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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2013
    In:  Probability Theory and Related Fields Vol. 156, No. 3-4 ( 2013-08), p. 795-825
    In: Probability Theory and Related Fields, Springer Science and Business Media LLC, Vol. 156, No. 3-4 ( 2013-08), p. 795-825
    Type of Medium: Online Resource
    ISSN: 0178-8051 , 1432-2064
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
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  • 2
    Online Resource
    Online Resource
    Institute of Mathematical Statistics ; 2016
    In:  The Annals of Probability Vol. 44, No. 4 ( 2016-7-1)
    In: The Annals of Probability, Institute of Mathematical Statistics, Vol. 44, No. 4 ( 2016-7-1)
    Type of Medium: Online Resource
    ISSN: 0091-1798
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2016
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  • 3
    In: Obstetrical & Gynecological Survey, Ovid Technologies (Wolters Kluwer Health), Vol. 75, No. 2 ( 2020-2), p. 104-105
    Abstract: (Abstracted from Nat Comm 2019;10:3043) Epidemiologic associations have been made between advanced paternal age and increased offspring risk of autism spectrum disorder (ASD), schizophrenia (SCZ), congenital heart disease (CHD), epilepsy (EPI), and intellectual disability (ID). This association have been often attributed to de novo single nucleotide variants (dnSNVs) that occur 3 to 4 times more often in paternal compared with maternal germ cells.
    Type of Medium: Online Resource
    ISSN: 1533-9866 , 0029-7828
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
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  • 4
    Online Resource
    Online Resource
    Institute of Mathematical Statistics ; 2014
    In:  Electronic Journal of Probability Vol. 19, No. none ( 2014-1-1)
    In: Electronic Journal of Probability, Institute of Mathematical Statistics, Vol. 19, No. none ( 2014-1-1)
    Type of Medium: Online Resource
    ISSN: 1083-6489
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2014
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  • 5
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 55, No. 9 ( 2023-09), p. 1494-1502
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 22_Supplement ( 2022-11-15), p. B016-B016
    Abstract: Pancreatic cancer is a lethal disease in part because tumor cells exist in distinct transcriptional phenotypes (e.g. basal and classical states), each with a selective ability to evade current chemotherapy regimens. Two major mechanisms have been suggested for treatment evasion: 1) intrinsic resistance of certain phenotypes to particular chemotherapy regimens and 2) plasticity of treatment sensitive phenotypes to adopt more resistant phenotypes. However, the relative contribution of these mechanisms to treatment resistance is still poorly understood. Whereas previous work has described the redistribution of tumor cell states under selective treatment pressure, there is no direct evidence that tumor cells exhibit phenotypic plasticity at steady state or with treatment. By leveraging technological advancements in single-cell methods, lineage tracing and functional genomics, we have now shown direct evidence of phenotypic state switching in human pancreatic cancer cell lines. By performing single-cell RNA-seq on 5 barcoded PDAC cell lines over a steady state timecourse and under chemotherapy selective pressure ( & gt;600k cells total), we identify unique plasticity phenotypes within these cell lines and infer regulators of these plastic states. We validate the role of several of these regulators using bulk phenotypic CRISPRi screens in these cell lines. We next perform CRISPRi perturbations along with lineage tracing and single-cell multiomics ( & gt;300k cells) to dissect the regulatory relationships that underlie these cell states. We identify several novel epithelial and mesenchymal biasing factors, including those with unique roles in the most plastic clones. Collectively, we nominate several regulators that bias PDAC cell states thus posing a paradigm whereby perturbations may be used to homogenize tumor populations towards treatment-sensitive phenotypes. We believe this approach combined with current chemotherapy regimens could benefit pancreatic cancer patients by targeting residual, resistant tumor cells in the localized and metastatic disease settings to improve patient survival. Citation Format: Arnav Mehta, Lynn Bi, Aziz Al'Khafaji, Martin Jankowiak, Milan Parikh, Mehrtash Babadi, Alex Bloemendal, Marc Schwartz, Glen Munson, Joeseph Chan, Cassandra Burdziak, Elisa Donnard, Ryan Park, Chen Lu, Philippe Rigollet, Andrew Aguirre, Vidya Subramanian, Ray Jones, Eric S. Lander, David T. Ting, Dana Pe'er, Nir Hacohen. Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B016.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 7
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Science Vol. 379, No. 6639 ( 2023-03-31), p. 1341-1348
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 379, No. 6639 ( 2023-03-31), p. 1341-1348
    Abstract: In statistical genetics, dominance is a deviation from an additive genetic effect on a trait and is well documented in model organisms, particularly in the context of measures of “fitness,” and in plant and animal breeding. In humans, however, evidence of nonadditive genetic effects on complex, polygenic traits is sparse. We looked for evidence of nonadditive effects in more than 1000 phenotypes in the UK Biobank population cohort ( N = 361,194). To test for “dominance heritability,” or the aggregate contribution of nonadditive genetic effects to trait variance genome-wide, we introduce dominance linkage disequilibrium (LD) score regression (d-LDSC). Our method builds upon existing software to include nonadditive effects site by site. RATIONALE Identifying nonadditive genetic effects on traits allows us to better understand their underlying biology. Although nonadditive effects are commonly tested in Mendelian disorders, they are rarely tested in human