Copula directional dependence for inference and statistical analysis of whole‐brain connectivity from <scp>fMRI</scp> data

Lee, Namgil; Kim, Jong Min

doi:10.1002/brb3.1191

Cited by 6 publications

(3 citation statements)

References 58 publications

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“…To infer pairwise relationships between regulatory data types while reducing indirect relations, partial correlation analysis was performed using 'GeneNet' (Opgen-Rhein and Strimmer 2007) for both allelic ratios and total count data. Directional dependence modeling was performed in a regression framework using copulas (Lee and Kim 2019) to infer the flow of information for significant pairwise relationships in partial correlation analyses (Supplemental Methods pg. 26).…”

Section: Allele-specific Changes Across Regulatory Layersmentioning

confidence: 99%

Cis-acting variation is common across regulatory layers but is often buffered during embryonic development

et al. 2020

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Precise patterns of gene expression are driven by interactions between transcription factors, regulatory DNA sequences, and chromatin. How DNA mutations affecting any one of these regulatory “layers” are buffered or propagated to gene expression remains unclear. To address this, we quantified allele-specific changes in chromatin accessibility, histone modifications, and gene expression in F1 embryos generated from eight Drosophila crosses at three embryonic stages, yielding a comprehensive data set of 240 samples spanning multiple regulatory layers. Genetic variation (allelic imbalance) impacts gene expression more frequently than chromatin features, with metabolic and environmental response genes being most often affected. Allelic imbalance in cis-regulatory elements (enhancers) is common and highly heritable, yet its functional impact does not generally propagate to gene expression. When it does, genetic variation impacts RNA levels through two alternative mechanisms involving either H3K4me3 or chromatin accessibility and H3K27ac. Changes in RNA are more predictive of variation in H3K4me3 than vice versa, suggesting a role for H3K4me3 downstream from transcription. The impact of a substantial proportion of genetic variation is consistent across embryonic stages, with 50% of allelic imbalanced features at one stage being also imbalanced at subsequent developmental stages. Crucially, buffering, as well as the magnitude and evolutionary impact of genetic variants, is influenced by regulatory complexity (i.e., number of enhancers regulating a gene), with transcription factors being most robust to cis-acting, but most influenced by trans-acting, variation.

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Section: Allele-specific Changes Across Regulatory Layersmentioning

confidence: 99%

Cis-acting variation is common across regulatory layers but is often buffered during embryonic development

et al. 2020

View full text Add to dashboard Cite

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“…To infer pairwise relationships between regulatory data types while reducing indirect relations, partial correlation analysis was performed using 'GeneNet' (Opgen-Rhein and Strimmer 2007) for both allelic ratios and total count data. Directional dependence modeling was performed in a regression framework using copulas to describe the bivariate distribution between our pairwise datasets (Lee and Kim 2019). Copula regression was used to infer the flow of information for pairwise relationships that showed a significant relationship in partial correlation analyses.…”

Section: Allele-specific Changes Across Regulatory Layersmentioning

confidence: 99%

Cis acting variation is common, can propagates across multiple regulatory layers, but is often buffered in developmental programs

Floc’hlay

Wong

Zhao³

et al. 2020

Preprint

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Precise patterns of gene expression are driven by interactions between transcription factors, regulatory DNA sequence, and chromatin. How DNA mutations affecting any one of these regulatory ‘layers’ is buffered or propagated to gene expression remains unclear. To address this, we quantified allele-specific changes in chromatin accessibility, histone modifications, and gene expression in F1 embryos generated from eight Drosophila crosses, at three embryonic stages, yielding a comprehensive dataset of 240 samples spanning multiple regulatory layers. Genetic variation in cis-regulatory elements is common, highly heritable, and surprisingly consistent in its effects across embryonic stages. Much of this variation does not propagate to gene expression. When it does, it acts through H3K4me3 or alternatively through chromatin accessibility and H3K27ac. The magnitude and evolutionary impact of mutations is influenced by a genes’ regulatory complexity (i.e. enhancer number), with transcription factors being most robust to cis-acting, and most influenced by trans-acting, variation. Overall, the impact of genetic variation on regulatory phenotypes appears context-dependent even within the constraints of embryogenesis.

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“…Questions concerning the causal direction of dependence have been asked in epidemiology (Rosenström et al, 2012), genetics (J. M. Kim et al, 2008), neuroscience (Lee & Kim, 2019), education (Wiedermann et al, 2020b), as well as developmental (von Eye & DeShon, 2012) and clinical psychology (García-Velázquez et al, 2020). As an example, consider Sussman & Gifford’s (2019) recent proposal to consider reverse-causal directions in the theory of planned behavior (Fishbein & Ajzen, 1975)—one of the most important theories to explain human behavior.…”

mentioning

confidence: 99%

Conditional Direction of Dependence Modeling: Application and Implementation in SPSS

Martens

Wiedermann

2022

Social Science Computer Review

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Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation ( x → y vs. y → x) and potential of confounding ( x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only change means, variances, and covariances, but also the distributional shape (i.e., skewness, kurtosis, co-skewness, and co-kurtosis) of another variable given the moderator. Such distributional changes can be used to study underlying mechanisms of heterogenous causal effects. The present study introduces conditional direction of dependence modeling and presents SPSS macros to make CDDA easily accessible to applied researchers. A real-world data example from the field of gambling addiction research is used to introduce the functionality of CDDA SPSS macros. Limitations of CDDA due to violated assumptions and poor data quality are discussed. The CDDA installation package is available at no charge from www.ddaproject.com .

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Copula directional dependence for inference and statistical analysis of whole‐brain connectivity from fMRI data

Cited by 6 publications

References 58 publications

Cis-acting variation is common across regulatory layers but is often buffered during embryonic development

Cis-acting variation is common across regulatory layers but is often buffered during embryonic development

Cis acting variation is common, can propagates across multiple regulatory layers, but is often buffered in developmental programs

Conditional Direction of Dependence Modeling: Application and Implementation in SPSS

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