Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression. - Wikidata - wikimedia - TriplyDB

Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.

Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.

http://www.wikidata.org/entity/Q30428383

about

Beyond the fourth wave of genome-wide obesity association studies Gene-environment interactions in genome-wide association studies: current approaches and new directions A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.SNP set association analysis for familial data.Family-based association tests for sequence data, and comparisons with population-based association tests.A generalized genetic random field method for the genetic association analysis of sequencing data.Module-based association analysis for omics data with network structure.FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data.A unified powerful set-based test for sequencing data analysis of GxE interactions.GEE-based SNP set association test for continuous and discrete traits in family-based association studies Joint genetic analysis using variant sets reveals polygenic gene-context interactions.GENE-LEVEL PHARMACOGENETIC ANALYSIS ON SURVIVAL OUTCOMES USING GENE-TRAIT SIMILARITY REGRESSION.Analysis of gene-gene interactions using gene-trait similarity regression.On the Aggregation of Multimarker Information for Marker-Set and Sequencing Data Analysis: Genotype Collapsing vs. Similarity Collapsing.Incorporating gene-environment interaction in testing for association with rare genetic variants Comparison of statistical tests for association between rare variants and binary traits.Gene-based testing of interactions in association studies of quantitative traits Correcting systematic inflation in genetic association tests that consider interaction effects: application to a genome-wide association study of posttraumatic stress disorder.Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies.A New Method for Detecting Associations with Rare Copy-Number Variants.Similarity-based multimarker association tests for continuous traits.Scan-statistic approach identifies clusters of rare disease variants in LRP2, a gene linked and associated with autism spectrum disorders, in three datasets.Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level Powerful Set-Based Gene-Environment Interaction Testing Framework for Complex Diseases.Haplotype-based methods for detecting uncommon causal variants with common SNPs Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.Approximate score-based testing with application to multivariate trait association analysis.Sequence kernel association tests for the combined effect of rare and common variants.Challenges and opportunities in genome-wide environmental interaction (GWEI) studies Multivariate phenotype association analysis by marker-set kernel machine regression SBERIA: set-based gene-environment interaction test for rare and common variants in complex diseases.Quantitative trait analysis in sequencing studies under trait-dependent sampling.Test for interactions between a genetic marker set and environment in generalized linear models.U-statistics in genetic association studies.A functional U-statistic method for association analysis of sequencing data.FGWAS: Functional genome wide association analysis.

P2860

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P2860

Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.

http://www.wikidata.org/entity/Q30428383

description

2011 nî lūn-bûn

@nan

2011 թուականի Օգոստոսին հրատարակուած գիտական յօդուած

@hyw

2011 թվականի օգոստոսին հրատարակված գիտական հոդված

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2011年の論文

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2011年論文

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2011年論文

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2011年論文

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2011年論文

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2011年論文

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2011年论文

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name

Studying gene and gene-environ ...... e-trait similarity regression.

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Studying gene and gene-environ ...... e-trait similarity regression.

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Studying gene and gene-environ ...... e-trait similarity regression.

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Studying gene and gene-environ ...... e-trait similarity regression.

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J.AJHG.2011.07.007

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American Journal of Human Genetics

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Studying gene and gene-environ ...... e-trait similarity regression.

@en

P2093

Bradford B Worrall

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Daowen Zhang

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Duncan C Thomas

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Fang-Chi Hsu

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Michèle M Sale

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Monnat Pongpanich

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Patrick F Sullivan

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P2860

Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods

Pooled association tests for rare variants in exon-resequencing studies

Genome-wide association studies for common diseases and complex traits

Score tests for association between traits and haplotypes when linkage phase is ambiguous

Transcobalamin 2 variant associated with poststroke homocysteine modifies recurrent stroke risk.

Gene, region and pathway level analyses in whole-genome studies

Group additive regression models for genomic data analysis.

Comparison of the power of haplotype-based versus single- and multilocus association methods for gene x environment (gene x sex) interactions and application to gene x smoking and gene x sex interactions in rheumatoid arthritis.

Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.

A groupwise association test for rare mutations using a weighted sum statistic.

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277-288

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10.1016/J.AJHG.2011.07.007

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2

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89

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Chris Tyler-Smith

Jung-Ying Tzeng

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2011-08-01T00:00:00Z

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51565388

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21835306

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3155192

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