complex traits. Population biobanks allow us to explore questions of genetic architecture at scale and to detect small effect sizes. We sought to test for evidence of nonadditive effects in the UK Biobank. We also investigated whether the less-correlated nature of dominance associations among sites at a locus relative to their additive counterparts can help pinpoint causal variants. RESULTS We identified 183 phenotype-locus pairs at genome-wide significance ( P 〈 4.7 × 10 −11 ). We replicated known associations for phenotypes with dominant and recessive patterns of inheritance, for example, hair color at the MC1R locus. Qualitatively, we observed stronger nonadditive effects in instances where additive effects are large or the underlying genetic architecture is concentrated in a few loci. The power to detect nonadditive loci was low: We estimate that around a 20- to 30-fold increase in sample size is necessary to capture evidence of dominance effects similar to that observed at additive loci. Applying LDSC and d-LDSC to 1060 traits, we confirmed strong evidence of additive heritability (700 traits, P 〈 4.7 × 10 −5 ). Despite analyzing a much larger collection of traits with increased power over existing studies, we found little evidence of dominance heritability. We introduced dominance fine-mapping to pinpoint causal variants in the presence of a dominance signal. Gains in fine-mapping resolution due to the rapid decay of dominance LD compared with additive LD are generally outweighed by weaker association signals. CONCLUSION We evaluated the contribution of nonadditive genetic effects on trait variation across 1060 traits in the UK Biobank. We identified a modest number of loci and confirmed that heritability explained by dominance is small, in line with previous analyses. Our results support the robustness of the additive model when modeling human complex traits, consistent with the view that most common variants induce small perturbations of continuous latent biological processes aggregated by a mean-field approximation. Furthermore, the additive model typically captures much of the trait variance at a population level, even under classical dominant or recessive patterns. We estimate that for most complex traits, minimum sample sizes of millions are required to detect nonadditive effects at the same strength of association as those reported for additive effects. Summary of the analysis of nonadditive common variant effects site by site and in aggregate across the human genome for more than 1000 traits. The unique recoding of allele counts (up to a linear rescaling) captures any residual genetic association signal at a variant after accounting for the additive effect, known as the dominance deviation (top left). This “dominance encoding” varies with allele frequency, as shown by the color legend. The dominance encoding is generally distinct from biological recessive-dominant architecture, which includes an additive contribution of allelic dosage (top center). The correlation structure between variants under the dominance encoding decays as the square of the additive correlation between variants, as shown by the color legend (top right). The areas under the blue and red curves are average additive and dominance LD scores, respectively. Aggregate Manhattan plots of nonadditive marginal effect sizes assess the dominance deviation across the genome for 1060 traits (bottom left). The y axis is on the −log 10 scale up to 30, after which it switches to a −log 10 [−log 10 ( P )] scale to aid presentation. The genome-wide significance threshold ( P 〈 4.7 × 10 −11 ) is displayed in orange. Additive and dominance effect-size estimates coupled with additive and dominance LD scores were used to estimate the additive and dominance contribution to phenotypic variance, known as additive and dominance SNP heritability (bottom right). Additive and dominance SNP heritability estimates across these traits are displayed in blue and red, respectively. The dashed gray lines display the mean estimates across traits.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    RVK:
    RVK:
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Nature Genetics Vol. 53, No. 12 ( 2021-12), p. 1634-1635
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 53, No. 12 ( 2021-12), p. 1634-1635
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2016
    In:  Probability Theory and Related Fields Vol. 164, No. 1-2 ( 2016-02), p. 459-552
    In: Probability Theory and Related Fields, Springer Science and Business Media LLC, Vol. 164, No. 1-2 ( 2016-02), p. 459-552
    Type of Medium: Online Resource
    ISSN: 0178-8051 , 1432-2064
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
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  • 10
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2019-07-10)
    Abstract: There are established associations between advanced paternal age and offspring risk for psychiatric and developmental disorders. These are commonly attributed to genetic mutations, especially de novo single nucleotide variants (dnSNVs), that accumulate with increasing paternal age. However, the actual magnitude of risk from such mutations in the male germline is unknown. Quantifying this risk would clarify the clinical significance of delayed paternity. Using parent-child trio whole-exome-sequencing data, we estimate the relationship between paternal-age-related dnSNVs and risk for five disorders: autism spectrum disorder (ASD), congenital heart disease, neurodevelopmental disorders with epilepsy, intellectual disability and schizophrenia (SCZ). Using Danish registry data, we investigate whether epidemiologic associations between each disorder and older fatherhood are consistent with the estimated role of dnSNVs. We find that paternal-age-related dnSNVs confer a small amount of risk for these disorders. For ASD and SCZ, epidemiologic associations with delayed paternity reflect factors that may not increase with age.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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