“nominally significant”

After filtering to the 176 clock-disease associations that were Bonferroni significant in the Cox models, there were 32 instances where the AUC improvement between the null and full model was greater than 0.01 and nominally significant at P < 0.05 (Fig. 2 and Supplementary Data 8 ).

Leave-one-out Analyses To identify gene-based results driven by one or more variants, we applied the following leave-one-out strategy: 1- among the variants seen more than twice in our stage 1 sample (cases and controls together), we identified the variant with the lowest single-variant analysis p value whenever it is nominally significant (p < 0.05); 2- we removed this variant and performed the stage 1 gene-based test again.

After conducting heritability analysis across 139 immune traits, we identified 10 immune traits with significant heritability (FDR p < 0.05), and 23 other traits with nominally significant heritability (p < 0.05) in at least one ancestry group.

Genetic pathway analysis We identified a gene set by selecting all genes showing nominally significant expression changes ( P ≤ 0.05) in our data set, and we tested if our gene set was enriched for associative signal with two phenotypes: (i) hippocampal volume in adults; (ii) antidepressant response.

In total, 433 of the 765 (57%) were concordantly nominally significant in the NFPCS under additive models ( P ≤ 0.05, Supplementary Fig. 1 and Supplementary Data File 1 ) .

All 15 representative traits remained nominally significant (p<0.05) and showed consistent effect directions between marginal and conditional analyses ( Supplementary file 1 ).

The conditional analysis shows that 45 (including 42 genes) out of the total 74 (~60.8%) unique associations are still nominally significant when conditioned on known GWAS variants ( p < 0.05, Supplementary Table S4 ).

Model 4 constrains the MZ pair correlation to be twice the correlation for DZ and sister pairs combined, and the comparison of the log-likelihoods with those of Model 2 shows that, except for Cirrocumulus , there was at least nominally significant evidence that the MZ correlations were more than twice the corresponding correlations for DZ and sister pairs combined (all p ≤ 0.05).

Pooled analyses were considered to be nominally significant if the effect estimate had a P value <0.05.

Replicated nominally significant results from ANOVA (p<0.05) were tested using a two sample t-test on body height according to genotype distribution ( Figure 1 ).

A few traits exhibited nominally significant associations ( p < 0.05), but the effect directions were inconsistent across traits and sensitivity analyses (weighted median, MR Egger) did not uniformly support these findings.

However, in successive models of Model 3 and 4 (addition of POAG established and suspected risk factors), and Model 5 (further adjust for co-morbidities), we observed that while no metabolite exhibited significance at the NEF < 0.2 level (except for LPC(16:0) in Model 3), all were nominally significant and the direction of associations for the metabolites was similar ( p < 0.05).

Because statistical power to evaluate >1000 phecodes would be modest, we restricted our attention to phenotypes that were nominally significant (ie, p value <0.05) and were previously associated with Lp(a).

We had 80% power to detect nominally significant associations (p = 0.05) with SNPs that account for at least 0.8% of a trait's variance (full sample) or at least 1.0% of variance (individuals with MRIs).

Post hoc contrasts revealed that REM theta energy shows a negative statistical trend with the response of the anterior–superior hypothalamus (t = −1.81; p = 0.07) and a positive nominally significant association with the response of the posterior hypothalamus (t = 1.90; p = 0.05; Figure 2 A,B).

In total, 116 associations were identified to be associated with AF at the nominally significant level ( p < 0.05, Supplementary Table S4 ).

A total of 26 metabolites comprising 15 known metabolites and 11 unknown metabolites displayed a nominally significant relationship ( p < 0.05, IVW method) with CHD risk ( Table 1 ).

All variables were tested for bivariate association with the primary clinical endpoint and if nominally significant ( P < 0.05) were simultaneously forced into a hierarchical multivariate Cox regression model to identify independent OCT outcome predictors and to calculate their adjusted hazard ratio (HR).

Nominally significant interactions ( P <0.05) were found for genetic variants in MVK , LIPC , PABPC 4, AMPD 3 with change in high‐density lipoprotein cholesterol; SPTLC 3 with change in low‐density lipoprotein cholesterol; TOM 1 with change in total cholesterol; PDXDC 1 and CYP 26A1 with change in triglycerides; and none for lipoprotein (a).

Despite limited overlap at the single-metabolite level between the two overall OC outcomes, we observed greater concordance at the pathway level: among nominally significant pathways (raw p < 0.05), arginine and proline metabolism and beta-alanine metabolism overlapped across the two outcomes ( Figure 1 E; Supplementary Table S4 ).

Only four of the 23 leading-edge genes (GMPPA, CYP11B1, POR, SMO) are themselves at least nominally significant ( p <0.05).

The associations between birth date and birth month with BRS were nominally significant ( P < 0.05) with those born across the summer months scoring lower on the BRS; however, these associations did not remain after correction for multiple testing.

Furthermore, these variants exhibited directionally concordant and nominally significant associations ( p < 0.05) with glycated hemoglobin levels in the UK Biobank ( n = 344,182) [ 34 ].

Most of these associations remained significant or nominally significant ( P <0.05) in analyses with adjustment for other LE8 components.

After these corrections, the average reference allele frequency at all heterozygous sites was 0.5, and 50.02% of nominally significant ( P < 0.05) allele-specific variants favored the reference allele ( Fig. 1 B).