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@article{noth2009,
author = {Michael Nothnagel and David Ellinghaus and Stefan Schreiber and Michael Krawczak and Andre Franke}, 
journal = {Hum Genet},
title = {A comprehensive evaluation of SNP genotype imputation},
abstract = {Genome-wide association studies have contributed significantly to the genetic dissection of complex diseases. In order to increase the power of existing marker sets even further, methods have been proposed to predict individual genotypes at un-typed loci from other marker sets by imputation, usually employing HapMap data as a reference. Although various imputation algorithms have been used in practice already, a comprehensive evaluation and comparison of these approaches, using genome-wide SNP data from one and the same population is still lacking. We therefore investigated four publicly available programs for genotype imputation (BEAGLE, IMPUTE, MACH, and PLINK) using data from 449 German individuals genotyped in our laboratory for three genome-wide SNP sets [Affymetrix 5.0 (500 k), Affymetrix 6.0 (1,000 k), and Illumina 550 k]. We observed that HapMap-based imputation in a northern European population is powerful and reliable, even in highly variable genomic regions such as the extended MHC on chromosome 6p21. However, while genotype predictions were found to be highly accurate with all four programs, the number of SNPs for which imputation was actually carried out ('imputation efficacy') varied substantially. BEAGLE, IMPUTE, and MACH yielded nearly identical trade-offs between imputation accuracy and efficacy whereas PLINK performed consistently poorer. We nevertheless recommend either MACH or BEAGLE for practical use because these two programs are more user-friendly and generally require less memory than IMPUTE.},
affiliation = {Institute of Medical Informatics and Statistics, Christian-Albrechts University, Kiel, Germany.},
number = {2},
pages = {163--71},
volume = {125},
year = {2009},
month = {Mar},
language = {eng},
keywords = {Genetic Markers, Linkage Disequilibrium, Software, Genetics: Population, Male, Genomics, Models: Genetic, Germany, Female, Genome-Wide Association Study, Humans, Genotype, Polymorphism: Single Nucleotide}, 
date-added = {2010-07-06 09:39:36 -0400},
date-modified = {2010-07-06 09:39:37 -0400},
doi = {10.1007/s00439-008-0606-5},
pmid = {19089453},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2009/Nothnagel/Hum%20Genet%202009%20Nothnagel.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1731},
read = {Yes},
rating = {0}
}

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@article{serv2007,
author = {Bertrand Servin and Matthew Stephens}, 
journal = {PLoS Genet},
title = {Imputation-based analysis of association studies: candidate regions and quantitative traits},
abstract = {We introduce a new framework for the analysis of association studies, designed to allow untyped variants to be more effectively and directly tested for association with a phenotype. The idea is to combine knowledge on patterns of correlation among SNPs (e.g., from the International HapMap project or resequencing data in a candidate region of interest) with genotype data at tag SNPs collected on a phenotyped study sample, to estimate ("impute") unmeasured genotypes, and then assess association between the phenotype and these estimated genotypes. Compared with standard single-SNP tests, this approach results in increased power to detect association, even in cases in which the causal variant is typed, with the greatest gain occurring when multiple causal variants are present. It also provides more interpretable explanations for observed associations, including assessing, for each SNP, the strength of the evidence that it (rather than another correlated SNP) is causal. Although we focus on association studies with quantitative phenotype and a relatively restricted region (e.g., a candidate gene), the framework is applicable and computationally practical for whole genome association studies. Methods described here are implemented in a software package, Bim-Bam, available from the Stephens Lab website http://stephenslab.uchicago.edu/software.html.},
affiliation = {Department of Statistics, University of Washington, Seattle, Washington, United States of America. bservin@toulouse.inra.fr},
number = {7},
pages = {e114},
volume = {3},
year = {2007},
month = {Jul},
language = {eng},
keywords = {Computational Biology, Software, Bayes Theorem, Models: Statistical, Polymorphism: Genetic, Genomics, Models: Biological, Odds Ratio, Chromosome Mapping, Data Interpretation: Statistical, Humans, Genotype, Phenotype, Polymorphism: Single Nucleotide}, 
date-added = {2010-07-06 09:50:02 -0400},
date-modified = {2010-07-06 09:50:12 -0400},
doi = {10.1371/journal.pgen.0030114},
pii = {06-PLGE-RA-0550},
pmid = {17676998},
URL = {http://www.plosgenetics.org/article/info%253Adoi%252F10.1371%252Fjournal.pgen.0030114},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Servin/PLoS%20Genet%202007%20Servin.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1744},
rating = {0}
}

