Impact of Measurement Error on Testing Genetic Association with Quantitative Traits
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Mendeley | Further Information

{"title"=>"Impact of Measurement Error on Testing Genetic Association with Quantitative Traits", "type"=>"journal", "authors"=>[{"first_name"=>"Jiemin", "last_name"=>"Liao"}, {"first_name"=>"Xiang", "last_name"=>"Li"}, {"first_name"=>"Tien-Yin", "last_name"=>"Wong"}, {"first_name"=>"Jie Jin", "last_name"=>"Wang"}, {"first_name"=>"Chiea Chuen", "last_name"=>"Khor"}, {"first_name"=>"E. Shyong", "last_name"=>"Tai"}, {"first_name"=>"Tin", "last_name"=>"Aung"}, {"first_name"=>"Yik-Ying", "last_name"=>"Teo"}, {"first_name"=>"Ching-Yu", "last_name"=>"Cheng"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"24475218", "issn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0087044"}, "id"=>"0c7ad801-b437-3c4a-a40c-f17e9cad7762", "abstract"=>"Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (p<10(-5)) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.", "link"=>"http://www.mendeley.com/research/impact-measurement-error-testing-genetic-association-quantitative-traits-1", "reader_count"=>13, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>6, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>6, "Other"=>1, "Student > Master"=>2, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>4}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Norway"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1360184"], "description"=>"<p>MAF, minor allele frequency.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "snp", "GWAS", "cataract"], "article_id"=>912909, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.t003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_p_value_8722_5_SNP_in_the_GWAS_of_nuclear_cataract_comparison_of_means_/912909", "title"=>"Significant (<i>p</i> value<10<sup>−5</sup>) SNP in the GWAS of nuclear cataract (comparison of means).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360183"], "description"=>"<p>EA, effect alleles.</p>*<p><i>p</i> value <5.5×10<sup>−<b>3</b></sup>. Significance level was set at 0.05/9 = 0.0055.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods"], "article_id"=>912908, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.t004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_association_results_for_9_blood_pressure_SNPs_/912908", "title"=>"Summary association results for 9 blood pressure SNPs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360180"], "description"=>"<p>(A) Standardized phenotype for DBP, (B) Bland-Altman plot of difference in standardized phenotype (DBP – DBP<sub>e</sub>) against the average of the two, (C) Standardized phenotype for SBP, and (D) Bland-Altman plot of difference in standardized phenotype (SBP – SBP<sub>e</sub>) against the average of the two.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods"], "article_id"=>912905, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deviation_between_blood_pressure_measurements_/912905", "title"=>"Deviation between blood pressure measurements.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360178"], "description"=>"<p>Measurement error is displayed in terms of the number of SD of the true phenotype (without errors). The top panel represents comparison of means and three configurations were considered with the rest of the parameters following the default configuration: <i>p</i> = 0.2, <i>n</i> = 15,000,  = 0.06. is interpreted as the change in the standardized phenotype for every increase in one effect allele. The bottom panel represents comparison of variances and three configurations were considered with the rest of the parameters following the default configuration: <i>p</i> = 0.2, <i>n</i> = 30,000,  = 0.06. is interpreted as the change in the standardized and squared phenotype for every increase in one effect allele.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "allele"], "article_id"=>912903, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_effect_size_sample_size_and_minor_allele_frequency_on_power_/912903", "title"=>"Impact of effect size, sample size and minor allele frequency on power.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360179"], "description"=>"<p>(A) Standardized phenotype for comparison of means, (B) Bland-Altman plot of difference in standardized phenotype (Wisconsin System – LOCS III) against the average of the two, (C) Standardized and squared phenotype for comparison of variances, and (D) Bland-Altman plot of difference in standardized and squared phenotype (Wisconsin System – LOCS III) against the average of the two.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "wisconsin", "locs"], "article_id"=>912904, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.g002", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deviation_between_Wisconsin_System_and_LOCS_III_/912904", "title"=>"Deviation between Wisconsin System and LOCS III.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360188", "https://ndownloader.figshare.com/files/1360189", "https://ndownloader.figshare.com/files/1360190"], "description"=>"<div><p>Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central <i>F</i> distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (<i>p</i><10<sup>−5</sup>) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "quantitative"], "article_id"=>912913, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0087044.s001", "https://dx.doi.org/10.1371/journal.pone.0087044.s002", "https://dx.doi.org/10.1371/journal.pone.0087044.s003"], "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_Measurement_Error_on_Testing_Genetic_Association_with_Quantitative_Traits_/912913", "title"=>"Impact of Measurement Error on Testing Genetic Association with Quantitative Traits", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360186"], "description"=>"<p>Additive model for phenotype variances with and without measurement error.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "phenotype", "variances"], "article_id"=>912911, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Additive_model_for_phenotype_variances_with_and_without_measurement_error_/912911", "title"=>"Additive model for phenotype variances with and without measurement error.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360185"], "description"=>"1<p>The following parameter values were used: <i>p</i> = 0.2, <i>n</i> = 15,000,  = 0.06.</p>2<p>The following parameter values were used: <i>p</i> = 0.2, <i>n</i> = 30,000,  = 0.06.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "coefficients"], "article_id"=>912910, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_coefficients_to_account_for_measurement_error_/912910", "title"=>"Cost coefficients to account for measurement error.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-24 03:02:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1360182"], "description"=>"<p>(A) By effect size, the parameter values used were <i>p</i> = 0.3, <i>n</i> = 2,490. (B) By MAF, the parameters values used were  = 0.05, <i>n</i> = 2,490.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Genome-wide association studies", "genetics", "Human genetics", "Computerized simulations", "statistics", "Biostatistics", "Statistical methods", "GWAS"], "article_id"=>912907, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Jiemin Liao", "Xiang Li", "Tien-Yin Wong", "Jie Jin Wang", "Chiea Chuen Khor", "E. Shyong Tai", "Tin Aung", "Yik-Ying Teo", "Ching-Yu Cheng"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087044.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_power_of_GWAS_of_blood_pressure_measurements_/912907", "title"=>"Comparison between power of GWAS of blood pressure measurements.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-24 03:02:45"}

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  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"1", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[306, 482]}, {"subject_area"=>"/Medicine and health sciences/Ophthalmology", "average_usage"=>[259, 385]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[286]}]}
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