MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes - Test2 - elhamkhani - TriplyDB

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

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

http://www.wikidata.org/entity/Q24635938

about

Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture APLP2 Regulates Refractive Error and Myopia Development in Mice and Humans Six novel susceptibility Loci for early-onset androgenetic alopecia and their unexpected association with common diseases Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh A meta-analysis and genome-wide association study of platelet count and mean platelet volume in african americans A two-stage meta-analysis identifies several new loci for Parkinson's disease Mega2: validated data-reformatting for linkage and association analyses Genome-wide association studies of quantitatively measured skin, hair, and eye pigmentation in four European populations Genetic architecture of skin and eye color in an African-European admixed population Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease Genetic variation associated with differential educational attainment in adults has anticipated associations with school performance in children GWAS of 126,559 individuals identifies genetic variants associated with educational attainment Genome-wide association meta-analysis identifies new endometriosis risk loci A map of human genome variation from population-scale sequencing Comprehensive evaluation of imputation performance in African Americans Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1 Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia Genotype imputation with thousands of genomes Altered Brain Activation during Emotional Face Processing in Relation to Both Diagnosis and Polygenic Risk of Bipolar Disorder Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome Application of computational methods in genetic study of inflammatory bowel disease Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation Rare-variant association analysis: study designs and statistical tests Disease mechanisms in rheumatology--tools and pathways: defining functional genetic variants in autoimmune diseases Clinical review: Genome-wide association studies of skeletal phenotypes: what we have learned and where we are headed Exome sequencing and complex disease: practical aspects of rare variant association studies PyPedia: using the wiki paradigm as crowd sourcing environment for bioinformatics protocols Fine mapping and functional studies of risk variants for type 1 diabetes at chromosome 16p13.13 Genome-wide scans of genetic variants for psychophysiological endophenotypes: a methodological overview.The Future is The Past: Methylation QTLs in Schizophrenia Genome-wide association study identifies multiple loci influencing human serum metabolite levels Meta-analysis of Parkinson's disease: identification of a novel locus, RIT2.Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers Genetic variants at PSMD3 interact with dietary fat and carbohydrate to modulate insulin resistance Effects of APOA5 -1131T>C (rs662799) on fasting plasma lipids and risk of metabolic syndrome: evidence from a case-control study in China and a meta-analysis A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci Nine loci for ocular axial length identified through genome-wide association studies, including shared loci with refractive error Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene

P2860

Q21090200-968115B8-08FD-4978-B0C4-AC8D2C2F72EF Q21144730-8A4569FE-6A9F-452C-AB0E-233A6B7DCC12 Q21144915-F225BADF-7ED7-44E3-BB7B-C9222FC26E6A Q21144927-8073D19E-8189-4F62-B8B2-DC6657A391CF Q21144930-CC5D09F9-6788-4CC1-A8F1-8536489CA9AA Q21144948-E14140AF-62B8-48FE-8F71-53660A0269BA Q21146646-7D4E6599-11F6-465B-8E6C-B0AD8F8501A8 Q21560748-CA2261B2-1EAF-4554-98D7-8B97F579E84B Q21563358-980C26CB-5BAC-4AA1-9260-6906571703E6 Q22251067-76BBC82F-1126-4863-B356-FED81AB535E9 Q24288683-87986201-1B2D-4B83-A428-40ADC99B0C21 Q24289119-6528839A-84D4-456B-9306-C231B4A67905 Q24597781-B1241BA2-B5EA-45D1-9FA4-FD2500E8D25E Q24617794-316AEB11-D131-48B1-8DA2-0EF96439AF4C Q24626490-80E89675-BBF5-4D17-A3F7-464201488926 Q24629883-AC3B4E61-F64C-483B-970E-64C32FB312AF Q24631992-27224EF8-3227-48FE-8176-D0BA72C2A334 Q24633967-2C0C4895-9CB2-4C1F-9ECC-3B6A0B6ED00B Q24843192-A5C86753-901F-4C5C-A0F2-2ACC442692AF Q26748481-4F11163F-A0DB-45B5-A8CB-B6D990173DFC Q26771497-97A38C68-1B86-4F8E-88C4-C630390326D5 Q26774595-70BB27F7-38AE-4FCE-AB15-426BFFEFEDED Q26782233-FD59B028-DFAA-456A-80C0-9770E1FB5369 Q26852733-77ABB1C0-A395-4BE1-ADE8-D0D95722DB27 Q27005017-B90CD92D-28C4-49CE-9D4B-8EAA9ED5B2EC Q27014915-FA9912F2-418F-424E-9455-49BBEA19B179 Q27027055-BEED6D0D-46BC-4707-BEED-5F9588D2D558 Q27218818-AE58DD59-E174-4F77-BE4D-B10CA021AB5D Q27335170-A52750E4-4FAD-4FDB-9140-AA0B37CF0A3D Q27347160-CB6DAAEF-CFB5-4E09-9A3E-82F5B6CCCE7F Q28073441-AC5C9F1B-60C4-45C8-9E66-F701F98385E3 Q28258384-20771D57-9342-4C77-BDFC-E03FACCE6788 Q28262863-BE37CCBE-D347-46F3-A2A5-7EAE9949B95C Q28266762-E923044D-6987-4316-874B-9CF377F75E14 Q28267312-3228D1A7-3F43-4666-B8F2-F2E709A0984B Q28282981-619A6344-6AAD-4F29-AD77-2659C02D0BC9 Q28286797-25DCAB0F-C487-486D-9609-F5BB6AEAD404 Q28293771-A5B4E0B0-8E72-46C0-A500-C682DCBC6EFF Q28300637-63A47E3F-B6DB-4F46-B0E7-C6CE2FE7C0C0 Q28383674-C06362DD-672A-4188-8746-A8BADB84249B

