Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine
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{"title"=>"Genome-wide prediction methods in highly diverse and heterozygous species: Proof-of-concept through simulation in grapevine", "type"=>"journal", "authors"=>[{"first_name"=>"Agota", "last_name"=>"Fodor", "scopus_author_id"=>"23984511900"}, {"first_name"=>"Vincent", "last_name"=>"Segura", "scopus_author_id"=>"12784789800"}, {"first_name"=>"Marie", "last_name"=>"Denis", "scopus_author_id"=>"55249820000"}, {"first_name"=>"Samuel", "last_name"=>"Neuenschwander", "scopus_author_id"=>"55503182800"}, {"first_name"=>"Alexandre", "last_name"=>"Fournier-Level", "scopus_author_id"=>"24278587800"}, {"first_name"=>"Philippe", "last_name"=>"Chatelet", "scopus_author_id"=>"16232683500"}, {"first_name"=>"Félix Abdel Aziz", "last_name"=>"Homa", "scopus_author_id"=>"56410734400"}, {"first_name"=>"Thierry", "last_name"=>"Lacombe", "scopus_author_id"=>"23485802300"}, {"first_name"=>"Patrice", "last_name"=>"This", "scopus_author_id"=>"55780314900"}, {"first_name"=>"Loic Le", "last_name"=>"Cunff", "scopus_author_id"=>"40461387900"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"600373127", "sgr"=>"84909606253", "pmid"=>"25365338", "scopus"=>"2-s2.0-84909606253", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0110436", "issn"=>"19326203"}, "id"=>"a933dd5f-c2cf-381b-99e5-dbbc58fdce96", "abstract"=>"Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.", "link"=>"http://www.mendeley.com/research/genomewide-prediction-methods-highly-diverse-heterozygous-species-proofofconcept-through-simulation", "reader_count"=>46, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>16, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>16, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>39, "Physics and Astronomy"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>39}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Uruguay"=>1, "Brazil"=>1, "Denmark"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1777697"], "description"=>"a<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110436#pone.0110436-Myles1\" target=\"_blank\">[30]</a>.</p><p>Descriptive statistics on the simulated meta-population.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227789, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.t002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptive_statistics_on_the_simulated_meta_population_/1227789", "title"=>"Descriptive statistics on the simulated meta-population.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777679"], "description"=>"<p>Mean values were calculated on the 10 replicates of the simulation.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227771, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heat_map_presenting_the_difference_between_the_phenotypic_mean_of_training_and_candidate_sets_/1227771", "title"=>"Heat map presenting the difference between the phenotypic mean of training and candidate sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777696"], "description"=>"<p>The standard deviation is between brackets.</p>a<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110436#pone.0110436-Lacombe2\" target=\"_blank\">[47]</a>,</p>b<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110436#pone.0110436-Laucou1\" target=\"_blank\">[27]</a>.</p><p>Population statistics on simulated data for the five scenarios and reference values from published data.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227788, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_statistics_on_simulated_data_for_the_five_scenarios_and_reference_values_from_published_data_/1227788", "title"=>"Population statistics on simulated data for the five scenarios and reference values from published data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777691"], "description"=>"<p>We also compared here two combinations of training – candidate sets (i.e. the two figures on the left present within sub-population predictions and the two figures on the right present between sub-population predictions) and simple and complex traits through 10 replicates of the simulation. Training sets are indicated on the x axis, the two colors representing the methods used (cof, cofRR). Training and candidate sets comprised all individuals of the sub-population (1,000 and 200 individuals respectively), except for the model constructed on Call, which was tested on the entire breeding population (800 individuals).</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227783, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_accuracy_of_prediction_in_structured_A_and_non_structured_B_trait_/1227783", "title"=>"Mean accuracy of prediction in structured (A) and non-structured (B) trait.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777698"], "description"=>"<p>This table presents the number of positive detection via associated markers of each simulated QTL using the mlmm method, out of 10 replicates for both simple and complex traits and for structured and non-structured traits.</p><p>Results of GWAs analyses.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227790, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.t003", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_GWAs_analyses_/1227790", "title"=>"Results of GWAs analyses.