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@article{marc2007,
author = {Jonathan Marchini and Bryan Howie and Simon Myers and Gil McVean and Peter Donnelly}, 
journal = {Nat Genet},
title = {A new multipoint method for genome-wide association studies by imputation of genotypes},
abstract = {Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.},
affiliation = {Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.},
number = {7},
pages = {906--13},
volume = {39},
year = {2007},
month = {Jul},
language = {eng},
keywords = {Genomics, Genetic Markers, Genotype, Genome: Human, Models: Genetic, Polymorphism: Single Nucleotide, Genetics: Population, Humans, Case-Control Studies}, 
date-added = {2010-07-06 09:47:21 -0400},
date-modified = {2010-07-06 09:47:30 -0400},
doi = {10.1038/ng2088},
pii = {ng2088},
pmid = {17572673},
URL = {http://www.nature.com/ng/journal/v39/n7/abs/ng2088.html},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Marchini/Nat%20Genet%202007%20Marchini.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1743},
rating = {0}
}

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@article{gabr2002,
author = {S. B Gabriel}, 
journal = {Science},
title = {The Structure of Haplotype Blocks in the Human Genome},
number = {5576},
pages = {2225--2229},
volume = {296},
year = {2002},
month = {Jun},
date-added = {2009-06-01 12:28:58 -0400},
date-modified = {2009-07-22 16:12:34 -0400},
doi = {10.1126/science.1069424},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2002/Gabriel/Science%202002%20Gabriel.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p235},
read = {Yes},
rating = {0}
}


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@article{clayton2007,
author = {David Clayton and Hin-Tak Leung}, 
journal = {Hum Hered},
title = {An R package for analysis of whole-genome association studies},
abstract = {OBJECTIVE: To provide data classes and methods to facilitate the analysis of whole genome association studies in the R language for statistical computing. METHODS: We have implemented data classes in which each genotype call is stored as a single byte. At this density, data for single chromosomes derived from large studies and new high-throughput gene chip platforms can be handled in memory. We use the object-oriented programming model introduced with version 4 of the S-plus package, usually termed 'S4 methods'. RESULTS: At the current state of development the package only supports population-based studies, although we would hope to provide support for family-based studies soon. Both quantitative and qualitative phenotypes may be analysed. Flexible association testing functions are provided which can carry out single SNP tests which control for potential confounding by quantitative and qualitative covariates. Tests involving several SNPs taken together as 'tags' are also supported. Efficient calculation of pair-wise linkage disequilibrium measures is implemented and data input functions include a function which can download data directly from the international HapMap project website.},
affiliation = {Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. david.clayton@cimr.cam.ac.uk},
number = {1},
pages = {45--51},
volume = {64},
year = {2007},
month = {Jan},
language = {eng},
keywords = {Humans, Genome: Human, Phenotype, Genotype, Linkage Disequilibrium, Databases: Genetic, Software, Models: Genetic, Polymorphism: Single Nucleotide}, 
date-added = {2009-07-02 10:50:50 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1159/000101422},
pii = {000101422},
pmid = {17483596},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p437},
rating = {0}
}


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@article{chapman2003,
author = {Juliet M Chapman and Jason D Cooper and John A Todd and David G Clayton}, 
journal = {Hum Hered},
title = {Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power},
abstract = {In the 'indirect' method of detecting genetic associations between a trait and a DNA variant, we type several markers in a gene or chromosome region of linkage disequilibrium. If there is association between markers and the trait, we presume the existence of one or more causal polymorphisms in the region. In order to obtain a sufficiently dense set of markers it will almost always be necessary to use single nucleotide polymorphisms (SNPs). Although there is an emerging literature on methods for choosing an optimal set of 'haplotype tag SNPs' (htSNPs) to detect association between a genetic region and a trait, less attention has been given to the problem of how such studies should be analysed when completed, and how the initial data which was used to select the htSNPs should be incorporated into the analysis. This paper discusses this problem for both population- and family-based association studies. The role of the R2 measure of association between a causal locus and various methods of scoring of marker haplotypes is highlighted. In most cases, the simplest method of scoring (locus coding), which does not require phase resolution, is shown generally to be more powerful than scoring methods that include haplotype information. A new 'multi-locus TDT' is also proposed.},
affiliation = {JDRF/WT Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.},
number = {1-3},
pages = {18--31},
volume = {56},
year = {2003},
month = {Jan},
language = {eng},
keywords = {Multivariate Analysis, Data Interpretation: Statistical, Haplotypes, Linkage Disequilibrium, Humans, Genetic Predisposition to Disease}, 
date-added = {2009-07-02 10:50:50 -0400},
date-modified = {2010-05-14 17:59:44 -0400},
doi = {10.1159/000073729},
pii = {HHE20030561_3018},
pmid = {14614235},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2003/Chapman/Hum%20Hered%202003%20Chapman-1.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p436},
read = {Yes},
rating = {2}
}


