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{"title"=>"Practical issues in imputation-based association mapping", "type"=>"journal", "authors"=>[{"first_name"=>"Yongtao", "last_name"=>"Guan", "scopus_author_id"=>"56290684000"}, {"first_name"=>"Matthew", "last_name"=>"Stephens", "scopus_author_id"=>"7201574987"}], "year"=>2008, "source"=>"PLoS Genetics", "identifiers"=>{"scopus"=>"2-s2.0-58149175796", "sgr"=>"58149175796", "issn"=>"15537390", "isbn"=>"1553-7404 (Electronic)\\r1553-7390 (Linking)", "pmid"=>"19057666", "doi"=>"10.1371/journal.pgen.1000279", "pui"=>"354027910"}, "id"=>"9b3b9429-154f-38c7-a83d-c82a717e56fd", "abstract"=>"Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption--specifically, that difficult-to-impute SNPs tend to have larger effects--and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate--their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html.", "link"=>"http://www.mendeley.com/research/practical-issues-imputationbased-association-mapping", "reader_count"=>180, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>20, "Researcher"=>61, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>53, "Student > Postgraduate"=>6, "Student > Master"=>16, "Other"=>3, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>20, "Researcher"=>61, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>53, "Student > Postgraduate"=>6, "Student > Master"=>16, "Other"=>3, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>19, "Agricultural and Biological Sciences"=>110, "Medicine and Dentistry"=>15, "Neuroscience"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Psychology"=>1, "Computer Science"=>15}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>110}, "Computer Science"=>{"Computer Science"=>15}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>19}, "Unspecified"=>{"Unspecified"=>5}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "Argentina"=>1, "United States"=>11, "United Kingdom"=>1, "Portugal"=>1, "Iceland"=>1, "New Zealand"=>1, "Canada"=>2, "Austria"=>1, "Sweden"=>1, "Finland"=>1, "Brazil"=>1, "Nigeria"=>1, "Germany"=>2}, "group_count"=>11}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/915369"], "description"=>"<p>Each point represents one SNP-phenotype combination, colored according to average confidence in imputed genotypes. Specifically, the SNPs were colored according to the value of <i>r</i>, defined to be the ratio of the variance of the (posterior mean) genotypes and the expected variance if the SNP were typed, calculated from the MAF assuming Hardy-Weinberg equilibrium (2<i>f</i>(1−<i>f</i>)). This scaling ensures that for typed and confidently-imputed SNPs <i>r</i>≈1, whereas for SNPs with low average confidence <i>r</i> will be close to 0. Colors indicate ranges of values of <i>r</i>: red <i>r</i>∈(0,0.001]; green <i>r</i>∈(0.001,0.01]; blue <i>r</i>∈(0.01,0.1]; cyan <i>r</i>∈(0.1,0.5]; black <i>r</i>>0.5.</p>", "links"=>[], "tags"=>["correspondance"], "article_id"=>585826, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000279.g003", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_showing_correspondance_between_log_10_BF_mean_values_and_Likelihood_ratio_statistics_923_left_and_923_mean_right_/585826", "title"=>"Graph showing correspondance between log₁₀(BF_mean) values and Likelihood ratio statistics Λ (left) and Λ_mean (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-12-05 01:37:06"}
• {"files"=>["https://ndownloader.figshare.com/files/451972", "https://ndownloader.figshare.com/files/452015", "https://ndownloader.figshare.com/files/452059"], "description"=>"<div><p>Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption—specifically, that difficult-to-impute SNPs tend to have larger effects—and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate—their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from <a href=\"http://stephenslab.uchicago.edu/software.html\">http://stephenslab.uchicago.edu/software.html</a>.</p></div>", "links"=>[], "tags"=>["issues", "imputation-based"], "article_id"=>149059, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000279.s001", "https://dx.doi.org/10.1371/journal.pgen.1000279.s002", "https://dx.doi.org/10.1371/journal.pgen.1000279.s003"], "stats"=>{"downloads"=>6, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Practical_Issues_in_Imputation_Based_Association_Mapping/149059", "title"=>"Practical Issues in Imputation-Based Association Mapping", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-12-05 02:30:59"}
• {"files"=>["https://ndownloader.figshare.com/files/915292"], "description"=>"<p>In each case the diagonal blue line is <i>y</i> = <i>x</i>, and the vertical red lines indicate ±2 standard errors of the Bayes factor estimate (for example, on the left they run between log₁₀(BF_IS±2 standard errors)).</p>", "links"=>[], "tags"=>["correspondence"], "article_id"=>585753, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000279.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphs_showing_correspondence_of_BF_mean_x_axis_vs_BF_IS_y_axis_on_left_panel_and_BF_naive_y_axis_on_right_panel_/585753", "title"=>"Graphs showing correspondence of BF_mean (<i>x</i>-axis) vs BF_IS (<i>y</i>-axis on left panel) and BF_naive (<i>y</i>-axis on right panel).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-12-05 01:35:53"}
• {"files"=>["https://ndownloader.figshare.com/files/915204"], "description"=>"<p>Numbers along the line indicate the thresholds that produce the corresponding call rate and error rate; small black points indicate results for intermediate thresholds in increments of 0.02. For example, if we call only those imputed genotypes assigned probability >0.9 of being correct, then approximately 74% of imputed genotypes are called, and of these called genotypes approximately 1% are incorrect.</p>", "links"=>[], "tags"=>["trade-off", "call-rate", "probability", "calling", "imputed", "genotype"], "article_id"=>585658, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000279.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_showing_the_trade_off_between_call_rate_and_error_rate_as_the_probability_threshold_for_calling_an_imputed_genotype_is_varied_/585658", "title"=>"Graph showing the trade-off between call-rate and error rate, as the probability threshold for calling an imputed genotype is varied.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-12-05 01:34:18"}
• {"files"=>["https://ndownloader.figshare.com/files/915511"], "description"=>"<p>We use <i>K</i> to denote the number of clusters used, and <i>E</i> to denote the number of EM runs over which genotype probabilities are averaged. In each case we used 10 iterations of the EM algorithm in each run.</p>", "links"=>[], "tags"=>["rates", "cases", "probable", "genotype", "imputation", "ceu", "hapmap", "parents"], "article_id"=>585964, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000279.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Error_rates_proportion_of_cases_where_the_most_probable_genotype_does_not_match_the_true_genotype_for_different_genotype_imputation_strategies_using_the_model_of_7_and_the_phased_CEU_HapMap_parents_as_a_panel_/585964", "title"=>"Error rates (proportion of cases where the most probable genotype does not match the true genotype) for different genotype imputation strategies, using the model of [7] and the (phased) CEU HapMap parents as a panel.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-12-05 01:39:24"}
• {"files"=>["https://ndownloader.figshare.com/files/915461"], "description"=>"<p>The blue line is <i>x</i> = <i>y</i>.</p>", "links"=>[], "tags"=>["x-axis", "linear", "y-axis", "logistic"], "article_id"=>585917, "categories"=>["Mathematics", "Genetics"], "users"=>["Yongtao Guan", "Matthew Stephens"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000279.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_x_axis_is_log_10_BF_of_linear_model_and_y_axis_is_log_10_BF_of_logistic_model_/585917", "title"=>"The x-axis is log₁₀ (<i>BF</i>) of linear model and y-axis is log₁₀<i>BF</i> of logistic model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-12-05 01:38:37"}
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Relative Metric

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F1000Prime