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To illustrate the difference between disrupted expression neighborhoods of significant candidate genes and not significant genes, we have added graphs containing the queried neighborhood of the candidates (Figures S1, S2, S3).
At this stage the number of significant candidate genes of inflammation related diseases were T2D ~1231, HT ~1000, OBS ~1845 and ROS ~417.
The number of significant candidate SNPs (genome-wide P < 0.05 correspond to F values of: AFI 23.7163; RFI 23.7750; ADG 23.7011) and the number of SNPs included in the final models for each analysis are shown in Table 2.
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This strategy helps to reduce the number of significant candidates and the time needed to check subsets and supersets.
Given the high number of associations with high p-values, traditional corrections for statistical significance in situations of multiple hypotheses testing (such as the Bonferroni or Benjamini-Hochberg methods) are not applicable – they falsely increase the number of tested hypotheses, reducing the number of significant candidates.
In addition, MMASS had a higher number of significant candidates (B value >0 as cutoff) than did DMH: MMASS-v1 selected 531 differential candidates and MMASS-v2 had 512; whereas DMH-v1 selected 232 and DMH-v2 had 142.
The functional role of some of the significant candidate genes needs to be established.
The complementary use of CGH and expression profiles can facilitate the identification of clinically significant candidate genes involved in medulloblastoma growth.
We have thus used a different, more conservative method for estimating the false positive rate of our ncRNA predictions, which is explained in the section "Identification of highly significant candidate ncRNAs".
We can observe that the closest neighborhood of a significant candidate is highly differentially expressed and belongs to a more disrupted expression module than the neighborhood of a nonsignificant candidate.
Meta-analysis also highlights a number of other significant candidate regions, which are summarised in table 8. Trait-specific QTL effects of varying significance were found on chromosomes 1, 2, 4, 6 and 18, after the merging of BXD and F2 data.
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