A SUPER Powerful Method for Genome Wide Association Study
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{"title"=>"A SUPER powerful method for genome wide association study", "type"=>"journal", "authors"=>[{"first_name"=>"Qishan", "last_name"=>"Wang", "scopus_author_id"=>"55537080400"}, {"first_name"=>"Feng", "last_name"=>"Tian", "scopus_author_id"=>"55722242800"}, {"first_name"=>"Yuchun", "last_name"=>"Pan", "scopus_author_id"=>"55339776600"}, {"first_name"=>"Edward S.", "last_name"=>"Buckler", "scopus_author_id"=>"6603927775"}, {"first_name"=>"Zhiwu", "last_name"=>"Zhang", "scopus_author_id"=>"9742909300"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84907586937", "sgr"=>"84907586937", "pui"=>"600067724", "isbn"=>"1932-6203", "pmid"=>"25247812", "doi"=>"10.1371/journal.pone.0107684"}, "id"=>"97caf27e-65e9-3972-8b43-60ba89e2a725", "abstract"=>"Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.", "link"=>"http://www.mendeley.com/research/super-powerful-method-genome-wide-association-study-6", "reader_count"=>130, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Researcher"=>32, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>48, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>6, "Student > Bachelor"=>3, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Researcher"=>32, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>48, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>6, "Student > Bachelor"=>3, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>2, "Agricultural and Biological Sciences"=>104, "Business, Management and Accounting"=>1, "Psychology"=>1, "Social Sciences"=>1, "Computer Science"=>4, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Social Sciences"=>{"Social Sciences"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>104}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>5, "Brazil"=>1, "United Kingdom"=>1, "Italy"=>2, "Slovenia"=>1, "Australia"=>1, "France"=>2, "Spain"=>1}, "group_count"=>6}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1686266", "https://ndownloader.figshare.com/files/1686267", "https://ndownloader.figshare.com/files/1686268", "https://ndownloader.figshare.com/files/1686269", "https://ndownloader.figshare.com/files/1686270", "https://ndownloader.figshare.com/files/1686271"], "description"=>"<div><p>Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.</p></div>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178380, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0107684.s001", "https://dx.doi.org/10.1371/journal.pone.0107684.s002", "https://dx.doi.org/10.1371/journal.pone.0107684.s003", "https://dx.doi.org/10.1371/journal.pone.0107684.s004", "https://dx.doi.org/10.1371/journal.pone.0107684.s005", "https://dx.doi.org/10.1371/journal.pone.0107684.s006"], "stats"=>{"downloads"=>7, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_SUPER_Powerful_Method_for_Genome_Wide_Association_Study_/1178380", "title"=>"A SUPER Powerful Method for Genome Wide Association Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-09-23 02:42:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1686263"], "description"=>"<p>P values that reached the Bonferroni correction threshold (1.6E-5) are shown in bold.</p><p>SNPs found to be significant by SUPER and other three methods for AIL mouse data.</p>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178377, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107684.t002", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SNPs_found_to_be_significant_by_SUPER_and_other_three_methods_for_AIL_mouse_data_/1178377", "title"=>"SNPs found to be significant by SUPER and other three methods for AIL mouse data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-23 02:42:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1686261"], "description"=>"<p>The mouse phenotype is methamphetamine-induced locomotor activity on day 3 measured on 688 Advanced Intercross Lines (AIL). The human phenotype is cholesterol collected by the Framingham Heart Study (FHS) Project. Each dataset was analyzed with three different methods (SUPER, EMMAX, and FaST-LMM-Select) except the combination between FaST-LMM-Select and human data. The missing genotypes in the human data were imputed in format of dosage, which is not accepted by FaST-LMM-Select. The most significant SNP is highlighted by a horizontal blue line and labeled by its corresponding False Discover Rate (FDR). The p value threshold of 0.05 (after bonferroni multiple test correction) is indicated by a horizontal red line.</p>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178375, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107684.g003", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_association_studies_on_real_mouse_and_human_phenotypes_/1178375", "title"=>"Results of association studies on real mouse and human phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-23 02:42:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1686262"], "description"=>"<p>A set of SNPs was randomly sampled as causal QTNs for the simulated traits (0.04%, 0.05%, 0.085% and 1%, of the total number of SNPs for <i>Arabidopsis</i>, Rice, Dog, and Maize, respectively). The statistical power was estimated with heritability of 0.75. Power is defined as the proportion of QTNs detected under type I error of 5%. A total of 100 replications was conducted for each method. The statistical power shown here is the average of 100 replications.