Literature DB >> 24336645

PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants.

Biao Li1, Gao Wang, Suzanne M Leal.   

Abstract

MOTIVATION: Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership; control for missing covariate data; remove related individuals, duplicate samples and individuals with incorrect sex specification; recode primary traits and covariates; transform data; remove or winsorize outliers; select covariates for analysis; and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data.
SUMMARY: Phenoman is freeware that provides approaches for efficient exploration and management of phenotype data. Proper QC of phenotypes before proceeding to the association analysis is critical to ensure control of type I and II errors, reliable effect estimates and consistent results between studies. PhenoMan is highly beneficial for the preparation of qualitative and quantitative trait data for association studies using new datasets as well as those obtained from public repositories.
AVAILABILITY AND IMPLEMENTATION: code.google.com/p/phenoman

Mesh:

Year:  2013        PMID: 24336645      PMCID: PMC3904519          DOI: 10.1093/bioinformatics/btt682

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


  6 in total

1.  GenABEL: an R library for genome-wide association analysis.

Authors:  Yurii S Aulchenko; Stephan Ripke; Aaron Isaacs; Cornelia M van Duijn
Journal:  Bioinformatics       Date:  2007-03-23       Impact factor: 6.937

2.  The NCBI dbGaP database of genotypes and phenotypes.

Authors:  Matthew D Mailman; Michael Feolo; Yumi Jin; Masato Kimura; Kimberly Tryka; Rinat Bagoutdinov; Luning Hao; Anne Kiang; Justin Paschall; Lon Phan; Natalia Popova; Stephanie Pretel; Lora Ziyabari; Moira Lee; Yu Shao; Zhen Y Wang; Karl Sirotkin; Minghong Ward; Michael Kholodov; Kerry Zbicz; Jeffrey Beck; Michael Kimelman; Sergey Shevelev; Don Preuss; Eugene Yaschenko; Alan Graeff; James Ostell; Stephen T Sherry
Journal:  Nat Genet       Date:  2007-10       Impact factor: 38.330

3.  The impact of data quality on the identification of complex disease genes: experience from the Family Blood Pressure Program.

Authors:  Yen-Pei Christy Chang; James Dae-Ok Kim; Karen Schwander; Dabeeru C Rao; Mike B Miller; Alan B Weder; Richard S Cooper; Nicholas J Schork; Michael A Province; Alanna C Morrison; Sharon L R Kardia; Thomas Quertermous; Aravinda Chakravarti
Journal:  Eur J Hum Genet       Date:  2006-04       Impact factor: 4.246

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

Authors:  Shaun Purcell; Benjamin Neale; Kathe Todd-Brown; Lori Thomas; Manuel A R Ferreira; David Bender; Julian Maller; Pamela Sklar; Paul I W de Bakker; Mark J Daly; Pak C Sham
Journal:  Am J Hum Genet       Date:  2007-07-25       Impact factor: 11.025

5.  Practical aspects of imputation-driven meta-analysis of genome-wide association studies.

Authors:  Paul I W de Bakker; Manuel A R Ferreira; Xiaoming Jia; Benjamin M Neale; Soumya Raychaudhuri; Benjamin F Voight
Journal:  Hum Mol Genet       Date:  2008-10-15       Impact factor: 6.150

Review 6.  Computational and statistical approaches to analyzing variants identified by exome sequencing.

Authors:  Nathan O Stitziel; Adam Kiezun; Shamil Sunyaev
Journal:  Genome Biol       Date:  2011-09-14       Impact factor: 13.583

  6 in total
  1 in total

1.  Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project.

Authors:  Mengyuan Kan; Paul L Auer; Gao T Wang; Kristine L Bucasas; Stanley Hooker; Alejandra Rodriguez; Biao Li; Jaclyn Ellis; L Adrienne Cupples; Yii-Der Ida Chen; Josée Dupuis; Caroline S Fox; Myron D Gross; Joshua D Smith; Nancy Heard-Costa; James B Meigs; James S Pankow; Jerome I Rotter; David Siscovick; James G Wilson; Jay Shendure; Rebecca Jackson; Ulrike Peters; Hua Zhong; Danyu Lin; Li Hsu; Nora Franceschini; Chris Carlson; Goncalo Abecasis; Stacey Gabriel; Michael J Bamshad; David Altshuler; Deborah A Nickerson; Kari E North; Leslie A Lange; Alexander P Reiner; Suzanne M Leal
Journal:  Eur J Hum Genet       Date:  2016-01-13       Impact factor: 4.246

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.