Literature DB >> 18358330

Study designs for genome-wide association studies.

Peter Kraft1, David G Cox.   

Abstract

Advances in high-throughput genotyping and a flood of data on human genetic variation from the Human Genome and HapMap projects have made genome-wide association studies technically feasible. However, researchers designing such studies face a number of challenges, including how to avoid subtle systematic biases and how to achieve sufficient statistical power to distinguish modest association signals from chance associations. In many situations, it remains prohibitively expensive to genotype all the desired samples using a genome-wide genotyping array, so multistage designs are an attractive cost-saving measure. Here, we review some of the basic design principles for genetic association studies, discuss the properties of fixed genome-wide and custom genotyping arrays as they relate to study design, and present a theoretical framework and practical tools for power calculations. We close with a discussion of the limitations of multistage designs.

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Year:  2008        PMID: 18358330     DOI: 10.1016/S0065-2660(07)00417-8

Source DB:  PubMed          Journal:  Adv Genet        ISSN: 0065-2660            Impact factor:   1.944


  21 in total

1.  Replication in genome-wide association studies.

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Journal:  Stat Sci       Date:  2009-11-01       Impact factor: 2.901

2.  Powerful SNP-set analysis for case-control genome-wide association studies.

Authors:  Michael C Wu; Peter Kraft; Michael P Epstein; Deanne M Taylor; Stephen J Chanock; David J Hunter; Xihong Lin
Journal:  Am J Hum Genet       Date:  2010-06-11       Impact factor: 11.025

3.  Predicting functional regulatory polymorphisms.

Authors:  Ali Torkamani; Nicholas J Schork
Journal:  Bioinformatics       Date:  2008-06-18       Impact factor: 6.937

4.  [Leukocyte count of puerperal sows].

Authors:  D Mäde; G Wujanz
Journal:  Berl Munch Tierarztl Wochenschr       Date:  1996-09       Impact factor: 0.328

5.  SNP characteristics predict replication success in association studies.

Authors:  Ivan P Gorlov; Jason H Moore; Bo Peng; Jennifer L Jin; Olga Y Gorlova; Christopher I Amos
Journal:  Hum Genet       Date:  2014-10-02       Impact factor: 4.132

6.  An Enrichment Strategy Yields Seven Novel Single Nucleotide Polymorphisms Associated With Mortality and Altered Th17 Responses Following Blunt Trauma.

Authors:  Lukas Schimunek; Rami A Namas; Jinling Yin; Dongmei Liu; Derek Barclay; Fayten El-Dehaibi; Andrew Abboud; Haley Lindberg; Ruben Zamora; Timothy R Billiar; Yoram Vodovotz
Journal:  Shock       Date:  2018-03       Impact factor: 3.454

7.  Testing gene-gene interactions in genome wide association studies.

Authors:  Jie Kate Hu; Xianlong Wang; Pei Wang
Journal:  Genet Epidemiol       Date:  2014-01-15       Impact factor: 2.135

8.  One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study.

Authors:  Mitchell J Machiela; Constance Chen; Liming Liang; W Ryan Diver; Victoria L Stevens; Konstantinos K Tsilidis; Christopher A Haiman; Stephen J Chanock; David J Hunter; Peter Kraft
Journal:  Prostate       Date:  2012-12-19       Impact factor: 4.104

Review 9.  Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies.

Authors:  Duncan Thomas
Journal:  Annu Rev Public Health       Date:  2010       Impact factor: 21.981

Review 10.  Bioinformatics challenges for genome-wide association studies.

Authors:  Jason H Moore; Folkert W Asselbergs; Scott M Williams
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

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