Analyses and comparison of accuracy of different genotype imputation methods - Wikidata - awgldk - TriplyDB

Analyses and comparison of accuracy of different genotype imputation methods

Analyses and comparison of accuracy of different genotype imputation methods

http://www.wikidata.org/entity/Q33380176

about

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies Clinical utility of sequence-based genotype compared with that derivable from genotyping arrays Association between a literature-based genetic risk score and cardiovascular events in women Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes New World cattle show ancestry from multiple independent domestication events Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis Combining family- and population-based imputation data for association analysis of rare and common variants in large pedigrees A Genomic Approach for Distinguishing between Recent and Ancient Admixture as Applied to Cattle 1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data On combining reference data to improve imputation accuracy.On the performance of multiple imputation based on chained equations in tackling missing data of the African α3.7 -globin deletion in a malaria association study Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.Scanning and Filling: Ultra-Dense SNP Genotyping Combining Genotyping-By-Sequencing, SNP Array and Whole-Genome Resequencing Data.Bias Characterization in Probabilistic Genotype Data and Improved Signal Detection with Multiple Imputation Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.Assessment of genotype imputation methods Single versus multiple imputation for genotypic data.Analyses and comparison of imputation-based association methods 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.FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model.Practical considerations for imputation of untyped markers in admixed populations.Performance of genotype imputations using data from the 1000 Genomes Project.Identification of Diabetic Retinopathy Genes through a Genome-Wide Association Study among Mexican-Americans from Starr County, Texas Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects.Assessment of genotype imputation performance using 1000 Genomes in African American studies.Imputation and quality control steps for combining multiple genome-wide datasets.Performance of genotype imputation for low frequency and rare variants from the 1000 genomes Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate.Molgenis-impute: imputation pipeline in a box Association between SNPs and gene expression in multiple regions of the human brain Exploiting genotyping by sequencing to characterize the genomic structure of the American cranberry through high-density linkage mapping Ascertainment bias from imputation methods evaluation in wheat CIITA is not associated with risk of developing rheumatoid arthritis Effects of mitochondrial haplogroup N9a on type 2 diabetes mellitus and its associated complications.Comparison of the performance of two commercial genome-wide association study genotyping platforms in Han Chinese samples Physical activity, genes for physical fitness, and risk of coronary heart disease

P2860

Q21092459-9FAD9648-7454-40E8-BFA2-EAE8E7214676 Q21283815-45578527-9B5A-4ECD-9A05-6C40C8280FC0 Q24289049-642E1C42-0C55-4786-9B1C-1D3EC9E57BBC Q24613965-4ADF8B12-9D3F-43D7-931B-96067DBAB228 Q24633262-7BB89FBF-7C63-4FD5-BDEE-0868DDDF2EB4 Q24635938-8BD84179-7F37-46F2-BC03-836F67B44B96 Q28287815-8E55CFEF-0776-475B-8460-4D579D968227 Q28387512-95FEE1F2-0125-4910-ADC3-9BA7D27A18C9 Q28395566-D83E7820-0F02-4768-82E8-455A5556C9FE Q28658679-0F5D8107-3FF2-488D-885A-FDFF90908951 Q28943529-F3CA8387-7CE0-4059-AF5C-41E09A2DBC8E Q30587059-C9DFDF18-5209-4C87-B808-35973C7E3D3B Q30831955-754BB19F-5D89-405F-BB7F-89894F78B319 Q30836450-29FAE971-8A4B-47D8-91C6-34F7239D72CA Q30979619-01F6CA3C-82D9-4825-942C-EE807EF18D38 Q31108535-176273E2-A473-470A-8095-CB899EDDD1A8 Q31148631-4D0DA104-F871-4D34-9D9C-7A08711A2B1A Q33519327-5D7FB648-4BC3-49D0-AB65-897A4C69AB55 Q33519363-8ABE7FD8-B091-46D3-B1A0-504022C1C4AA Q33593078-92A196F3-B32B-42B9-8E3B-EBE79C8104E0 Q33603074-3C13EC67-5FD1-44BF-9231-D0EBF7418399 Q33798550-25079570-76D8-4A54-92DA-CF007A9AA149 Q33807685-8BCD6109-81D6-4898-A55D-5F147DED03AD Q34030741-43DFED03-C860-4642-A92A-337B6D036E96 Q34115604-40E1555A-ED8B-4B79-B7AA-B7EAE401B656 Q34127757-32BBB9FD-FABE-4277-B99D-D5A6B3D1F1BC Q34253087-9E1EF2CD-CC7D-475C-9495-100EFA0D23E8 Q34285635-1E3A5A10-92C7-4CEB-A3A7-9A48619BADB7 Q34505607-193F9334-5B35-403F-8135-EA21E53303F2 Q34677930-275224FB-8DCA-4AC1-AA9F-0E884EBED95B Q35013642-408A36E5-E664-4132-A1A2-1A344E312E81 Q35720540-188B3CFF-F259-44D2-A203-C6DDFD55FDDD Q35749760-4D87F925-053D-4416-BAC8-4D1CB168AF25 Q36001595-B554971B-14BA-4B8D-AEF5-F130E524564F Q36050689-240CB16F-8BB0-4452-B93C-36167DA61866 Q36156494-A841CA2D-EE08-4757-A928-23408B9E0973 Q36253879-4DA748DE-53C5-426C-B1CB-994930B0E589 Q36334137-5ABF2C90-06E9-4E16-B24A-292B3A5271BC Q36512658-2FCECF23-0AEF-4F15-BEA8-01F7F95280BC Q36707460-C9B91A0A-7B92-4A92-BD09-0222854D6A82

