; FDR p-value = 0.045) gave ambiguous results on whether genetically predicted liability to schizophrenia would be associated with eosinophil count. MR Egger (b = 0.044; FDR p-value = 0.010) and MR-PRESSO (b = 0.009; FDR p-value = 0.045) supported genetically predicted liability to schizophrenia is associated with elevated monocyte count, and the opposite direction was also indicated by MR Egger (OR = 1.231; FDR p-value = 0.045). Lastly, unidirectional genetic liability from schizophrenia to neutrophil count were proposed by MR-PRESSO (b = 0.011; FDR p-value = 0.028) and contamination mixture (b = 0.011; FDR p-value = 0.045) method.

CONCLUSION

This MR study utilised multiple MR methods to obtain results suggesting bidirectional genetic genetically-predicted relationships for elevated lymphocyte counts and schizophrenia risk. In addition, moderate evidence also showed bidirectional genetically-predicted relationships between schizophrenia and monocyte counts, and unidirectional effect from genetic liability for eosinophil count to schizophrenia and from genetic liability for schizophrenia to neutrophil count. The influence of schizophrenia to eosinophil count is less certain. Our findings support the role of WBC in schizophrenia and concur with the hypothesis of neuroinflammation in schizophrenia.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

A high-density genome-wide association with absolute blood monocyte count in domestic sheep identifies novel loci

Monocytes are a core component of the immune system that arise from bone marrow and differentiate into cells responsible for phagocytosis and antigen presentation. Their derivatives are often responsible for the initiation of the adaptive immune response. Monocytes and macrophages are central in both controlling and propagating infectious diseases such as infection by Coxiella burnetii and small ruminant lentivirus in sheep. Genotypes from 513 Rambouillet, Polypay, and Columbia sheep (Ovis aries) were generated using the Ovine SNP50 BeadChip. Of these sheep, 222 animals were subsequently genotyped with the Ovine Infinium^® HD SNP BeadChip to increase SNP coverage. Data from the 222 HD genotyped sheep were combined with the data from an additional 258 unique sheep to form a 480-sheep reference panel; this panel was used to impute the low-density genotypes to the HD genotyping density. Then, a genome-wide association analysis was conducted to identify loci associated with absolute monocyte counts from blood. The analysis used a single-locus mixed linear model implementing EMMAX with age and ten principal components as fixed effects. Two genome-wide significant peaks (p < 5x10^-7) were identified on chromosomes 9 and 1, and ten genome-wide suggestive peaks (p < 1x10^-5) were identified on chromosomes 1, 2, 3, 4, 9, 10, 15, and 16. The identified loci were within or near genes including KCNK9, involved into cytokine production, LY6D, a member of a superfamily of genes, some of which subset monocyte lineages, and HMGN1, which encodes a chromatin regulator associated with myeloid cell differentiation. Further investigation of these loci is being conducted to understand their contributions to monocyte counts. Investigating the genetic basis of monocyte lineages and numbers may in turn provide information about pathogens of veterinary importance and elucidate fundamental immunology.

Integrated Analysis of Microarray Studies to Identify Novel Diagnostic Markers in Bladder Pain Syndrome/Interstitial Cystitis with Hunner Lesion

Background

The aim of this study was to identify novel genetic features of Hunner’s lesion interstitial cystitis (HIC) via comprehensive analysis of the Gene Expression Omnibus (GEO) database.

Methods

The GSE11783 and GSE28242 datasets were downloaded from GEO for further analysis. Differentially expressed genes (DEGs) were identified and analyzed for functional annotation. The diagnostic markers for HIC were screened and validated using the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms. Finally, the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was adopted to investigate the correlation between immune cell infiltration and diagnostic markers in HIC.

Results

A total of 7837 DEGs were identified in GSE11783 and 1583 DEGs in GSE28242. Venn diagrams were used to obtain 16 overlapping upregulated and 67 overlapping downregulated DEGs separately. The LASSO logistic model and SVM-RFE algorithm were used to identify 6 genes including KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 from DEGs as diagnostic markers for HIC. Their diagnostic potential in HIC and bladder pain syndrome/interstitial cystitis (BPS/IC) were acceptable. PLAC8 exhibited the best diagnostic performance in BPS/IC with an area under the curve of 0.916. The results of immune infiltration involving GSE11783 revealed that the plasma cell ratio (p = 0.017), activated memory CD4+ T cells (p = 0.009), activated dendritic cells (p = 0.01), eosinophils (p = 0.004), and neutrophils (p = 0.03) were significantly higher in HIC than in normal samples, in contrast to resting mast cells (p = 0.022). A positive correlation existed between diagnostic markers and infiltrating immune cells.

