Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues
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{"title"=>"Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues", "type"=>"journal", "authors"=>[{"first_name"=>"Kimberly R.", "last_name"=>"Kukurba", "scopus_author_id"=>"55338546500"}, {"first_name"=>"Rui", "last_name"=>"Zhang", "scopus_author_id"=>"35270909000"}, {"first_name"=>"Xin", "last_name"=>"Li", "scopus_author_id"=>"55938833700"}, {"first_name"=>"Kevin S.", "last_name"=>"Smith", "scopus_author_id"=>"55489786900"}, {"first_name"=>"David A.", "last_name"=>"Knowles", "scopus_author_id"=>"23397533000"}, {"first_name"=>"Meng", "last_name"=>"How Tan", "scopus_author_id"=>"55162812200"}, {"first_name"=>"Robert", "last_name"=>"Piskol", "scopus_author_id"=>"25822552100"}, {"first_name"=>"Monkol", "last_name"=>"Lek", "scopus_author_id"=>"26639403100"}, {"first_name"=>"Michael", "last_name"=>"Snyder", "scopus_author_id"=>"46561402300"}, {"first_name"=>"Daniel G.", "last_name"=>"MacArthur", "scopus_author_id"=>"7004309751"}, {"first_name"=>"Jin Billy", "last_name"=>"Li", "scopus_author_id"=>"8837276700"}, {"first_name"=>"Stephen B.", "last_name"=>"Montgomery", "scopus_author_id"=>"8708455800"}], "year"=>2014, "source"=>"PLoS Genetics", "identifiers"=>{"scopus"=>"2-s2.0-84901624486", "doi"=>"10.1371/journal.pgen.1004304", "isbn"=>"1553-7404 (Electronic)\\r1553-7390 (Linking)", "issn"=>"15537404", "pmid"=>"24786518", "pui"=>"373197086", "sgr"=>"84901624486"}, "id"=>"78983df4-530d-3170-8f04-5712a21a8b69", "abstract"=>"Personal exome and genome sequencing provides access to loss-of-function and rare deleterious alleles whose interpretation is expected to provide insight into individual disease burden. However, for each allele, accurate interpretation of its effect will depend on both its penetrance and the trait's expressivity. In this regard, an important factor that can modify the effect of a pathogenic coding allele is its level of expression; a factor which itself characteristically changes across tissues. To better inform the degree to which pathogenic alleles can be modified by expression level across multiple tissues, we have conducted exome, RNA and deep, targeted allele-specific expression (ASE) sequencing in ten tissues obtained from a single individual. By combining such data, we report the impact of rare and common loss-of-function variants on allelic expression exposing stronger allelic bias for rare stop-gain variants and informing the extent to which rare deleterious coding alleles are consistently expressed across tissues. This study demonstrates the potential importance of transcriptome data to the interpretation of pathogenic protein-coding variants.", "link"=>"http://www.mendeley.com/research/allelic-expression-deleterious-proteincoding-variants-across-human-tissues", "reader_count"=>91, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>5, "Other"=>6, "Student > Master"=>5, "Student > Bachelor"=>4, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>30, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>30, "Student > Postgraduate"=>5, "Other"=>6, "Student > Master"=>5, "Student > Bachelor"=>4, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>14, "Mathematics"=>2, "Agricultural and Biological Sciences"=>61, "Medicine and Dentistry"=>4, "Neuroscience"=>1, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>61}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>14}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Sweden"=>1, "Hong Kong"=>1, "United States"=>5, "United Kingdom"=>1, "Russia"=>1, "Spain"=>1}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/1480928"], "description"=>"<p>(<b>A</b>) The distribution of allelic ratios across tissues indicates that most heterozygous sites have bi-allelic expression across all tissues (no ASE, red). A subset of sites exhibits ASE that is consistent between all tissues (shared ASE, blue). However, a small fraction of sites exhibit ASE that is tissue-specific (variable ASE, green). The mean allelic ratio is on the x-axis and the variance (standard deviation) of the allelic ratio is on the y-axis. Allelic ratios were calculated for all sites tested by mmPCR-Seq. The reproducibility between replicates for the three groups (non-ASE, shared ASE and variable ASE), as well as the classification of non-ASE and ASE (shared ASE plus variable ASE) is is 93.3% and 95.7%, respectively. (<b>B</b>) Genes with rare and deleterious nsSNPs were stratified into those that exhibited no ASE (red), shared ASE (blue), and variable ASE (green) across different tissues. The reproducibility of genes classified as shared ASE and variable ASE between replicates is 100%.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "genomics", "Functional genomics", "gene expression", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "ase", "tissues", "deleterious", "variant"], "article_id"=>1013059, "categories"=>["Biological Sciences"], "users"=>["Kimberly R. Kukurba", "Rui Zhang", "Xin Li", "Kevin S. Smith", "David A. Knowles", "Meng How Tan", "Robert Piskol", "Monkol Lek", "Michael Snyder", "Daniel G. MacArthur", "Jin Billy Li", "Stephen B. Montgomery"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004304.