Collective Genetic Interaction Effects and the Role of Antigen-Presenting Cells in Autoimmune Diseases

A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions

MOTIVATION

Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions.

METHODS

A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate.

RESULT

Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/.

Transcriptional Changes in Regulatory T Cells From Patients With Autoimmune Polyendocrine Syndrome Type 1 Suggest Functional Impairment of Lipid Metabolism and Gut Homing

Autoimmune polyendocrine syndrome type I (APS-1) is a monogenic model disorder of organ-specific autoimmunity caused by mutations in the Autoimmune regulator (AIRE) gene. AIRE facilitates the expression of organ-specific transcripts in the thymus, which is essential for efficient removal of dangerous self-reacting T cells and for inducing regulatory T cells (Tregs). Although reduced numbers and function of Tregs have been reported in APS-I patients, the impact of AIRE deficiency on gene expression in these cells is unknown. Here, we report for the first time on global transcriptional patterns of isolated Tregs from APS-1 patients compared to healthy subjects. Overall, we found few differences between the groups, although deviant expression was observed for the genes TMEM39B, SKIDA1, TLN2, GPR15, FASN, BCAR1, HLA-DQA1, HLA-DQB1, HLA-DRA, GPSM3 and AKR1C3. Of significant interest, the consistent downregulation of GPR15 may indicate failure of Treg gut homing which could be of relevance for the gastrointestinal manifestations commonly seen in APS-1. Upregulated FASN expression in APS-1 Tregs points to increased metabolic activity suggesting a putative link to faulty Treg function. Functional studies are needed to determine the significance of these findings for the immunopathogenesis of APS-1 and for Treg immunobiology in general.

Interaction of HLA Class II rs9272219 and TMPO rs17028450 (Arg690Cys) Variants Affects Neuromyelitis Optica Spectrum Disorder Susceptibility in an Admixed Mexican Population

Neuromyelitis Optica Spectrum Disorder (NMOSD) is a demyelinating autoimmune disease of the central nervous system, more prevalent in individuals of non-European ancestry. Few studies have analyzed genetic risk factors in NMOSD, and HLA class II gene variation has been associated NMOSD risk in various populations including Mexicans. Thymopoietin (TMPO) has not been tested as a candidate gene for NMOSD or other autoimmune disease, however, experimental evidence suggests this gene may be involved in negative selection of autoreactive T cells and autoimmunity. We thus investigated whether the missense TMPO variant rs17028450 (Arg630Cys, frequent in Latin America) is associated with NMOSD, and whether this variant shows an interaction with HLA-class II rs9272219, previously associated with NMOSD risk. A total of 119 Mexican NMOSD patients, 1208 controls and 357 Native Mexican individuals were included. The HLA rs9272219 “T” risk allele frequency ranged from 21 to 68%, while the rs17028450 “T” minor allele frequency was as high as 18% in Native Mexican groups. Both rs9272219 and rs17028450 were significantly associated with NMOSD risk under additive models (OR = 2.48; p = 8 × 10^–10 and OR = 1.59; p = 0.0075, respectively), and a significant interaction between both variants was identified with logistic regression models (p = 0.048). Individuals bearing both risk alleles had an estimated 3.9-fold increased risk of NMOSD. To our knowledge, this is the first study reporting an association of TMPO gene variation with an autoimmune disorder and the interaction of specific susceptibility gene variants, that may contribute to the genetic architecture of NMOSD in admixed Latin American populations.

Genetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks

BACKGROUND

Common metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors.

METHODS

We analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group.

RESULTS

In contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing β-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension.

CONCLUSIONS

Our results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

Collective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis

BACKGROUND

Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes. These traits, however, are often highly polygenic, with individual loci contributing only small effects to the overall association. The polygenicity makes it challenging to explain, for example, the widely observed comorbidity between stress and cardiac disease.

METHODS

We present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait. Using simulated data, we demonstrate that by taking into account the non-uniform distribution of genotypes within a cohort, we can achieve greater power than regression-based methods for high-dimensional inference.

RESULTS

We analyzed genome-wide data sets of outbred mice and pet dogs, and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects: γ-carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear, consistent with our previous finding in human post-traumatic stress disorder (PTSD) data. Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis, and noise-induced fear in dogs with hemostasis.

CONCLUSIONS

Our findings suggest a novel explanation for the observed comorbidity between PTSD/anxiety and cardiovascular diseases: key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear.

Large-scale interaction effects reveal missing heritability in schizophrenia, bipolar disorder and posttraumatic stress disorder

Genetic susceptibility factors behind psychiatric disorders typically contribute small effects individually. A possible explanation for the missing heritability is that the effects of common variants are not only polygenic but also non-additive, appearing only when interactions within large groups are taken into account. Here, we tested this hypothesis for schizophrenia (SZ) and bipolar disorder (BP) disease risks, and identified genetic factors shared with posttraumatic stress disorder (PTSD). When considered independently, few single-nucleotide polymorphisms (SNPs) reached genome-wide significance. In contrast, when SNPs were selected in groups (containing up to thousands each) and the collective effects of all interactions were estimated, the association strength for SZ/BP rose dramatically with a combined sample size of 7187 cases and 8309 controls. We identified a large number of genes and pathways whose association was significant only when interaction effects were included. The gene with highest association was CSMD1, which encodes a negative regulator of complement activation. Pathways for glycosaminoglycan (GAG) synthesis exhibited strong association in multiple contexts. Taken together, highly associated pathways suggested a pathogenesis mechanism where maternal immune activation causes disruption of neurogenesis (compounded by impaired cell cycle, DNA repair and neuronal migration) and deficits in cortical interneurons, leading to symptoms triggered by synaptic pruning. Increased risks arise from GAG deficiencies causing complement activation and excessive microglial action. Analysis of PTSD data sets suggested an etiology common to SZ/BP: interneuron deficiency can also lead to impaired control of fear responses triggered by trauma. We additionally found PTSD risk factors affecting synaptic plasticity and fatty acid signaling, consistent with the fear extinction model. Our results suggest that much of the missing heritability of psychiatric disorders resides in non-additive interaction effects.