Conclusion

KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 represent novel and potent diagnostic markers for HIC. They also exhibit certain diagnostic potential in BPS/IC. Immune cell infiltration might play a key role in the pathogenesis and progression of BPS/IC.

Prediction of the Postoperative Fat Volume Retention Rate After Augmentation Mammoplasty with Autologous Fat Grafting: From the Perspective of Preoperative Inflammatory Level

BACKGROUND

Postoperative fat volume retention rate (PFVRR) after augmentation mammoplasty with autologous fat grafting is highly variable on an individual basis and challenging to be predicted. However, at present, there is a lack of further research on the relevant preoperative patient's self-related influencing factors. The early inflammatory response degree, directly influenced by preoperative inflammatory level, is an indispensable part of angiogenesis, which is a key factor in adipocyte survival.

METHODS

A retrospective review was conducted of patients who underwent breast augmentation with autologous fat grafting performed by a senior surgeon. Preoperative patient demographics and laboratory findings relevant to inflammatory level, such as monocyte to lymphocyte ratio (MLR), were included as the independent variables. The PFVRR more than 3 months after the operation was included as the dependent variable. Key factors influencing the PFVRR were analyzed.

RESULTS

Sixty-three patients were included. The total volume of bilateral fat injection was 375.00 (range, 320.00-400.00) mL, and the long-term bilateral volumetric change was 106.98 (range, 69.90-181.58) mL. The mean PFVRR was 35.36% ± 15.87%, and the preoperative MLR was an independent positive influencing factor of it, while the lymphocyte (L) count was negative. By ROC curve analysis, a value of MLR equal to 0.23 was the diagnostic cut-off point for whether PFVRR was greater than 50%, and its area under the curve was 0.870, with a sensitivity of 93.33% and a specificity of 81.25%. The other hematological parameters and demographics such as age, body mass index, and donor site were not significantly correlated with the PFVRR.

CONCLUSION

Preoperative peripheral blood inflammatory indicators can influence the PFVRR, with the MLR positively and L count negatively. Based on the diagnostic threshold of MLR = 0.23 derived from this study, clinicians can make reasonable predictions of whether half of the injected fat volume would be retained based on preoperative blood routine tests that are readily available.

LEVEL OF EVIDENCE IV

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Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.

Circulating biomarkers of immunity and inflammation, risk of Alzheimer's disease, and hippocampal volume: a Mendelian randomization study

The aim of this study was to explore the association between genetically predicted circulating levels of immunity and inflammation, and the risk of Alzheimer's disease (AD) and hippocampal volume, by conducting a two-sample Mendelian Randomization Study. We identified 12 markers of immune cells and derived ratios (platelet count, eosinophil count, neutrophil count, basophil count, monocyte count, lymphocyte count, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, CD4 count, CD8 count, CD4-to-CD8 ratio, and CD56) and 5 signaling molecules (IL-6, fibrinogen, CRP, and Lp-PLA2 activity and mass) as primary exposures of interest. Other genetically available immune biomarkers with a weaker a priori link to AD were considered secondary exposures. Associations with AD were evaluated in The International Genomics of Alzheimer's Project (IGAP) GWAS dataset (21,982 cases; 41,944 controls of European ancestry). For hippocampal volume, we extracted data from a GWAS meta-analysis on 33,536 participants of European ancestry. None of the primary or secondary exposures showed statistically significant associations with AD or with hippocampal volume following P-value correction for multiple comparisons using false discovery rate < 5% (Q-value < 0.05). CD4 count showed the strongest suggestive association with AD (odds ratio 1.32, P < 0.01, Q > 0.05). There was evidence for heterogeneity in the MR inverse variance-weighted meta-analyses as measured by Cochran Q, and weighted median and weighted mode for multiple exposures. Further cluster analyses did not reveal clusters of variants that could influence the risk factor in distinct ways. This study suggests that genetically predicted circulating biomarkers of immunity and inflammation are not associated with AD risk or hippocampal volume. Future studies should assess competing risk, explore in more depth the role of adaptive immunity in AD, in particular T cells and the CD4 subtype, and confirm these findings in other ethnicities.