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patterns_of_ASE_across_tissues_and_their_influence_on_rare_deleterious_variant_interpretation_/1013059", "title"=>"Patterns of ASE across tissues and their influence on rare deleterious variant interpretation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-01 02:56:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1480929"], "description"=>"<p>(<b>A</b>) ASE analysis of nonsense variants (red), rare deleterious nsSNPs (blue), and control sites (green) tested by mmPCR-Seq in different tissues. The control sites are random heterozygous sites in the individual's genome. Rare, deleterious nsSNPs and nonsense alleles have significantly reduced expression compared to controls. This observation is most significant for loss-of-function variants where the nonsense allele is likely removed through nonsense-mediated decay (student's t-test, p<0.05, see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004304#pgen.1004304.s025\" target=\"_blank\">Table S5</a>). (<b>B</b>) ASE analysis of rare (red) and common (pink) nonsense variants tested by mmPCR-Seq data across different tissues. Common nonsense variants are defined as those with a minor allele frequency greater than 5% across the 1000 Genomes population data. Rare nonsense alleles show significantly reduced expression compared to common nonsense alleles (student's t-test, p<0.05).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "genomics", "Functional genomics", "gene expression", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "deleterious", "nssnps", "nonsense", "variants"], "article_id"=>1013060, "categories"=>["Biological Sciences"], "users"=>["Kimberly R. Kukurba", "Rui Zhang", "Xin Li", "Kevin S. Smith", "David A. Knowles", "Meng How Tan", "Robert Piskol", "Monkol Lek", "Michael Snyder", "Daniel G. MacArthur", "Jin Billy Li", "Stephen B. Montgomery"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004304.g004", "stats"=>{"downloads"=>1, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ASE_analysis_of_rare_deleterious_nsSNPs_and_nonsense_variants_by_mmPCR_Seq_/1013060", "title"=>"ASE analysis of rare deleterious nsSNPs and nonsense variants by mmPCR-Seq.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-01 02:56:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1480926"], "description"=>"<p>(<b>A</b>) The two chromosomal copies (alleles) of a gene are shown in red and blue. In most cases, both alleles are transcribed; this is known as bi-allelic expression (left panel). In the case of allele-specific expression (middle panel), one allele exhibits greater expression than the other allele. When only one allele of a gene is actively transcribed, gene expression is termed monoallelic expression (right panel). (<b>B</b>) RNA-Seq reads across heterozygous sites can discriminate between the two alleles and quantify the relative abundance of expression. Although the relative gene expression levels may be similar, the allelic ratios can vary.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "genomics", "Functional genomics", "gene expression", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "allele-specific"], "article_id"=>1013057, "categories"=>["Biological Sciences"], "users"=>["Kimberly R. Kukurba", "Rui Zhang", "Xin Li", "Kevin S. Smith", "David A. Knowles", "Meng How Tan", "Robert Piskol", "Monkol Lek", "Michael Snyder", "Daniel G. MacArthur", "Jin Billy Li", "Stephen B. Montgomery"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004304.g001", "stats"=>{"downloads"=>9, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_allele_specific_expression_/1013057", "title"=>"Schematic of allele-specific expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-01 02:56:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1480927"], "description"=>"<p>(<b>A</b>) Shared patterns of gene expression were detected for tissues with shared functional roles or embryonic origins. For example, the small intestine and colon are both digestive system organs derived from the endoderm and have a high degree of pairwise correlation (Spearman Correlation, <i>R</i> = 0.92). Likewise, the frontal lobe and cerebellum, which are both vital tissues nervous system derived from the ectoderm, have a high degree of shared expression (<i>R</i> = 0.91). The hierarchical clustering was generated using pairwise Spearman correlation coefficients of FPKM expression values for all genes. (<b>B</b>) Shared patterns of ASE were detected by mmPCR-Seq. The concordance of ASE between tissues does not as strongly reflect the relationships seen for shared gene expression or shared embryonic origin. The allelic ratio is calculated as the alternate allele reads divided by the total reads. Each data point represents a single heterozygous site tested for ASE with a total read depth greater than 200. The plots are colored by the degree of correlation of allelic bias between the pairwise tissues. These results indicate that relationships of allelic expression across tissues are much more complex than those of total expression level.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "genomics", "Functional genomics", "gene expression", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "allelic", "ratios", "somatic"], "article_id"=>1013058, "categories"=>["Biological Sciences"], "users"=>["Kimberly R. Kukurba", "Rui Zhang", "Xin Li", "Kevin S. Smith", "David A. Knowles", "Meng How Tan", "Robert Piskol", "Monkol Lek", "Michael Snyder", "Daniel G. MacArthur", "Jin Billy Li", "Stephen B. Montgomery"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004304.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_of_gene_expression_and_allelic_ratios_across_ten_somatic_tissues_/1013058", "title"=>"Correlation of gene expression and allelic ratios across ten somatic tissues.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-01 02:56:42"}

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Relative Metric

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