Expressed Exome Capture Sequencing (EecSeq): a method for cost-effective exome sequencing for all organisms

Exome capture is an effective tool for surveying the genome for loci under selection. However, traditional methods require annotated genomic resources. Here, we present a method for creating cDNA probes from expressed mRNA, which are then used to enrich and capture genomic DNA for exon regions. This approach, called “EecSeq,” eliminates the need for costly probe design and synthesis. We tested EecSeq in the eastern oyster, Crassostrea virginica, using a controlled exposure experiment. Four adult oysters were heat shocked at 36°C for 1 hr along with four control oysters kept at 14°C. Stranded mRNA libraries were prepared for two individuals from each treatment and pooled. Half of the combined library was used for probe synthesis, and half was sequenced to evaluate capture efficiency. Genomic DNA was extracted from all individuals, enriched via captured probes, and sequenced directly. We found that EecSeq had an average capture sensitivity of 86.8% across all known exons and had over 99.4% sensitivity for exons with detectable levels of expression in the mRNA library. For all mapped reads, over 47.9% mapped to exons and 37.0% mapped to expressed targets, which is similar to previously published exon capture studies. EecSeq displayed relatively even coverage within exons (i.e., minor “edge effects”) and even coverage across exon GC content. We discovered 5,951 SNPs with a minimum average coverage of 80×, with 3,508 SNPs appearing in exonic regions. We show that EecSeq provides comparable, if not superior, specificity and capture efficiency compared to costly, traditional methods

Genetic, Spatial, and Temporal Components of Precise Spawning Synchrony in Reef Building Corals of the Montastraea annularis Species Complex

When organisms release gametes into the sea, synchrony must be precise to increase fertilization and decrease hybridization. We tagged and genotyped over 400 spawning corals from the three species in the Montastraea annularis species complex. We report on the influence of species, individuals, and genotypes on timing of spawning from 2002 through 2009. During their annual spawning event M. franksi spawns on average 2 h after sunset, whereas M. annularis and M. faveolata spawn 3.5 h after sunset. Only M. franksi and M. annularis have compatible gametes. Individual colonies of the same genotype spawn at approximately the same time after sunset within and across years (within minutes), but different genotypes have significantly different spawning times. Neighboring colonies, regardless of genotype, spawn more synchronously than individuals spaced further apart. At a given distance, clone-mates spawn more synchronously than nonclone-mates. A transplant experiment indicates a genetic and environmental influence on spawn time. There is strong, but not absolute, concordance between spawn time, morphology, and genetics. Tight precision in spawning is achieved via a combination of external cues, genetic precision, and perhaps conspecific signaling. These mechanisms are likely to influence reproductive success and reproductive isolation in a density-dependent manner

All scripts and simulation output

This zip file contains all scripts and simulation outputs from SLiM, including raw tree files. See the GitHub repository https://github.com/TestTheTests/TTT_RecombinationGenomeScans for more information, and filtered vcf files used for all analyses

Data from: The effect of neutral recombination variation on genome scans for selection

Recently, there has been an increasing interest in identifying the role that regions of low recombination or inversion play in adaptation of species to local environments. Many examples of groups of adapted genes located within inversions are arising in the literature, in part inspired by theory that predicts the evolution of these so-called “supergenes.” We still, however, have a poor understanding of how genomic heterogeneity, such as varying rates of recombination, may confound signals of selection. Here, I evaluate the effect of neutral inversions and recombination variation on genome scans for selection, including tests for selective sweeps, differentiation outlier tests, and association tests. There is considerable variation among methods in their performance, with some methods being unaffected and some showing elevated false positive signals within a neutral inversion or region of low recombination. In some cases the false positive signal can be dampened or removed, if it is possible to use a quasi-independent set of SNPs to parameterize the model before performing the test. These results will be helpful to those seeking to understand the importance of regions of low recombination in adaptation

Understanding the effects of linkage and pleiotropy on evolutionary adaptation

A recommendation – based on reviews by Pär Ingvarsson and one anonymous reviewer – of the article: Chebib, J. and Guillaume, F. (2019). Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies. bioRxiv, 656413, v3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/656413. doi: 10.1101/65641