The role of lymphocyte-monocyte ratio on axial spondyloarthritis diagnosis and sacroiliitis staging

Background

Axial spondyloarthritis (axial SpA) is a chronic inflammatory disorder could lead to disability due to the failure of timely treatment. The role of lymphocyte-to-monocyte ratio (LMR) in axial SpA remains unclear. The aim of this study was to investigate the role of LMR in axial SpA diagnosis, disease activity classification and sacroiliitis staging.

Methods

Seventy-eight axial SpA patients [51males and 27 females; mean age 41.0 (29–52) years] and 78 healthy controls (HCs) [55males and 23 females; mean age 40 (30–53) years] were enrolled in this study. The diagnosis of axial SpA was performed according to the New York criteria or the Assessment of Spondyloarthritis international Society (ASAS) classification criteria, whereas the staging of sacroiliitis in axial SpA patients was determined by X-ray examination. Comparisons of LMR levels between groups were performed using t test. Pearson or Spearman correlation analysis were used to assess correlations between LMR and other indicators. Receiver operating characteristic (ROC) curves were used to determine the role of LMR in the diagnosis of axial SpA.

Results

Higher neutrophil-to-lymphocyte ratio(NLR), red blood cell distribution width(RDW), platelet-to-lymphocyte ratio(PLR), mean platelet volume(MPV), erythrocyte sedimentation rate (ESR), and C-reactive protein(CRP) levels and lower red blood cell (RBC), hemoglobin (Hb), Hematocrit (Hct), LMR, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL) and albumin/globulin (A/G) levels were noted in axial SpA patients compared to HCs. Positive correlations were observed between LMR and RBC, Hb, Hct and A/G, whereas negative correlations were found between LMR and NLR, PLR, AST, and TBIL (P < 0.05). ROC curves showed that the area under the curve (AUC) for LMR in the diagnosis of ankylosing spondylitis was 0.803 (95% CI = 0.734–0.872) with a sensitivity and specificity of 62.8 and 87.2%, respectively, and the AUC (95% CI) for the combination of ESR, CRP and LMR was 0.975 (0.948–1.000) with a sensitivity and specificity of 94.9 and 97.4%, respectively. LMR levels were lower (P < 0.05) and significant differences in LMR values were observed among different stages (P < 0.05).

Conclusions

Our study suggested that LMR might be an important inflammatory marker to identify axial SpA and assess disease activity and X-ray stage of sacroiliitis.

Combined oral contraceptives modify the effect of smoking on inflammatory cellular indexes and endothelial function in healthy subjects

Little information is available on the influence of sex in combination with smoking habits and combined oral contraceptives (COC) use on cellular inflammatory indexes such as neutrophil/lymphocyte ratio (NLR), derived NRL (dNLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR), mean platelet volume/platelet count (MPV/PLT), aggregate inflammation systemic index (AISI), and systemic inflammation response index (SIRI), which are cost-effective biomarkers to assessing inflammation. Therefore, the effect of COC was studied alone or in association with smoking and compared with results from healthy COC-free women and men. Furthermore, the association of cellular inflammatory indexes with endothelial function (arginine (Arg), asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA) and lipid peroxidation (malondialdehyde MDA) biomarkers was evaluated. Blood was collected for hematological and biochemical analysis, which were used to calculate PLR, NLR, dNLR, MLR, MPV/PLT, AISI, and SIRI. Serum samples were assayed for Arg, ADMA, SDMA, and MDA. Monocytes, MLR, SIRI, and MPV/PLT were higher in men, while PLT count was higher in women. COC use increased lymphocytes and lowered PLR and MLR. Smoking reduced sexually divergent parameters, especially in COC users: smoking and non-smoking COC-free women displayed six divergent parameters, while COC users displayed only two (monocytes and MPV). In addition, COC affected endothelial function, reducing ADMA and Arg. Moreover, COC-free women had lower Arg levels than men. In conclusion, COC use strongly influence the effects of tobacco smoking, which are sex and parameter specific. Further, these data stress that COC use and smoking attitude select different cohorts indicating that sex and gender studies need intersectionality.

Substance use: Interplay between polygenic risk and neighborhood environment

BACKGROUND

Tobacco, alcohol, and cannabis use are prevalent behaviors that pose considerable health risks. Genetic vulnerability and characteristics of the neighborhood of residence form important risk factors for substance use. Possibly, these factors do not act in isolation. This study tested the interaction between neighborhood characteristics and genetic risk (gene-environment interaction, GxE) and the association between these classes of risk factors (gene-environment correlation, rGE) in substance use.

