Impact of pre-imputation SNP-filtering on genotype imputation results. - Wikidata - awgldk - TriplyDB

Impact of pre-imputation SNP-filtering on genotype imputation results.

Impact of pre-imputation SNP-filtering on genotype imputation results.

http://www.wikidata.org/entity/Q34537985

about

Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration.Impact of genetic similarity on imputation accuracy.Comparing performance of modern genotype imputation methods in different ethnicities A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Pediatric Cohorts.Genome-wide association study of a nicotine metabolism biomarker in African American smokers: impact of chromosome 19 genetic influences.Testing Departure from Hardy-Weinberg Proportions.Genetic study of multimodal imaging Alzheimer's disease progression score implicates novel loci.

P2860

Q35509349-B65487FB-5CFF-4ABC-8747-7D69CC1A0684 Q35700710-41EB057C-B1AA-415A-A58A-75AA23F46E39 Q37306979-D38B6F08-CE5E-4553-9213-1D0F4C420BB4 Q37347399-55315F39-C7A1-4945-8953-BE002144CE81 Q46297334-FA228A5E-4624-4AF7-86BA-E2150672AE90 Q47860901-6BB9C89F-BD54-471B-A87C-70F324C7C7B3 Q55514197-1782ED53-EAD5-4A2B-9C25-D40BFAD9336E

P2860

Impact of pre-imputation SNP-filtering on genotype imputation results.

http://www.wikidata.org/entity/Q34537985

description

2014 nî lūn-bûn

@nan

2014 թուականի Օգոստոսին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի օգոստոսին հրատարակված գիտական հոդված

@hy

2014年の論文

@ja

2014年論文

@yue

2014年論文

@zh-hant

2014年論文

@zh-hk

2014年論文

@zh-mo

2014年論文

@zh-tw

2014年论文

@wuu

name

Impact of pre-imputation SNP-filtering on genotype imputation results.

@ast

Impact of pre-imputation SNP-filtering on genotype imputation results.

@en

Impact of pre-imputation SNP-filtering on genotype imputation results.

@nl

type

label

Impact of pre-imputation SNP-filtering on genotype imputation results.

@ast

Impact of pre-imputation SNP-filtering on genotype imputation results.

@en

Impact of pre-imputation SNP-filtering on genotype imputation results.

@nl

prefLabel

Impact of pre-imputation SNP-filtering on genotype imputation results.

@ast

Impact of pre-imputation SNP-filtering on genotype imputation results.

@en

Impact of pre-imputation SNP-filtering on genotype imputation results.

@nl

P1433

Q34537985-62B409B1-C5AC-4891-9752-01914B7832B1

P1476

Q34537985-09B8E98A-09CA-4A1D-87DC-6934688CA1C9

P2093

Q34537985-4CE1674F-93C5-4438-885B-EC923DBF5E9C

Q34537985-4EE9FE05-83E1-4AF5-8AFF-E7FC0C6554E6

P2860

Q34537985-04F8F59A-9911-457F-B3D4-80CEEDB222E9

Q34537985-10BCA27D-B551-4AB5-852B-9151DC2850FD

Q34537985-1F2F5E77-510F-43C0-94C3-266027A91012

Q34537985-3870C79D-E9A5-4055-97B8-A729C4C1DD8B

Q34537985-3A1C84B3-2C06-4C77-8FF4-DBF83FA674E5

Q34537985-4288E115-7127-410D-807E-491557349DDF

Q34537985-540EB14F-6435-473B-88FF-04E1AC312DEE

Q34537985-6194AD9B-09DB-42EE-BFB9-731886F5D62C

Q34537985-6319A369-3C40-4224-8A2B-8A34E4E85C83

Q34537985-68CC8601-4FC4-4944-B6EF-71FF31E445F0

P2888

Q34537985-A78B658F-0723-4212-BBCA-37629B22E4E8

P304

Q34537985-7ABA9B27-9267-4D8E-AB58-8C9CAC924BDF

P31

Q34537985-6C78392A-440F-4D12-8A05-51CE20AC8B14

P356

Q34537985-42237DE5-4E57-4173-96EF-06A202A65387

P478

Q34537985-3A411D9E-0D09-45AF-A6E4-BA16F7261F1E

P50

Q34537985-279E832A-FD38-47E1-B39C-5E92A89A0A29

Q34537985-9878BF55-0DD5-4627-9A63-93E7422C77FA

Q34537985-EF5C178C-37E6-4BC2-8DE1-1950A2124D59

P577

Q34537985-DBAA1A43-D830-4A60-B484-4E43D0EF86E8

P5875

Q34537985-9AB18C9B-D238-4EEE-8F28-5E616CFDF5B6

P6179

Q34537985-883D358E-52D5-4AF9-876E-B833E93B46A1

P698

Q34537985-FD1F850E-3AAB-4BF6-844A-B35018EAD505

P932

Q34537985-65D00F6D-9C81-431B-8DB7-06603B52BB4C

P356

S12863-014-0088-5

P1433

P1476

Impact of pre-imputation SNP-filtering on genotype imputation results

@en

P2093

Markus Scholz

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

Nab Raj Roshyara

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

P2860

A flexible and accurate genotype imputation method for the next generation of genome-wide association studies

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

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

A map of human genome variation from population-scale sequencing

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

A second generation human haplotype map of over 3.1 million SNPs

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

Principal components analysis corrects for stratification in genome-wide association studies

Practical aspects of imputation-driven meta-analysis of genome-wide association studies

P2888

s12863-014-0088-5

P304

88

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

P31

scholarly article

P356

10.1186/S12863-014-0088-5

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

P478

15

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

P50

P577

2014-08-12T00:00:00Z

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

P5875

264711877

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

P6179

1044354259

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

P698

25112433

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

P932

4236550

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