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{"title"=>"Pedigree-Free Estimates of Heritability in the Wild: Promising Prospects for Selfing Populations", "type"=>"journal", "authors"=>[{"first_name"=>"Laurene", "last_name"=>"Gay", "scopus_author_id"=>"17341885300"}, {"first_name"=>"Mathieu", "last_name"=>"Siol", "scopus_author_id"=>"22938753800"}, {"first_name"=>"Joelle", "last_name"=>"Ronfort", "scopus_author_id"=>"6603621602"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84879344955", "doi"=>"10.1371/journal.pone.0066983", "sgr"=>"84879344955", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"23825602", "issn"=>"19326203", "pui"=>"369184597"}, "id"=>"775b0301-2bbd-38ca-be04-0894c3cef744", "abstract"=>"Estimating the genetic variance available for traits informs us about a population’s ability to evolve in response to novel selective challenges. In selfing species, theory predicts a loss of genetic diversity that could lead to an evolutionary dead-end, but empirical support remains scarce. Genetic variability in a trait is estimated by correlating the phenotypic resemblance with the proportion of the genome that two relatives share identical by descent (‘realized relatedness’). The latter is traditionally predicted from pedigrees (ΦA: expected value) but can also be estimated using molecular markers (average number of alleles shared). Nevertheless, evolutionary biologists, unlike animal breeders, remain cautious about using marker-based relatedness coefficients to study complex phenotypic traits in populations. In this paper, we review published results comparing five different pedigree-free methods and use simulations to test individual-based models (hereafter called animal models) using marker-based relatedness coefficients, with a special focus on the influence of mating systems. Our literature review confirms that Ritland’s regression method is unreliable, but suggests that animal models with marker-based estimates of relatedness and genomic selection are promising and that more testing is required. Our simulations show that using molecular markers instead of pedigrees in animal models seriously worsens the estimation of heritability in outcrossing populations, unless a very large number of loci is available. In selfing populations the results are less biased. More generally, populations with high identity disequilibrium (consanguineous or bottlenecked populations) could be propitious for using marker-based animal models, but are also more likely to deviate from the standard assumptions of quantitative genetics models (non-additive variance).", "link"=>"http://www.mendeley.com/research/pedigreefree-estimates-heritability-wild-promising-prospects-selfing-populations", "reader_count"=>65, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>7, "Researcher"=>23, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>7, "Researcher"=>23, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>1, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>49, "Medicine and Dentistry"=>1, "Psychology"=>1, "Social Sciences"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>3}}, "reader_count_by_country"=>{"Belgium"=>1, "Iran"=>1, "United States"=>2, "Poland"=>1}, "group_count"=>0}

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• http://doi.org/10.3389/fgene.2015.00057
• http://doi.org/10.1080/12538078.2013.822827
• http://doi.org/10.1111/2041-210X.12129
• http://doi.org/10.3390/brainsci7070091
• http://doi.org/10.1002/ece3.2346
• http://doi.org/10.1002/ece3.2171
• http://doi.org/10.1038/s41598-018-36050-7
• http://doi.org/10.1016/j.tree.2017.09.004
• http://doi.org/10.1007/s11295-016-1062-3
• http://doi.org/10.1186/s12864-017-3487-y
• http://doi.org/10.1111/1755-0998.12536
• http://doi.org/10.1111/eva.12149
• http://doi.org/10.1111/mec.12827
• http://doi.org/10.1534/g3.115.025957

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• {"id"=>"427836625293279233", "text"=>"Good test of current methods to measure heritability without a pedigree #PLOSONE http://t.co/yhMyT3K13b", "created_at"=>"2014-01-27T16:12:54Z", "user"=>"jessstapley", "user_name"=>"Jessica Stapley", "user_profile_image"=>"http://pbs.twimg.com/profile_images/344513261572193168/aecf889b4ba03e7509ebcde77e9586c4_normal.png"}
• {"id"=>"427837745181257728", "text"=>"RT @jessstapley: Good test of current methods to measure heritability without a pedigree #PLOSONE http://t.co/yhMyT3K13b", "created_at"=>"2014-01-27T16:17:21Z", "user"=>"adhayward18", "user_name"=>"Adam Hayward", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3560479279/d645b14d2e35e4109b8b56fa11406f42_normal.jpeg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1100192"], "description"=>"1<p>The column “Method” is the same as described for <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone-0066983-t001\" target=\"_blank\">Table 1</a>, with the additional category “Ped” that stands for pedigree-based animal models. Numbers in italics indicate that we report the correlation with the simulated heritability (<i>corr</i>) rather than the bias.</p>2<p>Method used to estimate pairwise kinship coefficients using molecular markers: L = Loiselle <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Loiselle1\" target=\"_blank\">[20]</a>; UAR = raw unified additive relationship (estimator of the genome-wide relationship between individuals) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Powell1\" target=\"_blank\">[14]</a>; SA = % of shared alleles <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Srensen1\" target=\"_blank\">[135]</a>; pedigree = Φ_A.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "simulation", "studies", "comparing", "estimates", "quantitative", "pedigree-free"], "article_id"=>730786, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.t002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_simulation_studies_comparing_estimates_of_quantitative_genetics_parameters_using_pedigree_free_methods_/730786", "title"=>"Summary of simulation studies comparing estimates of quantitative genetics parameters using pedigree-free methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100194"], "description"=>"1<p>Method: 1 = Ritland’s method; 2 = maximum likelihood on relatedness classes; 3 = sibship or pedigree reconstruction; 4 = animal model with marker-based relatedness matrix; 5 = genomic selection.