Literature DB >> 16783633

A review of statistical methods for expression quantitative trait loci mapping.

Christina Kendziorski1, Ping Wang.   

Abstract

With high-throughput technologies now widely available, investigators can easily measure thousands of phenotypes for quantitative trait loci (QTL) mapping. Microarray measurements are particularly amenable to QTL mapping, as evidenced by a number of recent studies demonstrating utility across a broad range of biological endeavors. The early success stories have impelled a rapid increase in both the number and complexity of expression QTL (eQTL) experiments. Consequently, there is a need to consider the statistical principles involved in the design and analysis of these experiments and the methods currently being used. In this article we review these principles and methods and discuss the open questions most likely to yield significant progress toward increasing the amount of meaningful information obtained from eQTL mapping experiments.

Mesh:

Year:  2006        PMID: 16783633     DOI: 10.1007/s00335-005-0189-6

Source DB:  PubMed          Journal:  Mamm Genome        ISSN: 0938-8990            Impact factor:   2.957


  48 in total

1.  Power and sample size for DNA microarray studies.

Authors:  Mei-Ling Ting Lee; G A Whitmore
Journal:  Stat Med       Date:  2002-12-15       Impact factor: 2.373

Review 2.  Design issues for cDNA microarray experiments.

Authors:  Yee Hwa Yang; Terry Speed
Journal:  Nat Rev Genet       Date:  2002-08       Impact factor: 53.242

3.  Sample size determination in microarray experiments for class comparison and prognostic classification.

Authors:  Kevin Dobbin; Richard Simon
Journal:  Biostatistics       Date:  2005-01       Impact factor: 5.899

4.  Selective phenotyping for increased efficiency in genetic mapping studies.

Authors:  Chunfang Jin; Hong Lan; Alan D Attie; Gary A Churchill; Dursun Bulutuglo; Brian S Yandell
Journal:  Genetics       Date:  2004-12       Impact factor: 4.562

5.  Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits.

Authors:  Margarete Mehrabian; Hooman Allayee; Jirina Stockton; Pek Yee Lum; Thomas A Drake; Lawrence W Castellani; Michael Suh; Christopher Armour; Stephen Edwards; John Lamb; Aldons J Lusis; Eric E Schadt
Journal:  Nat Genet       Date:  2005-10-02       Impact factor: 38.330

6.  An integrative genomics approach to infer causal associations between gene expression and disease.

Authors:  Eric E Schadt; John Lamb; Xia Yang; Jun Zhu; Steve Edwards; Debraj Guhathakurta; Solveig K Sieberts; Stephanie Monks; Marc Reitman; Chunsheng Zhang; Pek Yee Lum; Amy Leonardson; Rolf Thieringer; Joseph M Metzger; Liming Yang; John Castle; Haoyuan Zhu; Shera F Kash; Thomas A Drake; Alan Sachs; Aldons J Lusis
Journal:  Nat Genet       Date:  2005-06-19       Impact factor: 38.330

7.  Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'.

Authors:  Leonid Bystrykh; Ellen Weersing; Bert Dontje; Sue Sutton; Mathew T Pletcher; Tim Wiltshire; Andrew I Su; Edo Vellenga; Jintao Wang; Kenneth F Manly; Lu Lu; Elissa J Chesler; Rudi Alberts; Ritsert C Jansen; Robert W Williams; Michael P Cooke; Gerald de Haan
Journal:  Nat Genet       Date:  2005-02-13       Impact factor: 38.330

8.  Statistical methods for mapping quantitative trait loci from a dense set of markers.

Authors:  J Dupuis; D Siegmund
Journal:  Genetics       Date:  1999-01       Impact factor: 4.562

9.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

10.  Cluster analysis and display of genome-wide expression patterns.

Authors:  M B Eisen; P T Spellman; P O Brown; D Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-12-08       Impact factor: 11.205

View more
  33 in total

1.  Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study.

Authors:  Xiang Zhang; Shunping Huang; Wei Sun; Wei Wang
Journal:  Genetics       Date:  2012-01-31       Impact factor: 4.562

2.  Matrix eQTL: ultra fast eQTL analysis via large matrix operations.

Authors:  Andrey A Shabalin
Journal:  Bioinformatics       Date:  2012-04-06       Impact factor: 6.937

Review 3.  Systems genetics, bioinformatics and eQTL mapping.

Authors:  Hong Li; Hongwen Deng
Journal:  Genetica       Date:  2010-09-03       Impact factor: 1.082

Review 4.  Physiological insights gained from gene expression analysis in obesity and diabetes.

Authors:  Mark P Keller; Alan D Attie
Journal:  Annu Rev Nutr       Date:  2010-08-21       Impact factor: 11.848

5.  A model selection approach for expression quantitative trait loci (eQTL) mapping.

Authors:  Ping Wang; John A Dawson; Mark P Keller; Brian S Yandell; Nancy A Thornberry; Bei B Zhang; I-Ming Wang; Eric E Schadt; Alan D Attie; C Kendziorski
Journal:  Genetics       Date:  2010-11-29       Impact factor: 4.562

Review 6.  Computational tools for discovery and interpretation of expression quantitative trait loci.

Authors:  Fred A Wright; Andrey A Shabalin; Ivan Rusyn
Journal:  Pharmacogenomics       Date:  2012-02       Impact factor: 2.533

7.  A statistical framework for eQTL mapping using RNA-seq data.

Authors:  Wei Sun
Journal:  Biometrics       Date:  2011-08-12       Impact factor: 2.571

8.  RNA Sequencing and Analysis.

Authors:  Kimberly R Kukurba; Stephen B Montgomery
Journal:  Cold Spring Harb Protoc       Date:  2015-04-13

9.  A statistical framework for expression quantitative trait loci mapping.

Authors:  Meng Chen; Christina Kendziorski
Journal:  Genetics       Date:  2007-07-29       Impact factor: 4.562

10.  A SPARSE CONDITIONAL GAUSSIAN GRAPHICAL MODEL FOR ANALYSIS OF GENETICAL GENOMICS DATA.

Authors:  Jianxin Yin; Hongzhe Li
Journal:  Ann Appl Stat       Date:  2011-12       Impact factor: 2.083

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.