http://lod.bco-dmo.org/id/dataset/889769 eng; USA utf8 dataset Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact 2023-02-13 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project) 2023-02-13 publication 2023-02-13 revision Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA) 2023-02-22 publication https://doi.org/10.26008/1912/bco-dmo.889769.1 Katie Lotterhos Northeastern University principalInvestigator Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. publisher Cite this dataset as: Lotterhos, K. (2023) Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2023-02-13 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.889769.1 [access date] Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes Dataset Description: &lt;p&gt;See metadata files associated with simulation outputs in the repository.&lt;/p&gt; &lt;p&gt;The associated modeling code is archived at&amp;nbsp;10.5281/zenodo.7622893.&lt;/p&gt; &lt;p&gt;The tutorial associated with the publication is published at&amp;nbsp;https://marineomics.github.io/RDAtraitPredictionTutorial.html.&lt;/p&gt; Methods and Sampling: &lt;p&gt;&lt;strong&gt;Landscapes and demographies&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;All simulations consisted of 100 demes arranged on a 10 x 10 landscape grid. The 15 levels of landscape-demography were broadly divided into three landscape categories: (i) a stepping stone landscape with latitudinal and longitudinal selective clines (Stepping-Stone Clines, the most commonly simulated scenario in testing methods) (Coop et al. 2010; Frichot et al. 2013; Günther &amp;amp; Coop 2013; de Villemereuil et al. 2014; Lotterhos &amp;amp; Whitlock 2015; Rellstab et al. 2015; Gautier 2015; Forester et al. 2016, 2018), (ii) a stepping stone landscape with one latitudinal cline and one non-linear longitudinal mountain range (Stepping-Stone Mountain, which left the potential for unique architectures to arise to the same selective pressure at different geographic locations), and (iii) an estuary landscape with a latitudinal and longitudinal selective clines (Estuary Clines, which simulated repeated independent bouts of adaptation analogous to oysters or sticklebacks that repeatedly colonize and adapt to isolated freshwater environments connected by gene flow in the marine environment). For simplicity, I refer to the latitudinal environment as Temperature and the longitudinal environment as Env2.&amp;nbsp;&lt;/p&gt; &lt;p&gt;In summary, Stepping-Stone Mountain had a different environmental pattern than Stepping-Stone Clines but the same demography, while Estuary Clines had the same environmental pattern as Stepping-Stone Clines but different demography. The demographic parameters were chosen such that different landscapes achieved similar levels of neutral genetic differentiation and local adaptation. Within each of the three landscapes, 5 demographies were simulated that described the migration rates and effective population sizes on the landscape (see Supplemental Methods).&lt;/p&gt; &lt;p&gt;See the Supplemental Methods for a description of the multivariate continuous space simulations with 6 traits.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Genetic map&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The Wright-Fisher simulations were based on a previously published quantitative genetic model and a genetic map (Lotterhos 2019). The genome consisted of 20 linkage groups each with 50,000 sites. The scaled recombination rate (N metapop r = 0.01) gave a resolution of 0.001 cM between proximate bases and a total length of 50 cM for each linkage group. This resolution mimicked a SNP chip, in which SNPs were collected across a large genetic map (Lotterhos 2019). The population-scaled neutral mutation rate was (N metapop μ= 0.001). QTNs could evolve on the first 10 linkage groups, while on the second 10 linkage groups only neutral loci could evolve.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Genetic Architecture and Stabilizing Selection&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Mutation: Quantitative trait nucleotides (QTNs) contributed additively to the optimal phenotype for each individual without dominance. Three genic levels were simulated: oligogenic (few loci of large effect on the trait), moderately polygenic (dozens to hundreds of loci with intermediate effects), and highly polygenic (hundreds of loci with small effects). For QTN mutations under 1 trait or 2 traits without pleiotropy, the univariate effect size of a new QTN mutation was drawn from a normal distribution with a mean of 0 and standard deviation sigma QTN. For QTN mutations under 2 traits with pleiotropy, the bivariate effect size was drawn from a multivariate normal distribution with a standard deviation of sigma QTN for both traits and no covariance, which gave flexibility for mutations to evolve with effects on one or both traits. Thus, the distribution of effect sizes and linkage relationships among QTNs was allowed to evolve.&amp;nbsp;&lt;/p&gt; &lt;p&gt;Pleiotropy: Within each genic category were 5 levels of pleiotropy and selection: (i) 1 temperature trait (which adapted to the latitudinal cline),&amp;nbsp; (ii) 2 traits without pleiotropy and equal strengths of selection on both traits, (iii) 2 traits without pleiotropy and with weaker selection on the temperature trait, (iv) 2 traits with pleiotropy (QTNs could evolve effects on one or both traits) and equal strengths of selection on both traits, and (v) 2 traits with pleiotropy and with weaker selection on the latitudinal temperature trait.&lt;/p&gt; &lt;p&gt;Selection: The trait was subject to spatially heterogeneous stabilizing selection with the optimum for each location in space given by the environment. For each individual in each generation, the fitness was determined by a Gaussian function given the difference between the individual’s phenotype and the optimum at that location.&amp;nbsp;&lt;/p&gt; &lt;p&gt;See trait equation details in the supplemental document: equation_details_genetic_architecture_and_stabilizing_selection.pdf&lt;/p&gt; &lt;p&gt;For information on burn-in, adding neutral loci with tree sequencing, filtering, and sampling, see the Supplemental Methods.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Quantifying the degree of local adaptation, divergence, and structure&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;For each replicate, the degree of local adaptation was measured as (i) the difference between population fitness in sympatry and allopatry following (Blanquart et al. 2013) and (ii) the correlation between the phenotype and environmental cline for each trait. Overall divergence (genetic differentiation) was calculated as Weir and Cockerham’s FST&amp;nbsp; (Weir &amp;amp; Cockerham 1984) in OutFLANK (Whitlock &amp;amp; Lotterhos 2015). Population structure was estimated with a principal component analysis on the genotype matrix.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Quantifying trait and allelic clines&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The degree of a trait cline was measured as Kendall's tau rank correlation coefficient between individual trait values and deme environment. The degree of an allele frequency cline was measured as Kendall's tau rank correlation coefficient (Kendall 1938, 1945) between deme allele frequency and deme environment, with significance being determined after Bonferroni correction based on the number of SNPs in the data. QTNs that were significant by this criteria were deemed “clinal QTNs.” The proportion of clinal QTNs excluded minor alleles with frequency &amp;lt; 0.01.