Literature DB >> 20607129

Methodological Issues in Multistage Genome-wide Association Studies.

Duncan C Thomas1, Graham Casey, David V Conti, Robert W Haile, Juan Pablo Lewinger, Daniel O Stram.   

Abstract

Because of the high cost of commercial genotyping chip technologies, many investigations have used a two-stage design for genome-wide association studies, using part of the sample for an initial discovery of "promising" SNPs at a less stringent significance level and the remainder in a joint analysis of just these SNPs using custom genotyping. Typical cost savings of about 50% are possible with this design to obtain comparable levels of overall type I error and power by using about half the sample for stage I and carrying about 0.1% of SNPs forward to the second stage, the optimal design depending primarily upon the ratio of costs per genotype for stages I and II. However, with the rapidly declining costs of the commercial panels, the generally low observed ORs of current studies, and many studies aiming to test multiple hypotheses and multiple endpoints, many investigators are abandoning the two-stage design in favor of simply genotyping all available subjects using a standard high-density panel. Concern is sometimes raised about the absence of a "replication" panel in this approach, as required by some high-profile journals, but it must be appreciated that the two-stage design is not a discovery/replication design but simply a more efficient design for discovery using a joint analysis of the data from both stages. Once a subset of highly-significant associations has been discovered, a truly independent "exact replication" study is needed in a similar population of the same promising SNPs using similar methods. This can then be followed by (1) "generalizability" studies to assess the full scope of replicated associations across different races, different endpoints, different interactions, etc.; (2) fine-mapping or re-sequencing to try to identify the causal variant; and (3) experimental studies of the biological function of these genes. Multistage sampling designs may be more useful at this stage, say for selecting subsets of subjects for deep re-sequencing of regions identified in the GWAS.

Entities:  

Year:  2009        PMID: 20607129      PMCID: PMC2895324          DOI: 10.1214/09-sts288

Source DB:  PubMed          Journal:  Stat Sci        ISSN: 0883-4237            Impact factor:   2.901


  95 in total

Review 1.  Genome-wide association studies for common diseases and complex traits.

Authors:  Joel N Hirschhorn; Mark J Daly
Journal:  Nat Rev Genet       Date:  2005-02       Impact factor: 53.242

2.  Optimal two-stage genotyping designs for genome-wide association scans.

Authors:  Hansong Wang; Duncan C Thomas; Itsik Pe'er; Daniel O Stram
Journal:  Genet Epidemiol       Date:  2006-05       Impact factor: 2.135

3.  Optimal designs for two-stage genome-wide association studies.

Authors:  Andrew D Skol; Laura J Scott; Gonçalo R Abecasis; Michael Boehnke
Journal:  Genet Epidemiol       Date:  2007-11       Impact factor: 2.135

4.  Multistage sampling for latent variable models.

Authors:  Duncan C Thomas
Journal:  Lifetime Data Anal       Date:  2007-10-18       Impact factor: 1.588

5.  A Bayesian measure of the probability of false discovery in genetic epidemiology studies.

Authors:  Jon Wakefield
Journal:  Am J Hum Genet       Date:  2007-07-03       Impact factor: 11.025

6.  Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms.

Authors:  Carl A Anderson; Fredrik H Pettersson; Jeffrey C Barrett; Joanna J Zhuang; Jiannis Ragoussis; Lon R Cardon; Andrew P Morris
Journal:  Am J Hum Genet       Date:  2008-06-26       Impact factor: 11.025

7.  Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs.

Authors:  Bhramar Mukherjee; Jaeil Ahn; Stephen B Gruber; Gad Rennert; Victor Moreno; Nilanjan Chatterjee
Journal:  Genet Epidemiol       Date:  2008-11       Impact factor: 2.135

Review 8.  The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling.

Authors:  N Risch; J Teng
Journal:  Genome Res       Date:  1998-12       Impact factor: 9.043

Review 9.  Study designs for genome-wide association studies.

Authors:  Peter Kraft; David G Cox
Journal:  Adv Genet       Date:  2008       Impact factor: 1.944

10.  Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.

Authors:  Christian Gieger; Ludwig Geistlinger; Elisabeth Altmaier; Martin Hrabé de Angelis; Florian Kronenberg; Thomas Meitinger; Hans-Werner Mewes; H-Erich Wichmann; Klaus M Weinberger; Jerzy Adamski; Thomas Illig; Karsten Suhre
Journal:  PLoS Genet       Date:  2008-11-28       Impact factor: 5.917

View more
  24 in total

1.  Replication in genome-wide association studies.

Authors:  Peter Kraft; Eleftheria Zeggini; John P A Ioannidis
Journal:  Stat Sci       Date:  2009-11-01       Impact factor: 2.901

2.  Don't split your data.

Authors:  Henrik Källberg; Lars Alfredsson; Maria Feychting; Anders Ahlbom
Journal:  Eur J Epidemiol       Date:  2010-03-26       Impact factor: 8.082

3.  Enriching targeted sequencing experiments for rare disease alleles.

Authors:  Todd L Edwards; Zhuo Song; Chun Li
Journal:  Bioinformatics       Date:  2011-06-23       Impact factor: 6.937

4.  Two-stage design of sequencing studies for testing association with rare variants.

Authors:  Fan Yang; Duncan C Thomas
Journal:  Hum Hered       Date:  2011-07-02       Impact factor: 0.444

5.  Genome-wide meta-analysis identifies a novel susceptibility signal at CACNA2D3 for nicotine dependence.

Authors:  Xianyong Yin; Chris Bizon; Jeffrey Tilson; Yuan Lin; Ian R Gizer; Cindy L Ehlers; Kirk C Wilhelmsen
Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2017-04-25       Impact factor: 3.568

6.  GIGI: an approach to effective imputation of dense genotypes on large pedigrees.

Authors:  Charles Y K Cheung; Elizabeth A Thompson; Ellen M Wijsman
Journal:  Am J Hum Genet       Date:  2013-04-04       Impact factor: 11.025

Review 7.  Genome-wide association studies of albuminuria: towards genetic stratification in diabetes?

Authors:  Cristian Pattaro
Journal:  J Nephrol       Date:  2017-09-16       Impact factor: 3.902

Review 8.  Gene--environment-wide association studies: emerging approaches.

Authors:  Duncan Thomas
Journal:  Nat Rev Genet       Date:  2010-04       Impact factor: 53.242

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

10.  Some surprising twists on the road to discovering the contribution of rare variants to complex diseases.

Authors:  Duncan C Thomas
Journal:  Hum Hered       Date:  2013-04-11       Impact factor: 0.444

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.