Combinations of SNPs related to signal transduction in bipolar disorder

Gene variants for the WNT pathway are associated with severity in periodontal disease

OBJECTIVE

Studies of Wnt variants-related to bone resorption in periodontitis are limited. The aim of this study was to establish the genotype and allele frequency of gene variants associated with the Wnt pathway in systemically healthy individuals with and without periodontitis (PD).

MATERIALS AND METHODS

One hundred fifty-seven systemically healthy individuals were evaluated, 90 with PD and 67 without PD. Periodontal clinical indexes, serological and clinical indices of inflammation, and the following variants associated with the Wnt pathway: DKK, SOST, LRP5, and KREMEN were analyzed by high resolution melting and confirmed by Sanger sequencing.

RESULTS

In the PD-free group, 67.2% of the individuals presented the variant for DKKrs1896367 (p = 0.008) and 82.6% had the variant for KREMEN rs132274 (p = 0.016). The heterozygous variant for the DKK rs1896367 polymorphism was associated with the absence of PD and lower severity OR: 0.33 (CI95% 0.15-0.70) and OR: 0.24 (CI95% 0.11-0.53), respectively. Similarly, KREMEN rs132274 was the homozygous variant associated with the absence of PD (OR: 0.33 (CI95% 0.13-0.88)). On the contrary, 85.6% of individuals with PD presented a variant for DKK rs1896368 (p = 0.042), all suffering severe forms of periodontitis.

CONCLUSION

The presence of DKKrs1896367 and KREMENrs132274 variants in individuals without PD suggests that these single nucleotide polymorphisms could be protective factors for bone loss in PD. A very interesting finding is that the DKKrs1896368 variant was found in a high percentage of severe cases, suggesting that the presence of this variant may be related to the severe bone loss observed in PD.

Boltzmann's Theorem Revisited: Inaccurate Time-to-Action Clocks in Affective Disorders

Timely goal-oriented behavior is essential for survival and is shaped by experience. In this paper, a multileveled approach was employed, ranging from the polymorphic level through thermodynamic molecular, cellular, intracellular, extracellular, non-neuronal organelles and electrophysiological waves, attesting for signal variability. By adopting Boltzmann's theorem as a thermodynamic conceptualization of brain work, we found deviations from excitation-inhibition balance and wave decoupling, leading to wider signal variability in affective disorders compared to healthy individuals. Recent evidence shows that the overriding on-off design of clock genes paces the accuracy of the multilevel parallel sequencing clocks and that the accuracy of the time-to-action is more crucial for healthy behavioral reactions than their rapidity or delays. In affective disorders, the multilevel clocks run free and lack accuracy of responsivity to environmentally triggered time-to-action as the clock genes are not able to rescue mitochondria organelles from oxidative stress to produce environmentally-triggered energy that is required for the accurate time-to-action and maintenance of the thermodynamic equilibrium. This maintenance, in turn, is dependent on clock gene transcription of electron transporters, leading to higher signal variability and less signal accuracy in affective disorders. From a Boltzmannian thermodynamic and energy-production perspective, the option of reversibility to a healthier time-toaction, reducing entropy is implied. We employed logic gates to show deviations from healthy levelwise communication and the reversed conditions through compensations implying the role of nonneural cells and the extracellular matrix in return to excitation-inhibition balance and accuracy in the time-to-action signaling.

Genetic risk factors for ME/CFS identified using combinatorial analysis

Background

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

Methods

We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case–control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility.

Results

Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10–10 to 1.6 × 10–72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

Conclusions

This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients.

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated.

METHODS AND RESULTS

In this paper, we proposed a model for predicting BD based on single nucleotide polymorphisms (SNPs) screening by genome-wide association study (GWAS), which was constructed by a convolutional neural network (CNN) that predicted the probability of the disease. According to the difference of GWAS threshold, two sets of data were named: group P001 and group P005. And different convolutional neural networks are set for the two sets of data. The training accuracy of the model trained with group P001 data is 96%, and the test accuracy is 91%. The training accuracy of the model trained with group P005 data is 94.5%, and the test accuracy is 92%. At the same time, we used gradient weighted class activation mapping (Grad-CAM) to interpret the prediction model, indirectly to identify high-risk SNPs of BD. In the end, we compared these high-risk SNPs with human gene annotation information.

