Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Sall, Saveen; Thompson, Willie; Santos, Aurianna; Dwyer, Donard S.

doi:10.3389/fpsyt.2021.698029

Cited by 15 publications

(25 citation statements)

References 72 publications

(122 reference statements)

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“…Consistent with the studies of gene interactions in bipolar disorder and depression [ 34 , 35 ], candidate risk genes for schizophrenia showed greater network interaction than random gene sets of the same size. These gene–gene interactions may cause network ripple effects among the connected genes that amplify the impact of small individual VAOs.…”

Section: Discussionsupporting

confidence: 80%

“…To determine the statistical significance for comparisons between the number of genetic interactions among genes in the different sets, we first calculated the mean and standard deviation from data derived from 4 random lists of genes expressed as the number of links per gene obtained from GeneMANIA, as previously published [ 34 , 35 ]. Based on the standard deviations from the random gene lists, we established confidence intervals (CIs) for significant differences at the p < 0.01 or p < 0.05 levels.…”

Section: Methodsmentioning

confidence: 99%

“…Although epistasis is acknowledged as a theoretical contributor, the magnitude of its effects is viewed as ranging from negligible [ 27 , 28 ] to highly significant [ 29 , 30 , 31 , 32 ]. Gene interactions may help to explain the so-called missing heritability in psychiatric disorders [ 33 ], and genetic connectivity is extensive among the risk genes for bipolar disorder [ 34 ] and major depressive disorder [ 35 ]. Davierwala et al [ 36 ] reported that essential genes are much more interactive than non-essential genes and schizophrenia risk genes are highly enriched for ones that are essential for life and broadly conserved during evolution [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Kasap

Dwyer

2022

Genes

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Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene–gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.

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Section: Discussionsupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Kasap

Dwyer

2022

Genes

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“…Additionally, in Crohn’s disease, CONTENT implicated the following genes, whereas previous methods did not: STAT3 56 and CTBP2 57 with GTEx weights, as well as ATG16L 58 and PKAR2A 59 using CLUES weights. For major depression disorder (MDD), CONTENT implicated SYN2M 60 and CYB56AD1 61 using GTEx weights, and GAB1 62 , TLR4 61 and ARL3 63 using CLUES weights.…”

Section: Resultsmentioning

confidence: 99%

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

Thompson

Gordon

et al. 2022

Nat Commun

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A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT—a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

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“…Here, we summarize connections revealed in studies of the nematode, C. elegans , that suggest certain aspects of suicidal behavior may be fundamental to life, evolutionarily conserved and controlled by genetics. Previously, we ( 59 ) identified a state of diminished motivation in C. elegans regulated by signaling pathways (e.g., Akt) associated with psychosis ( 60 ) and major depressive disorder ( 61 ). Animals with defects in the insulin receptor gene and downstream signaling components fail to forage in response to food deprivation and will remain in place until they die ( 59 ).…”

Section: Catalysis Of Stbs By Genetic and Downstream Biological Factorsmentioning

confidence: 99%

Catalytic Reaction Model of Suicide

McPherson¹,

Sall²,

Santos³

et al. 2022

Front. Psychiatry

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Suicide is a devastating outcome of unresolved issues that affect mental health, general wellbeing and socioeconomic stress. The biology of suicidal behavior is still poorly understood, although progress has been made. Suicidal behavior runs in families and genetic studies have provided initial glimpses into potential genes that contribute to suicide risk. Here, we attempt to unify the biology and behavioral dimensions into a model that can guide research in this area. The proposed model envisions suicidal behavior as a catalytic reaction that may result in suicide depending on the conditions, analogously to enzyme catalysis of chemical reactions. A wide array of substrates or reactants, such as hopelessness, depression, debilitating illnesses and diminished motivation can mobilize suicidal thoughts and behaviors (STBs), which can then catalyze the final step/act of suicide. Here, we focus on three biological substrates in particular: threat assessment, motivation to engage in life and impulsivity. Genetic risk factors can affect each of these processes and tilt the balance toward suicidal behavior when existential crises (real or perceived) emerge such as loss of a loved one, sudden changes in social status or serious health issues. Although suicide is a uniquely human behavior, many of the fundamental biological processes are evolutionarily conserved. Insights from animal models may help to shape our understanding of suicidal behavior in man. By examining counterparts of the major biological processes in other organisms, new ideas about the role of genetic risk factors may emerge along with possible therapeutic interventions or preventive measures.

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Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Cited by 15 publications

References 72 publications

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

Catalytic Reaction Model of Suicide

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