Accuracy of CNV Detection from GWAS Data
Publication Date
January 13, 2011
Journal
PLOS ONE
Authors
Dandan Zhang, Yudong Qian, Nirmala Akula, Ney Alliey Rodriguez, et al
Volume
6
Issue
1
Pages
e14511
DOI
https://dx.plos.org/10.1371/journal.pone.0014511
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0014511
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21249187
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3020939
Europe PMC
http://europepmc.org/abstract/MED/21249187
Web of Science
000286515000001
Scopus
79251545821
Mendeley
http://www.mendeley.com/research/accuracy-cnv-detection-gwas-data
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Accuracy of CNV detection from GWAS data", "type"=>"journal", "authors"=>[{"first_name"=>"Dandan", "last_name"=>"Zhang", "scopus_author_id"=>"57194560639"}, {"first_name"=>"Yudong", "last_name"=>"Qian", "scopus_author_id"=>"10338826400"}, {"first_name"=>"Nirmala", "last_name"=>"Akula", "scopus_author_id"=>"6602291399"}, {"first_name"=>"Ney", "last_name"=>"Alliey-Rodriguez", "scopus_author_id"=>"25928289700"}, {"first_name"=>"Jinsong", "last_name"=>"Tang", "scopus_author_id"=>"55713958600"}, {"first_name"=>"Elliot S.", "last_name"=>"Gershon", "scopus_author_id"=>"7006407060"}, {"first_name"=>"Chunyu", "last_name"=>"Liu", "scopus_author_id"=>"35315983600"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"21249187", "doi"=>"10.1371/journal.pone.0014511", "sgr"=>"79251545821", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "scopus"=>"2-s2.0-79251545821", "issn"=>"19326203", "pui"=>"361180286"}, "id"=>"38a0fdbf-b368-332a-8eb5-dc2b9182742b", "abstract"=>"Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two \"gold standards,\" the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a \"gold standard\" for detection of CNVs remains to be established.", "link"=>"http://www.mendeley.com/research/accuracy-cnv-detection-gwas-data", "reader_count"=>117, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Student > Doctoral Student"=>6, "Researcher"=>30, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>8, "Student > Doctoral Student"=>6, "Researcher"=>30, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>2, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>11, "Mathematics"=>4, "Agricultural and Biological Sciences"=>68, "Medicine and Dentistry"=>19, "Neuroscience"=>1, "Psychology"=>3, "Computer Science"=>5, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>68}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>11}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "United States"=>2, "United Kingdom"=>1, "Canada"=>2, "Sweden"=>1, "Turkey"=>1, "Korea (South)"=>1, "China"=>1, "Brazil"=>1, "South Africa"=>1, "Italy"=>1, "France"=>2, "Germany"=>2}, "group_count"=>9}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/807305"], "description"=>"<p>qPCR validation of the calls made by each program for three common CNVs identified by Canary in the BiGS dataset.</p>", "links"=>[], "tags"=>["validation", "calls", "cnvs", "canary", "bigs"], "article_id"=>477664, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t008", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_qPCR_validation_of_the_calls_made_by_each_program_for_three_common_CNVs_identified_by_Canary_in_the_BiGS_dataset_/477664", "title"=>"qPCR validation of the calls made by each program for three common CNVs identified by Canary in the BiGS dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:07:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/807015"], "description"=>"<p>*Positive predictive value: true positive/(true positive + false positive). For each region, five samples were tested, one with a putative deletion/duplication, the other four with two putative).</p>", "links"=>[], "tags"=>["predictive", "cnvs", "qpcr", "validation", "program-specific"], "article_id"=>477376, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Positive_predictive_value_for_rare_CNVs_of_each_program_based_on_qPCR_validation_of_their_program_specific_singletons_/477376", "title"=>"Positive predictive value for rare CNVs of each program, based on qPCR validation of their program-specific singletons.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:02:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/807050"], "description"=>"<p>Frequencies with which each program calls three common CNVs identified by Canary in the BiGS dataset.</p>", "links"=>[], "tags"=>["calls", "cnvs", "canary", "bigs"], "article_id"=>477410, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t007", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequencies_with_which_each_program_calls_three_common_CNVs_identified_by_Canary_in_the_BiGS_dataset_/477410", "title"=>"Frequencies with which each program calls three common CNVs identified by Canary in the BiGS dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:03:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/807341"], "description"=>"<p>For HelixTree and Partek, default settings were used; normalization was done before CNV calling. For PennCNV-Affy, the standard procedure was followed without wave adjustment. For Birdsuite, population-specific prior models were employed.</p>*<p>Platewise normalization was done by Affy Power Tools (APT1.10.0) plug in Birdsuite/PennCNV-Affy.</p>**<p>Normalization was done by HelixTree.</p>***<p>For Canary, the appropriate prior model was selected based on the ancestry of the sample.</p>", "links"=>[], "tags"=>["genetics and genomics", "genetics and genomics/bioinformatics", "genetics and genomics/complex traits", "genetics and genomics/genetics of disease", "genetics and genomics/genomics", "genetics and genomics/medical genetics"], "article_id"=>477702, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t009", "stats"=>{"downloads"=>5, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Settings_used_for_each_of_the_four_software_suites_/477702", "title"=>"Settings used for each of the four software suites.