CONCLUSION

The model prediction results of the group P001 yielded 137 risk genes, of which 22 were reported to be associated with the occurrence of BD. The model prediction results of the group P005 yielded 407 risk genes, of which 51 were reported to be associated with the occurrence of BD.

Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

Combinations of SNP genotypes from the Wellcome Trust Case Control Study of bipolar patients

OBJECTIVES

Combinations of genetic variants are the basis for polygenic disorders. We examined combinations of SNP genotypes taken from the 446 729 SNPs in The Wellcome Trust Case Control Study of bipolar patients.

METHODS

Parallel computing by graphics processing units, cloud computing, and data mining tools were used to scan The Wellcome Trust data set for combinations.

RESULTS

Two clusters of combinations were significantly associated with bipolar disorder. One cluster contained 68 combinations, each of which included five SNP genotypes. Of the 1998 patients, 305 had combinations from this cluster in their genome, but none of the 1500 controls had any of these combinations in their genome. The other cluster contained six combinations, each of which included five SNP genotypes. Of the 1998 patients, 515 had combinations from the cluster in their genome, but none of the 1500 controls had any of these combinations in their genome.

CONCLUSION

Clusters of combinations of genetic variants can be considered general risk factors for polygenic disorders, whereas accumulation of combinations from the clusters in the genome of a patient can be considered a personal risk factor.

Combinations of genetic variants associated with bipolar disorder

The main objective of the study was to find genetic variants that in combination are significantly associated with bipolar disorder. In previous studies of bipolar disorder, combinations of three and four single nucleotide polymorphisms (SNP) genotypes taken from 803 SNPs were analyzed, and five clusters of combinations were found to be significantly associated with bipolar disorder. In the present study, combinations of ten SNP genotypes taken from the same 803 SNPs were analyzed, and one cluster of combinations was found to be significantly associated with bipolar disorder. Combinations from the new cluster and from the five previous clusters were identified in the genomes of 266 or 44% of the 607 patients in the study whereas none of the 1355 control participants had any of these combinations in their genome.The SNP genotypes in the smaller combinations were the normal homozygote, heterozygote or variant homozygote. In the combinations containing 10 SNP genotypes almost all the genotypes were the normal homozygote. Such a finding may indicate that accumulation in the genome of combinations containing few SNP genotypes may be a risk factor for bipolar disorder when those combinations contain relatively many rare SNP genotypes, whereas combinations need to contain many SNP genotypes to be a risk factor when most of the SNP genotypes are the normal homozygote.

Combinations of Genetic Variants Occurring Exclusively in Patients

In studies of polygenic disorders, scanning the genetic variants can be used to identify variant combinations. Combinations that are exclusively found in patients can be separated from those combinations occurring in control persons. Statistical analyses can be performed to determine whether the combinations that occur exclusively among patients are significantly associated with the investigated disorder. This research strategy has been applied in materials from various polygenic disorders, identifying clusters of patient-specific genetic variant combinations that are significant associated with the investigated disorders. Combinations from these clusters are found in the genomes of up to 55% of investigated patients, and are not present in the genomes of any control persons.

Combinations of genetic data in a study of oral cancer

In the single locus strategy a number of genetic variants are analyzed, in order to find variants that are distributed significantly different between controls and patients. A supplementary strategy is to analyze combinations of genetic variants. A combination that is the genetic basis for a polygenic disorder will not occur in in control persons genetically unrelated to patients, so the strategy is to analyze combinations of genetic variants present exclusively in patients. In a previous study of oral cancer and leukoplakia 325 SNPs were analyzed. This study has been supplemented with an analysis of combinations of two SNP genotypes from among the 325 SNPs. Two clusters of combinations containing 95 patient specific combinations were significantly associated with oral cancer or leukoplakia. Of 373 patients with oral cancer 205 patients had a number of these 95 combinations in their genome, whereas none of 535 control persons had any of these combinations in their genome.

