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  1. AU="Pritchard, Jonathan"
  2. AU="Memeo, Lorenzo"
  3. AU="Taylan, Gokay"
  4. AU="Tijssen, Robert J. W."
  5. AU="Silva, Marcelina Jasmine"
  6. AU="Egbuna, Chukwuebuka"

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  1. Artikel: Bayesian estimation of gene constraint from an evolutionary model with gene features.

    Zeng, Tony / Spence, Jeffrey P / Mostafavi, Hakhamanesh / Pritchard, Jonathan K

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at ... ...

    Abstract Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ∼25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric,
    Sprache Englisch
    Erscheinungsdatum 2024-04-10
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.05.19.541520
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Toward the Identifiability of Comparative Deep Generative Models.

    Lopez, Romain / Huetter, Jan-Christian / Hajiramezanali, Ehsan / Pritchard, Jonathan / Regev, Aviv

    ArXiv

    2024  

    Abstract: Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of ... ...

    Abstract Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of comparing data sets from different sources. One such example is the setting of contrastive analysis that focuses on describing patterns that are enriched in a target data set compared to a background data set. The practical deployment of those models often assumes that DGMs naturally infer interpretable and modular latent representations, which is known to be an issue in practice. Consequently, existing methods often rely on ad-hoc regularization schemes, although without any theoretical grounding. Here, we propose a theory of identifiability for comparative DGMs by extending recent advances in the field of non-linear independent component analysis. We show that, while these models lack identifiability across a general class of mixing functions, they surprisingly become identifiable when the mixing function is piece-wise affine (e.g., parameterized by a ReLU neural network). We also investigate the impact of model misspecification, and empirically show that previously proposed regularization techniques for fitting comparative DGMs help with identifiability when the number of latent variables is not known in advance. Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization. We empirically validate our theory and new methodology using simulated data as well as a recent data set of genetic perturbations in cells profiled via single-cell RNA sequencing.
    Sprache Englisch
    Erscheinungsdatum 2024-01-29
    Erscheinungsland United States
    Dokumenttyp Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Scaling the discrete-time Wright-Fisher model to biobank-scale datasets.

    Spence, Jeffrey P / Zeng, Tony / Mostafavi, Hakhamanesh / Pritchard, Jonathan K

    Genetics

    2023  Band 225, Heft 3

    Abstract: The discrete-time Wright-Fisher (DTWF) model and its diffusion limit are central to population genetics. These models can describe the forward-in-time evolution of allele frequencies in a population resulting from genetic drift, mutation, and selection. ... ...

    Abstract The discrete-time Wright-Fisher (DTWF) model and its diffusion limit are central to population genetics. These models can describe the forward-in-time evolution of allele frequencies in a population resulting from genetic drift, mutation, and selection. Computing likelihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large samples or in the presence of strong selection. Existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here, we present a scalable algorithm that approximates the DTWF model with provably bounded error. Our approach relies on two key observations about the DTWF model. The first is that transition probabilities under the model are approximately sparse. The second is that transition distributions for similar starting allele frequencies are extremely close as distributions. Together, these observations enable approximate matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the tens of millions, paving the way for rigorous biobank-scale inference. Finally, we use our results to estimate the impact of larger samples on estimating selection coefficients for loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.
    Mesh-Begriff(e) Biological Specimen Banks ; Gene Frequency ; Genetics, Population ; Genetic Drift ; Probability ; Models, Genetic ; Selection, Genetic
    Sprache Englisch
    Erscheinungsdatum 2023-10-11
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2167-2
    ISSN 1943-2631 ; 0016-6731
    ISSN (online) 1943-2631
    ISSN 0016-6731
    DOI 10.1093/genetics/iyad168
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: Systematic differences in discovery of genetic effects on gene expression and complex traits.

    Mostafavi, Hakhamanesh / Spence, Jeffrey P / Naqvi, Sahin / Pritchard, Jonathan K

    Nature genetics

    2023  Band 55, Heft 11, Seite(n) 1866–1875

    Abstract: Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), ... ...