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;GEA performance&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Latent-factor mixed models (LFMM) assess the linear relationship between genotype and environment while controlling for structure as latent factors. LFMM was implemented using the function lfmm2 in the R package LEA v.4.0.3 (Frichot et al. 2013; Frichot &amp;amp; François 2015; Caye et al. 2019). Redundancy analysis (RDA) and the partial RDA (pRDA) including a structure correction (conditional on the first 2 PC axes) were implemented using the ‘rda’ function in the R package vegan (Dixon 2003). See Supplemental Methods for details of implementation and correction for false discovery rate.&lt;/p&gt; &lt;p&gt;The performance of the association metrics were summarized as: (i) false discovery rate (FDR, proportion of outliers that are neutral, lower is better), (ii) true positive rate (TPR, proportion of QTNs that are significant outliers, higher is better), and (iii) the area under the precision-recall curve (AUC-PR, higher is better) (Lotterhos et al. 2022). In order to provide the most optimistic estimate of a method’s performance, the performance statistics were calculated by only including truly neutral loci unaffected by selection on linkage groups 11-20 and the QTNs.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Importance of clinal QTNs to local adaptation&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;First framework: A linear model was used to conduct the GWAS with individual trait value as the response variable and SNP genotype, PC1, and PC2 as explanatory variables. The proportion of GWAS hits that also showed clines with the environmental variable was compared to the known number of clinal QTNs. Second framework: The proportion of additive genetic variance (VA) for each QTN was calculated as the additive genetic variance for the focal QTN standardized by the total additive genetic variance following (Lotterhos 2019). The proportion of additive genetic variance (VA) explained by clinal QTNs was compared to a null expectation equal to the proportion of QTNs that were clinal. Third framework: I estimated the proportion of local explained by different subsets of QTNs: (i) QTNs with MAF &amp;gt; 0.01, (ii) clinal QTNs, and (iii) clinal QTNs inferred from latent factor mixed models that include a structure correction. For (ii) and (iii), a GEA model was performed for each environment, and then outlier QTNs were combined into a focal QTN set that was used for the local adaptation prediction. For each focal subset of QTNs, the counts of the derived allele were multiplied by the QTN effect size, summed to get a phenotype, and that phenotype was used in an in silico reciprocal transplant using the known phenotype-fitness function to estimate the degree of local adaptation. This estimate was then divided by the total degree of local adaptation (using all QTNs including those below the MAF threshold) to get an estimate of the proportion of local adaptation explained by that focal subset.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2043905 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2043905 completed Katie Lotterhos Northeastern University 781-581-7370 X304 Northeastern University Marine Sciences Center 430 Nahant Rd. Nahant MA 01908 USA k.lotterhos@northeastern.edu pointOfContact asNeeded Dataset Version: 1 Unknown seed n_samp_tot n_samp_per_pop sd_fitness_among_inds sd_fitness_among_pops final_LA K Bonf_alpha numCausalLowMAFsample all_corr_phen_temp subsamp_corr_phen_temp all_corr_phen_sal subsamp_corr_phen_sal num_causal_prefilter num_causal_postfilter num_non_causal num_neut_prefilter num_neut_postfilter num_neut_neutralgenome num_causal_temp num_causal_sal num_multiallelic meanFst va_temp_total va_sal_total Va_temp_sample Va_sal_sample nSNPs median_causal_temp_cor median_causal_sal_cor median_neut_temp_cor median_neut_sal_cor cor_VA_temp_prop cor_VA_sal_prop cor_TPR_temp cor_TPR_sal cor_FDR_allSNPs_temp cor_FDR_neutSNPs_temp cor_FDR_allSNPs_sal cor_FDR_neutSNPs_sal num_causal_sig_temp_corr num_causal_sig_sal_corr num_notCausal_sig_temp_corr num_notCausal_sig_sal_corr num_neut_sig_temp_corr num_neut_sig_sal_corr cor_AUCPR_temp_allSNPs cor_AUCPR_temp_neutSNPs cor_AUCPR_sal_allSNPs cor_AUCPR_sal_neutSNPs cor_af_temp_noutliers cor_af_sal_noutliers cor_FPR_temp_neutSNPs cor_FPR_sal_neutSNPs LEA3_2_lfmm2_Va_temp_prop LEA3_2_lfmm2_Va_sal_prop LEA3_2_lfmm2_TPR_temp LEA3_2_lfmm2_TPR_sal LEA3_2_lfmm2_FDR_allSNPs_temp LEA3_2_lfmm2_FDR_allSNPs_sal LEA3_2_lfmm2_FDR_neutSNPs_temp LEA3_2_lfmm2_FDR_neutSNPs_sal LEA3_2_lfmm2_AUCPR_temp_allSNPs LEA3_2_lfmm2_AUCPR_temp_neutSNPs LEA3_2_lfmm2_AUCPR_sal_allSNPs LEA3_2_lfmm2_AUCPR_sal_neutSNPs LEA3_2_lfmm2_mlog10P_tempenv_noutliers LEA3_2_lfmm2_mlog10P_salenv_noutliers LEA3_2_lfmm2_num_causal_sig_temp LEA3_2_lfmm2_num_neut_sig_temp LEA3_2_lfmm2_num_causal_sig_sal LEA3_2_lfmm2_num_neut_sig_sal LEA3_2_lfmm2_FPR_neutSNPs_temp LEA3_2_lfmm2_FPR_neutSNPs_sal RDA1_propvar RDA2_propvar RDA1_propvar_corr RDA2_propvar_corr RDA1_temp_cor RDA1_sal_cor RDA2_temp_cor RDA2_sal_cor RDA_Va_temp_prop RDA_Va_temp_prop_corr RDA_Va_sal_prop RDA_Va_sal_prop_corr RDA_TPR RDA_TPR_corr RDA_FDR_allSNPs RDA_FDR_allSNPs_corr num_RDA_sig_causal num_RDA_sig_neutral num_RDA_sig_causal_corr num_RDA_sig_neutral_corr RDA_FDR_neutSNPs RDA_FDR_neutSNPs_corr RDA_AUCPR_allSNPs RDA_AUCPR_neutSNPs RDA_AUCPR_neutSNPs_corr RDA_FPR_neutSNPs RDA_FPR_neutSNPs_corr RDA_RDAmutpred_cor_tempEffect RDA_RDAmutpred_cor_salEffect RDA_absRDAmutpred_cor_tempVa RDA_absRDAmutpred_cor_salVa RDA_RDAmutpred_cor_tempEffect_structcorr RDA_RDAmutpred_cor_salEffect_structcorr RDA_absRDAmutpred_cor_tempVa_structcorr RDA_absRDAmutpred_cor_salVa_structcorr RDA_cor_RDA20000temppredict_tempPhen RDA_cor_RDA20000salpredict_salPhen RDA_cor_RDA20000temppredict_tempPhen_structcorr RDA_cor_RDA20000salpredict_salPhen_structcorr cor_PC1_temp cor_PC1_sal cor_PC2_temp cor_PC2_sal cor_LFMMU1_temp cor_LFMMU1_sal cor_LFMMU2_temp cor_LFMMU2_sal cor_PC1_LFMMU1_temp cor_PC1_LFMMU1_sal cor_PC2_LFMMU1_temp cor_PC2_LFMMU1_sal gwas_TPR_sal gwas_TPR_temp gwas_FDR_sal_neutbase gwas_FDR_temp_neutbase clinalparadigm_sal_proptop5GWASclines clinalparadigm_temp_proptop5GWASclines clinalparadigm_sal_propsigGWASclines clinalparadigm_temp_propsigGWASclines Northeastern's High Performance Computing Cluster theme None, User defined count ratio featureType BCO-DMO Standard Parameters High-Performance Computing Cluster instrument BCO-DMO Standard Instruments otherRestrictions otherRestrictions Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use. CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP) https://osprey.bco-dmo.org/project/876610 CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP) <p><strong>NSF Award Abstract:</strong><br /> Environmental change can be rapid and involve multiple aspects of the environment changing at the same time, such as warming and increased disease pressure. Rapid environmental change threatens the productivity of aquaculture and crops on which humans depend. Predicting organisms' vulnerabilities to rapid and multifactor environmental change, however, is a major scientific challenge. A hurdle to addressing this challenge arises from the complex and non-intuitive ways that organisms adapt, through changes at the level of the DNA sequence, to many environmental stresses at the same time. Thus, there is a need for new approaches to understand and predict adaptation in multivariate environments. To address this need, this project integrates research and education with a Model Validation Program (MVP). The research is developing and evaluating Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. To evaluate MLAs, this research combines both data simulation and an empirical test in the