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:08:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/806959"], "description"=>"<p>The average number of CNVs per individual varies greatly among programs, especially for CNVs less than 10 kb. The X axis represents length of CNVs. The Y axis is the average number of CNVs per individual.</p>", "links"=>[], "tags"=>["cnvs", "called", "cnv-calling"], "article_id"=>477319, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.g001", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_number_of_CNVs_per_individual_called_by_each_of_the_four_CNV_calling_programs_/477319", "title"=>"The average number of CNVs per individual called by each of the four CNV-calling programs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-01-13 02:01:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/807223"], "description"=>"<p>*CNVs spanned by more than 20 markers were included in this analysis.</p><p>**Pedigree information was incorporated.</p>", "links"=>[], "tags"=>["rates", "cnv-calling", "programs", "cnv", "conrad", "90", "ceu"], "article_id"=>477580, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_recovery_rates_of_each_of_the_CNV_calling_programs_depending_on_CNV_frequency_based_on_data_by_Conrad_et_al_in_90_CEU_samples_/477580", "title"=>"The average recovery rates of each of the CNV-calling programs depending on CNV frequency based on data by Conrad <i>et al</i>. in 90 CEU samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:06:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/402719"], "description"=>"<div><p>Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites—Birdsuite, Partek, HelixTree, and PennCNV-Affy—in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two “gold standards,” the sequence data of Kidd <em>et al</em>., and aCGH data of Conrad <em>et al</em>. Algorithms for calling CNVs especially common ones need substantial improvement, and a “gold standard” for detection of CNVs remains to be established.</p></div>", "links"=>[], "tags"=>["cnv", "detection", "GWAS"], "article_id"=>139566, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511", "stats"=>{"downloads"=>8, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Accuracy_of_CNV_Detection_from_GWAS_Data/139566", "title"=>"Accuracy of CNV Detection from GWAS Data", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:39:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/807095"], "description"=>"<p>*Criterion: Two CNVs from Conrad <i>et al</i> and Kidd <i>et al</i> were considered as the same if they shared at least 25% of the total length spanned. One CNV from Conrad <i>et al</i> can share with more than one CNV from Kidd <i>et al</i>, vice versa.</p>", "links"=>[], "tags"=>["hapmap", "individuals"], "article_id"=>477460, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CNVs_detected_in_8_HapMap_individuals_shared_between_two_studies_/477460", "title"=>"CNVs detected in 8 HapMap individuals shared between two studies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:04:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/807265"], "description"=>"<p>*The pedigree information was incorporated with the calling of CNVs.</p><p>Recovery rate was calculated with the requirement of copy number consistency.</p>", "links"=>[], "tags"=>["rates", "cnv-calling", "programs", "cnv", "kidd", "et"], "article_id"=>477621, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_recovery_rates_of_each_of_the_CNV_calling_programs_depending_on_CNV_length_based_on_data_of_Kidd_et_al_/477621", "title"=>"The recovery rates of each of the CNV-calling programs depending on CNV length based on data of Kidd et al.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:07:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/807139"], "description"=>"<p>*Data format: number of events (percentage of events shared by other programs).</p><p>**Shared singleton deletions or duplications were defined as CNVs called by one program that overlapped at all with singleton deletions or duplications called by any other program.</p><p>***Program-specific CNVs are those that did not overlap at all with any singleton deletions or duplications called by any other program.</p>", "links"=>[], "tags"=>["singleton", "cnvs", "called", "overlap", "calls"], "article_id"=>477502, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t005", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_number_of_singleton_CNVs_called_by_each_program_and_percentage_by_which_they_overlap_with_calls_made_by_the_other_programs_/477502", "title"=>"The number of singleton CNVs called by each program, and percentage by which they overlap with calls made by the other programs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:05:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/807178"], "description"=>"<p>*5,341 CNVs spanned by >20 markers were included in this analysis.</p><p>**The pedigree information was incorporated with the calling of CNVs.</p>", "links"=>[], "tags"=>["rates", "cnv-calling", "programs", "cnv", "conrad", "90", "ceu"], "article_id"=>477535, "categories"=>["Genetics"], "users"=>["Dandan Zhang", "Yudong Qian", "Nirmala Akula", "Ney Alliey-Rodriguez", "Jinsong Tang", "Elliot S. Gershon", "Chunyu Liu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014511.t002", "stats"=>{"downloads"=>6, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_recovery_rates_of_each_of_the_CNV_calling_programs_depending_on_CNV_length_based_on_data_by_Conrad_et_al_in_90_CEU_samples_/477535", "title"=>"The recovery rates of each of the CNV-calling programs depending on CNV length based on data by Conrad <i>et al</i>. in 90 CEU samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-01-13 02:05:35"}

PMC Usage Stats | Further Information

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