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777694"], "description"=>"<p>Mean prediction accuracy was calculated on all 10 replicates of the simulation using four methods (cof, RR, BLR, cofRR). Models were built on the core-collection (Call) and applied to the four breeding sub-populations separately (dWW, dWE, dTE and Mixed, each composed of 200 individuals) and on the whole breeding meta-population (dall, 800 individuals). The two figures on the left side represent accuracies observed on structured traits and the other two figures accuracies on non-structured traits.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227786, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g007", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_accuracy_in_structured_A_and_non_structured_B_traits_using_the_core_collection_as_training_population_/1227786", "title"=>"Prediction accuracy in structured (A) and non-structured (B) traits using the core-collection as training population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777676"], "description"=>"<p>Distributions are presented on one replicate of the simulation for the structured and non-structured simple (<b>A</b>) and complex (<b>B</b>) traits. The colored vertical lines show the phenotypes of the founder individuals of descendent populations. Call corresponds to the core-collection.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227768, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_phenotypes_in_training_WW_WE_TE_populations_/1227768", "title"=>"Distribution of phenotypes in training (WW, WE, TE) populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777673"], "description"=>"<p>Mean estimation of LD (in Kb) around the QTLs, calculated at r<sup>2</sup>SV  = 0.2 between all loci in the 600 Kb neighborhood of each QTL locus on 3,000 individuals, for simple traits (<b>A</b>) and complex traits (<b>B</b>) on the 10 replicates of the simulation. The two figures on the left side represent LD around structured trait's QTLs and the other two figures around non-structured traits QTLs. QTL loci were ranked as a function of theirs effects from negative to positive values. Error bars were calculated with 95% confidence intervals on the estimates of the means.</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227766, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimation_of_LD_around_QTLs_/1227766", "title"=>"Estimation of LD around QTLs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777671"], "description"=>"<p>This scheme, implemented with quantiNemo, is composed of three steps: burn-in, domestication and breeding. Burn-in and domestication steps had the purpose to obtain grapevine diversity groups corresponding to Western Europe wine group (WW), Eastern Europe and Balkan wine group (WE) and Eastern Europe and Caucasus table group (TE) as described by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110436#pone.0110436-Bacilieri1\" target=\"_blank\">[22]</a>. Breeding step models crosses between selected individuals of these groups. At the right side of the figure are represented generation numbers and historical events with dates. White area is representing wild grape, after domestication it is showed grey. “Wine” and “Table” symbolize the two different definitions of selection applied on the trait under selection (selection optima and intensity). Black arrows show the direction of migration and its intensity is indicated by boldface numbers, specifying the number of migrating individuals. The stringency of each bottleneck is indicated by specifying the number of selected individuals (in regular font).</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227763, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scheme_of_the_demographical_scenario_based_on_our_working_hypothesis_on_grapevine_evolution_/1227763", "title"=>"Scheme of the demographical scenario based on our working hypothesis on grapevine evolution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777702"], "description"=>"<div><p>Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.</p></div>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227794, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436", "stats"=>{"downloads"=>14, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_Wide_Prediction_Methods_in_Highly_Diverse_and_Heterozygous_Species_Proof_of_Concept_through_Simulation_in_Grapevine_/1227794", "title"=>"Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-03 03:57:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1777683"], "description"=>"<p>Results are showed on simple and complex traits through the 10 replicates of the simulation. Figure <b>A</b> presents the prediction within sub-population (candidate set derived from the training set). Figure <b>B</b> shows the mean accuracy of prediction between sub-population (candidate sub-populations derived from a different training set). Training sets are indicated on the x axis, the four colors representing the four methods used (cof, RR, BLR, cofRR). Training and candidate sets comprised all individuals of the indicated sub-population (1,000 and 200 individuals respectively). In figure <b>C</b> the prediction models were built on the core-collection (Call) and applied to the four breeding sub-populations separately (dWW, dWE, dTE and Mixed, each composed of 200 individuals) and to the whole meta-population (dall, 800 individuals).</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227775, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_prediction_accuracy_as_a_function_of_the_training_8211_candidate_combination_/1227775", "title"=>"Mean prediction accuracy as a function of the training – candidate combination.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}

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Relative Metric

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