field with the Eastern Oyster, which provide important ecosystem services and support a multi-million dollar industry. For oysters, this research is studying how temperature, disease pressure, and salinity interact with evolutionary history to determine fitness in the field. This research advances efforts toward addressing the major scientific challenge of predicting adaptation in complex environments by integrating concepts across the frontiers of marine, evolutionary, and statistical sciences in a new way. Machine learning and model validation are not traditionally taught in the marine and environmental sciences, but are becoming increasingly relevant to these fields. As part of a broader education program, this research is developing MVP Learning Modules for high school students and undergraduates, which help students build the foundational knowledge they need to critically evaluate and apply models. Modules are being disseminated to hundreds of students in the greater Boston area and are being made available online for widespread use. The MVP mentoring program is training graduate students, undergraduates, and high school students in marine evolutionary ecology, statistical genomics, and machine learning. This research addresses a pressing societal need to more informatively match genotypes to environments for restoration, farming, and assisted gene flow efforts. Results are being disseminated to stakeholders in the oyster industry.</p> <p>The goal of this research is to evaluate if MLAs, which can model non-linearities, can be used to understand and predict adaptation to multivariate environments under a wide range of scenarios. In Objective 1, the Principal Investigator (PI) is creating simulated datasets with different aspects of realism, and using them to evaluate and refine the MLAs. This novel set of simulations is studying genome evolution under high gene flow in complex, multivariate environments. In Objective 2, the PI is building on their expertise with the Eastern oyster to evaluate the MLAs in a field setting. The PI is first developing a comprehensive seascape genomic dataset and using it to train MLAs to predict an individual's multivariate environment based on a single nucleotide polymorphism genotype. Then, the PI is testing if the MLA prediction can predict the fitness of different genotypes from across the species range when raised in common garden field conditions. In Objective 3, the PI is integrating research and education by using the data obtained from Objs. 1 and 2 to develop a series of original "MVP Learning Modules" with interactive web apps for persons at different levels of understanding, using the relatable example of an oyster restoration project. This research lays the foundation for future studies by producing datasets that could become classical examples for developing and benchmarking innovative modeling approaches.</p> <p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p> Model Validation Program largerWorkCitation project eng; USA oceans 2020-01-01 2022-12-31 East coast of North America 0 BCO-DMO catalogue of parameters from Output model data from paradox of adaptive trait clines with non-clinal patterns in the underlying genes (Model Validation Program project) Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact http://lod.bco-dmo.org/id/dataset-parameter/890146.rdf Name: seed Units: unitless Description: <p>Simulation seed.</p> http://lod.bco-dmo.org/id/dataset-parameter/890147.rdf Name: n_samp_tot Units: unitless Description: <p>Total number of individuals sampled.</p> http://lod.bco-dmo.org/id/dataset-parameter/890148.rdf Name: n_samp_per_pop Units: unitless Description: <p>Number of individuals sampled from each deme.</p> http://lod.bco-dmo.org/id/dataset-parameter/890149.rdf Name: sd_fitness_among_inds Units: unitless Description: <p>Variance in fitness among all sampled individuals in the simulation (sampling prob. is proportional to fitness to mimic viability selection).</p> http://lod.bco-dmo.org/id/dataset-parameter/890150.rdf Name: sd_fitness_among_pops Units: unitless Description: <p>Variance in fitness among all demes in the simulation after sampling (sampling prob. is proportional to fitness to mimic viability selection).</p> http://lod.bco-dmo.org/id/dataset-parameter/890151.rdf Name: final_LA Units: unitless Description: <p>Final amount of local adaptation in the simulation.</p> http://lod.bco-dmo.org/id/dataset-parameter/890152.rdf Name: K Units: unitless Description: <p>Number of populations used in analyses.</p> http://lod.bco-dmo.org/id/dataset-parameter/890153.rdf Name: Bonf_alpha Units: unitless Description: <p>The significance threshold for P-values applied to the correlation.</p> http://lod.bco-dmo.org/id/dataset-parameter/890154.rdf Name: numCausalLowMAFsample Units: unitless Description: <p>Number of causal loci that were not filtered out, but were below the MAF cutoff. These were included in the calculations.</p> http://lod.bco-dmo.org/id/dataset-parameter/890155.rdf Name: all_corr_phen_temp Units: unitless Description: <p>For all individuals, correlation between individual temp phenotype and environment temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890156.rdf Name: subsamp_corr_phen_temp Units: unitless Description: <p>After sampling 10 individuals from each deme with a probability based on their fitness, correlation individual temp phenotype and environment temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890157.rdf Name: all_corr_phen_sal Units: unitless Description: <p>For all individuals, correlation between individual sal phenotype and environment salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890158.rdf Name: subsamp_corr_phen_sal Units: unitless Description: <p>After sampling 10 individuals from each deme with a probability based on their fitness, correlation between individual sal phenotype and environment salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890159.rdf Name: num_causal_prefilter Units: unitless Description: <p>Number of causal loci in sim before filtering for MAF &gt; 0.01.</p> http://lod.bco-dmo.org/id/dataset-parameter/890160.rdf Name: num_causal_postfilter Units: unitless Description: <p>Number of causal loci in sim before filtering for MAF &gt; 0.01.</p> http://lod.bco-dmo.org/id/dataset-parameter/890161.rdf Name: num_non_causal Units: unitless Description: <p>Number of neutral loci in sim arising on the half of the genome where they could be linked to causal loci.</p> http://lod.bco-dmo.org/id/dataset-parameter/890162.rdf Name: num_neut_prefilter Units: unitless Description: <p>Total number of neutral loci on all LG before filtering MAF &gt; 0.01. This is not really accurate, since many neutral loci were filtered out after output by pyslim - causal loci were not subject to filtering.</p> http://lod.bco-dmo.org/id/dataset-parameter/890163.rdf Name: num_neut_postfilter Units: unitless Description: <p>Total number of neutral loci on all LG after filtering MAF &gt; 0.01.</p> http://lod.bco-dmo.org/id/dataset-parameter/890164.rdf Name: num_neut_neutralgenome Units: unitless Description: <p>Number of truly neutral loci in sim, unlinked to causal loci, on LG 11-20.</p> http://lod.bco-dmo.org/id/dataset-parameter/890165.rdf Name: num_causal_temp Units: unitless Description: <p>Number of loci with non-zero phenotypic effects on the temperature phenotype.</p> http://lod.bco-dmo.org/id/dataset-parameter/890166.rdf Name: num_causal_sal Units: unitless Description: <p>Number of loci with non-zero phenotypic effects on the salinity phenotype.</p> http://lod.bco-dmo.org/id/dataset-parameter/890167.rdf Name: num_multiallelic Units: unitless Description: <p>Rarely there is a back-mutation in SLiM, leading to a 0/0 0/1 1/1 1/2 0/2 2/2 genotypes. These were filtered for analysis.