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Similarly, the combination of weak effects on drug resistance due to expression changes at many genes may be more significant than the effect of any single gene.
The larger size of this data set could reduce the effect of sampling and may facilitate the detection of weak effects.
Another notable GWAS success story (17, 18), Crohn's disease, can be reasonably well predicted by a large number of weak effects.
Despite the co-localization of the compensatory interactions, at this point it is not clear whether or not these compensations are specific, such as those expected in strong interactions, or general, as may be expected for mutations of weak effects.
As illustrated in additional file 1: SupFig1, minor genetic factors of weak effects that cannot be detected (and, therefore, not included in the simulations) may modify the expected responses to selection significantly.
Finally, no comparisons have been made using genomic data involving very large numbers of loci (e.g. thousands of genes) and it is possible that larger genomic samples may allow the detection of weak effects of selfing despite the stochasticity and slow rate of mutation accumulation.
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Because of its larger population size, fewer advantageous mutations will be effectively neutral in D. melanogaster and many adaptive substitutions of weak effect can fix in this species.
Subsequent analyses indicate that the causes of ASDs include rarer single gene mutations and chromosomal abnormalities, as well as ASDs caused by multiple interacting genes of weak effect.
This does not exclude SNPs of weak effect or those showing heterogeneity between the sample collections.
Indeed, a major drawback of GWASs is the observation of many significant association signals from loci of weak effect.
Evidence from family studies suggest some unidentified common gene variants (presumably of weak effect) are likely to be important in the aetiology of ASD.
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