</p><p>Statistical power of using different kinship for four species (<i>Arabidopsis</i>, Rice, Dog and Maize).</p>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178376, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107684.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_power_of_using_different_kinship_for_four_species_Arabidopsis_Rice_Dog_and_Maize_/1178376", "title"=>"Statistical power of using different kinship for four species (<i>Arabidopsis</i>, Rice, Dog and Maize).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-23 02:42:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1686257"], "description"=>"<p><b>A</b>) Distribution of statistical power by using a kinship derived from a set of SNPs selected randomly. The dataset contained ∼3,000 SNPs genotyped on 282 maize inbred lines. The number of selected SNPs was the same as the number of individuals used to derive kinship. Power was examined on a trait simulated from 27 causative mutations, i.e. Quantitative Trait Nucleotide (QTNs), sampled from the ∼3,000 SNPs except the ones on the last chromosome. The SNPs on the last chromosome were used to derive the null distribution of Type I error. The heritability of the trait was set to 0.75. A total of 100 replications were conducted. The average and the median power are 0.476 and 0.444. The power of using kinship derived from all SNPs is 0.511 (red line). <b>B</b>) Conception of kinship for association study. Pedigree is the first available information used to calculate kinship. It is the expectation for a pair of individuals to be identical by descent at any locus, (e.g., full siblings have a kinship of 50% in cases of no inbreeding). Pedigree kinship can be used across traits. A realized kinship derived from genetic markers covering entire genome is more precise than pedigree based (e.g., full siblings could have a kinship of 60% - or 40% - instead of 50%). However, it is still general and can be used for all traits. A complete trait specific realized kinship is using all the QTNs underlying the trait. This complete trait specific kinship is ideal for genome prediction, but not for GWAS. The ideal kinship for GWAS is its complement (using all QTNs except the one being tested) to remove the confounding between the kinship and the tested SNPs. <b>C</b>) and <b>D</b>) display the performance of statistical power and effectiveness of genomic control of inflation factor by using different kinship. The statistical power is about 50% when using all the SNPs. Inclusion or exclusion of the 27 QTNs did not have a significant impact. When only the 27 QTNs were used to derive a complete trait specific kinship, the statistical power was dramatically reduced to 30%. When each of the 27 QTNs was tested by using the complementary trait specific kinship derived from the other 26 QTNs (SUPER with known QTNs), the statistical power was boosted to 66%. A statistical power of 61% was retained by using SUPER with masked QTNs. The genomic control of SUPER was similar with known QTNs and with masked QTNs, closer to expectation (1.00) than other methods.</p>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178371, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107684.g001", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conception_and_performances_of_different_methods_/1178371", "title"=>"Conception and performances of different methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-23 02:42:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1686259"], "description"=>"<p><b>A</b>) Statistical power was examined on a trait simulated from 27 causative mutations (QTNs) sampled from SNPs on chromosomes 1 to 9 in maize data. The SNPs on the last chromosome (10) were used to derive null distribution. Power was defined as the proportion of detected QTNs under type I error of 5%. A total of 100 replications was conducted for each method. The heritability of the trait was set to 75%. Four methods were examined: 1) SUPER; 2) EMMAX; 3) FaST-LMM-Select; and 4) General linear model (GLM). <b>B</b>) Statistical power of four methods under different heritability levels. The four methods are SUPER, LMM-Selected, EMMAX and GLM.</p>", "links"=>[], "tags"=>["Mixed Linear Model", "association", "relationship", "mlm", "method SUPER", "GAPIT software package", "population structure", "snp"], "article_id"=>1178373, "categories"=>["Biological Sciences"], "users"=>["Qishan Wang", "Feng Tian", "Yuchun Pan", "Edward S. Buckler", "Zhiwu Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107684.g002", "stats"=>{"downloads"=>5, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_power_under_different_ranges_of_type_1_error_and_heritability_/1178373", "title"=>"Statistical power under different ranges of type 1 error and heritability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-23 02:42:34"}

PMC Usage Stats | Further Information

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Relative Metric

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