    Abstract Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
    Mesh-Begriff(e) Genome-Wide Association Study ; Multifactorial Inheritance ; Gene Expression Regulation/genetics ; Quantitative Trait Loci/genetics ; Gene Expression ; Polymorphism, Single Nucleotide/genetics
    Sprache Englisch
    Erscheinungsdatum 2023-10-19
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 1108734-1
    ISSN 1546-1718 ; 1061-4036
    ISSN (online) 1546-1718
    ISSN 1061-4036
    DOI 10.1038/s41588-023-01529-1
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel: Bayesian estimation of gene constraint from an evolutionary model with gene features.

    Zeng, Tony / Spence, Jeffrey P / Mostafavi, Hakhamanesh / Pritchard, Jonathan K

    Research square

    2023  

    Abstract: Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at ... ...

    Abstract Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric,
    Sprache Englisch
    Erscheinungsdatum 2023-06-13
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.21203/rs.3.rs-3012879/v1
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel: Scaling the Discrete-time Wright Fisher model to biobank-scale datasets.

    Spence, Jeffrey P / Zeng, Tony / Mostafavi, Hakhamanesh / Pritchard, Jonathan K

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The Discrete-Time Wright Fisher (DTWF) model and its large population diffusion limit are central to population genetics. These models describe the forward-in-time evolution of the frequency of an allele in a population and can include the fundamental ... ...

    Abstract The Discrete-Time Wright Fisher (DTWF) model and its large population diffusion limit are central to population genetics. These models describe the forward-in-time evolution of the frequency of an allele in a population and can include the fundamental forces of genetic drift, mutation, and selection. Computing like-lihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large sample sizes or in the presence of strong selection. Unfortunately, existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here we present an algorithm that approximates the DTWF model with provably bounded error and runs in time linear in the size of the population. Our approach relies on two key observations about Binomial distributions. The first is that Binomial distributions are approximately sparse. The second is that Binomial distributions with similar success probabilities are extremely close as distributions, allowing us to approximate the DTWF Markov transition matrix as a very low rank matrix. Together, these observations enable matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the billions, paving the way for rigorous biobank-scale population genetic inference. Finally, we use our results to estimate how increasing sample sizes will improve the estimation of selection coefficients acting on loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.
    Sprache Englisch
    Erscheinungsdatum 2023-05-22
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.05.19.541517
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Buch ; Online: Measuring the cosmological 21-cm dipole with 21-cm global experiments

    Ignatov, Yordan D. / Pritchard, Jonathan R. / Wu, Yuqing

    2023  

    Abstract: A measurement of the 21-cm global signal would be a revealing probe of the Dark Ages, the era of first star formation, and the Epoch of Reionization. It has remained elusive owing to bright galactic and extra-galactic foreground contaminants, coupled ... ...

    Abstract A measurement of the 21-cm global signal would be a revealing probe of the Dark Ages, the era of first star formation, and the Epoch of Reionization. It has remained elusive owing to bright galactic and extra-galactic foreground contaminants, coupled with instrumental noise, ionospheric effects, and beam chromaticity. The simultaneous detection of a consistent 21-cm dipole signal alongside the 21-cm global signal would provide confidence in a claimed detection. We use simulated data to investigate the possibility of using drift-scan dipole antenna experiments to achieve a detection of both monopole and dipole. We find that at least two antennae located at different latitudes are required to localise the dipole. In the absence of foregrounds, a total integration time of $\sim 10^4$ hours is required to detect the dipole. With contamination by simple foregrounds, we find that the integration time required increases to $\sim 10^5$ hours. We show that the extraction of the 21-cm dipole from more realistic foregrounds requires a more sophisticated foreground modelling approach. Finally, we motivate a global network of dipole antennae that could reasonably detect the dipole in $\sim 10^3$ hours of integration time.

    Comment: 12 pages, 15 figures
    Schlagwörter Astrophysics - Cosmology and Nongalactic Astrophysics
    Thema/Rubrik (Code) 621
    Erscheinungsdatum 2023-10-09
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  8. Artikel ; Online: On the number of genealogical ancestors tracing to the source groups of an admixed population.

    Mooney, Jazlyn A / Agranat-Tamir, Lily / Pritchard, Jonathan K / Rosenberg, Noah A

    Genetics

    2023  Band 224, Heft 3

    Abstract: Members of genetically admixed populations possess ancestry from multiple source groups, and studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral ... ...

