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False and true positives in arthropod thermal adaptation candidate gene lists

Citation

Yampolsky, Lev; Herrmann, Maike (2022), False and true positives in arthropod thermal adaptation candidate gene lists, Dryad, Dataset, https://doi.org/10.5061/dryad.m0cfxpp3r

Abstract

Genome-wide studies are prone to false positives due to inherently low priors and statistical power. One approach to ameliorate this problem is to seek validation of reported candidate genes across independent studies: genes with  repeatedly discovered effects are less likely to be false positives. Inversely, genes reported only as many times as expected by chance alone, while possibly representing novel discoveries, are also more likely to be false positives. We show that, across over 30 genome-wide studies that reported Drosophila and Daphnia genes with possible roles in thermal adaptation, the combined lists of candidate genes and orthologous groups are rapidly approaching the total number of genes and orthologous groups in the genome, respectively, consistent with the expectation of high frequency of false positives. The majority of these spurious candidates have been identified by one or a few studies, as expected by chance alone. In contrast, a noticeable minority of genes have been identified by numerous studies with the probabilities of such discoveries occurring by chance alone being exceedingly small. For this subset of genes, different studies are in agreement with each other despite differences in the ecological settings, genomic tools and methodology, and reporting thresholds.  We provide a reference set of presumed true positives among Drosophila candidate genes and orthologous groups involved in response to changes in temperature, suitable for cross-validation purposes. Despite this approach being prone to false negatives, this list of presumed true positives includes several hundred genes, consistent with the "omnigenic" concept of genetic architecture of complex traits.

Methods

Meta-analysis of literature data. Studies included used either transcriptomics or GWAS/local adaptation approaches.