</p>2<p>SSR = microsatellite; SNP = single nucleotide substitution.</p>3<p>Method used to estimate pairwise kinship coefficients using molecular markers: LR = Lynch & Ritland <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Lynch2\" target=\"_blank\">[16]</a>; QG = Queller & Goodnight <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Queller1\" target=\"_blank\">[15]</a>; W = Wang <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Wang1\" target=\"_blank\">[17]</a>; R = Ritland <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Ritland1\" target=\"_blank\">[21]</a>; SA = % of shared alleles <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Toro1\" target=\"_blank\">[118]</a>; GRM = genomic relationship matrix <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-VanRaden1\" target=\"_blank\">[119]</a>; IBS(i,j) = probability of identity by state <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Oliehoek1\" target=\"_blank\">[120]</a>. For method 3, this column indicates the method of pedigree reconstruction: MCMC (maximizes the pairwise likelihood ratios of being full siblings or unrelated <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Smith1\" target=\"_blank\">[121]</a>); CERVUS <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Marshall1\" target=\"_blank\">[122]</a>; COLONY <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Wang2\" target=\"_blank\">[123]</a>; PAPA <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Duchesne1\" target=\"_blank\">[124]</a>.</p>4<p>The bias is defined as the difference between heritability estimated from the pedigree (Φ<i>_A</i>) and heritability estimated using molecular markers in one of the five methods presented <i>_marker</i>–<i>_ped</i>. When no information was available on <i>_ped</i> estimated using pedigrees, the bias was measured relative to estimated using method 3 (pedigree reconstruction), as indicated in the column “Note”. When several traits were analyzed (column “traits”), we present the average bias.</p>5<p>Mating regime: “Out” stands for outcrossing, “Mix” for mixed mating, “Self” for predominantly selfing and “C” for clonality.</p>6<p>Single Locus Probe.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "studies", "comparing", "estimates", "quantitative", "pedigree-free"], "article_id"=>730787, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.t001", "stats"=>{"downloads"=>7, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_studies_comparing_estimates_of_quantitative_genetics_parameters_using_pedigree_free_methods_/730787", "title"=>"Summary of studies comparing estimates of quantitative genetics parameters using pedigree-free methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100195", "https://ndownloader.figshare.com/files/1100197", "https://ndownloader.figshare.com/files/1100198", "https://ndownloader.figshare.com/files/1100199", "https://ndownloader.figshare.com/files/1100200"], "description"=>"<div><p>Estimating the genetic variance available for traits informs us about a population’s ability to evolve in response to novel selective challenges. In selfing species, theory predicts a loss of genetic diversity that could lead to an evolutionary dead-end, but empirical support remains scarce. Genetic variability in a trait is estimated by correlating the phenotypic resemblance with the proportion of the genome that two relatives share identical by descent (‘realized relatedness’). The latter is traditionally predicted from pedigrees (Φ<i>_A</i>: expected value) but can also be estimated using molecular markers (average number of alleles shared). Nevertheless, evolutionary biologists, unlike animal breeders, remain cautious about using marker-based relatedness coefficients to study complex phenotypic traits in populations. In this paper, we review published results comparing five different pedigree-free methods and use simulations to test individual-based models (hereafter called animal models) using marker-based relatedness coefficients, with a special focus on the influence of mating systems. Our literature review confirms that Ritland’s regression method is unreliable, but suggests that animal models with marker-based estimates of relatedness and genomic selection are promising and that more testing is required. Our simulations show that using molecular markers instead of pedigrees in animal models seriously worsens the estimation of heritability in outcrossing populations, unless a very large number of loci is available. In selfing populations the results are less biased. More generally, populations with high identity disequilibrium (consanguineous or bottlenecked populations) could be propitious for using marker-based animal models, but are also more likely to deviate from the standard assumptions of quantitative genetics models (non-additive variance).</p></div>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "estimates", "heritability", "prospects", "selfing"], "article_id"=>730788, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0066983.s001", "https://dx.doi.org/10.1371/journal.pone.0066983.s002", "https://dx.doi.org/10.1371/journal.pone.0066983.s003", "https://dx.doi.org/10.1371/journal.pone.0066983.s004", "https://dx.doi.org/10.1371/journal.pone.0066983.s005"], "stats"=>{"downloads"=>33, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pedigree_Free_Estimates_of_Heritability_in_the_Wild_Promising_Prospects_for_Selfing_Populations_/730788", "title"=>"Pedigree-Free Estimates of Heritability in the Wild: Promising Prospects for Selfing Populations", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100175"], "description"=>"<p>The efficiency was assessed in a review of 24 empirical studies (A) or 15 simulation studies (B), comparing heritability estimates using pedigree or one of the following methods: 1 - Ritland; 2 - relatedness classes; 3 - reconstructed pedigrees; 4 - marker-based animal model or 5 - genomic selection. Details of the number of studies for each method are given in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone-0066983-t001\" target=\"_blank\">Table 1</a>. The bias was measured as <i>_marker</i> - <i>_pedigree</i> in A and as E( – <i>h²</i>) in B, where <i>h²</i> is the simulated parameter. The horizontal line shows the median bias for each method. The bottom and top of the box show the 25th and 75th percentiles. The vertical dashed lines show the maximum and minimum biases and the circles are outliers.