scholarly journals Shared associations identify causal relationships between gene expression and immune cell phenotypes

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Christiane Gasperi ◽  
Sung Chun ◽  
Shamil R. Sunyaev ◽  
Chris Cotsapas
Keyword(s):  
Complex Traits ◽  
Direct Evidence ◽  
Immune Cell ◽  
Genetic Locus ◽  
Causal Variant ◽  
Causal Relationships ◽  
Genetic Studies ◽  
Two Factors ◽  
Gene Regulatory ◽  
Cell Phenotypes

AbstractGenetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.

scholarly journals Shared associations identify causal relationships between gene expression and immune cell phenotypes

2020 ◽  
Author(s):  
Christiane Gasperi ◽  
Sung Chun ◽  
Shamil R. Sunyaev ◽  
Chris Cotsapas
Keyword(s):  
Complex Traits ◽  
Direct Evidence ◽  
Immune Cell ◽  
Genetic Locus ◽  
Causal Variant ◽  
Causal Relationships ◽  
Genetic Studies ◽  
Two Factors ◽  
Gene Regulatory ◽  
Cell Phenotypes

AbstractGenetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with most due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.

The Genetics of Personality Disorder

1992 ◽  
Vol 160 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Peter McGuffin ◽  
Anita Thapar
Keyword(s):  
Family Environment ◽  
Complex Traits ◽  
Direct Evidence ◽  
Adoption Studies ◽  
Genetic Studies ◽  
Normal Personality ◽  
Psychophysiological Research ◽  
Sources Of Variation ◽  
Positive Effect ◽  
Shed Light

Most measurable aspects of normal personality appear to be at least moderately heritable, with direct evidence coming from family, twin and adoption studies and indirect support deriving from psychophysiological research and breeding experiments on animals. Interestingly, genetic studies also shed light on the environmental sources of variation in personality and suggest that shared family environment rarely, if ever, has any positive effect on similarity between relatives. Despite problems of classification, and variations in the use of terms, a survey of the literature provides reasonably consistent evidence of a genetic contribution to several categories of abnormal personality, which we here divide into three groups, antisocial, anxious/avoidant, and schizoid–schizotypal personalities. However, personality disorders are complex traits that do not show simple mendelian patterns of inheritance and so far molecular genetics has been of no help in understanding their aetiology. Fortunately, techniques are now becoming available that enable the detection and potential localisation of genes of small effect and which may help elucidate the molecular basis even of (probably) polygenic traits such as abnormal personality.

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

scholarly journals Combination-Feeding Causes Differences in Aspects of Systemic and Mucosal Immune Cell Phenotypes and Functions Compared to Exclusive Sow-Rearing or Formula-Feeding in Piglets

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1097
Author(s):  
Emily C. Radlowski ◽  
Mei Wang ◽  
Marcia H. Monaco ◽  
Sarah S. Comstock ◽  
Sharon M. Donovan
Keyword(s):  
Immune Cell ◽  
Arginase Activity ◽  
T Helper ◽  
Mesenteric Lymph Nodes ◽  
Immune Development ◽  
Mesenteric Lymph ◽  
Tnf Α ◽  
Activation Pathways ◽  
Cell Phenotypes ◽  
Oxide Synthase

Combination feeding (human milk and formula) is common and influences immune development compared to exclusive breastfeeding. Infant formulas contain prebiotics, which influence immune development. Herein, immune development of combination-fed (CF), sow-reared (SR) and formula-fed (FF) piglets, and the effect of prebiotics was tested. Piglets (n = 47) were randomized to: SR, FF, CF, FF+prebiotic (FP), and CF+prebiotic (CP). FP and CP received formula with galactooligosaccharides and inulin (4 g/L in a 4:1 ratio). CF and CP piglets were sow-reared for until d5 and then rotated between a sow and formula every 12 h. On day 21, piglets received an intraperitoneal injection of lipopolysaccharide 2 h prior to necropsy. Immune cells from blood, mesenteric lymph nodes (MLN), and spleen were phenotyped. Classical (nitric oxide synthase) and alternative (arginase activity) activation pathways were measured in isolated macrophages. Serum IL-6 and TNF-α were measured by ELISA. SR piglets had lower (p < 0.0001) CD4+ T-helper cells and higher (p < 0.0001) B-cells in PBMC than all other groups. CP piglets had higher (p < 0.0001) arginase activity compared to all other groups. FF piglets had higher (p < 0.05) IL-6 compared to both CF and SR, but were similar to FP and CP. Thus, CF, with or without prebiotics, differentially affected immunity compared to exclusively fed groups.