</p> http://lod.bco-dmo.org/id/dataset-parameter/890168.rdf Name: meanFst Units: unitless Description: <p>Overall FST (fixation index) calculated from mean(T1)/mean(T2) in outflank.</p> http://lod.bco-dmo.org/id/dataset-parameter/890169.rdf Name: va_temp_total Units: unitless Description: <p>Total additive genetic variance in the temperature trait, based on the entire 10,000 individual sample.</p> http://lod.bco-dmo.org/id/dataset-parameter/890170.rdf Name: va_sal_total Units: unitless Description: <p>Total additive genetic variance in the salinity trait, based on the entire 10,000 individual sample.</p> http://lod.bco-dmo.org/id/dataset-parameter/890171.rdf Name: Va_temp_sample Units: unitless Description: <p>Total additive genetic variance in the temperature trait, based on the 1,000 individual (10 ind/deme x 100 demes) sample.</p> http://lod.bco-dmo.org/id/dataset-parameter/890172.rdf Name: Va_sal_sample Units: unitless Description: <p>Total additive genetic variance in the salinity trait, based on the entire 1,000 individual (10 ind/deme x 100 demes) sample.</p> http://lod.bco-dmo.org/id/dataset-parameter/890173.rdf Name: nSNPs Units: unitless Description: <p>Total number of SNPs in analysis.</p> http://lod.bco-dmo.org/id/dataset-parameter/890174.rdf Name: median_causal_temp_cor Units: unitless Description: <p>Median abs(Spearman&#039;s correlation) between allele frequency and temperature for causal loci.</p> http://lod.bco-dmo.org/id/dataset-parameter/890175.rdf Name: median_causal_sal_cor Units: unitless Description: <p>Median abs(Spearman&#039;s correlation) between allele frequency and salinity for causal loci.</p> http://lod.bco-dmo.org/id/dataset-parameter/890176.rdf Name: median_neut_temp_cor Units: unitless Description: <p>Median abs(Spearman&#039;s correlation) between allele frequency and temperature for neutral loci.</p> http://lod.bco-dmo.org/id/dataset-parameter/890177.rdf Name: median_neut_sal_cor Units: unitless Description: <p>Median abs(Spearman&#039;s correlation) between allele frequency and salinity for neutral loci.</p> http://lod.bco-dmo.org/id/dataset-parameter/890178.rdf Name: cor_VA_temp_prop Units: unitless Description: <p>Proportion of VA in temperature phenotype explained by clinal outliers for temperature, based on kendall&#039;s correlation between deme allele frequency and deme temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890179.rdf Name: cor_VA_sal_prop Units: unitless Description: <p>Proportion of VA in salinity phenotype explained by clinal outliers for salinity, based on kendall&#039;s correlation between deme allele frequency and deme salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890180.rdf Name: cor_TPR_temp Units: unitless Description: <p>True positive rate for loci with non-zero effects on temperature, based on kendall&#039;s correlation between deme allele frequency and deme temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890181.rdf Name: cor_TPR_sal Units: unitless Description: <p>True positive rate for loci with non-zero effects on salinity, based on kendall&#039;s correlation between deme allele frequency and deme salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890182.rdf Name: cor_FDR_allSNPs_temp Units: unitless Description: <p>False discovery rate of (kendall&#039;s correlation between deme allele frequency and deme temperature) for loci with non-zero effects on temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890183.rdf Name: cor_FDR_neutSNPs_temp Units: unitless Description: <p>An optimistic calculation for false discovery rate of (kendall&#039;s correlation between deme allele frequency and deme temperature) for loci with non-zero effects on temperature, excluding non-causal loci in half of genome affected by selection.</p> http://lod.bco-dmo.org/id/dataset-parameter/890184.rdf Name: cor_FDR_allSNPs_sal Units: unitless Description: <p>False discovery rate of (kendall&#039;s correlation between deme allele frequency and deme salinity) for loci with non-zero effects on salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890185.rdf Name: cor_FDR_neutSNPs_sal Units: unitless Description: <p>An optimistic calculation for false discovery rate of (kendall&#039;s correlation between deme allele frequency and deme temperature) for loci with non-zero effects on temperature, excluding non-causal loci in half of genome affected by selection.</p> http://lod.bco-dmo.org/id/dataset-parameter/890186.rdf Name: num_causal_sig_temp_corr Units: unitless Description: <p>Number of causal loci on temperature trait that are significant cor(af,temp) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890187.rdf Name: num_causal_sig_sal_corr Units: unitless Description: <p>Number of causal loci on salinity trait that are significant cor(af,salinity) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890188.rdf Name: num_notCausal_sig_temp_corr Units: unitless Description: <p>Number of non-causal (neutral and neutral-linked) loci that are significant cor(af,temp) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890189.rdf Name: num_notCausal_sig_sal_corr Units: unitless Description: <p>Number of non-causal (neutral and neutral-linked) loci that are significant cor(af,salinity) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890190.rdf Name: num_neut_sig_temp_corr Units: unitless Description: <p>Number of truly neutral loci (LG 11-20) that are significant cor(af,temp) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890191.rdf Name: num_neut_sig_sal_corr Units: unitless Description: <p>Number of truly neutral loci (LG 11-20) that are significant cor(af,salinity) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890192.rdf Name: cor_AUCPR_temp_allSNPs Units: unitless Description: <p>AUC-PR of kendall&#039;s correlation between deme allele frequency and deme temperature, based on the whole genome and causal loci for temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890193.rdf Name: cor_AUCPR_temp_neutSNPs Units: unitless Description: <p>An optimistic estimate of AUC-PR of kendall&#039;s correlation between deme allele frequency and deme temperature, based on causal loci for temperature and neutral loci not affected by selection (excluding non-causal loci in half of genome affected by selection).</p> http://lod.bco-dmo.org/id/dataset-parameter/890194.rdf Name: cor_AUCPR_sal_allSNPs Units: unitless Description: <p>AUC-PR of kendall&#039;s correlation between deme allele frequency and deme salinity, based on the whole genome and causal loci for salinity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890195.rdf Name: cor_AUCPR_sal_neutSNPs Units: unitless Description: <p>An optimistic estimate of AUC-PR of kendall&#039;s correlation between deme allele frequency and deme salinity, based on causal loci for salinity and neutral loci not affected by selection (excluding non-causal loci in half of genome affected by selection).</p> http://lod.bco-dmo.org/id/dataset-parameter/890196.rdf Name: cor_af_temp_noutliers Units: unitless Description: <p>Number of outliers for cor(af,temp) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890197.rdf Name: cor_af_sal_noutliers Units: unitless Description: <p>Number of outliers for cor(af,salinity) after Bonferroni correction.</p> http://lod.bco-dmo.org/id/dataset-parameter/890198.rdf Name: cor_FPR_temp_neutSNPs Units: unitless Description: <p>False positive rate in cor(af,temp) after Bonferroni correction, based on neutral loci unaffected by selection (LG 11-20).</p> http://lod.bco-dmo.org/id/dataset-parameter/890199.rdf Name: cor_FPR_sal_neutSNPs Units: unitless Description: <p>False positive rate in cor(af,sal) after Bonferroni correction, based on neutral loci unaffected by selection (LG 11-20).