P2860

Analyses and comparison of accuracy of different genotype imputation methods

http://www.wikidata.org/entity/Q33380176

description

2008 nî lūn-bûn

@nan

2008 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2008 թվականի հոտեմբերին հրատարակված գիտական հոդված

@hy

2008年の論文

@ja

2008年論文

@yue

2008年論文

@zh-hant

2008年論文

@zh-hk

2008年論文

@zh-mo

2008年論文

@zh-tw

2008年论文

@wuu

name

Analyses and comparison of accuracy of different genotype imputation methods

@ast

Analyses and comparison of accuracy of different genotype imputation methods

@en

type

label

Analyses and comparison of accuracy of different genotype imputation methods

@ast

Analyses and comparison of accuracy of different genotype imputation methods

@en

prefLabel

Analyses and comparison of accuracy of different genotype imputation methods

@ast

Analyses and comparison of accuracy of different genotype imputation methods

@en

P1433

Q33380176-218DE724-6288-4962-8FD4-D5D1CCCF8362

P1476

Q33380176-2F5351E6-6E2B-4ECE-8EC1-D1F968303DC2

P2093

Q33380176-1BFF8154-F73A-44A6-BDF3-192A952B1C11

Q33380176-3DA19A75-97D4-4EA9-823D-B650EDC9C0EA

Q33380176-9C2924CD-621E-4DDF-929B-A8F3933CB21F

Q33380176-D1FC8D6A-586B-47D9-B734-A11F0EB6A9B7

P2860

Q33380176-08F2FD80-28AA-48B3-A08A-8D88BB22880B

Q33380176-1354F2A5-5BB6-4872-9C20-7B4FC230D8C0

Q33380176-1EBC1E52-3E94-4FD5-A664-150A90FF80A8

Q33380176-209243B5-3AC8-4107-A691-4EAC79E9F0DE

Q33380176-37F502E9-E210-4FEB-84AE-6485B31E31ED

Q33380176-3A2A2D13-86D8-42CF-84DB-E890EFE1C011

Q33380176-3C415195-A659-4131-B451-8C58AC324C45

Q33380176-3C6D6580-69A8-46E6-AE34-4A09AC0A9AFE

Q33380176-3ECA7973-CD10-44F8-A10D-995469559809

Q33380176-43AFC1EC-ADAD-4C0D-9152-382CF294ADBF

P304

Q33380176-BBADFC07-B346-4279-BE9F-5F2E2FDA0F37

P31

Q33380176-6D236964-3325-4512-9CCB-F710E84AD24B

P356

Q33380176-7A175F88-4F6F-41F1-9478-CADA264984CA

P407

Q33380176-DC05577E-D334-4AA8-A57C-D6CB52717C58

P433

Q33380176-6447BEFC-3197-4DB5-80C0-5C1DEBF6E420

P478

Q33380176-3D6850BB-DBE9-468B-BC80-DC23F5DAC1F4

P50

Q33380176-0B0ACF0C-4675-46E4-A22E-F82F4495E230

P577

Q33380176-1CA7F81C-1485-4075-BFF6-97AC393A97A3

P5875

Q33380176-BFDB8129-2AAD-4D9F-9043-B10C0199CA07

P698

Q33380176-E49F20FC-FFBF-4E07-8AD8-62C7EE82628B

P932

Q33380176-4944721D-3742-49A9-A946-B0A927A67A6C

P356

JOURNAL.PONE.0003551

P1433

P1476

Analyses and comparison of accuracy of different genotype imputation methods

@en

P2093

Christopher J Papasian

http://www.w3.org/2001/XMLSchema#string

Jian Li

http://www.w3.org/2001/XMLSchema#string

Lei Zhang

http://www.w3.org/2001/XMLSchema#string

Yu-Fang Pei

http://www.w3.org/2001/XMLSchema#string

P2860

A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase

A second generation human haplotype map of over 3.1 million SNPs

Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia

Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering

PLINK: a tool set for whole-genome association and population-based linkage analyses

A haplotype map of the human genome

A new look at the statistical model identification

A New Statistical Method for Haplotype Reconstruction from Population Data

Newly identified loci that influence lipid concentrations and risk of coronary artery disease

Efficiency and power in genetic association studies

P304

e3551

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1371/JOURNAL.PONE.0003551

http://www.w3.org/2001/XMLSchema#string

P407

P433

10

http://www.w3.org/2001/XMLSchema#string

P478

3

http://www.w3.org/2001/XMLSchema#string

P50

P577

2008-10-29T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

23425855

http://www.w3.org/2001/XMLSchema#string

P698

18958166

http://www.w3.org/2001/XMLSchema#string

P932

2569208

http://www.w3.org/2001/XMLSchema#string