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On the split-pedigree data, 781 individuals are available for FAM-MDR without main effects correction, and 726 for FAM-MDR with main effects correction.
PGMDR without main effects correction does not lead to a significant interaction.
Every analysis is carried out once with and without main effects correction.
When performing a main effects correction, the same best interaction model is identified.
Without main effects correction, FAM-MDR on the original data uses all 620 individuals with GAUC value available.
As a result, PGMDR uses only 537 individuals in an analysis without correction for main effects, and 501 individuals in an analysis with main effects correction, amounting to reductions of 13% and 12% compared to the corresponding FAM-MDR analyses.
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None of the SNPs were found to have statistically significant main effects after correction for multiple testing.
When we used in the MLM model the structure based on the STS detected on chromosome 2 (all or a subset excluding the positions with the main effects), the correction was much better, and many associations were no more significant (Table 2), confirming that many associations could be due to the structure.
Hence, in reality the balance between necessary corrections for important main effects and avoiding over-correction needs to be considered to optimize the performance of any epistasis detection method.
Furthermore, Marchini et al (2005) simulated data for 300 000 loci in a similar sized case control study to ours (2000/2000) under plausible scenarios for epistatic interaction and showed that the logistic regression method has reasonable power to detect gene gene interactions even in the absence of main effects and with conservative correction for multiple testing.
The analysis with correction for main effects leads to the detection of two-locus effects beyond co-dominant main effects.
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