</p> http://lod.bco-dmo.org/id/dataset-parameter/890200.rdf Name: LEA3_2_lfmm2_Va_temp_prop Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the temperature trait explained by outliers in the LFMM temp model.</p> http://lod.bco-dmo.org/id/dataset-parameter/890201.rdf Name: LEA3_2_lfmm2_Va_sal_prop Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the saliniity trait explained by outliers in the LFMM salinity model.</p> http://lod.bco-dmo.org/id/dataset-parameter/890202.rdf Name: LEA3_2_lfmm2_TPR_temp Units: unitless Description: <p>True positive rate of the LFMM temp model for loci with alleles that have non-zero effects on the temperature phenotype.</p> http://lod.bco-dmo.org/id/dataset-parameter/890203.rdf Name: LEA3_2_lfmm2_TPR_sal Units: unitless Description: <p>True positive rate of the LFMM salinity model for loci with alleles that have non-zero effects on the salnity phenotype.</p> http://lod.bco-dmo.org/id/dataset-parameter/890204.rdf Name: LEA3_2_lfmm2_FDR_allSNPs_temp Units: unitless Description: <p>False discovery rate of the LFMM temp model for the entire genome.</p> http://lod.bco-dmo.org/id/dataset-parameter/890205.rdf Name: LEA3_2_lfmm2_FDR_allSNPs_sal Units: unitless Description: <p>False discovery rate of the LFMM temp model for the entire genome.</p> http://lod.bco-dmo.org/id/dataset-parameter/890206.rdf Name: LEA3_2_lfmm2_FDR_neutSNPs_temp Units: unitless Description: <p>An optimistic calculation of the false discovery rate of the LFMM temp model, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded).</p> http://lod.bco-dmo.org/id/dataset-parameter/890207.rdf Name: LEA3_2_lfmm2_FDR_neutSNPs_sal Units: unitless Description: <p>An optimistic calculation of the false discovery rate of the LFMM salinity model, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded).</p> http://lod.bco-dmo.org/id/dataset-parameter/890208.rdf Name: LEA3_2_lfmm2_AUCPR_temp_allSNPs Units: unitless Description: <p>The AUC-PR of the lfmm temp model based on the entire genome.</p> http://lod.bco-dmo.org/id/dataset-parameter/890209.rdf Name: LEA3_2_lfmm2_AUCPR_temp_neutSNPs Units: unitless Description: <p>An optimistic calculation of the AUC-PR of the LFMM temp model, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded).</p> http://lod.bco-dmo.org/id/dataset-parameter/890210.rdf Name: LEA3_2_lfmm2_AUCPR_sal_allSNPs Units: unitless Description: <p>The AUC-PR of the lfmm salinity model based on the entire genome.</p> http://lod.bco-dmo.org/id/dataset-parameter/890211.rdf Name: LEA3_2_lfmm2_AUCPR_sal_neutSNPs Units: unitless Description: <p>An optimistic calculation of the AUC-PR of the LFMM salinity model, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded).</p> http://lod.bco-dmo.org/id/dataset-parameter/890212.rdf Name: LEA3_2_lfmm2_mlog10P_tempenv_noutliers Units: unitless Description: <p>Number of outliers for the lfmm temp model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890213.rdf Name: LEA3_2_lfmm2_mlog10P_salenv_noutliers Units: unitless Description: <p>Number of outliers for the lfmm salinity model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890214.rdf Name: LEA3_2_lfmm2_num_causal_sig_temp Units: unitless Description: <p>Number of causal loci on the temp trait, significant in the lfmm temp model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890215.rdf Name: LEA3_2_lfmm2_num_neut_sig_temp Units: unitless Description: <p>Number of neutral loci false positives (only neutral loci not affected by selection), significant in the lfmm temp model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890216.rdf Name: LEA3_2_lfmm2_num_causal_sig_sal Units: unitless Description: <p>Number of causal loci on the salinity trait, significant in the lfmm salinity model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890217.rdf Name: LEA3_2_lfmm2_num_neut_sig_sal Units: unitless Description: <p>Number of neutral loci false positives (only neutral loci not affected by selection), significant in the lfmm salinity model (qvalue &lt;0.05).</p> http://lod.bco-dmo.org/id/dataset-parameter/890218.rdf Name: LEA3_2_lfmm2_FPR_neutSNPs_temp Units: unitless Description: <p>False positive rate of lfmm temperature model.</p> http://lod.bco-dmo.org/id/dataset-parameter/890219.rdf Name: LEA3_2_lfmm2_FPR_neutSNPs_sal Units: unitless Description: <p>False positive rate of lfmm salinity model.</p> http://lod.bco-dmo.org/id/dataset-parameter/890220.rdf Name: RDA1_propvar Units: unitless Description: <p>Proportion of variance explained by first RDA axis. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890221.rdf Name: RDA2_propvar Units: unitless Description: <p>Proportion of variance explained by second RDA axis. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890222.rdf Name: RDA1_propvar_corr Units: unitless Description: <p>Proportion of variance explained by first RDA axis. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890223.rdf Name: RDA2_propvar_corr Units: unitless Description: <p>Proportion of variance explained by second RDA axis. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890224.rdf Name: RDA1_temp_cor Units: unitless Description: <p>Output of `summary(rdaout)$biplot[2,1]`, which is the correlation between RDA1 and the temperature environmental variable. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890225.rdf Name: RDA1_sal_cor Units: unitless Description: <p>Output of `summary(rdaout)$biplot[1,1]`, which is the correlation between RDA1 and the salinity environmental variable. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890226.rdf Name: RDA2_temp_cor Units: unitless Description: <p>Output of `summary(rdaout)$biplot[2,2]`, which is the correlation between RDA2 and the temperature environmental variable. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890227.rdf Name: RDA2_sal_cor Units: unitless Description: <p>Output of `summary(rdaout)$biplot[1,2]`, which is the correlation between RDA2 and the salinity environmental variable. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890228.rdf Name: RDA_Va_temp_prop Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the temperature trait explained by outliers in the RDA outlier analysis, following (Capblanq 2018, <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906">https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906</a>). RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890229.rdf Name: RDA_Va_temp_prop_corr Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the temperature trait explained by outliers in the RDA outlier analysis, following (Capblanq 2018, <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906">https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906</a>). RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890230.rdf Name: RDA_Va_sal_prop Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the salinity trait explained by outliers in the RDA outlier analysis, following (Capblanq 2018, <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906">https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906</a>). RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890231.rdf Name: RDA_Va_sal_prop_corr Units: unitless Description: <p>Proportion of additive genetic variance (Va) in the salinity trait explained by outliers in the RDA outlier analysis, following (Capblanq 2018, <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906">https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906</a>). RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890232.rdf Name: RDA_TPR Units: unitless Description: <p>True positive rate of the RDA for all causal loci. Since RDA is a multidimensional analysis, I did not differentiate between loci that had causal effects on temperature or salinity. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890233.rdf Name: RDA_TPR_corr Units: unitless Description: <p>True positive rate of the RDA for all causal loci. Since RDA is a multidimensional analysis, I did not differentiate between loci that had causal effects on temperature or salinity. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890234.rdf Name: RDA_FDR_allSNPs Units: unitless Description: <p>False discovery rate of the RDA outlier analysis based on the entire genome. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890235.rdf Name: RDA_FDR_allSNPs_corr Units: unitless Description: <p>False discovery rate of the RDA outlier analysis based on the entire genome. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890236.rdf Name: num_RDA_sig_causal Units: unitless Description: <p>Number of causal loci that are significant in the RDA analysis at q &gt; 0.05. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890237.rdf Name: num_RDA_sig_neutral Units: unitless Description: <p>Number of neutral loci (LG 11-20) that are significant in the RDA analysis at q &gt; 0.05. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890238.rdf Name: num_RDA_sig_causal_corr Units: unitless Description: <p>Number of causal loci that are significant in the RDA analysis at q &gt; 0.05. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890239.rdf Name: num_RDA_sig_neutral_corr Units: unitless Description: <p>Number of neutral loci (LG 11-20) that are significant in the RDA analysis at q &gt; 0.05. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890240.rdf Name: RDA_FDR_neutSNPs Units: unitless Description: <p>An optimistic calculation of the false discovery rate of the RDA, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded). RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890241.rdf Name: RDA_FDR_neutSNPs_corr Units: unitless Description: <p>An optimistic calculation of the false discovery rate of the RDA, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded). RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890242.rdf Name: RDA_AUCPR_allSNPs Units: unitless Description: <p>AUC-PR of the RDA outlier analysis based on the entire genome. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890243.rdf Name: RDA_AUCPR_neutSNPs Units: unitless Description: <p>An optimistic calculation of the AUC-PR of the RDA, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded). RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890244.rdf Name: RDA_AUCPR_neutSNPs_corr Units: unitless Description: <p>An optimistic calculation of the AUC-PR of the RDA, including only causal loci and neutral loci not affected by selection (any non-causal loci that arises on the half of the genome affected by selection was excluded). RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890245.rdf Name: RDA_FPR_neutSNPs Units: unitless Description: <p>False positive rate of the RDA analysis, based only on neutral SNPs. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890246.rdf Name: RDA_FPR_neutSNPs_corr Units: unitless Description: <p>False positive rate of the RDA analysis, based only on neutral SNPs. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890247.rdf Name: RDA_RDAmutpred_cor_tempEffect Units: unitless Description: <p># pearson&#039;s correlation between the predicted temperature effect from RDA and the true mutation effect on temperature. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890248.rdf Name: RDA_RDAmutpred_cor_salEffect Units: unitless Description: <p># pearson&#039;s correlation between the predicted salinity effect from RDA and the true mutation effect on salinity. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890249.rdf Name: RDA_absRDAmutpred_cor_tempVa Units: unitless Description: <p># pearson&#039;s correlation between the abs(predicted temperature effect from RDA) and the true mutation Va on temperature. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890250.rdf Name: RDA_absRDAmutpred_cor_salVa Units: unitless Description: <p># pearson&#039;s correlation between the abs(predicted salinity effect from RDA) and the true mutation Va on salinity. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890251.rdf Name: RDA_RDAmutpred_cor_tempEffect_structcorr Units: unitless Description: <p># pearson&#039;s correlation between the predicted temperature effect from RDA and the true mutation effect on temperature. RDA model: genotype ~ environment. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890252.rdf Name: RDA_RDAmutpred_cor_salEffect_structcorr Units: unitless Description: <p># pearson&#039;s correlation between the predicted salinity effect from RDA and the true mutation effect on salinity. RDA model: genotype ~ environment. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890253.rdf Name: RDA_absRDAmutpred_cor_tempVa_structcorr Units: unitless Description: <p># pearson&#039;s correlation between the abs(predicted temperature effect from RDA) and the true mutation Va on temperature. RDA model: genotype ~ environment. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890254.rdf Name: RDA_absRDAmutpred_cor_salVa_structcorr Units: unitless Description: <p># pearson&#039;s correlation between the abs(predicted salinity effect from RDA) and the true mutation Va on salinity. RDA model: genotype ~ environment. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890255.rdf Name: RDA_cor_RDA20000temppredict_tempPhen Units: unitless Description: <p>Correlation between the true temperature phenotype and that predicted from an RDA based on 20K SNPs. see `seed_Rout_RDA_predictions` for correlations with less loci used to make the prediction. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890256.rdf Name: RDA_cor_RDA20000salpredict_salPhen Units: unitless Description: <p>Correlation between the true salinity phenotype and that predicted from an RDA based on 20K SNPs. see `seed_Rout_RDA_predictions` for correlations with less loci used to make the prediction. RDA model: genotype ~ environment.</p> http://lod.bco-dmo.org/id/dataset-parameter/890257.rdf Name: RDA_cor_RDA20000temppredict_tempPhen_structcorr Units: unitless Description: <p>Correlation between the true temperature phenotype and that predicted from an RDA based on 20K SNPs. see `seed_Rout_RDA_predictions` for correlations with less loci used to make the prediction. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890258.rdf Name: RDA_cor_RDA20000salpredict_salPhen_structcorr Units: unitless Description: <p>Correlation between the true salinity phenotype and that predicted from an RDA based on 20K SNPs. see `seed_Rout_RDA_predictions` for correlations with less loci used to make the prediction. RDA model with structure correction: genotype ~ environment + Condition(PC1 + PC2).</p> http://lod.bco-dmo.org/id/dataset-parameter/890259.rdf Name: cor_PC1_temp Units: unitless Description: <p>Correlation between individual loading on PC1 from the principle components based on the Genotype-matrix (individual genotypes labeled as 0,1,2) and temperature of the deme where it was sampled.</p> http://lod.bco-dmo.org/id/dataset-parameter/890260.rdf Name: cor_PC1_sal Units: unitless Description: <p>Correlation between individual loading on PC1 from the principle components based on the Genotype-matrix and salnity of the deme where it was sampled.</p> http://lod.bco-dmo.org/id/dataset-parameter/890261.rdf Name: cor_PC2_temp Units: unitless Description: <p>Correlation between individual loading on PC2 from the principle components based on the Genotype-matrix and temperature of the deme where it was sampled.