Effects of Environment, Genetics and Data Analysis Pitfalls in an Esophageal Cancer Genome-Wide Association Study
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{"title"=>"Effects of environment, genetics and data analysis pitfalls in an esophageal cancer genome-wide association study", "type"=>"journal", "authors"=>[{"first_name"=>"Alexander", "last_name"=>"Statnikov", "scopus_author_id"=>"10241774000"}, {"first_name"=>"Chun", "last_name"=>"Li", "scopus_author_id"=>"36078361000"}, {"first_name"=>"Constantin F.", "last_name"=>"Aliferis", "scopus_author_id"=>"6602844802"}], "year"=>2007, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"351472232", "doi"=>"10.1371/journal.pone.0000958", "sgr"=>"41549136980", "scopus"=>"2-s2.0-41549136980", "isbn"=>"1932-6203 (Electronic)", "pmid"=>"17895998"}, "id"=>"32adf3d1-a7ab-3854-bbcc-090069a367e9", "abstract"=>"BACKGROUND: The development of new high-throughput genotyping technologies has allowed fast evaluation of single nucleotide polymorphisms (SNPs) on a genome-wide scale. Several recent genome-wide association studies employing these technologies suggest that panels of SNPs can be a useful tool for predicting cancer susceptibility and discovery of potentially important new disease loci. METHODOLOGY/PRINCIPAL FINDINGS: In the present paper we undertake a careful examination of the relative significance of genetics, environmental factors, and biases of the data analysis protocol that was used in a previously published genome-wide association study. That prior study reported a nearly perfect discrimination of esophageal cancer patients and healthy controls on the basis of only genetic information. On the other hand, our results strongly suggest that SNPs in this dataset are not statistically linked to the phenotype, while several environmental factors and especially family history of esophageal cancer (a proxy to both environmental and genetic factors) have only a modest association with the disease. CONCLUSIONS/SIGNIFICANCE: The main component of the previously claimed strong discriminatory signal is due to several data analysis pitfalls that in combination led to the strongly optimistic results. Such pitfalls are preventable and should be avoided in future studies since they create misleading conclusions and generate many false leads for subsequent research.", "link"=>"http://www.mendeley.com/research/effects-environment-genetics-data-analysis-pitfalls-esophageal-cancer-genomewide-association-study", "reader_count"=>17, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Master"=>3, "Professor"=>2, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Master"=>3, "Professor"=>2, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>4, "Chemistry"=>2, "Psychology"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Chemistry"=>{"Chemistry"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}}, "reader_count_by_country"=>{"United States"=>1, "Egypt"=>1, "United Kingdom"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/464969", "https://ndownloader.figshare.com/files/465014"], "description"=>"<div><h3>Background</h3><p>The development of new high-throughput genotyping technologies has allowed fast evaluation of single nucleotide polymorphisms (SNPs) on a genome-wide scale. Several recent genome-wide association studies employing these technologies suggest that panels of SNPs can be a useful tool for predicting cancer susceptibility and discovery of potentially important new disease loci.</p><h3>Methodology/Principal Findings</h3><p>In the present paper we undertake a careful examination of the relative significance of genetics, environmental factors, and biases of the data analysis protocol that was used in a previously published genome-wide association study. That prior study reported a nearly perfect discrimination of esophageal cancer patients and healthy controls on the basis of only genetic information. On the other hand, our results strongly suggest that SNPs in this dataset are not statistically linked to the phenotype, while several environmental factors and especially family history of esophageal cancer (a proxy to both environmental and genetic factors) have only a modest association with the disease.</p><h3>Conclusions/Significance</h3><p>The main component of the previously claimed strong discriminatory signal is due to several data analysis pitfalls that in combination led to the strongly optimistic results. Such pitfalls are preventable and should be avoided in future studies since they create misleading conclusions and generate many false leads for subsequent research.</p></div>", "links"=>[], "tags"=>["effects", "genetics", "pitfalls", "esophageal", "cancer", "genome-wide"], "article_id"=>151654, "categories"=>["Genetics", "Mathematics"], "users"=>["Alexander Statnikov", "Chun Li", "Constantin F. Aliferis"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000958.s001", "https://dx.doi.org/10.1371/journal.pone.0000958.s002"], "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effects_of_Environment_Genetics_and_Data_Analysis_Pitfalls_in_an_Esophageal_Cancer_Genome_Wide_Association_Study/151654", "title"=>"Effects of Environment, Genetics and Data Analysis Pitfalls in an Esophageal Cancer Genome-Wide Association Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2007-09-26 00:27:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/945710"], "description"=>"<p>The classification algorithm is Support Vector Machines (SVM). Only SNPs selected by Recursive Feature Elimination (RFE) are used. The following abbreviations are used for variable names: Age (age at interview), Smk (tobacco use), Alc (alcohol consumption), Fh (family history of esophageal cancer), and Pck (consumption of pickled vegetables). The “+” symbol in the Data column denotes that the analysis was performed by ensembling approach.</p>", "links"=>[], "tags"=>["classification", "repeated", "10-fold", "cross-validation"], "article_id"=>616083, "categories"=>["Genetics", "Mathematics"], "users"=>["Alexander Statnikov", "Chun Li", "Constantin F. Aliferis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000958.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_classification_performance_obtained_by_repeated_10_fold_cross_validation_procedure_/616083", "title"=>"Estimates of classification performance obtained by repeated 10-fold cross-validation procedure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-26 01:41:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/945649"], "description"=>"<p>The figure is shown in logarithmic scale for convenience. The vertical line is the Bonferroni adjusted α-level (0.05/10,009). While there are SNPs that are significant according to GLM1 method, no SNP is significant by GLM2. The distribution of p-values for GLM2 is uniform, however the distribution for GLM1 is not.</p>", "links"=>[], "tags"=>["p-values", "computed", "glm1", "glm2", "snp"], "article_id"=>616022, "categories"=>["Genetics", "Mathematics"], "users"=>["Alexander Statnikov", "Chun Li", "Constantin F. Aliferis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000958.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_p_values_computed_by_GLM1_and_GLM2_SNP_selection_methods_/616022", "title"=>"Distribution of p-values computed by GLM1 and GLM2 SNP selection methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-26 01:40:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/945533"], "description"=>"<p>The first principal component provides a nearly perfect separation of cases from controls.</p>", "links"=>[], "tags"=>["components", "extracted", "snps"], "article_id"=>615906, "categories"=>["Genetics", "Mathematics"], "users"=>["Alexander Statnikov", "Chun Li", "Constantin F. Aliferis"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000958.g001", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_First_two_principal_components_extracted_from_SNPs_that_were_selected_by_the_method_GLM1_/615906", "title"=>"First two principal components extracted from SNPs that were selected by the method GLM1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-26 01:38:26"}

PMC Usage Stats | Further Information

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Relative Metric

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