cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1384844.v1
title: Prenatal antidepressant exposure associated with <i>CYP2E1</i> DNA methylation change in neonates

description: Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these two exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at <i>CYP2E1</i>(β<sub>Non-exposed</sub> = 0.06, β<sub>SRI-exposed</sub> = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of <i>CYP2E1</i> regulatory regions in an expanded cohort, higher DNA methylation status—both the mean across 16 CpG sites (<i>P</i> &lt; 0.01) and at each specific CpG site (<i>P</i> &lt; 0.05)—was associated with exposure to lower 3<sup>rd</sup> trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood × SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (<i>P</i> = 0.04), CpG9 (<i>P</i> = 0.04) and CpG10 (<i>P</i> = 0.02), in the interrogated <i>CYP2E1</i> region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal <i>CYP2E1</i> methylation status, which, in turn, appeared to be associated with birth weight.
DOI: 10.6084/m9.figshare.1384844.v2
title: Prenatal antidepressant exposure associated with <i>CYP2E1</i> DNA methylation change in neonates

description: Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these 2 exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at <i>CYP2E1</i>(β<sub>Non-exposed</sub> = 0.06, β<sub>SRI-exposed</sub> = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of <i>CYP2E1</i> regulatory regions in an expanded cohort, higher DNA methylation status—both the mean across 16 CpG sites (<i>P</i> &lt; 0.01) and at each specific CpG site (<i>P</i> &lt; 0.05)—was associated with exposure to lower 3rd trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood x SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (<i>P</i> = 0.04), CpG9 (<i>P</i> = 0.04) and CpG10 (<i>P</i> = 0.02), in the interrogated <i>CYP2E1</i> region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal <i>CYP2E1</i> DNA methylation status, which, in turn, appeared to be associated with birth weight.
DOI: 10.6084/m9.figshare.1384844.v3
title: Prenatal antidepressant exposure associated with <i>CYP2E1</i> DNA methylation change in neonates

description: Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these 2 exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at <i>CYP2E1</i>(β<sub>Non-exposed</sub> = 0.06, β<sub>SRI-exposed</sub> = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of <i>CYP2E1</i> regulatory regions in an expanded cohort, higher DNA methylation status—both the mean across 16 CpG sites (<i>P</i> &lt; 0.01) and at each specific CpG site (<i>P</i> &lt; 0.05)—was associated with exposure to lower 3rd trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood x SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (<i>P</i> = 0.04), CpG9 (<i>P</i> = 0.04) and CpG10 (<i>P</i> = 0.02), in the interrogated <i>CYP2E1</i> region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal <i>CYP2E1</i> DNA methylation status, which, in turn, appeared to be associated with birth weight.
DOI: 10.6084/m9.figshare.1384844.v5
title: Prenatal antidepressant exposure associated with <i>CYP2E1</i> DNA methylation change in neonates

description: Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these 2 exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at <i>CYP2E1</i>(β<sub>Non-exposed</sub> = 0.06, β<sub>SRI-exposed</sub> = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of <i>CYP2E1</i> regulatory regions in an expanded cohort, higher DNA methylation status—both the mean across 16 CpG sites (<i>P</i> &lt; 0.01) and at each specific CpG site (<i>P</i> &lt; 0.05)—was associated with exposure to lower 3rd trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood x SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (<i>P</i> = 0.04), CpG9 (<i>P</i> = 0.04) and CpG10 (<i>P</i> = 0.02), in the interrogated <i>CYP2E1</i> region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal <i>CYP2E1</i> DNA methylation status, which, in turn, appeared to be associated with birth weight.
DOI: 10.6084/m9.figshare.1384844.v4
title: Prenatal antidepressant exposure associated with <i>CYP2E1</i> DNA methylation change in neonates