</p> http://lod.bco-dmo.org/id/dataset-parameter/890262.rdf Name: cor_PC2_sal Units: unitless Description: <p>Correlation between individual loading on PC2 from the principle components based on the Genotype-matrix and salnity of the deme where it was sampled.</p> http://lod.bco-dmo.org/id/dataset-parameter/890263.rdf Name: cor_LFMMU1_temp Units: unitless Description: <p>Correlation between the individual loading on the latent factor 1 from the lfmm model based on temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890264.rdf Name: cor_LFMMU1_sal Units: unitless Description: <p>Correlation between the individual loading on the latent factor 1 from the lfmm model based on salnity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890265.rdf Name: cor_LFMMU2_temp Units: unitless Description: <p>Correlation between the individual loading on the latent factor 2 from the lfmm model based on temperature.</p> http://lod.bco-dmo.org/id/dataset-parameter/890266.rdf Name: cor_LFMMU2_sal Units: unitless Description: <p>Correlation between the individual loading on the latent factor 2 from the lfmm model based on salnity.</p> http://lod.bco-dmo.org/id/dataset-parameter/890267.rdf Name: cor_PC1_LFMMU1_temp Units: unitless Description: <p>Correlation between (individual loading on PC1 from the principle components based on the Genotype-matrix) and (individual loading on the latent factor 1 from the lfmm model based on temperature).</p> http://lod.bco-dmo.org/id/dataset-parameter/890268.rdf Name: cor_PC1_LFMMU1_sal Units: unitless Description: <p>Correlation between (individual loading on PC1 from the principle components based on the Genotype-matrix) and (individual loading on the latent factor 1 from the lfmm model based on salinity).</p> http://lod.bco-dmo.org/id/dataset-parameter/890269.rdf Name: cor_PC2_LFMMU1_temp Units: unitless Description: <p>Correlation between (individual loading on PC2 from the principle components based on the Genotype-matrix) and (individual loading on the latent factor 1 from the lfmm model based on temperature).</p> http://lod.bco-dmo.org/id/dataset-parameter/890270.rdf Name: cor_PC2_LFMMU1_sal Units: unitless Description: <p>Correlation between (individual loading on PC2 from the principle components based on the Genotype-matrix) and (individual loading on the latent factor 1 from the lfmm model based on salinity).</p> http://lod.bco-dmo.org/id/dataset-parameter/890271.rdf Name: gwas_TPR_sal Units: unitless Description: <p>True positive rate of the GWAS model for the salinity trait.</p> http://lod.bco-dmo.org/id/dataset-parameter/890272.rdf Name: gwas_TPR_temp Units: unitless Description: <p>True positive rate of the GWAS model for the temperature trait.</p> http://lod.bco-dmo.org/id/dataset-parameter/890273.rdf Name: gwas_FDR_sal_neutbase Units: unitless Description: <p>False discovery rate of the GWAS model for the salinity trait, only including QTNs and purely neutral loci unaffected by selection.</p> http://lod.bco-dmo.org/id/dataset-parameter/890274.rdf Name: gwas_FDR_temp_neutbase Units: unitless Description: <p>False discovery rate of the GWAS model for the temperature trait, only including QTNs and purely neutral loci unaffected by selection.</p> http://lod.bco-dmo.org/id/dataset-parameter/890275.rdf Name: clinalparadigm_sal_proptop5GWASclines Units: unitless Description: <p>Proportion of the top 5% of GWAS loci with the smallest P-values for the salinity trait (true and false positives) that show clines.</p> http://lod.bco-dmo.org/id/dataset-parameter/890276.rdf Name: clinalparadigm_temp_proptop5GWASclines Units: unitless Description: <p>Proportion of the top 5% of GWAS loci with the smallest P-values for the temperature trait (true and false positives) that show clines.</p> http://lod.bco-dmo.org/id/dataset-parameter/890277.rdf Name: clinalparadigm_sal_propsigGWASclines Units: unitless Description: <p>Proportion of GWAS hits for the salinity trait (true and false positives) that show clines.</p> http://lod.bco-dmo.org/id/dataset-parameter/890278.rdf Name: clinalparadigm_temp_propsigGWASclines Units: unitless Description: <p>Proportion of GWAS hits for the temperature trait (true and false positives) that show clines.</p> GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact https://doi.org/10.26008/1912/bco-dmo.889769.1 download onLine dataset &lt;p&gt;&lt;strong&gt;Landscapes and demographies&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;All simulations consisted of 100 demes arranged on a 10 x 10 landscape grid. The 15 levels of landscape-demography were broadly divided into three landscape categories: (i) a stepping stone landscape with latitudinal and longitudinal selective clines (Stepping-Stone Clines, the most commonly simulated scenario in testing methods) (Coop et al. 2010; Frichot et al. 2013; Günther &amp;amp; Coop 2013; de Villemereuil et al. 2014; Lotterhos &amp;amp; Whitlock 2015; Rellstab et al. 2015; Gautier 2015; Forester et al. 2016, 2018), (ii) a stepping stone landscape with one latitudinal cline and one non-linear longitudinal mountain range (Stepping-Stone Mountain, which left the potential for unique architectures to arise to the same selective pressure at different geographic locations), and (iii) an estuary landscape with a latitudinal and longitudinal selective clines (Estuary Clines, which simulated repeated independent bouts of adaptation analogous to oysters or sticklebacks that repeatedly colonize and adapt to isolated freshwater environments connected by gene flow in the marine environment). For simplicity, I refer to the latitudinal environment as Temperature and the longitudinal environment as Env2.&amp;nbsp;&lt;/p&gt; &lt;p&gt;In summary, Stepping-Stone Mountain had a different environmental pattern than Stepping-Stone Clines but the same demography, while Estuary Clines had the same environmental pattern as Stepping-Stone Clines but different demography. The demographic parameters were chosen such that different landscapes achieved similar levels of neutral genetic differentiation and local adaptation. Within each of the three landscapes, 5 demographies were simulated that described the migration rates and effective population sizes on the landscape (see Supplemental Methods).&lt;/p&gt; &lt;p&gt;See the Supplemental Methods for a description of the multivariate continuous space simulations with 6 traits.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Genetic map&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The Wright-Fisher simulations were based on a previously published quantitative genetic model and a genetic map (Lotterhos 2019). The genome consisted of 20 linkage groups each with 50,000 sites. The scaled recombination rate (N metapop r = 0.01) gave a resolution of 0.001 cM between proximate bases and a total length of 50 cM for each linkage group. This resolution mimicked a SNP chip, in which SNPs were collected across a large genetic map (Lotterhos 2019). The population-scaled neutral mutation rate was (N metapop μ= 0.001). QTNs could evolve on the first 10 linkage groups, while on the second 10 linkage groups only neutral loci could evolve.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Genetic Architecture and Stabilizing Selection&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Mutation: Quantitative trait nucleotides (QTNs) contributed additively to the optimal phenotype for each individual without dominance. Three genic levels were simulated: oligogenic (few loci of large effect on the trait), moderately polygenic (dozens to hundreds of loci with intermediate effects), and highly polygenic (hundreds of loci with small effects). For QTN mutations under 1 trait or 2 traits without pleiotropy, the univariate effect size of a new QTN mutation was drawn from a normal distribution with a mean of 0 and standard deviation sigma QTN. For QTN mutations under 2 traits with pleiotropy, the bivariate effect size was drawn from a multivariate normal distribution with a standard deviation of sigma QTN for both traits and no covariance, which gave flexibility for mutations to evolve with effects on one or both traits. Thus, the distribution of effect sizes and linkage relationships among QTNs was allowed to evolve.