Combinatorial analytics: An essential tool for the delivery of precision medicine and precision agriculture

Gardner, Steve

doi:10.1016/j.ailsci.2021.100003

Cited by 8 publications

(9 citation statements)

References 46 publications

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“…This suggests that the two diseases may share similar etiologies with possible overlap in the biological drivers and risk genes. Our analysis of the first UK Biobank COVID-19 population identified four genes out of 68 associated specifically with the risk of severe COVID that we had previously identified as having strong association with neurodegenerative processes [23], including ATXN1, SORCS2 and STH and MAPT from loci on chromosome 17 that were subsequently validated by the results from the COVID-19 Host Genetics Initiative [119]. This analysis also revealed several other disease and symptom associated mechanisms, such as viral host response factors and pro-inflammatory cytokine production.…”

Section: Similarities With Other Diseasesmentioning

confidence: 88%

“…The PrecisionLife combinatorial analysis platform enables hypothesis-free identification of such high-order combinatorial multi-modal features (known as disease signatures) at scale on modest computational hardware. These combinatorial disease signatures capture both linear and non-linear effects of genetic and molecular interaction networks in a way that is complementary to GWAS analysis [23].…”

Section: Combinatorial Analysismentioning

confidence: 99%

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Genetic risk factors for ME/CFS identified using combinatorial analysis

Das¹,

Taylor²,

Kozubek³

et al. 2022

J Transl Med

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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.

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Section: Similarities With Other Diseasesmentioning

confidence: 88%

Section: Combinatorial Analysismentioning

confidence: 99%

Genetic risk factors for ME/CFS identified using combinatorial analysis

Das¹,

Taylor²,

Kozubek³

et al. 2022

J Transl Med

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“…Combinatorial analysis uses advanced analytics and AI methods to identify such combinations of features in complex chronic diseases. 33 It is a hypothesis-free method for the detection of high-order, disease-associated combinations of features (disease signatures—typically comprising three to ten features) that together are strongly associated with variations in disease risk, symptoms, progression rates, and therapy response commonly seen in subgroups of patients using a case-control cohort design. 34 , 35 …”

Section: High-resolution Patient Stratification Using Combinatorial A...mentioning

confidence: 99%

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

Das¹,

Taylor²,

Beaulah³

et al. 2022

Patterns

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“…These features may include genetic variants (SNPs, CNVs) or other multi-omics features, such as transcripts; CpG sites; or even epidemiological, environmental, or other nongenetic/nonbiological factors. A foundational component of the platform is the same as that used in Precision Life for human biomedical applications ( Gardner, 2021 ). The algorithm is based on constructing combinations of features progressively from low to higher order (so called “layers”) and validating these networks of features using multiple testing correction to adjust the p -value of the statistical test including Fisher’s exact test and the Z-score test.…”

Section: Introductionmentioning

confidence: 99%

Host Genome–Metagenome Analyses Using Combinatorial Network Methods Reveal Key Metagenomic and Host Genetic Features for Methane Emission and Feed Efficiency in Cattle

Cardinale¹,

Kadarmideen²

2022

Front. Genet.

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Cattle production is one of the key contributors to global warming due to methane emission, which is a by-product of converting feed stuff into milk and meat for human consumption. Rumen hosts numerous microbial communities that are involved in the digestive process, leading to notable amounts of methane emission. The key factors underlying differences in methane emission between individual animals are due to, among other factors, both specific enrichments of certain microbial communities and host genetic factors that influence the microbial abundances. The detection of such factors involves various biostatistical and bioinformatics methods. In this study, our main objective was to reanalyze a publicly available data set using our proprietary Synomics Insights platform that is based on novel combinatorial network and machine learning methods to detect key metagenomic and host genetic features for methane emission and residual feed intake (RFI) in dairy cattle. The other objective was to compare the results with publicly available standard tools, such as those found in the microbiome bioinformatics platform QIIME2 and classic GWAS analysis. The data set used was publicly available and comprised 1,016 dairy cows with 16S short read sequencing data from two dairy cow breeds: Holstein and Nordic Reds. Host genomic data consisted of both 50 k and 150 k SNP arrays. Although several traits were analyzed by the original authors, here, we considered only methane emission as key phenotype for associating microbial communities and host genetic factors. The Synomics Insights platform is based on combinatorial methods that can identify taxa that are differentially abundant between animals showing high or low methane emission or RFI. Focusing exclusively on enriched taxa, for methane emission, the study identified 26 order-level taxa that combinatorial networks reported as significantly enriched either in high or low emitters. Additionally, a Z-test on proportions found 21/26 (81%) of these taxa were differentially enriched between high and low emitters (p value <.05). In particular, the phylum of Proteobacteria and the order Desulfovibrionales were found enriched in high emitters while the order Veillonellales was found to be more abundant in low emitters as previously reported for cattle (Wallace et al., 2015). In comparison, using the publicly available tool ANCOM only the order Methanosarcinales could be identified as differentially abundant between the two groups. We also investigated a link between host genome and rumen microbiome by applying our Synomics Insights platform and comparing it with an industry standard GWAS method. This resulted in the identification of genetic determinants in cows that are associated with changes in heritable components of the rumen microbiome. Only four key SNPs were found by both our platform and GWAS, whereas the Synomics Insights platform identified 1,290 significant SNPs that were not found by GWAS. Gene Ontology (GO) analysis found transcription factor as the dominant biological function. We estimated heritability of a core 73 taxa from the original set of 150 core order-level taxonomies and showed that some species are medium to highly heritable (0.25–0.62), paving the way for selective breeding of animals with desirable core microbiome characteristics. We identified a set of 113 key SNPs associated with >90% of these core heritable taxonomies. Finally, we have characterized a small set (<10) of SNPs strongly associated with key heritable bacterial orders with known role in methanogenesis, such as Desulfobacterales and Methanobacteriales.

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Combinatorial analytics: An essential tool for the delivery of precision medicine and precision agriculture

Cited by 8 publications

References 46 publications

Genetic risk factors for ME/CFS identified using combinatorial analysis

Genetic risk factors for ME/CFS identified using combinatorial analysis

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

Host Genome–Metagenome Analyses Using Combinatorial Network Methods Reveal Key Metagenomic and Host Genetic Features for Methane Emission and Feed Efficiency in Cattle

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