description: Some but not all neonates are affected by prenatal exposure to serotonin reuptake inhibitor antidepressants (SRI) and maternal mood disturbances. Distinguishing the impact of these 2 exposures is challenging and raises critical questions about whether pharmacological, genetic, or epigenetic factors can explain the spectrum of reported outcomes. Using unbiased DNA methylation array measurements followed by a detailed candidate gene approach, we examined whether prenatal SRI exposure was associated with neonatal DNA methylation changes and whether such changes were associated with differences in birth outcomes. Prenatal SRI exposure was first associated with increased DNA methylation status primarily at <i>CYP2E1</i>(β<sub>Non-exposed</sub> = 0.06, β<sub>SRI-exposed</sub> = 0.30, FDR = 0); however, this finding could not be distinguished from the potential impact of prenatal maternal depressed mood. Then, using pyrosequencing of <i>CYP2E1</i> regulatory regions in an expanded cohort, higher DNA methylation status—both the mean across 16 CpG sites (<i>P</i> &lt; 0.01) and at each specific CpG site (<i>P</i> &lt; 0.05)—was associated with exposure to lower 3rd trimester maternal depressed mood symptoms only in the SRI-exposed neonates, indicating a maternal mood x SRI exposure interaction. In addition, higher DNA methylation levels at CpG2 (<i>P</i> = 0.04), CpG9 (<i>P</i> = 0.04) and CpG10 (<i>P</i> = 0.02), in the interrogated <i>CYP2E1</i> region, were associated with increased birth weight independently of prenatal maternal mood, SRI drug exposure, or gestational age at birth. Prenatal SRI antidepressant exposure and maternal depressed mood were associated with altered neonatal <i>CYP2E1</i> DNA methylation status, which, in turn, appeared to be associated with birth weight.
DOI: 10.6084/m9.figshare.1384849
title: Algorithms for Envelope Estimation

description: Envelopes were recently proposed as methods for reducing estimative variation in multivariate linear regression. Estimation of an envelope usually involves optimization over Grassmann manifolds. We propose a fast and widely applicable one-dimensional (1D) algorithm for estimating an envelope in general. We reveal an important structural property of envelopes that facilitates our algorithm, and we prove both Fisher consistency and n-consistency of the algorithm.
DOI: 10.6084/m9.figshare.1384849.v1
title: Algorithms for Envelope Estimation

description: Envelopes were recently proposed as methods for reducing estimative variation in multivariate linear regression. Estimation of an envelope usually involves optimization over Grassmann manifolds. We propose a fast and widely applicable one-dimensional (1D) algorithm for estimating an envelope in general. We reveal an important structural property of envelopes that facilitates our algorithm, and we prove both Fisher consistency and n-consistency of the algorithm.
DOI: 10.6084/m9.figshare.1385367
title: False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

description: This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1385367.v1
title: False discovery rate regression: an application to neural synchrony detection in primary visual cortex

description: This paper introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a pre-specified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper.
DOI: 10.6084/m9.figshare.1385367.v2
title: False discovery rate regression: an application to neural synchrony detection in primary visual cortex

description: This paper introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a pre-specified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper.
DOI: 10.6084/m9.figshare.1385367.v3
title: False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

description: This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1385367.v4
title: False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

description: This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1385367.v5
title: False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

description: This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1385367.v6
title: False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex

description: This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390605
title: Quantile Correlations and Quantile Autoregressive Modeling

description: In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box–Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from classical autoregressive models to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the fitted model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR and QPCOR, but also show the large sample results of QACF, QPACF, and the quantile version of the Box–Pierce test. Moreover, we obtain the bootstrap approximations to the distributions of parameter estimators and proposed measures. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate usefulness. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390605.v1
title: Quantile Correlations and Quantile Autoregressive Modeling

description: In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box–Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from classical autoregressive models to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the fitted model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR and QPCOR, but also show the large sample results of QACF, QPACF, and the quantile version of the Box–Pierce test. Moreover, we obtain the bootstrap approximations to the distributions of parameter estimators and proposed measures. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate usefulness. Supplementary materials for this article are available online.