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@article{Williams:2007p21,
author = {R. B.H Williams and E. K.F Chan and M. J Cowley and P. F.R Little}, 
journal = {Genome Research},
title = {The influence of genetic variation on gene expression},
number = {12},
pages = {1707--1716},
volume = {17},
year = {2007},
month = {Dec},
date-added = {2009-06-01 12:30:54 -0400},
date-modified = {2009-07-22 16:12:35 -0400},
doi = {10.1101/gr.6981507},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2007/Williams/Genome%20Research%202007%20Williams.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p21},
read = {Yes},
rating = {0}
}


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@article{Degner:2009p1333,
author = {J Degner and J Marioni and A Pai and J Pickrell and E Nkadori and Y Gilad and J Pritchard}, 
journal = {Bioinformatics},
title = {Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data},
abstract = {MOTIVATION: Next-generation sequencing has become an important tool for genome-wide quantification of DNA and RNA. However, a major technical hurdle lies in the need to map short sequence reads back to their correct locations in a reference genome. Here we investigate the impact of SNP variation on the reliability of read-mapping in the context of detecting allele-specific expression (ASE). RESULTS: We generated sixteen million 35 bp reads from mRNA of each of two HapMap Yoruba individuals. When we mapped these reads to the human genome we found that, at heterozygous SNPs, there was a significant bias towards higher mapping rates of the allele in the reference sequence, compared to the alternative allele. Masking known SNP positions in the genome sequence eliminated the reference bias but, surprisingly, did not lead to more reliable results overall. We find that even after masking, approximately 5-10% of SNPs still have an inherent bias towards more effective mapping of one allele. Filtering out inherently biased SNPs removes 40% of the top signals of ASE. The remaining SNPs showing ASE are enriched in genes previously known to harbor cis-regulatory variation or known to show uniparental imprinting. Our results have implications for a variety of applications involving detection of alternate alleles from short-read sequence data. AVAILABILITY: Scripts, written in Perl and R, for simulating short reads, masking SNP variation in a reference genome, and analyzing the simulation output are available upon request from JFD. Raw short read data were deposited in GEO (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE18156. CONTACT: jdegner@uchicago.edu, marioni@uchicago.edu, gilad@uchicago.edu, pritch@uchicago.edu.},
affiliation = {Department of Human Genetics, Howard Hughes Medical Institute, and Committee on Genetics, Genomics and Systems Biology, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637 .},
pages = {},
year = {2009},
month = {Oct},
language = {ENG},
date-added = {2009-11-30 13:08:29 -0500},
date-modified = {2010-02-08 14:22:08 -0500},
doi = {10.1093/bioinformatics/btp579},
pii = {btp579},
pmid = {19808877},
local-url = {file://localhost/Users/stvjc/Documents/Papers/2009/Degner/Bioinformatics%202009%20Degner.pdf},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1333},
read = {Yes},
rating = {0}
}


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@article{Stranger:2007p1342,
author = {Barbara E Stranger and Alexandra C Nica and Matthew S Forrest and Antigone Dimas and Christine P Bird and Claude Beazley and Catherine E Ingle and Mark Dunning and Paul Flicek and Daphne Koller and Stephen Montgomery and Simon Tavar{\'e} and Panos Deloukas and Emmanouil T Dermitzakis}, 
journal = {Nat Genet},
title = {Population genomics of human gene expression},
abstract = {Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.},
affiliation = {The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.},
number = {10},
pages = {1217--24},
volume = {39},
year = {2007},
month = {Oct},
language = {eng},
keywords = {Genetics: Population, Transcription Initiation Site, Repressor Proteins, Polymorphism: Single Nucleotide, Alleles, Chromosomes: Human: Pair 2, Genomics, Phenotype, Genetic Variation, Gene Expression Profiling, Cell Line: Tumor, Gene Expression, Humans, Genome: Human}, 
date-added = {2009-12-08 12:32:30 -0500},
date-modified = {2009-12-08 12:32:30 -0500},
doi = {10.1038/ng2142},
pii = {ng2142},
pmid = {17873874},
uri = {papers://C760FBA3-C68E-4E3E-8645-15A5D6D0F91F/Paper/p1342},
rating = {0}
}

