The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Philip C Haycock - One of the best experts on this subject based on the ideXlab platform.
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phenospd an integrated toolkit for Phenotypic Correlation estimation and multiple testing correction using gwas summary statistics
GigaScience, 2018Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Benjamin Elsworth, Benjamin M Neale, Philip C HaycockAbstract:Background Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate Phenotypic Correlation using only genome-wide association study (GWAS) summary results. Results Here, we present an integrated R toolkit, PhenoSpD, to use LD score regression to estimate Phenotypic Correlations using GWAS summary statistics and to utilize the estimated Phenotypic Correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated Phenotypic Correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic Correlations rather than Phenotypic Correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true Phenotypic Correlation. We further applied PhenoSpD to an estimated 5,618 pair-wise Phenotypic Correlations among 107 metabolites using GWAS summary statistics from Kettunen's publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites. Conclusions PhenoSpD extends the use of summary-level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.
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phenospd an integrated toolkit for Phenotypic Correlation estimation and multiple testing correction using gwas summary statistics
bioRxiv, 2017Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Philip C Haycock, Tom R GauntAbstract:Background: Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate Phenotypic Correlation using genome-wide association study (GWAS) summary statistics. Results: Here, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate Phenotypic Correlations using GWAS summary statistics; and 2) to utilize the estimated Phenotypic Correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate Phenotypic Correlation using samples with only a partial overlap, but as overlap decreases Correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true Phenotypic Correlation. We further applied PhenoSpD to estimated 7,503 pair-wise Phenotypic Correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites. Conclusion: PhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits. Availability: R code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).
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phenospd an atlas of Phenotypic Correlations and a multiple testing correction for the human phenome
bioRxiv, 2017Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Philip C Haycock, Tom R GauntAbstract:Summary: Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. Here, we present a novel method, PhenoSpD, to estimate Phenotypic Correlations using genome-wide association study (GWAS) summary statistics from the same sample and utilizes the Correlations to inform correction of multiple testing for human GWAS studies. In a case study using GWAS summary results, PhenoSpD suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true Phenotypic Correlation. We then estimated 120,713 pair-wise Phenotypic Correlations among 24 categories of human traits and diseases (total 862 traits) and further corrected multiple testing for these traits using PhenoSpD. The atlas of Phenotypic Correlations provides novel insights into the relationships between traits, while the PhenoSpD multiple testing correction function provides a simple and conservative way to reduce dimensionality for GWAS of complex molecular traits. Availability: R codes and Documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).
Evelyne Costes - One of the best experts on this subject based on the ideXlab platform.
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using numerical plant models and Phenotypic Correlation space to design achievable ideotypes
Plant Cell and Environment, 2017Co-Authors: Victor Picheny, Pierre Casadebaig, Ronan Trepos, Robert Faivre, David Da Silva, Patrick Vincourt, Evelyne CostesAbstract:: Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for Correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modelling approach, which identified paths for desirable trait modification, including direction and intensity.
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using numerical plant models and Phenotypic Correlation space to design achievable ideotypes
arXiv: Quantitative Methods, 2016Co-Authors: Victor Picheny, Pierre Casadebaig, Ronan Trepos, Robert Faivre, David Da Silva, Patrick Vincourt, Evelyne CostesAbstract:Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for Correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.
Jie Zheng - One of the best experts on this subject based on the ideXlab platform.
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identifying the genetic basis and molecular mechanisms underlying Phenotypic Correlation between complex human traits using a gene based approach
bioRxiv, 2020Co-Authors: C K Fuller, Jie ZhengAbstract:Phenotypic Correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. The recent accumulation of GWAS data has made it possible to analyze the genetic similarity between human traits through comparative analysis. Here we developed a gene-based approach to measure genetic similarity between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data; 2) measuring the similarity between a pair of traits by a normalized distance between the two gene-phenotype association profiles; 3) delineating genes/pathways supporting the similarity. Application of this approach to a set of GWAS data covering 59 human traits detected a significant similarity between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based similarity measures. Examples include Height and Schizophrenia, Cancer and Alzheimer9s Disease, and Rheumatoid Arthritis and Crohn9s disease. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Height and Schizophrenia are co-associated with genes involved in neural development, skeletal muscle regeneration, protein synthesis, magnesium homeostasis, and immune response, suggesting growth and development as a common theme underlying both traits. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses, and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.
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phenospd an integrated toolkit for Phenotypic Correlation estimation and multiple testing correction using gwas summary statistics
GigaScience, 2018Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Benjamin Elsworth, Benjamin M Neale, Philip C HaycockAbstract:Background Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate Phenotypic Correlation using only genome-wide association study (GWAS) summary results. Results Here, we present an integrated R toolkit, PhenoSpD, to use LD score regression to estimate Phenotypic Correlations using GWAS summary statistics and to utilize the estimated Phenotypic Correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated Phenotypic Correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic Correlations rather than Phenotypic Correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true Phenotypic Correlation. We further applied PhenoSpD to an estimated 5,618 pair-wise Phenotypic Correlations among 107 metabolites using GWAS summary statistics from Kettunen's publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites. Conclusions PhenoSpD extends the use of summary-level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.