scholarly journals The limits of normal approximation for adult height

2021 ◽  
Author(s):  
Sergei A. Slavskii ◽  
Ivan A. Kuznetsov ◽  
Tatiana I. Shashkova ◽  
Georgii A. Bazykin ◽  
Tatiana I. Axenovich ◽  
...  
Keyword(s):  
Complex Traits ◽  
Adult Height ◽  
Normal Approximation ◽  
Classical Model ◽  
Model Complexity ◽  
Test Case ◽  
Genetic Studies ◽  
Quantitative Genetic ◽  
Profound Influence ◽  
Log Normal

AbstractAdult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

scholarly journals Sex- and age‐dependent alterations of splenic immune cell profile and NK cell phenotypes and function in C57BL/6J mice

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Kelly B. Menees ◽  
Rachael H. Earls ◽  
Jaegwon Chung ◽  
Janna Jernigan ◽  
Nikolay M. Filipov ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Sex Differences ◽  
Nk Cells ◽  
Immune Cell ◽  
Nk Cell ◽  
Spleen Weight ◽  
Age Related ◽  
And Function ◽  
Cell Phenotypes

Abstract Background Physiological homeostasis decline, immunosenescence, and increased risk for multiple diseases, including neurodegeneration, are all hallmarks of ageing. Importantly, it is known that the ageing process is sex-biased. For example, there are sex differences in predisposition for multiple age-related diseases, including neurodegenerative and autoimmune diseases. However, sex differences in age-associated immune phenotypes are not clearly understood. Results Here, we examined the effects of age on immune cell phenotypes in both sexes of C57BL/6J mice with a particular focus on NK cells. We found female-specific spleen weight increases with age and concordant reduction in the number of splenocytes per gram of spleen weight compared to young females. To evaluate sex- and age-associated changes in splenic immune cell composition, we performed flow cytometry analysis. In male mice, we observed an age-associated reduction in the frequencies of monocytes and NK cells; female mice displayed a reduction in B cells, NK cells, and CD8 + T cells and increased frequency of monocytes and neutrophils with age. We then performed a whole blood stimulation assay and multiplex analyses of plasma cytokines and observed age- and sex-specific differences in immune cell reactivity and basal circulating cytokine concentrations. As we have previously illustrated a potential role of NK cells in Parkinson’s disease, an age-related neurodegenerative disease, we further analyzed age-associated changes in NK cell phenotypes and function. There were distinct differences between the sexes in age-associated changes in the expression of NK cell receptors, IFN-γ production, and impairment of α-synuclein endocytosis. Conclusions This study demonstrates sex- and age-specific alterations in splenic lymphocyte composition, circulating cytokine/chemokine profiles, and NK cell phenotype and effector functions. Our data provide evidence that age-related physiological perturbations differ between the sexes which may help elucidate sex differences in age-related diseases, including neurodegenerative diseases, particularly Parkinson’s disease, where immune dysfunction is implicated in their etiology.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Genomic Risk Score impact on susceptibility to systemic sclerosis

2020 ◽  
Vol 80 (1) ◽  
pp. 118-127 ◽  
Author(s):  
Lara Bossini-Castillo ◽  
Gonzalo Villanueva-Martin ◽  
Martin Kerick ◽  
Marialbert Acosta-Herrera ◽  
Elena López-Isac ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Immune Cell ◽  
European Ancestry ◽  
Risk Scores ◽  
Immunological Parameters ◽  
Cell Counts ◽  
Best Fitting ◽  
Genomic Risk

ObjectivesGenomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.MethodsAllelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.ResultsThe best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.ConclusionsGRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.

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