Genotype imputation for genome-wide association studies

[1]  Tariq Ahmad,et al.  Meta-analysis and imputation refines the association of 15q25 with smoking quantity , 2010, Nature Genetics.

[2]  Zhaoxia Yu,et al.  Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. , 2009, American journal of human genetics.

[3]  Peter Donnelly,et al.  A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies , 2009, 1010.4670.

[4]  M. Stephens,et al.  Bayesian statistical methods for genetic association studies , 2009, Nature Reviews Genetics.

[5]  L. Bernardinelli,et al.  Association between Protective and Deleterious HLA Alleles with Multiple Sclerosis in Central East Sardinia , 2009, PloS one.

[6]  Ludwig Kappos,et al.  Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci , 2009, Nature Genetics.

[7]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[8]  Laura J. Scott,et al.  Edinburgh Research Explorer Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution , 2022 .

[9]  Eran Halperin,et al.  Inference of locus-specific ancestry in closely related populations , 2009, Bioinform..

[10]  P. Donnelly,et al.  Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip , 2009, PLoS genetics.

[11]  B. Browning,et al.  A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. , 2009, American journal of human genetics.

[12]  Gonçalo Abecasis,et al.  Genotype-imputation accuracy across worldwide human populations. , 2009, American journal of human genetics.

[13]  Jon Wakefield,et al.  Bayes factors for genome‐wide association studies: comparison with P‐values , 2009, Genetic epidemiology.

[14]  Eric E Schadt,et al.  Accuracy of Genome-wide Imputation of Untyped Markers and Impacts on Statistical Power for Association Studies , 2009 .

[15]  Vincent Plagnol,et al.  Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci , 2008, Nature Genetics.

[16]  Steven Gallinger,et al.  Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer , 2008, Nature Genetics.

[17]  Yongtao Guan,et al.  Practical Issues in Imputation-Based Association Mapping , 2008, PLoS genetics.

[18]  Ion I. Mandoiu,et al.  Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity , 2007, WABI.

[19]  Hong-Wen Deng,et al.  Analyses and Comparison of Accuracy of Different Genotype Imputation Methods , 2008, PloS one.

[20]  Manuel A. R. Ferreira,et al.  Practical aspects of imputation-driven meta-analysis of genome-wide association studies. , 2008, Human molecular genetics.

[21]  Sharon R. Browning,et al.  Missing data imputation and haplotype phase inference for genome-wide association studies , 2008, Human Genetics.

[22]  Jonathan Marchini,et al.  Comparing algorithms for genotype imputation. , 2008, American journal of human genetics.

[23]  Pall I. Olason,et al.  Detection of sharing by descent, long-range phasing and haplotype imputation , 2008, Nature Genetics.

[24]  Subhajyoti De,et al.  Common variants near MC4R are associated with fat mass, weight and risk of obesity , 2008, Nature Genetics.

[25]  M. McCarthy,et al.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes , 2008, Nature Genetics.

[26]  D. Lin,et al.  Simple and efficient analysis of disease association with missing genotype data. , 2008, American journal of human genetics.

[27]  Peter Donnelly,et al.  A statistical method for predicting classical HLA alleles from SNP data. , 2008, American journal of human genetics.

[28]  B. Browning,et al.  Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. , 2007, American journal of human genetics.

[29]  Zhaohui S. Qin,et al.  A second generation human haplotype map of over 3.1 million SNPs , 2007, Nature.

[30]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[31]  B. Browning,et al.  Efficient multilocus association testing for whole genome association studies using localized haplotype clustering , 2007, Genetic epidemiology.

[32]  P. Donnelly,et al.  A new multipoint method for genome-wide association studies by imputation of genotypes , 2007, Nature Genetics.

[33]  M. McCarthy,et al.  Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes , 2007, Science.

[34]  M. Stephens,et al.  Imputation-Based Analysis of Association Studies: Candidate Regions and Quantitative Traits , 2007, PLoS genetics.

[35]  Dan L Nicolae,et al.  Testing Untyped Alleles (TUNA)—applications to genome‐wide association studies , 2006, Genetic epidemiology.

[36]  R. Durbin,et al.  Mapping trait loci by use of inferred ancestral recombination graphs. , 2006, American journal of human genetics.

[37]  Joshua T. Burdick,et al.  In silico method for inferring genotypes in pedigrees , 2006, Nature Genetics.

[38]  Sharon R Browning,et al.  Multilocus association mapping using variable-length Markov chains. , 2006, American journal of human genetics.

[39]  Paul Scheet,et al.  A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. , 2006, American journal of human genetics.

[40]  Zhaohui S. Qin,et al.  A comparison of phasing algorithms for trios and unrelated individuals. , 2006, American journal of human genetics.

[41]  S. Gabriel,et al.  Efficiency and power in genetic association studies , 2005, Nature Genetics.

[42]  Sebastian Zöllner,et al.  Coalescent-Based Association Mapping and Fine Mapping of Complex Trait Loci , 2005, Genetics.

[43]  David M. Evans,et al.  Genotype prediction using a dense map of SNPs , 2004, Genetic epidemiology.

[44]  C. Carlson,et al.  Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. , 2004, American journal of human genetics.

[45]  M. Stephens,et al.  Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. , 2003, Genetics.

[46]  G. McVean,et al.  Estimating recombination rates from population-genetic data , 2003, Nature Reviews Genetics.

[47]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[48]  Frank Dudbridge,et al.  Haplotype tagging for the identification of common disease genes , 2001, Nature Genetics.

[49]  P. Donnelly,et al.  A new statistical method for haplotype reconstruction from population data. , 2001, American journal of human genetics.

[50]  P. Donnelly,et al.  Inference in molecular population genetics , 2000 .

[51]  L. Excoffier,et al.  Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. , 1995, Molecular biology and evolution.

[52]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[53]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[54]  E. Lander,et al.  Construction of multilocus genetic linkage maps in humans. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[55]  T. Louis.  Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .

[56]  R. Elston,et al.  A general model for the genetic analysis of pedigree data. , 1971, Human heredity.