P2860

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

http://www.wikidata.org/entity/Q24635938

description

2010 nî lūn-bûn

@nan

2010 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2010 թվականի դեկտեմբերին հրատարակված գիտական հոդված

@hy

2010年の論文

@ja

2010年論文

@yue

2010年論文

@zh-hant

2010年論文

@zh-hk

2010年論文

@zh-mo

2010年論文

@zh-tw

2010年论文

@wuu

name

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

@ast

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

@en

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

@nl

type

label

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

@ast

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

@en

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

@nl

prefLabel

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

@ast

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

@en

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

@nl

P1433

Q24635938-2A40E168-B6F5-4A38-A6F1-954E00050B22

P1476

Q24635938-F950E8ED-76C8-4238-AB0E-77A6953E05E3

P2093

Q24635938-0A535FC4-D49F-47AB-A997-E8B3A0F2AC29

Q24635938-54321787-F72B-425D-ADA2-13AF3E3EF3FD

Q24635938-C51226E2-9F9C-4A03-A11E-DA7E997AFECB

P2860

Q24635938-023FD93C-4D9D-4F9B-8F6C-1596FF954E9F

Q24635938-0984A8E9-6BCB-44E2-B945-3F8B9D14B863

Q24635938-0E01C8F4-B7E1-494B-AD99-C11E87FCB899

Q24635938-0F9E36EC-874A-41E9-90A0-E5A7CCFC56E1

Q24635938-1F5CE0FA-DA14-4637-8C98-84132E45ED9C

Q24635938-200BB00F-4B5C-441B-B100-DEAD5C8A5233

Q24635938-2072CB4F-F113-41EE-B909-42E4CB72A5B7

Q24635938-20D307FF-16BF-4779-8F33-5591B74AEC29

Q24635938-23DA3BBA-69EC-4079-A2E8-959DF1D7BD22

Q24635938-23E267C8-C417-4DB2-B935-B8D0C3A1B58C

P304

Q24635938-2DE7A6FA-139F-4118-8594-7BBB3CC7C42F

P31

Q24635938-016CFBC1-E4C3-4F02-B041-A02889AB126D

P3181

Q24635938-A625BF09-ED19-4276-ABF3-F06FE2F820B2

P356

Q24635938-4C9231E4-629A-45E1-823E-22EA65499E82

P407

Q24635938-C1060FE5-B28A-442D-A2DF-147A885CAC1F

P433

Q24635938-617815DC-7ACE-4AA8-9C65-4522C07F2EDA

P478

Q24635938-7FD0F59D-27E7-4915-A356-F6E88F614A83

P50

Q24635938-75C3D664-321A-4E9F-95DC-90ECC1ED994A

Q24635938-78E7A17F-5CDA-44D9-93E2-FCB18E1E8625

P577

Q24635938-1EA668DA-9F7E-4030-82E8-81FF3BC05455

P5875

Q24635938-F07409F5-7265-40B6-A0B2-4E951F97BC00

P698

Q24635938-F672A1A4-0E4D-40AD-9BCE-24738A73D074

P932

Q24635938-7D109744-5B92-4952-983B-52BA1F68AE0A

P3181

P356

P1433

Genetic Epidemiology

P1476

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

@en

P2093

Gonçalo R Abecasis

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

Jun Ding

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

Yun Li

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

Mapping short DNA sequencing reads and calling variants using mapping quality scores

Six new loci associated with body mass index highlight a neuronal influence on body weight regulation

A second generation human haplotype map of over 3.1 million SNPs

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

A haplotype map of the human genome

A New Statistical Method for Haplotype Reconstruction from Population Data

Merlin--rapid analysis of dense genetic maps using sparse gene flow trees

The Sequence Alignment/Map format and SAMtools

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

P304

816-34

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

P31

scholarly article

P3181

257567

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

P356

10.1002/GEPI.20533

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

P407

P433

8

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

P478

34

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

P50

Cristen J. Willer

P577

2010-12-01T00:00:00Z

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

P5875

47702130

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

P698

21058334

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

P932

3175618

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