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "marker-based", "methods", "empirical", "simulation"], "article_id"=>730773, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_five_different_marker_based_methods_to_estimate_heritability_8211_review_of_empirical_and_simulation_studies_/730773", "title"=>"Accuracy of five different marker-based methods to estimate heritability– review of empirical and simulation studies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100181"], "description"=>"<p>This figure shows the correlation between the heritability simulated and heritability estimates obtained using pedigree-based animal models (A), marker-based animal models (B) or marker-based relatedness coefficients truncated before the analysis (C). Each dot stands for a simulated population, with 90% selfing (in grey) or complete outcrossing (in black). Circles stand for means across 20 replicates and solid lines show the 95% confidence intervals, as estimated by Asreml (and averaged across replicates). The dashed lines represent <i>y = x</i>.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "pedigree", "marker-based", "methods"], "article_id"=>730775, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.g002", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_results_testing_the_accuracy_of_pedigree_or_marker_based_methods_to_estimate_heritability_/730775", "title"=>"Simulation results testing the accuracy of pedigree or marker-based methods to estimate heritability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100184"], "description"=>"<p>Regression between pairwise Loiselle coefficients estimated using 1500 SNP and Φ<i>_A</i>. The population comprised 500 individuals with 90% selfing (grey crosses) or complete outcrossing (black circles). The legend indicates the slope of the regression of Φ<i>_A</i> against Loiselle and the correlation coefficient <i>r</i>. The variance in relatedness was 0.0026 in the outcrossing population and 0.0108 in the selfing population (within the range of variances observed in wild populations, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone-0066983-t001\" target=\"_blank\">Table 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066983#pone.0066983-Csillery1\" target=\"_blank\">[23]</a>).</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "larger", "variance", "pairwise", "relatedness", "coefficients", "selfing", "compared", "outcrossing"], "article_id"=>730778, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Higher_mean_and_larger_variance_in_pairwise_relatedness_coefficients_in_selfing_compared_to_outcrossing_populations_/730778", "title"=>"Higher mean and larger variance in pairwise relatedness coefficients in selfing compared to outcrossing populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100185"], "description"=>"<p>Regression between pairwise Loiselle coefficients estimated using 1500 SNPs and pairwise Loiselle coefficients estimated using the allele frequency at QTLs determining the phenotypic trait. Outcrossing populations are shown in black and selfing populations (selfing rate 90%) in grey. The legend indicates the slope of the regression and the correlation coefficient <i>r</i>. The slope is expected to be close to one if the relatedness at causal loci is accurately predicted by the relatedness at observed SNPs.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "causal", "loci", "correlated", "selfing", "outcrossing"], "article_id"=>730779, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relatednesses_at_causal_and_marker_loci_are_more_closely_correlated_in_selfing_than_in_outcrossing_populations_/730779", "title"=>"Relatednesses at causal and marker loci are more closely correlated in selfing than in outcrossing populations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-25 01:49:45"}
• {"files"=>["https://ndownloader.figshare.com/files/1100187"], "description"=>"<p>Influence of the number of loci used to estimate pairwise relatedness coefficients (Loiselle coefficients) on the bias in heritability estimates, when using a marker-based animal model. Each dot stands for a simulated population of 500 individuals, with complete outcrossing (panel A, in black) or 90% selfing (panel B, in grey). Panel C shows the results when marker-based relatedness coefficients are truncated before the analysis. Large circles stands for the average heritability over the 20 replicated simulations. The confidence intervals estimated in Asreml for each replicate were averaged over the 20 replicates and are shown as solid lines. The dashed line stands for the simulated heritability.</p>", "links"=>[], "tags"=>["ecology", "Evolutionary ecology", "Evolutionary biology", "Evolutionary processes", "adaptation", "Evolutionary genetics", "evolutionary theory", "genetics", "Heredity", "Complex traits", "Genetic determinism", "phenotypes", "Quantitative traits", "Plant science", "Plant evolution", "Plant genetics", "estimates", "loci"], "article_id"=>730781, "categories"=>["Biological Sciences"], "users"=>["Laurene Gay", "Mathieu Siol", "Joëlle Ronfort"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066983.g005", "stats"=>{"downloads"=>7, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heritability_estimates_become_more_accurate_with_the_number_of_marker_loci_used_to_estimate_relatedness_/730781", "title"=>"Heritability estimates become more accurate with the number of marker loci used to estimate relatedness.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-25 01:49:45"}

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Relative Metric

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F1000Prime