Literature DB >> 19931040

Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies.

Brian L Browning1, Zhaoxia Yu.   

Abstract

We present a novel method for simultaneous genotype calling and haplotype-phase inference. Our method employs the computationally efficient BEAGLE haplotype-frequency model, which can be applied to large-scale studies with millions of markers and thousands of samples. We compare genotype calls made with our method to genotype calls made with the BIRDSEED, CHIAMO, GenCall, and ILLUMINUS genotype-calling methods, using genotype data from the Illumina 550K and Affymetrix 500K arrays. We show that our method has higher genotype-call accuracy and yields fewer uncalled genotypes than competing methods. We perform single-marker analysis of data from the Wellcome Trust Case Control Consortium bipolar disorder and type 2 diabetes studies. For bipolar disorder, the genotype calls in the original study yield 25 markers with apparent false-positive association with bipolar disorder at a p < 10(-7) significance level, whereas genotype calls made with our method yield no associated markers at this significance threshold. Conversely, for markers with replicated association with type 2 diabetes, there is good concordance between genotype calls used in the original study and calls made by our method. Results from single-marker and haplotypic analysis of our method's genotype calls for the bipolar disorder study indicate that our method is highly effective at eliminating genotyping artifacts that cause false-positive associations in genome-wide association studies. Our new genotype-calling methods are implemented in the BEAGLE and BEAGLECALL software packages.

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Year:  2009        PMID: 19931040      PMCID: PMC2790566          DOI: 10.1016/j.ajhg.2009.11.004

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  38 in total

1.  Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data.

Authors:  Benilton Carvalho; Henrik Bengtsson; Terence P Speed; Rafael A Irizarry
Journal:  Biostatistics       Date:  2006-12-22       Impact factor: 5.899

2.  Efficient multilocus association testing for whole genome association studies using localized haplotype clustering.

Authors:  Brian L Browning; Sharon R Browning
Journal:  Genet Epidemiol       Date:  2007-07       Impact factor: 2.135

3.  A new multipoint method for genome-wide association studies by imputation of genotypes.

Authors:  Jonathan Marchini; Bryan Howie; Simon Myers; Gil McVean; Peter Donnelly
Journal:  Nat Genet       Date:  2007-06-17       Impact factor: 38.330

4.  Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis.

Authors:  John D Rioux; Ramnik J Xavier; Kent D Taylor; Mark S Silverberg; Philippe Goyette; Alan Huett; Todd Green; Petric Kuballa; M Michael Barmada; Lisa Wu Datta; Yin Yao Shugart; Anne M Griffiths; Stephan R Targan; Andrew F Ippoliti; Edmond-Jean Bernard; Ling Mei; Dan L Nicolae; Miguel Regueiro; L Philip Schumm; A Hillary Steinhart; Jerome I Rotter; Richard H Duerr; Judy H Cho; Mark J Daly; Steven R Brant
Journal:  Nat Genet       Date:  2007-04-15       Impact factor: 38.330

5.  Risk alleles for multiple sclerosis identified by a genomewide study.

Authors:  David A Hafler; Alastair Compston; Stephen Sawcer; Eric S Lander; Mark J Daly; Philip L De Jager; Paul I W de Bakker; Stacey B Gabriel; Daniel B Mirel; Adrian J Ivinson; Margaret A Pericak-Vance; Simon G Gregory; John D Rioux; Jacob L McCauley; Jonathan L Haines; Lisa F Barcellos; Bruce Cree; Jorge R Oksenberg; Stephen L Hauser
Journal:  N Engl J Med       Date:  2007-07-29       Impact factor: 91.245

Review 6.  Genome-wide association studies provide new insights into type 2 diabetes aetiology.

Authors:  Timothy M Frayling
Journal:  Nat Rev Genet       Date:  2007-09       Impact factor: 53.242

7.  Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.

Authors:  Eleftheria Zeggini; Michael N Weedon; Cecilia M Lindgren; Timothy M Frayling; Katherine S Elliott; Hana Lango; Nicholas J Timpson; John R B Perry; Nigel W Rayner; Rachel M Freathy; Jeffrey C Barrett; Beverley Shields; Andrew P Morris; Sian Ellard; Christopher J Groves; Lorna W Harries; Jonathan L Marchini; Katharine R Owen; Beatrice Knight; Lon R Cardon; Mark Walker; Graham A Hitman; Andrew D Morris; Alex S F Doney; Mark I McCarthy; Andrew T Hattersley
Journal:  Science       Date:  2007-04-26       Impact factor: 47.728

8.  Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.