&amp;nbsp;&lt;/p&gt; &lt;p&gt;Pleiotropy: Within each genic category were 5 levels of pleiotropy and selection: (i) 1 temperature trait (which adapted to the latitudinal cline),&amp;nbsp; (ii) 2 traits without pleiotropy and equal strengths of selection on both traits, (iii) 2 traits without pleiotropy and with weaker selection on the temperature trait, (iv) 2 traits with pleiotropy (QTNs could evolve effects on one or both traits) and equal strengths of selection on both traits, and (v) 2 traits with pleiotropy and with weaker selection on the latitudinal temperature trait.&lt;/p&gt; &lt;p&gt;Selection: The trait was subject to spatially heterogeneous stabilizing selection with the optimum for each location in space given by the environment. For each individual in each generation, the fitness was determined by a Gaussian function given the difference between the individual’s phenotype and the optimum at that location.&amp;nbsp;&lt;/p&gt; &lt;p&gt;See trait equation details in the supplemental document: equation_details_genetic_architecture_and_stabilizing_selection.pdf&lt;/p&gt; &lt;p&gt;For information on burn-in, adding neutral loci with tree sequencing, filtering, and sampling, see the Supplemental Methods.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Quantifying the degree of local adaptation, divergence, and structure&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;For each replicate, the degree of local adaptation was measured as (i) the difference between population fitness in sympatry and allopatry following (Blanquart et al. 2013) and (ii) the correlation between the phenotype and environmental cline for each trait. Overall divergence (genetic differentiation) was calculated as Weir and Cockerham’s FST&amp;nbsp; (Weir &amp;amp; Cockerham 1984) in OutFLANK (Whitlock &amp;amp; Lotterhos 2015). Population structure was estimated with a principal component analysis on the genotype matrix.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Quantifying trait and allelic clines&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The degree of a trait cline was measured as Kendall's tau rank correlation coefficient between individual trait values and deme environment. The degree of an allele frequency cline was measured as Kendall's tau rank correlation coefficient (Kendall 1938, 1945) between deme allele frequency and deme environment, with significance being determined after Bonferroni correction based on the number of SNPs in the data. QTNs that were significant by this criteria were deemed “clinal QTNs.” The proportion of clinal QTNs excluded minor alleles with frequency &amp;lt; 0.01.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;GEA performance&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Latent-factor mixed models (LFMM) assess the linear relationship between genotype and environment while controlling for structure as latent factors. LFMM was implemented using the function lfmm2 in the R package LEA v.4.0.3 (Frichot et al. 2013; Frichot &amp;amp; François 2015; Caye et al. 2019). Redundancy analysis (RDA) and the partial RDA (pRDA) including a structure correction (conditional on the first 2 PC axes) were implemented using the ‘rda’ function in the R package vegan (Dixon 2003). See Supplemental Methods for details of implementation and correction for false discovery rate.&lt;/p&gt; &lt;p&gt;The performance of the association metrics were summarized as: (i) false discovery rate (FDR, proportion of outliers that are neutral, lower is better), (ii) true positive rate (TPR, proportion of QTNs that are significant outliers, higher is better), and (iii) the area under the precision-recall curve (AUC-PR, higher is better) (Lotterhos et al. 2022). In order to provide the most optimistic estimate of a method’s performance, the performance statistics were calculated by only including truly neutral loci unaffected by selection on linkage groups 11-20 and the QTNs.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Importance of clinal QTNs to local adaptation&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;First framework: A linear model was used to conduct the GWAS with individual trait value as the response variable and SNP genotype, PC1, and PC2 as explanatory variables. The proportion of GWAS hits that also showed clines with the environmental variable was compared to the known number of clinal QTNs. Second framework: The proportion of additive genetic variance (VA) for each QTN was calculated as the additive genetic variance for the focal QTN standardized by the total additive genetic variance following (Lotterhos 2019). The proportion of additive genetic variance (VA) explained by clinal QTNs was compared to a null expectation equal to the proportion of QTNs that were clinal. Third framework: I estimated the proportion of local explained by different subsets of QTNs: (i) QTNs with MAF &amp;gt; 0.01, (ii) clinal QTNs, and (iii) clinal QTNs inferred from latent factor mixed models that include a structure correction. For (ii) and (iii), a GEA model was performed for each environment, and then outlier QTNs were combined into a focal QTN set that was used for the local adaptation prediction. For each focal subset of QTNs, the counts of the derived allele were multiplied by the QTN effect size, summed to get a phenotype, and that phenotype was used in an in silico reciprocal transplant using the known phenotype-fitness function to estimate the degree of local adaptation. This estimate was then divided by the total degree of local adaptation (using all QTNs including those below the MAF threshold) to get an estimate of the proportion of local adaptation explained by that focal subset.&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; Specified by the Principal Investigator(s) &lt;p&gt;&lt;strong&gt;Processing notes from researcher:&amp;nbsp;&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Conda environments used to create and process the simulations are included in the code repository&lt;/li&gt; &lt;/ul&gt; &lt;div class=&quot;field&quot; id=&quot;form_data_processing&quot;&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;/div&gt; &lt;div class=&quot;field&quot; id=&quot;form_problems&quot;&gt;&amp;nbsp;&lt;/div&gt; Specified by the Principal Investigator(s) asNeeded 7.x-1.1 Biological and Chemical Oceanography Data Management Office (BCO-DMO) Unavailable 508-289-2009 WHOI MS#36 Woods Hole MA 02543 USA info@bco-dmo.org http://www.bco-dmo.org Monday - Friday 8:00am - 5:00pm For questions regarding this resource, please contact BCO-DMO via the email address provided. pointOfContact Northeastern's High Performance Computing Cluster Northeastern's High Performance Computing Cluster PI Supplied Instrument Name: Northeastern's High Performance Computing Cluster Instrument Name: High-Performance Computing Cluster Instrument Short Name:HPC Instrument Description: "High-Performance Computing" (HPC) refers to a class of evolving technologies that provide leading-edge computational capabilities, including scalable high-performance computers, high-end graphic systems, and high-speed networks. HPC may be used for molecular modeling, genome analysis, and image processing, among others.