The Effect of Neutral Recombination Variation on Genome Scans for Selection

Recently, there has been an increasing interest in identifying the role that regions of low recombination or inversion play in adaptation of species to local environments. Many examples of groups of adapted genes located within inversions are arising in the literature, in part inspired by theory that predicts the evolution of these so-called “supergenes.” We still, however, have a poor understanding of how genomic heterogeneity, such as varying rates of recombination, may confound signals of selection. Here, I evaluate the effect of neutral inversions and recombination variation on genome scans for selection, including tests for selective sweeps, differentiation outlier tests, and association tests. There is considerable variation among methods in their performance, with some methods being unaffected and some showing elevated false positive signals within a neutral inversion or region of low recombination. In some cases the false positive signal can be dampened or removed, if it is possible to use a quasi-independent set of SNPs to parameterize the model before performing the test. These results will be helpful to those seeking to understand the importance of regions of low recombination in adaptation

R code for simulations

This .zip file contains R code and functions used for the landscape simulator in R and the source code for the implementation of FDIST2 in R

Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests

FST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to non-equilibrium demographic histories has been under-studied. We developed a landscape-genetics simulator to test the effects of isolation by distance (IBD) and range expansion on FST outlier methods. We evaluated the two most commonly used methods for the identification of FST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary non-independence). Parameterization with a set of neutral loci (“neutral parameterization”) always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely-used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary non-independence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives under balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical p-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published FST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially-divergent selection

Results from a meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results in published literature

Dataset: Results of meta-analysis on CovGE in phenotypic resultsCovariance can exist between the genetic and environmental influences on phenotype (CovGE) and can have an important role in ecological and evolutionary processes in nature and population responses to environmental change. CovGE is commonly called countergradient variation (CnGV; negative CovGE)or cogradient variation (CoGV; positive CovGE)and has been recognized in classic studies that have established several long-standing hypotheses about CnGV and CoGV. For instance, it is hypothesized that CnGV is more prevalent in nature than CoGV, that CnGV is more prevalent in fish, amphibian, and invertebrate taxa, across latitudinal or altitudinal environmental gradients, and more frequently occurs in metabolic compensation traits, including development, growth, feeding, metabolism, and activity, while CoGV is more commonly observed in morphological traits. The recent development of a standardized method to measure CovGE allows for the first rigorous quantitative exploration of these hypotheses. We use meta-analysis and apply the novel quantitative method to test whether the above hypotheses are supported in the literature. We found no differences in frequency of CnGV and CoGV, and no systematic patterns relative to taxa, environmental gradient, or trait type. However, our analyses suggest that CovGE may be as common as gene by environment (GxE) interactions. Given that CovGE is likely to have a strong impact on future outcomes for organisms experiencing environmental change, that significant CovGE occurred frequently, and the lack of systematic patterns in the occurrence of CovGE, we encourage a more widespread application of measuring CovGE. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/877425NSF Division of Ocean Sciences (NSF OCE) OCE-176431

Metadata for studies from meta-analysis investigating covariance between genetic and environmental (CovGE) effects in phenotypic results

Dataset: Metadata from meta-analysis on CovGE in phenotypic resultsCovariance can exist between the genetic and environmental influences on phenotype (CovGE) and can have an important role in ecological and evolutionary processes in nature and population responses to environmental change. CovGE is commonly called countergradient variation (CnGV; negative CovGE)or cogradient variation (CoGV; positive CovGE)and has been recognized in classic studies that have established several long-standing hypotheses about CnGV and CoGV. For instance, it is hypothesized that CnGV is more prevalent in nature than CoGV, that CnGV is more prevalent in fish, amphibian, and invertebrate taxa, across latitudinal or altitudinal environmental gradients, and more frequently occurs in metabolic compensation traits, including development, growth, feeding, metabolism, and activity, while CoGV is more commonly observed in morphological traits. The recent development of a standardized method to measure CovGE allows for the first rigorous quantitative exploration of these hypotheses. We use meta-analysis and apply the novel quantitative method to test whether the above hypotheses are supported in the literature. We found no differences in frequency of CnGV and CoGV, and no systematic patterns relative to taxa, environmental gradient, or trait type. However, our analyses suggest that CovGE may be as common as gene by environment (GxE) interactions. Given that CovGE is likely to have a strong impact on future outcomes for organisms experiencing environmental change, that significant CovGE occurred frequently, and the lack of systematic patterns in the occurrence of CovGE, we encourage a more widespread application of measuring CovGE. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/877414NSF Division of Ocean Sciences (NSF OCE) OCE-176431