Authors:  John A Todd; Neil M Walker; Jason D Cooper; Deborah J Smyth; Kate Downes; Vincent Plagnol; Rebecca Bailey; Sergey Nejentsev; Sarah F Field; Felicity Payne; Christopher E Lowe; Jeffrey S Szeszko; Jason P Hafler; Lauren Zeitels; Jennie H M Yang; Adrian Vella; Sarah Nutland; Helen E Stevens; Helen Schuilenburg; Gillian Coleman; Meeta Maisuria; William Meadows; Luc J Smink; Barry Healy; Oliver S Burren; Alex A C Lam; Nigel R Ovington; James Allen; Ellen Adlem; Hin-Tak Leung; Chris Wallace; Joanna M M Howson; Cristian Guja; Constantin Ionescu-Tîrgovişte; Matthew J Simmonds; Joanne M Heward; Stephen C L Gough; David B Dunger; Linda S Wicker; David G Clayton
Journal:  Nat Genet       Date:  2007-06-06       Impact factor: 38.330

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

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

10.  Genomewide association analysis of coronary artery disease.

Authors:  Nilesh J Samani; Jeanette Erdmann; Alistair S Hall; Christian Hengstenberg; Massimo Mangino; Bjoern Mayer; Richard J Dixon; Thomas Meitinger; Peter Braund; H-Erich Wichmann; Jennifer H Barrett; Inke R König; Suzanne E Stevens; Silke Szymczak; David-Alexandre Tregouet; Mark M Iles; Friedrich Pahlke; Helen Pollard; Wolfgang Lieb; Francois Cambien; Marcus Fischer; Willem Ouwehand; Stefan Blankenberg; Anthony J Balmforth; Andrea Baessler; Stephen G Ball; Tim M Strom; Ingrid Braenne; Christian Gieger; Panos Deloukas; Martin D Tobin; Andreas Ziegler; John R Thompson; Heribert Schunkert
Journal:  N Engl J Med       Date:  2007-07-18       Impact factor: 91.245

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  118 in total

1.  M(3): an improved SNP calling algorithm for Illumina BeadArray data.

Authors:  Gengxin Li; Joel Gelernter; Henry R Kranzler; Hongyu Zhao
Journal:  Bioinformatics       Date:  2011-12-08       Impact factor: 6.937

2.  Family-based association tests using genotype data with uncertainty.

Authors:  Zhaoxia Yu
Journal:  Biostatistics       Date:  2011-12-08       Impact factor: 5.899

3.  Genotype calling from next-generation sequencing data using haplotype information of reads.

Authors:  Degui Zhi; Jihua Wu; Nianjun Liu; Kui Zhang
Journal:  Bioinformatics       Date:  2012-01-27       Impact factor: 6.937

4.  High-resolution detection of identity by descent in unrelated individuals.

Authors:  Sharon R Browning; Brian L Browning
Journal:  Am J Hum Genet       Date:  2010-03-18       Impact factor: 11.025

Review 5.  Genotype imputation for genome-wide association studies.

Authors:  Jonathan Marchini; Bryan Howie
Journal:  Nat Rev Genet       Date:  2010-07       Impact factor: 53.242

6.  Increased power of mixed models facilitates association mapping of 10 loci for metabolic traits in an isolated population.

Authors:  Eimear E Kenny; Minseung Kim; Alexander Gusev; Jennifer K Lowe; Jacqueline Salit; J Gustav Smith; Sirisha Kovvali; Hyun Min Kang; Christopher Newton-Cheh; Mark J Daly; Markus Stoffel; David M Altshuler; Jeffrey M Friedman; Eleazar Eskin; Jan L Breslow; Itsik Pe'er
Journal:  Hum Mol Genet       Date:  2010-11-30       Impact factor: 6.150

7.  SNP detection and genotyping from low-coverage sequencing data on multiple diploid samples.

Authors:  Si Quang Le; Richard Durbin
Journal:  Genome Res       Date:  2010-10-27       Impact factor: 9.043

8.  Correcting for Sample Contamination in Genotype Calling of DNA Sequence Data.

Authors:  Matthew Flickinger; Goo Jun; Gonçalo R Abecasis; Michael Boehnke; Hyun Min Kang
Journal:  Am J Hum Genet       Date:  2015-07-30       Impact factor: 11.025

9.  Imputation of coding variants in African Americans: better performance using data from the exome sequencing project.

Authors:  Qing Duan; Eric Yi Liu; Paul L Auer; Guosheng Zhang; Ethan M Lange; Goo Jun; Chris Bizon; Shuo Jiao; Steven Buyske; Nora Franceschini; Chris S Carlson; Li Hsu; Alex P Reiner; Ulrike Peters; Jeffrey Haessler; Keith Curtis; Christina L Wassel; Jennifer G Robinson; Lisa W Martin; Christopher A Haiman; Loic Le Marchand; Tara C Matise; Lucia A Hindorff; Dana C Crawford; Themistocles L Assimes; Hyun Min Kang; Gerardo Heiss; Rebecca D Jackson; Charles Kooperberg; James G Wilson; Gonçalo R Abecasis; Kari E North; Deborah A Nickerson; Leslie A Lange; Yun Li
Journal:  Bioinformatics       Date:  2013-08-16       Impact factor: 6.937

10.  PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies.

Authors:  Peizhou Liao; Glen A Satten; Yi-Juan Hu
Journal:  Genet Epidemiol       Date:  2017-05-31       Impact factor: 2.135

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