The relationship between imputation error and statistical power in genetic association studies in diverse populations
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Genotype imputation with thousands of genomesStrategies for Imputing and Analyzing Rare Variants in Association StudiesWhole-exome imputation of sequence variants identified two novel alleles associated with adult body height in African Americans.A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health RecordsPredicting HLA alleles from high-resolution SNP data in three Southeast Asian populations.Effective filtering strategies to improve data quality from population-based whole exome sequencing studies.Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.Value of Mendelian laws of segregation in families: data quality control, imputation, and beyondEvaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.Assessing accuracy of genotype imputation in American Indians.Genotype and SNP calling from next-generation sequencing data.Multistudy fine mapping of chromosome 2q identifies XRCC5 as a chronic obstructive pulmonary disease susceptibility gene.Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypesAssessment of genotype imputation performance using 1000 Genomes in African American studies.Genome-wide association of breast cancer: composite likelihood with imputed genotypesAssessing the impact of differential genotyping errors on rare variant tests of association.Genome-wide association studies in diverse populationsRe-ranking sequencing variants in the post-GWAS era for accurate causal variant identificationAssessing the impact of non-differential genotyping errors on rare variant tests of association.Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate.Identification of Mendelian inconsistencies between SNP and pedigree information of sibs.Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants.A coalescent model for genotype imputation.Haplotype variation and genotype imputation in African populations.A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibilityVariation at HLA-DRB1 is associated with resistance to enteric feverA generic coalescent-based framework for the selection of a reference panel for imputationGenome-wide association analyses using electronic health records identify new loci influencing blood pressure variation.Windfalls and pitfalls: Applications of population genetics to the search for disease genes.A large multi-ethnic genome-wide association study identifies novel genetic loci for intraocular pressure.Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection.Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies.Strategies for phasing and imputation in a population isolate.PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables.Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population.A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci.Genetic variation in the locus is associated with erectile dysfunction
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The relationship between imputation error and statistical power in genetic association studies in diverse populations
description
article científic
@ca
article scientifique
@fr
articolo scientifico
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artigo científico
@pt
bilimsel makale
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scientific article published on 22 October 2009
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
The relationship between imput ...... studies in diverse populations
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The relationship between imput ...... tudies in diverse populations.
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type
label
The relationship between imput ...... studies in diverse populations
@en
The relationship between imput ...... tudies in diverse populations.
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prefLabel
The relationship between imput ...... studies in diverse populations
@en
The relationship between imput ...... tudies in diverse populations.
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P2093
P2860
P1476
The relationship between imput ...... studies in diverse populations
@en
P2093
Chaolong Wang
Lucy Huang
Noah A Rosenberg
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P304
P356
10.1016/J.AJHG.2009.09.017
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P577
2009-10-22T00:00:00Z