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 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.

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.

Combinations of SNPs related to signal transduction in bipolar disorder.

Any given single nucleotide polymorphism (SNP) in a genome may have little or no functional impact. A biologically significant effect may possibly emerge only when a number of key SNP-related genotypes occur together in a single organism. Thus, in analysis of many SNPs in association studies of complex diseases, it may be useful to look at combinations of genotypes. Genes related to signal transmission, e.g., ion channel genes, may be of interest in this respect in the context of bipolar disorder. In the present study, we analysed 803 SNPs in 55 genes related to aspects of signal transmission and calculated all combinations of three genotypes from the 3×803 SNP genotypes for 1355 controls and 607 patients with bipolar disorder. Four clusters of patient-specific combinations were identified. Permutation tests indicated that some of these combinations might be related to bipolar disorder. The WTCCC bipolar dataset were use for replication, 469 of the 803 SNP were present in the WTCCC dataset either directly (n = 132) or by imputation (n = 337) covering 51 of our selected genes. We found three clusters of patient-specific 3×SNP combinations in the WTCCC dataset. Different SNPs were involved in the clusters in the two datasets. The present analyses of the combinations of SNP genotypes support a role for both genetic heterogeneity and interactions in the genetic architecture of bipolar disorder.