METHODS

Two polygenic scores (PGS) each (based on different discovery datasets) were created for smoking initiation, cigarettes per day, and glasses of alcohol per week based on summary statistics of different genome-wide association studies (GWAS). For cannabis initiation one PGS was created. These PGS were used to predict their respective phenotype in a large population-based sample from the Netherlands Twin Register (N = 6,567). Neighborhood characteristics as retrieved from governmental registration systems were factor analyzed and resulting measures of socioeconomic status (SES) and metropolitanism were used as predictors.

RESULTS

There were (small) main effects of neighborhood characteristics and PGS on substance use. One of the 14 tested GxE effects was significant, such that the PGS was more strongly associated with alcohol use in individuals with high SES. This was effect was only significant for one out of two PGS. There were weak indications of rGE, mainly with age and cohort covariates.

CONCLUSION

We conclude that both genetic and neighborhood-level factors are predictors for substance use. More research is needed to establish the robustness of the findings on the interplay between these factors.

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.

High monocyte to lymphocyte ratio is associated with impaired protection after subcutaneous administration of BCG in a mouse model of tuberculosis

Background: The only available tuberculosis (TB) vaccine, Bacillus Calmette-Guérin (BCG), has variable efficacy. New vaccines are therefore urgently needed. Why BCG fails is incompletely understood, and the tools used for early assessment of new vaccine candidates do not account for BCG variability. Taking correlates of risk of TB disease observed in human studies and back-translating them into mice to create models of BCG variability should allow novel vaccine candidates to be tested early in animal models that are more representative of the human populations most at risk. Furthermore, this could help to elucidate the immunological mechanisms leading to BCG failure. We have chosen the monocyte to lymphocyte (ML) ratio as a correlate of risk of TB disease and have back-translated this into a mouse model.

Methods: Four commercially available, inbred mouse strains were chosen. We investigated their baseline ML ratio by flow cytometry; extent of BCG-mediated protection from M ycobacterium tuberculosis infection by experimental challenge; vaccine-induced interferon gamma (IFNγ) response by ELISPOT assay; and tissue distribution of BCG by plating tissue homogenates.

Results: The ML ratio varied significantly between A/J, DBA/2, C57Bl/6 and 129S2 mice. A/J mice showed the highest BCG-mediated protection and lowest ML ratio, while 129S2 mice showed the lowest protection and higher ML ratio. We also found that A/J mice had a lower antigen specific IFNγ response than 129S2 mice. BCG tissue distribution appeared higher in A/J mice, although this was not statistically significant.

Conclusions: These results suggest that the ML ratio has an impact on BCG-mediated protection in mice, in alignment with observations from clinical studies. A/J and 129S2 mice may therefore be useful models of BCG vaccine variability for early TB vaccine testing. We speculate that failure of BCG to protect from TB disease is linked to poor tissue distribution in a ML high immune environment.

The association between the lymphocyte-monocyte ratio and disease activity in rheumatoid arthritis

The lymphocyte-monocyte ratio (LMR) is a systemic inflammatory marker for prediction of disease development, progress, and survival. Recently, a genome-wide association study identified genetic variations in ITGA4 and HLA-DRB1 that affect the LMR levels and were widely believed to be susceptibility genes for autoimmune diseases, including rheumatoid arthritis (RA). However, the role of LMR in RA patients remains unclear. The LMR level and other laboratory data of 66 RA patients, 163 osteoarthritis (OA) patients, and 131 healthy controls (HC) were compared using binary logistic regression. The correlations between LMR and disease activity and other inflammatory markers were measured using the Spearman rank test. ROC curve analyses assessed the diagnostic accuracy of LMR in RA. The LMR and lymphocyte count were significantly lower in RA patients, whereas the monocyte count was significantly higher relative to the HC group/OA patients (p < 0.01). A decreased LMR has been associated with increased disease activity (p = 0.012). In addition, the DAS28 and traditional inflammatory markers, including ESR, CRP, RDW, PLR, and NLR, and immune-related factors, such as C4, IgA, and IgM, were inversely correlated with LMR, while hemoglobin and albumin were positively correlated with LMR. The ROC curve showed that the area under the curve of LMR was 0.705 (95%CI = 0.630-0.781). The corresponding specificity and sensitivity were 82.82 and 45.45%, respectively. The present study shows that the LMR is an important inflammatory marker which could be used to identify disease activity in RA patients and to distinguish RA from OA patients.