Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology

D'Silva, Sheldon; Chakraborty, Shreya; Kahali, Bratati

doi:10.1038/s41598-022-11270-0

Cited by 9 publications

(5 citation statements)

References 72 publications

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“…Another essential gene for obesity, MC4R ( Loos et al, 2008 ) was not selected because it was filtered out for having less than three SNPs mapped. A recent epistasis analysis found two pairs of SNPs whose interactions were associated with BMI ( FTO – MC4R and RHBDD1 – MAPK1 ) ( D’Silva et al, 2022 ). SmCCNet did not detect RHBDD1 or MAPK1 , possibly caused by CCA’s focus on inter-modal rather than intra-modal interactions.…”

Section: Resultsmentioning

confidence: 99%

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Li,

Melograna,

Hoskens

et al. 2023

Front. Genet.

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Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up.Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations.Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups.Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

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Section: Resultsmentioning

confidence: 99%

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Li,

Melograna,

Hoskens

et al. 2023

Front. Genet.

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“…Even though, a number of epistatic interactions may affect body mass index [50], GWAS statistics revealed nearly a thousand of SNPs highly associated with the BMI (with p-values < 1×10 − 8 ) [51]. Therefore, it is possible that the impact of these interactions can be overshadowed by SNPs with linear contribution to phenotype.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning captures the effect of epistasis in multifactorial diseases

Perelygin,

Kamelin,

Syzrantsev

et al. 2024

Preprint

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Background Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as Alzheimer's disease, diabetes, cardiovascular disease, cancer, and others, association between individual SNPs and disease could be non-linear due to epistatic interactions. The aim of the presented study is to explore the power of non-linear machine learning algorithms and deep learning models to predict the risk of multifactorial diseases with epistasis. Results First, we tested ensemble tree methods and deep learning neural networks against LASSO linear regression model on simulated data with different types and strength of epistasis. The results showed that with the increase of strength of epistasis effect, non-linear models significantly outperform linear. Then the higher performance of non-linear models over linear was confirmed on real genetic data for multifactorial phenotypes such as obesity, type 1 diabetes, and psoriasis. From non-linear models, gradient boosting appeared to be the best model in obesity and psoriasis while deep learning methods significantly outperform linear approaches in type 1 diabetes. Conclusions Overall, our study underscores the efficacy of non-linear models and deep learning approaches in more accurately accounting for the effects of epistasis in simulations with specific configurations and in the context of certain diseases.

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“…Complex traits, such as OUD, are controlled by many genes that interact with each other and their environment (Crist et al, 2019). While improved statistical approach (D’Silva et al,2022; Wei et al, 2014) and novel machine learning methods (Chicco and Faultless, 2021) are starting to enable the study of epistasis in human data, most human genetic studies either did not have the power to detect epistasis or ignored its effect (Carlborg and Haley, 2004;Uffelmann et al, 2021). In contrast, many epistatic interactions have been identified using model organisms (Mackay, 2014), mostly due to the ability to obtain data from individuals with controlled genotypes and measuring phenotypes in well controlled environments (Campbell et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Opiate responses are controlled by interactions ofOprm1andFgf12loci in rodents: Correspondence to human GWAS findings

Lemen

Zuo²,

Hatoum

et al. 2022

Preprint

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We analyzed time dependent behavioral responses to morphine and naloxone obtained from a large family of young adult BXD mice (n = 70 strains, including C57BL/6J and DBA/2J parents, 4 to 9 cases per strain) using the latest whole genome sequencing (WGS)-based genetic markers. These data include quantitative locomotor and behavior responses measured three hours after an acute morphine injection (50 mg/kg i.p.), followed by naloxone-induced withdrawal, obtained by Philip et al (2010). Locomotor data were analyzed in 15 min bins and mapped jointly for both sexes or independently for males and females. We confirmed a highly significant association between locomotor response and a genomic region that overlaps with Oprm1 on Chr 10 at 6.8 Mb (LOD maximum of 11.4) between 15-105 min, with a peak at 75 min. Effects were modestly dependent of sex. Strains that were B homozygotes run 76 meters farther than those that are D homozygotes after a morphine injection. We discovered a novel association between a locus on Chr 16 and a late phase locomotor response (after 150 min) in both sexes. This locus had a significant but transient epistatic interaction with the Oprm1 locus between 45-90 min, well before the main effect was detectable. The Chr 16 locus includes one compelling candidate fibroblast growth factor 12 (Fgf12). Null mutation of Fgf12 has been shown to cause locomotor deficits (e.g., ataxia) in mice. Analysis of genes correlated with both OPRM1 and FGF12 in six human brain regions (GTEx, v8) demonstrated an enrichment of genetic signals associated with SUD phenotypes, and a modest corroboration of variants in the FGF12 loci on Chr 3q28. To the best of our knowledge this is the first demonstration of a transient time dependent epistatic interaction modulating drug response in mammals; a finding with interesting mechanistic implications. Finally, this work demonstrates how high-quality FAIR+ data can be used with newly acquired data sets to yield striking results, and how joint mouse and human neurogenomic and mapping data can be merged at gene and network levels for bidirectional validation of potential SUD variants and molecular networks.

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Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology

Cited by 9 publications

References 72 publications

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Deep Learning captures the effect of epistasis in multifactorial diseases

Opiate responses are controlled by interactions ofOprm1andFgf12loci in rodents: Correspondence to human GWAS findings

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