Inferring causal phenotype networks using structural equation models.
about
Breeding and Genetics Symposium: inferring causal effects from observational data in livestock.Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic dataDiscovering phenotypic causal structure from nonexperimental data.A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data.Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.Analyzing networks of phenotypes in complex diseases: methodology and applications in COPDInference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.Investigating perturbed pathway modules from gene expression data via structural equation models.Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathwaysThe Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster.The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models.Searching for phenotypic causal networks involving complex traits: an application to European quail.Is structural equation modeling advantageous for the genetic improvement of multiple traits?Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.).Inferring relationships between Phosphorus utilization, feed per gain, and bodyweight gain in an F2 cross of Japanese quail using recursive models.Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs.Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattleA new method to infer causal phenotype networks using QTL and phenotypic information.Assessing Predictive Properties of Genome-Wide Selection in Soybeans.A novel network analysis approach reveals DNA damage, oxidative stress and calcium/cAMP homeostasis-associated biomarkers in frontotemporal dementia.Bayesian Networks Analysis of Malocclusion Data.Inferring causal structures and comparing the causal effects among calving difficulty, gestation length and calf size in Japanese Black cattle.Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle.Quantitative genetic analysis of causal relationships among feather pecking, feather eating, and general locomotor activity in laying hens using structural equation models.Genotype-phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes.Health and body condition of lactating females on rabbit farms.Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach.What Is the Influence of Morphological Knowledge in the Early Stages of Reading Acquisition Among Low SES Children? A Graphical Modeling Approach.Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models
P2860
Q30581061-E48B5360-020C-4971-B2B3-815CA6213A66Q30993859-935441C6-405D-4913-98B6-CCF53210B249Q31062044-1FA05650-12B9-4F14-8F77-06F64939FED0Q31141263-06EADEDE-96A9-4FEE-9BB7-237701D61266Q33711455-E2C23324-AFDE-4EFA-B7AA-1123ECAE0469Q33929376-C12976CB-A49C-4A45-9784-DAFD531B5184Q34743724-2F5B1DD0-5020-426F-88F8-D687874A1A2BQ35178635-50C23DE6-ACDE-4DFB-93E9-DB3EC9AAFED1Q35230437-E20C7E74-C4D2-40F2-8284-3DB18441D7E4Q35763294-3E6C4B09-D8AC-45BB-8434-5582975A8B0AQ35821062-155A747A-3632-4B70-A7AF-30761F1C63FFQ35966240-9E71EFB2-D0E9-4525-807B-F50C53415EAAQ36972186-2367FCD3-E47D-4DE2-B420-5AA7F58A7504Q38715012-D7BB9C57-5ECA-4183-853C-D188C984BA83Q40134458-75AC13B8-F3D6-4678-941D-07EDFE898550Q40367995-1BC4F8FA-73FC-4A3E-A2A1-F06B364F5EB8Q40616644-0169A0DD-C621-4F91-9A10-087E704D3A90Q42034557-A4E56684-CFE0-4507-88DC-FE59894C7F07Q42369185-5C428FCB-CF41-49D7-B019-59AFCE5B2CBBQ42705252-D5B751D7-9FFC-4F16-94F1-ABF42959FD63Q47178029-1F7F355D-65CB-40CB-9EAC-8B3D29F801D8Q47215241-9C6EA034-0E15-4635-A1F5-0E52E1C8E1CBQ48920562-800B3DCB-417A-4ADB-A79A-3D172AA50B63Q50634443-CDBFCE11-6BE9-4C84-812A-A023DD508241Q50858546-164CC1E9-AE08-4F2E-8CE3-C29A23B0908EQ51366340-BA0AC3E1-5B88-42D6-9CD4-DC983679E141Q52375410-D985FE07-320F-4C3A-9453-FD5E69099A2AQ55257943-AE18A830-1C7C-4D14-AABA-A65AD3D910D2Q57816654-9A42BDA9-0FD7-4F89-8E37-C860DE5BFC19
P2860
Inferring causal phenotype networks using structural equation models.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 10 February 2011
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Inferring causal phenotype networks using structural equation models.
@en
Inferring causal phenotype networks using structural equation models.
@nl
type
label
Inferring causal phenotype networks using structural equation models.
@en
Inferring causal phenotype networks using structural equation models.
@nl
prefLabel
Inferring causal phenotype networks using structural equation models.
@en
Inferring causal phenotype networks using structural equation models.
@nl
P2093
P2860
P356
P1476
Inferring causal phenotype networks using structural equation models.
@en
P2093
Bruno D Valente
Daniel Gianola
Guilherme J M Rosa
Gustavo de los Campos
Martinho A Silva
Xiao-Lin Wu
P2860
P2888
P356
10.1186/1297-9686-43-6
P577
2011-02-10T00:00:00Z
P5875
P6179
1004664519