15 research outputs found

    Latent variable model for stroking items and IBQ negative emotionality.

    No full text
    <p>The figure shows the factor loadings for mothers' reports of stroking at 5 and 9 weeks, and IBQ negative emotionality (Distress to Limitations and Fear) at 29 weeks. The values of λ (direct effect of stroking on IBQ), δ and β (associations of risks and confounders with stroking and IBQ respectively), and γ (interaction between maternal stroking and prenatal maternal depression), are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045446#pone-0045446-t004" target="_blank">Table 4</a>.</p

    Interaction between maternal stroking and prenatal depression on infant vagal withdrawal.

    No full text
    <p>Simple regression lines and 95% confidence envelopes showing the interaction between maternal reports of stroking (median split) and prenatal depression, with infant vagal withdrawal at 29 weeks (p = 0.01 from multivariate regression).</p

    Interaction between maternal stroking and prenatal depression on infant distress to limitations.

    No full text
    <p>Simple regression lines and 95% confidence envelopes showing the interaction between maternal reports of stroking (median split) and prenatal depression, with infant IBQ distress to limitations at 29 weeks (p = 0.007 from multivariate regression).</p

    Latent variable model for stroking items and respiratory sinus arrhythmia (RSA) estimate of vagal tone.

    No full text
    <p>The figure shows the factor loadings for mothers' reports of stroking at 5 and 9 weeks, and RSA at 29 weeks. The values of λ (direct effect of stroking on vagal withdrawal), δ<sub>1</sub>, δ<sub>2</sub>, and β (associations of risks and confounders with stroking, vagal tone and vagal withdrawal respectively), and γ (interaction between maternal stroking and prenatal maternal depression), are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045446#pone.0045446-t003" target="_blank">Table 3</a>.</p

    Additional file 3: of PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores

    No full text
    Figure S2. PRSice v1.25, PRSice-2, and PRSoS performance across datasets. Bar plot shows the results of the performance test comparing running PRSice v1.25, PRSice-2, and PRSoS across the datasets. Processing time (y-axis) uses a log base 10 scale. Error bars indicate standard deviations. Numbers in boxed inserts indicate the size of the genotype data input. †Note that the file sizes used for the Imputed PP are same for PRSice v1.25 and PRSoS, thus illustrating the processing speed difference with same file size input. Genotype input formats are different across all three software for the other performance tests. Imputed PP = imputed posterior probabilities, Imputed HC = imputed posterior probabilities converted to “hard calls”, Array Data = observed genotypes. (PDF 34 kb

    Additional file 2: of PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores

    No full text
    Figure S1. PRSice v1.25 and PRSoS performance across the number of cores used to generate PRS and five thresholds using the Imputed Hard Call dataset. PRSice v1.25 could only run on 1 core. PRSoS performance was tested with 1, 4, 12, 20, and 24 cores on a Linux CentOS 7, 24-core Intel Xeon server. Error bars indicate standard deviations. (PDF 4 kb
    corecore