Literature DB >> 22592383

Fast computation for genome-wide association studies using boosted one-step statistics.

Arend Voorman1, Ken Rice, Thomas Lumley.   

Abstract

MOTIVATION: Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical.
RESULTS: In this article, we present simple methods that capitalize on the structure inherent in GWAS studies to dramatically speed up computation for a wide variety of problems, with a special focus on methods for correlated phenotypes. AVAILABILITY: The R package 'boss' is available on the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/web/packages/boss/

Mesh:

Year:  2012        PMID: 22592383      PMCID: PMC3389774          DOI: 10.1093/bioinformatics/bts291

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  14 in total

1.  Estimating equations for association structures.

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Journal:  Stat Med       Date:  2004-03-30       Impact factor: 2.373

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

Authors:  Paul Scheet; Matthew Stephens
Journal:  Am J Hum Genet       Date:  2006-02-17       Impact factor: 11.025

3.  Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis.

Authors:  Yurii S Aulchenko; Dirk-Jan de Koning; Chris Haley
Journal:  Genetics       Date:  2007-07-29       Impact factor: 4.562

4.  A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals.

Authors:  Brian L Browning; Sharon R Browning
Journal:  Am J Hum Genet       Date:  2009-02-05       Impact factor: 11.025

5.  Efficient control of population structure in model organism association mapping.

Authors:  Hyun Min Kang; Noah A Zaitlen; Claire M Wade; Andrew Kirby; David Heckerman; Mark J Daly; Eleazar Eskin
Journal:  Genetics       Date:  2008-03       Impact factor: 4.562

Review 6.  Regression analysis for correlated data.

Authors:  K Y Liang; S L Zeger
Journal:  Annu Rev Public Health       Date:  1993       Impact factor: 21.981

7.  Performance of generalized estimating equations in practical situations.

Authors:  S R Lipsitz; G M Fitzmaurice; E J Orav; N M Laird
Journal:  Biometrics       Date:  1994-03       Impact factor: 2.571

8.  Partial Correlation Estimation by Joint Sparse Regression Models.

Authors:  Jie Peng; Pei Wang; Nengfeng Zhou; Ji Zhu
Journal:  J Am Stat Assoc       Date:  2009-06-01       Impact factor: 5.033

9.  Correcting "winner's curse" in odds ratios from genomewide association findings for major complex human diseases.

Authors:  Hua Zhong; Ross L Prentice
Journal:  Genet Epidemiol       Date:  2010-01       Impact factor: 2.135

10.  Behavior of QQ-plots and genomic control in studies of gene-environment interaction.

Authors:  Arend Voorman; Thomas Lumley; Barbara McKnight; Kenneth Rice
Journal:  PLoS One       Date:  2011-05-12       Impact factor: 3.240

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  10 in total

1.  Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.

Authors:  Colleen M Sitlani; Kenneth M Rice; Thomas Lumley; Barbara McKnight; L Adrienne Cupples; Christy L Avery; Raymond Noordam; Bruno H C Stricker; Eric A Whitsel; Bruce M Psaty
Journal:  Stat Med       Date:  2014-10-09       Impact factor: 2.373

2.  Genome-wide association study of rate of cognitive decline in Alzheimer's disease patients identifies novel genes and pathways.

Authors:  Richard Sherva; Alden Gross; Shubhabrata Mukherjee; Ryan Koesterer; Philippe Amouyel; Celine Bellenguez; Carole Dufouil; David A Bennett; Lori Chibnik; Carlos Cruchaga; Jorge Del-Aguila; Lindsay A Farrer; Richard Mayeux; Leanne Munsie; Ashley Winslow; Stephen Newhouse; Andrew J Saykin; John S K Kauwe; Paul K Crane; Robert C Green
Journal:  Alzheimers Dement       Date:  2020-06-23       Impact factor: 16.655

3.  Two-stage genome-wide search for epistasis with implementation to Recombinant Inbred Lines (RIL) populations.

Authors:  Pavel Goldstein; Abraham B Korol; Anat Reiner-Benaim
Journal:  PLoS One       Date:  2014-12-23       Impact factor: 3.240

4.  Identification of CDC42BPG as a novel susceptibility locus for hyperuricemia in a Japanese population.

Authors:  Yoshiki Yasukochi; Jun Sakuma; Ichiro Takeuchi; Kimihiko Kato; Mitsutoshi Oguri; Tetsuo Fujimaki; Hideki Horibe; Yoshiji Yamada
Journal:  Mol Genet Genomics       Date:  2017-11-09       Impact factor: 3.291

5.  Identification of nine novel loci related to hematological traits in a Japanese population.

Authors:  Yoshiki Yasukochi; Jun Sakuma; Ichiro Takeuchi; Kimihiko Kato; Mitsutoshi Oguri; Tetsuo Fujimaki; Hideki Horibe; Yoshiji Yamada
Journal:  Physiol Genomics       Date:  2018-06-29       Impact factor: 3.107

6.  A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci.

Authors:  David A Hinds; George McMahon; Amy K Kiefer; Chuong B Do; Nicholas Eriksson; David M Evans; Beate St Pourcain; Susan M Ring; Joanna L Mountain; Uta Francke; George Davey-Smith; Nicholas J Timpson; Joyce Y Tung
Journal:  Nat Genet       Date:  2013-06-30       Impact factor: 38.330

7.  Identification of three genetic variants as novel susceptibility loci for body mass index in a Japanese population.

Authors:  Yoshiki Yasukochi; Jun Sakuma; Ichiro Takeuchi; Kimihiko Kato; Mitsutoshi Oguri; Tetsuo Fujimaki; Hideki Horibe; Yoshiji Yamada
Journal:  Physiol Genomics       Date:  2018-01-12       Impact factor: 3.107

8.  Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits.

Authors:  Baolin Wu; James S Pankow
Journal:  Comput Math Methods Med       Date:  2018-03-18       Impact factor: 2.238

9.  Longitudinal exome-wide association study to identify genetic susceptibility loci for hypertension in a Japanese population.

Authors:  Yoshiki Yasukochi; Jun Sakuma; Ichiro Takeuchi; Kimihiko Kato; Mitsutoshi Oguri; Tetsuo Fujimaki; Hideki Horibe; Yoshiji Yamada
Journal:  Exp Mol Med       Date:  2017-12-08       Impact factor: 8.718

10.  Six novel susceptibility loci for coronary artery disease and cerebral infarction identified by longitudinal exome-wide association studies in a Japanese population.

Authors:  Yoshiki Yasukochi; Jun Sakuma; Ichiro Takeuchi; Kimihiko Kato; Mitsutoshi Oguri; Tetsuo Fujimaki; Hideki Horibe; Yoshiji Yamada
Journal:  Biomed Rep       Date:  2018-06-05
  10 in total

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