    Abstract Members of genetically admixed populations possess ancestry from multiple source groups, and studies of human genetic admixture frequently estimate ancestry components corresponding to fractions of individual genomes that trace to specific ancestral populations. However, the same numerical ancestry fraction can represent a wide array of admixture scenarios within an individual's genealogy. Using a mechanistic model of admixture, we consider admixture genealogically: how many ancestors from the source populations does the admixture represent? We consider African-Americans, for whom continent-level estimates produce a 75-85% value for African ancestry on average and 15-25% for European ancestry. Genetic studies together with key features of African-American demographic history suggest ranges for parameters of a simple three-epoch model. Considering parameter sets compatible with estimates of current ancestry levels, we infer that if all genealogical lines of a random African-American born during 1960-1965 are traced back until they reach members of source populations, the mean over parameter sets of the expected number of genealogical lines terminating with African individuals is 314 (interquartile range 240-376), and the mean of the expected number terminating in Europeans is 51 (interquartile range 32-69). Across discrete generations, the peak number of African genealogical ancestors occurs in birth cohorts from the early 1700s, and the probability exceeds 50% that at least one European ancestor was born more recently than 1835. Our genealogical perspective can contribute to further understanding the admixture processes that underlie admixed populations. For African-Americans, the results provide insight both on how many of the ancestors of a typical African-American might have been forcibly displaced in the Transatlantic Slave Trade and on how many separate European admixture events might exist in a typical African-American genealogy.
    Mesh-Begriff(e) Humans ; Black People/genetics ; Black or African American/genetics ; Genetics, Population
    Sprache Englisch
    Erscheinungsdatum 2023-03-08
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2167-2
    ISSN 1943-2631 ; 0016-6731
    ISSN (online) 1943-2631
    ISSN 0016-6731
    DOI 10.1093/genetics/iyad079
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Toward the Identifiability of Comparative Deep Generative Models

    Lopez, Romain / Huetter, Jan-Christian / Hajiramezanali, Ehsan / Pritchard, Jonathan / Regev, Aviv

    2024  

    Abstract: Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of ... ...

    Abstract Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task of comparing data sets from different sources. One such example is the setting of contrastive analysis that focuses on describing patterns that are enriched in a target data set compared to a background data set. The practical deployment of those models often assumes that DGMs naturally infer interpretable and modular latent representations, which is known to be an issue in practice. Consequently, existing methods often rely on ad-hoc regularization schemes, although without any theoretical grounding. Here, we propose a theory of identifiability for comparative DGMs by extending recent advances in the field of non-linear independent component analysis. We show that, while these models lack identifiability across a general class of mixing functions, they surprisingly become identifiable when the mixing function is piece-wise affine (e.g., parameterized by a ReLU neural network). We also investigate the impact of model misspecification, and empirically show that previously proposed regularization techniques for fitting comparative DGMs help with identifiability when the number of latent variables is not known in advance. Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization. We empirically validate our theory and new methodology using simulated data as well as a recent data set of genetic perturbations in cells profiled via single-cell RNA sequencing.

    Comment: 45 pages, 3 figures
    Schlagwörter Computer Science - Machine Learning ; Quantitative Biology - Genomics ; Statistics - Methodology
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2024-01-29
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Gaussian state-based quantum illumination with simple photodetection.

    Yang, Hao / Roga, Wojciech / Pritchard, Jonathan D / Jeffers, John

    Optics express

    2021  Band 29, Heft 6, Seite(n) 8199–8215

    Abstract: Proofs of the quantum advantage available in imaging or detecting objects under quantum illumination can rely on optimal measurements without specifying what they are. We use the continuous-variable Gaussian quantum information formalism to show that ... ...

    Abstract Proofs of the quantum advantage available in imaging or detecting objects under quantum illumination can rely on optimal measurements without specifying what they are. We use the continuous-variable Gaussian quantum information formalism to show that quantum illumination is better for object detection compared with coherent states of the same mean photon number, even for simple direct photodetection. The advantage persists if signal energy and object reflectivity are low and background thermal noise is high. The advantage is even greater if we match signal beam detection probabilities rather than mean photon number. We perform all calculations with thermal states, even for non-Gaussian conditioned states with negative Wigner functions. We simulate repeated detection using a Monte-Carlo process that clearly shows the advantages obtainable.
    Sprache Englisch
    Erscheinungsdatum 2021-04-06
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.416151
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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