Biology subjects

Yeaman, S.

Publications and source records attributed to Yeaman, S..

4 recordsLinked to original sources

bioRxiv2018

Quantifying how constraints limit the diversity of viable routes to adaptation

Convergent adaptation can occur at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel metric to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This metric is explicitly based on the probability of observing a given amount of repeatability by chance under an arbitrary null hypothesis, and is readily compared among different species and types of trait. We also formulate a metric to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these metrics can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to antibiotics in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution.

evolutionary biology↗

bioRxiv2017

Effect of migration and environmental heterogeneity on the maintenance of quantitative variation: a simulation study

The paradox of high genetic variation observed in traits under stabilizing selection is a longstanding problem in evolutionary theory, as mutation rates are 10-100 times too low to explain observed levels of standing genetic variation under classic models of mutation-selection balance. Here, we use individual-based simulations to explore the effect of various types of environmental heterogeneity on the maintenance of genetic variation (VA) for a quantitative trait under stabilizing selection. We find that VA is maximized at intermediate migration rates in spatially heterogeneous environments, and that the observed patterns are robust to changes in population size. Spatial environmental heterogeneity increased variation by as much as 10-fold over mutation-selection-balance alone, whereas pure temporal environmental heterogeneity increased variance by only 45% at max. Our results show that some combinations of spatial heterogeneity and migration can maintain considerably more variation than mutation-selection balance, potentially reconciling the discrepancy between theoretical predictions and empirical observations. However, given the narrow regions of parameter space required for this effect, this is unlikely to provide a general explanation for the maintenance of variation. Nonetheless, our results suggest that habitat fragmentation may affect the maintenance of VA and thereby reduce the adaptive capacity of populations.

evolutionary biology↗

bioRxiv2017

Modular environmental pleiotropy of genes involved in local adaptation to climate despite physical linkage

BackgroundPhysical linkage among genes shaped by different sources of selection is a fundamental aspect of genetic architecture. Theory predicts that evolution in complex environments selects for modular genetic architectures and high recombination rates among loci shaped by different sources of selection. However, limited data exist to test these predictions because the field lacks consensus for how to control for intercorrelated environmental variables. Here, we introduce a co-association network analysis, which clusters loci based on differing environmental associations, and use it to study the genetic architecture of local adaptation to climate in lodgepole pine (Pinus contorta).\n\nResultsWe identified many modules of genes associated with distinct environments (aridity, freezing, geography), and discovered low recombination rates among some candidate genes in different modules. We observed limited evidence for environmental pleiotropic effects on distinct aspects of climate. We also found limited correspondence between the modularity of co-association networks and gene regulatory networks. We compared co-association networks to associations with principal components, and found the latter can lead to misinterpretation. Finally, we used simulations to illustrate the benefits and caveats of co-association networks.\n\nConclusionsCo-association networks provided a useful framework for studying modularity. Our results supported the prediction that evolution to complex environments selects for modular genetic architectures, but some of our results went against the prediction that selection would increase recombination among loci experiencing different sources of selection. These results give new insight into evolutionary debates about the extent of modularity and pleiotropy in the evolution of genetic architectures.

evolutionary biology↗

bioRxiv2017

A Novel Post Hoc Method For Detecting Index Switching Finds No Evidence For Increased Switching On The Illumina HiSeq X

High throughput sequencing using the Illumina HiSeq platform is a pervasive and critical molecular ecology resource, and has provided the data underlying many recent advances. A recent study has suggested that index switching, where reads are misattributed to the wrong sample, may be higher in new versions of the HiSeq platform. This has the potential to invalidate both published and in-progress work across the field. Here, we test for evidence of index switching in an exemplar whole genome shotgun dataset sequenced on both the Illumina HiSeq 2500, which should not have the problem, and the Illumina HiSeq X, which may. We leverage unbalanced heterozygotes, which may be produced by index switching, and ask whether the under-sequenced allele is more likely to be found in other samples in the same lane than expected based on the allele frequency. Although we validate the sensitivity of this method using simulations, we find that neither the HiSeq 2500 nor the HiSeq X have evidence of index switching. This suggests that, thankfully, index switching may not be a ubiquitous problem in HiSeq X sequence data. Lastly, we provide scripts for applying our method so that index switching can be tested for in other datasets.

bioinformatics↗