Combinations of Genetic Data Present in Bipolar Patients, but Absent in Control Persons

The main objective of the study was to find combinations of genetic variants significantly associated with bipolar disorder. In a previous study of bipolar disorder, combinations of three single nucleotide polymorphism (SNP) genotypes taken from 803 SNPs were analyzed, and four clusters of combinations were found to be significantly associated with bipolar disorder. In the present study, combinations of four SNP genotypes taken from the same 803 SNPs were analyzed, and one cluster of combinations was found to be significantly associated with bipolar disorder. Combinations from the new cluster and from the four previous clusters were identified in the genomes of 209 of the 607 patients in the study whereas none of the 1355 control participants had any of these combinations in their genome.

Searching High-Order SNP Combinations for Complex Diseases Based on Energy Distribution Difference

Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual's genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

A combination of single nucleotide polymorphisms in the 3'untranslated region of HLA-G is associated with preeclampsia

Reduced expression of human leukocyte antigen-G (HLA-G) has been linked to onset of preeclampsia. Associations have also been reported between preeclampsia and single nucleotide polymorphisms (SNP) in the 3'-untranslated region (UTR) of the HLA-G gene. However, there are conflicting results between studies. This studied examined whether a SNP, by itself or in combination with other SNPs, in the 3'UTR of the HLA-G gene is associated with an increased risk of preeclampsia. Placenta samples were obtained from 47 preeclamptic and 68 control cases. DNA was extracted, and the 3'UTR was sequenced and analyzed for nine polymorphisms using different genetic models of inheritance. Four of these polymorphisms have never been analyzed for an association with preeclampsia. Disputing existing reports, preeclamptic cases were suggestively associated with a G/G-genotype at SNP +3187 (p<0.05). Several SNP combinations were more prevalent in preeclampsia cases. Following corrections for multiple testing, one SNP combination (+3027C/C and +3187G/G) was significantly more prevalent in preeclampsia cases using co-dominant, additive, and dominant models (p<0.001). Taken together with the current literature, the data suggests that HLA-G 3'UTR SNP-pair associations, and not individual SNPs, could be useful in a predictive test for the susceptibility to preeclampsia.

Combinations of genetic data in a study of neuroblastoma risk genotypes

Analysis of combinations of genetic changes that occur exclusively in patients may be a supplementary strategy to the single-locus strategy used in many genetic studies. The genotypes of 16 SNPs within susceptibility loci for neuroblastoma (NB) were analyzed in a previous study. In the present study, combinations of these genotypes have been analyzed. The theoretical number of combinations of 3 SNP genotypes taken from 16 SNPs is 15,120. Of these, 14,307 were found in 370 patients and 803 controls; 12,772 combinations were common to both patients and controls; 1,213 were found in controls only; and 322 combinations were found in patients only. Among the latter, a cluster of 24 combinations was found to be significantly associated with NB (P < 0.00001).

Big data and clinicians: a review on the state of the science

BACKGROUND

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.

OBJECTIVE

The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.

METHODS

We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.

RESULTS

This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.

CONCLUSIONS

Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.

Connection between genetic and clinical data in bipolar disorder

Complex diseases may be associated with combinations of changes in DNA, where the single change has little impact alone. In a previous study of patients with bipolar disorder and controls combinations of SNP genotypes were analyzed, and four large clusters of combinations were found to be significantly associated with bipolar disorder. It has now been found that these clusters may be connected to clinical data.

Genetics of complex diseases: variations on a theme

A complex disease with an inheritable component is polygenic, meaning that several different changes in DNA are the genetic basis for the disease. Such a disease may also be genetically heterogeneous, meaning that independent changes in DNA, i.e. various genotypes, can be the genetic basis for the disease. Each of these genotypes may be characterized by specific combinations of key genetic changes. It is suggested that even if all key changes are found in genes related to the biology of a certain disease, the number of combinations may be so large that the number of different genotypes may be close to the number of patients suffering from the disease. This hypothesis is based on a study of bipolar disorder.