Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained

The influence of a colony's queen state on the drifting of drone honey bees (Apis mellifera L)

Drones were marked individually with numbered tags and introduced into pairs of hives that were spaced 1 m apart facing south. The queen state of one colony in each pair was altered (ie with a caged-virgin queen, caged-mated queen, mated-laying queen, queenless or pheromone trans-9-oxodecenoic acid) and the other member of the pair remained queenright. Drifting of 4 independent age groups of drones (5-10, 10-15, 15-20 and 20-25-d old) was studied. A higher proportion of drones drifted to colonies with caged-virgin queens or to colonies with lures containing a component of the virgin queen's pheromone, trans-9-oxodec-2-enoic acid, than to either queenless colonies, queenright colonies or colonies with caged-mated queens. The proportion of drones that drifted to colonies containing virgin queens increased with the age of the drone. There was a tendency for drones from queenright colonies to drift westward. This appears to have masked the attraction of drones to pairs of hives that had the pheromone or virgin queen-treated colonies in the westerly position. Drift of drones away from colonies with virgin queens was not significantly lower than drift from either queenright or queenless colonies