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phenospd an integrated toolkit for Phenotypic Correlation estimation and multiple testing correction using gwas summary statistics
bioRxiv, 2017Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Philip C Haycock, Tom R GauntAbstract:Background: Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate Phenotypic Correlation using genome-wide association study (GWAS) summary statistics. Results: Here, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate Phenotypic Correlations using GWAS summary statistics; and 2) to utilize the estimated Phenotypic Correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate Phenotypic Correlation using samples with only a partial overlap, but as overlap decreases Correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true Phenotypic Correlation. We further applied PhenoSpD to estimated 7,503 pair-wise Phenotypic Correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites. Conclusion: PhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits. Availability: R code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).
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phenospd an atlas of Phenotypic Correlations and a multiple testing correction for the human phenome
bioRxiv, 2017Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Philip C Haycock, Tom R GauntAbstract:Summary: Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. Here, we present a novel method, PhenoSpD, to estimate Phenotypic Correlations using genome-wide association study (GWAS) summary statistics from the same sample and utilizes the Correlations to inform correction of multiple testing for human GWAS studies. In a case study using GWAS summary results, PhenoSpD suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true Phenotypic Correlation. We then estimated 120,713 pair-wise Phenotypic Correlations among 24 categories of human traits and diseases (total 862 traits) and further corrected multiple testing for these traits using PhenoSpD. The atlas of Phenotypic Correlations provides novel insights into the relationships between traits, while the PhenoSpD multiple testing correction function provides a simple and conservative way to reduce dimensionality for GWAS of complex molecular traits. Availability: R codes and Documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).
Benjamin M Neale - One of the best experts on this subject based on the ideXlab platform.
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phenospd an integrated toolkit for Phenotypic Correlation estimation and multiple testing correction using gwas summary statistics
GigaScience, 2018Co-Authors: Jie Zheng, Tom G Richardson, Louise A C Millard, Gibran Hemani, Christopher A Raistrick, Bjarni J Vilhjalmsson, Benjamin Elsworth, Benjamin M Neale, Philip C HaycockAbstract:Background Identifying Phenotypic Correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale Phenotypic Correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate Phenotypic Correlation using only genome-wide association study (GWAS) summary results. Results Here, we present an integrated R toolkit, PhenoSpD, to use LD score regression to estimate Phenotypic Correlations using GWAS summary statistics and to utilize the estimated Phenotypic Correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated Phenotypic Correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic Correlations rather than Phenotypic Correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true Phenotypic Correlation. We further applied PhenoSpD to an estimated 5,618 pair-wise Phenotypic Correlations among 107 metabolites using GWAS summary statistics from Kettunen's publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites. Conclusions PhenoSpD extends the use of summary-level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.
M M Van Den Heuveleibrink - One of the best experts on this subject based on the ideXlab platform.
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frequency of wt1 and 11p15 constitutional aberrations and Phenotypic Correlation in childhood wilms tumour patients
European Journal of Cancer, 2012Co-Authors: Heidi Segers, Rogier Kersseboom, Marielle Alders, Rob Pieters, Anja Wagner, M M Van Den HeuveleibrinkAbstract:Abstract Introduction In 9–17% of Wilms tumour patients a predisposing syndrome is present, in particular WT1-associated syndromes and overgrowth syndromes. Constitutional WT1 mutations or epigenetic changes on chromosome 11p15 have also been described in Wilms tumour patients without Phenotypic abnormalities. Thus, the absence of Phenotypic abnormalities does not exclude the presence of a genetic predisposition, suggesting that more Wilms tumour patients may have a constitutional abnormality. Therefore, we investigated the frequency of constitutional aberrations in combination with phenotype. Patients & methods Clinical genetic assessment, as well as molecular analysis of WT1 and locus 11p15 was offered to a single-centre cohort of 109 childhood Wilms tumour patients. Results Twelve patients (11%) had a WT1 aberration and eight patients (8%) had an 11p15 aberration. Of the 12 patients with a WT1 aberration, four had WAGR syndrome (Wilms tumor, aniridia, genitourinary malformations and mental retardation), one had Denys-Drash syndrome, four had genitourinary anomalies without other syndromic features and three had bilateral disease with stromal-predominant histology at young age without congenital anomalies. Of the eight patients with an 11p15 aberration, four had Beckwith–Wiedemann syndrome (BWS), two had minor features of BWS and two had no stigmata of BWS or hemihypertrophy. Conclusion Constitutional WT1 or 11p15 aberrations are frequent in Wilms tumour patients and careful clinical assessment can identify the majority of these patients. Therefore, we would recommend offering clinical genetic counselling to all Wilms tumour patients, as well as molecular analysis to patients with clinical signs of a syndrome or with features that may indicate a constitutional WT1 or 11p15 aberration.
