Methods to impute missing genotypes for population data.
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Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studiesStructured Matrix Completion with Applications to Genomic Data Integration.Missing data imputation and haplotype phase inference for genome-wide association studies.Analyses and comparison of accuracy of different genotype imputation methodsImputation of missing genotypes: an empirical evaluation of IMPUTE.Genotype determination for polymorphisms in linkage disequilibriumIterative two-pass algorithm for missing data imputation in SNP arrays.Modeling Informatively Missing Genotypes in Haplotype Analysis.Utilizing genotype imputation for the augmentation of sequence data.An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.Recombination locations and rates in beef cattle assessed from parent-offspring pairs.USING LINEAR PREDICTORS TO IMPUTE ALLELE FREQUENCIES FROM SUMMARY OR POOLED GENOTYPE DATA.Identity by descent estimation with dense genome-wide genotype data.Reducing bias of allele frequency estimates by modeling SNP genotype data with informative missingnessFast accurate missing SNP genotype local imputation.APOBEC3H haplotypes and HIV-1 pro-viral vif DNA sequence diversity in early untreated human immunodeficiency virus-1 infection.Whole genome SNP genotype piecemeal imputationLung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility RegionsGenome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank.Comparison of different imputation methods from low- to high-density panels using Chinese Holstein cattle.Using population mixtures to optimize the utility of genomic databases: linkage disequilibrium and association study design in IndiaGenotype-imputation accuracy across worldwide human populations.Candidate gene analysis using imputed genotypes: cell cycle single-nucleotide polymorphisms and ovarian cancer risk.Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML versus a Bayesian model in multi-environmental field trialsThe relationship between imputation error and statistical power in genetic association studies in diverse populationsGenetic variants in urinary bladder cancer: collective power of the "wimp SNPs".Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays.Genotype imputation via matrix completion.Coverage and efficiency in current SNP chips.Windfalls and pitfalls: Applications of population genetics to the search for disease genes.Efficient genomewide selection of PCA-correlated tSNPs for genotype imputation.
P2860
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P2860
Methods to impute missing genotypes for population data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Methods to impute missing genotypes for population data.
@ast
Methods to impute missing genotypes for population data.
@en
Methods to impute missing genotypes for population data.
@nl
type
label
Methods to impute missing genotypes for population data.
@ast
Methods to impute missing genotypes for population data.
@en
Methods to impute missing genotypes for population data.
@nl
prefLabel
Methods to impute missing genotypes for population data.
@ast
Methods to impute missing genotypes for population data.
@en
Methods to impute missing genotypes for population data.
@nl
P1433
P1476
Methods to impute missing genotypes for population data.
@en
P2093
Daniel J Schaid
Zhaoxia Yu
P2888
P304
P356
10.1007/S00439-007-0427-Y
P577
2007-09-13T00:00:00Z
P6179
1023972121