“highly significant”

Highly significant associations (log-rank test, P < 2 × 10 –16 ) for all three variables were observed, in the whole series and in the two subsets (Fig. 1 ).

There was significant evidence of genome-wide selection for stripe rust resistance over the 11 years in the population, as demonstrated by a negative G ^ value ( G ^ = − 0.26) and a highly significant ( p = 2 × 10 –16 ) difference between the observed G ^ and the null distribution of 1000 permuted G ^ perm values (Fig. 3 A).

This was found to be a highly significant over-representation by goseq (Bonferroni adjusted p -value < 2 × 10 –16 , see Figure 2A and Supplementary Data File 2 ).

The models (for each plate and for each type of error) explained between 14.32% and 37.38% of the error distribution and were highly significant (P < 2 × 10 -16 ).

This difference in wing length was highly significant (t = −57.479, p < 2e-16).

The difference in this distribution was highly significant ( χ 2 p-value<2e-16).

We obtained a highly significant interaction effect between hosts and isolates nested within each population for virulence (degrees of freedom = 1,540, F = 2.11, p < 2e −16 ) and reproduction (degrees of freedom = 1,540, F = 2.68, p < 2e −16 ).

This leads to a highly significant coefficient ( β ˆ = 4.68 ; p<2e-16).

We note that this overlap was highly significant ( Figure 2b , Fisher test P <2×10 −16 ), suggesting that age and tumor presence elicit common changes in the DNAm pattern of peripheral blood.

To statistically confirm the effectiveness of this discrimination, we also conducted an ANOVA test, which showed a highly significant separation among the groups (F 2, 117 = 355.6, p < 2e –16 ).

Results Bacterial quantification The effect of extraction methodology on the concentration of the bacterial 16S rRNA gene was highly significant (Repeated measures ANOVA, F = 61.5, P < 2 × 10 −16 ; Fig. 1 A).

To determine the significance of the results, we fit three one-way analysis of variance (ANOVA) models comparing ssGSEA scores of CD4, CD8, and macrophage cell type markers, which were all highly significant ( p = 2 × 10 − 16 , p = 6.28 × 10 − 15 and p = 2 × 10 − 16 , respectively).

t-PGCS DNMs and likely post-zygotic DNMs) we first fit a simple Poisson regression model that calculated the effect of paternal age on total autosomal DNM counts in the R statistical language (v3.5.1) as follows: glm(autosomal_dnms ~ dad_age, family = poisson(link='identity’)) This model returned a highly significant effect of paternal age on total DNM counts (1.72 DNMs per year of paternal age, p<2e-16), but was agnostic to the family from which each third-generation individual was ‘sampled.’ Importantly, a number of third-generation individuals in the CEPH/Utah cohort share grandparents, and may therefore be considered members of the same family, despite having unique second-generation parents ( Figure 3—figure supplement 1 ).

In parallel, a linear model assessing the direct association between mean relative abundance and response demonstrated a highly significant positive effect (estimate = 0.30, P < 2.0e−16), with dataset terms again showing no significant influence (all P > 0.05), underscoring the robustness of abundance-based signals across cohorts.

A model without interaction showed a highly significant effect of voxel on spectral quality ( Fig. 3 a ; F (1,79.12) = 183.73, p < 2 × 10 −16 ), with LO linewidth broader than EVC.

Finally, note that RRF was actually so fast at small chunk sizes that there was a significant overhead associated with our parallelization of the algorithm (Methods), as linear models fitted to only chunk sizes less than 200 were highly significant ( P <2.00×10 −16 ), with negative slopes (Fig. 1 b).

Smooth terms for latitude and longitude were highly significant (p < 2 × 10−16), further confirming the spatial structure of life expectancy outcomes.

found that incorporating more than 2000 PRSs to predict coronary artery disease, the increase in AUC ranged from 0.003 to 0.023 compared to a model without MPS, and the p-value for the increase in AUC were all highly significant ( p < 2e-16) 19 .

Specifically, comparisons between the ≥5-year group and both the 3–4-year and 0–2-year groups were highly significant (both p < 2 × 10 −16 ), as was the comparison between the 3–4-year and 0–2-year groups ( p = 0.0037).

The regression coefficients for height, weight, age and sex were all highly significant (p<2x10 -16 ) but none of the principal components for ancestry had a significant association.

We found highly significant differences ( p < 2 e - 16 ) between most cluster pairs at K = 5, except clusters 2 and 5 ( p ≈ 1 ) and clusters 2 and 3 ( p = 1.2 e - 09 ).

General linear model showed that clinical symptoms have a highly significant effect on the number of cMBs (N = 2.78, 95%CI [2.51–3.07], p<2*10–16; ndf = 78, AIC = 1218).

A highly significant difference in allelic frequencies was observed across all analyzed polymorphisms (chi-squared test, p < 2 −16 ).

As expected, the PY effect was highly significant (P < 2 × 10 −16 ), and the smoking group W5Y remained significant after correcting for PY (P = 0.008).

3 ), with a highly significant genotype effect ( p -value < 2 × 10 −16 ).