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The science of siestas: New research reveals the genetic basis for daytime napping -- ScienceDaily

For this study, the MGH researchers and their colleagues used data from the UK Biobank, which includes genetic information from 452,633 people. All participants were asked whether they nap during the day "never/rarely," "sometimes" or "usually." The GWAS identified 123 regions in the human genome that are associated with daytime napping. A subset of participants wore activity monitors called accelerometers, which provide data about daytime sedentary behavior, which can be an indicator of napping. This objective data indicated that the self-reports about napping were accurate. "That gave an extra layer of confidence that what we found is real and not an artifact," says Dashti. Several other features of the study bolster its results. For example, the researchers independently replicated their findings in an analysis of the genomes of 541,333 people collected by 23andMe, the consumer genetic-testing company. Also, a significant number of the genes near or at regions identified by the GWAS are already known to play a role in sleep. One example is KSR2, a gene that the MGH team and collaborators had previously found plays a role in sleep regulation.

After GWAS studies, how to narrow the search for genes? -- ScienceDaily

Borrowing the machine-learning concept of "cross-validation," Benchmarker enables investigators to use the GWAS data itself as its own control. The idea is to take the GWAS dataset and single out one chromosome. The algorithm being benchmarked then uses the data from the remaining 21 chromosomes (all but X and Y) to make predictions about what genes on the single chromosome are most likely to contribute to the trait being investigated. As this process is repeated for each chromosome in turn, the genes that the algorithm has flagged are pooled. The algorithm is then validated by comparing this group of prioritized genes with the original GWAS results. "You train the algorithm on the GWAS with one chromosome withheld, then go back to that chromosome and ask whether those genes were actually associated with a strong p-value in the original GWAS results," explains Fine. "While these p-values don't represent the exact 'right answers,' they do tell you roughly where some true genetic associations are. The end product is an evaluation of how each algorithm performed."