Comprehensive Approach to Analyzing Rare Genetic Variants
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{"title"=>"Comprehensive approach to analyzing rare genetic variants", "type"=>"journal", "authors"=>[{"first_name"=>"Thomas J.", "last_name"=>"Hoffmann", "scopus_author_id"=>"55557672300"}, {"first_name"=>"Nicholas J.", "last_name"=>"Marini", "scopus_author_id"=>"6603012056"}, {"first_name"=>"John S.", "last_name"=>"Witte", "scopus_author_id"=>"7202012047"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0013584", "pui"=>"359941786", "scopus"=>"2-s2.0-78149479773", "issn"=>"19326203", "pmid"=>"21072163", "sgr"=>"78149479773", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"46a79d58-2e5d-3399-b5a7-162d318e8183", "abstract"=>"Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic \"step-up\" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.", "link"=>"http://www.mendeley.com/research/comprehensive-approach-analyzing-rare-genetic-variants", "reader_count"=>30, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Researcher"=>13, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>9, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>4, "Agricultural and Biological Sciences"=>18, "Medicine and Dentistry"=>4, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>18}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>4}}, "reader_count_by_country"=>{"United States"=>3, "Brazil"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/820124"], "description"=>"<p>Overlap of SIFT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-Ng1\" target=\"_blank\">[13]</a>, T - Tolerated, I - Intolerant (tolerance index score , as suggested by the software documentation), I-LC - Intolerant with Low Confidence (tolerance index score , but median sequence conservation score ); PMUT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-FerrerCosta1\" target=\"_blank\">[14]</a>, Neut - Neutral, Path - Pathological; and PolyPhen <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-Ramensky1\" target=\"_blank\">[15]</a>, Ben - Benign, Poss = Possibly Damaging, Prob = Probably Damaging. Bolded counts indicate where one method is the opposite of the other, where we allow I-LC and Poss to go either way. There was a pairwise concordance between SIFT and PMUT, where we allowed SIFT I-LC to match to either PMUT pathological or PMUT neutral; concordance between SIFT and PolyPhen, where we allowed SIFT I-LC to match to anything PolyPhen; and concordance between PMUT and PolyPhen where we allowed SIFT I-LC to match to anything and PolyPhen Poss to match to anything.</p>", "links"=>[], "tags"=>["genetics and genomics/complex traits", "genetics and genomics/disease models", "mathematics/statistics"], "article_id"=>490493, "categories"=>["Genetics", "Mathematics"], "users"=>["Thomas J. Hoffmann", "Nicholas J. Marini", "John S. Witte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013584.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Protein_Function_by_Gene_/490493", "title"=>"Protein Function by Gene.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 22:30:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/819961"], "description"=>"<p>500 simulations were based on haplotype distribution for each of 13 deep sequenced candidate genes, and averaged. 500 permutations were run per test. Information for each situation on the bottom of each plot consists of three parts that indicate the test used: (‘C’ for constant, ‘W’ for weighted by allele frequency); (‘’ if signed, ‘’ if constant); and the range of groupings (‘NS’ for nonsynonymous, ‘F’ for all protein coding, ‘F’ for nongenerating protein coding, ‘MAF’ for all MAF, ‘step’ for step-up, and ‘Perf’ for the exact generating alleles when appropriate). Results in plots A-C are sorted by the plot that has the highest area, i.e., the most powerful overall. In D, each value of indicates how much common variants affect disease and must be considered separately; to emphasize this, we have sorted by the power when .</p>", "links"=>[], "tags"=>["simulation", "comparing", "variant"], "article_id"=>490327, "categories"=>["Genetics", "Mathematics"], "users"=>["Thomas J. Hoffmann", "Nicholas J. Marini", "John S. Witte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013584.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_from_simulation_study_comparing_power_for_rare_variant_analysis_approaches_/490327", "title"=>"Results from simulation study comparing power for rare variant analysis approaches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 22:29:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/820049"], "description"=>"<p>Results in Figures A and B show the effect of having both deleterious and protective rare variants. Figures C and D switches to a continuous trait, with Figure D showing the effect of having both deleterious and protective rare variants. Results are sorted by the plot that has the highest area, i.e., the most powerful overall. See the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone-0013584-g002\" target=\"_blank\">Figure 2</a> legend for additional details about the different simulations.</p>", "links"=>[], "tags"=>["comparing", "variant"], "article_id"=>490419, "categories"=>["Genetics", "Mathematics"], "users"=>["Thomas J. Hoffmann", "Nicholas J. Marini", "John S. Witte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013584.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Further_results_comparing_power_across_rare_variant_approaches_/490419", "title"=>"Further results comparing power across rare variant approaches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 22:30:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/819836"], "description"=>"<p>For each gene, the number of variants from sequencing that are nonsynonymous, and then deemed deleterious by three different methods (SIFT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-Ng1\" target=\"_blank\">[13]</a>, PMUT <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-FerrerCosta1\" target=\"_blank\">[14]</a>, or PolyPhen <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013584#pone.0013584-Ramensky1\" target=\"_blank\">[15]</a>) plotted by ranges of the variant's minor allele frequency. The SIFT designations are generally used here for our simulation studies (except those with common variants, where we used PMUT designations to have more genes with deleterious mutations for simulation purposes).</p>", "links"=>[], "tags"=>["variants", "sequencing", "one-carbon", "folate", "metabolic", "pathway"], "article_id"=>490199, "categories"=>["Genetics", "Mathematics"], "users"=>["Thomas J. Hoffmann", "Nicholas J. Marini", "John S. Witte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013584.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deleteriousness_of_variants_detected_by_sequencing_one_carbon_folate_metabolic_pathway_candidate_genes_/490199", "title"=>"Deleteriousness of variants detected by sequencing one-carbon folate metabolic pathway candidate genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 22:28:55"}

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Relative Metric

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