Literature DB >> 16443924

Aspects of the design and analysis of high-dimensional SNP studies for disease risk estimation.

Ross L Prentice1, Lihong Qi.   

Abstract

The state of readiness for high-dimensional single nucleotide polymorphism (SNP) epidemiologic association studies is described, as background for a discussion of statistical aspects of case-control study design and analysis. Specifically, the important role that multistage designs can play in the elimination of false-positive associations and in the control of study costs will be noted. Also, the trade-offs associated with using pooled DNA at early design stages for additional important cost reductions will be discussed in some detail. An odds ratio approach to relating SNP alleles to disease risk using pooled DNA will be proposed, in conjunction with a simple empirical variance estimator, based on comparisons among log-odds ratio estimators from distinct pairs of case and control pools. Simulation studies will be presented to evaluate the moderate sample size properties of such multistage designs and estimation procedures. The design of an ongoing three-stage study in the Women's Health Initiative to relate 250,000 SNPs to the risk of coronary heart disease, stroke, and breast cancer will provide illustration, and will be used to motivate the choice of simulation configurations.

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Year:  2006        PMID: 16443924     DOI: 10.1093/biostatistics/kxj020

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  14 in total

1.  Mapping genes that predict treatment outcome in admixed populations.

Authors:  T M Baye; R A Wilke
Journal:  Pharmacogenomics J       Date:  2010-10-05       Impact factor: 3.550

2.  Sample-size properties of a case-control association analysis of multistage SNP studies for identifying disease susceptibility genes.

Authors:  Nobutaka Kitamura; Kouhei Akazawa; Shin-Ichi Toyabe; Akinori Miyashita; Ryozo Kuwano; Junichiro Nakamura
Journal:  J Hum Genet       Date:  2008-02-21       Impact factor: 3.172

3.  Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies.

Authors:  Hua Zhong; Ross L Prentice
Journal:  Biostatistics       Date:  2008-02-28       Impact factor: 5.899

4.  A comparison of association statistics between pooled and individual genotypes.

Authors:  Jo Knight; Scott F Saccone; Zhehao Zhang; Dennis G Ballinger; John P Rice
Journal:  Hum Hered       Date:  2009-01-27       Impact factor: 0.444

5.  Optimal DNA pooling-based two-stage designs in case-control association studies.

Authors:  Yihong Zhao; Shuang Wang
Journal:  Hum Hered       Date:  2008-10-17       Impact factor: 0.444

6.  Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction.

Authors:  James Y Dai; Charles Kooperberg; Michael Leblanc; Ross L Prentice
Journal:  Biometrika       Date:  2012-09-25       Impact factor: 2.445

7.  A targeted maximum likelihood estimator for two-stage designs.

Authors:  Sherri Rose; Mark J van der Laan
Journal:  Int J Biostat       Date:  2011-03-11       Impact factor: 0.968

8.  Variation in the FGFR2 gene and the effect of a low-fat dietary pattern on invasive breast cancer.

Authors:  Ross L Prentice; Ying Huang; David A Hinds; Ulrike Peters; David R Cox; Erica Beilharz; Rowan T Chlebowski; Jacques E Rossouw; Bette Caan; Dennis G Ballinger
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-01       Impact factor: 4.254

9.  Variation in the FGFR2 gene and the effects of postmenopausal hormone therapy on invasive breast cancer.

Authors:  Ross L Prentice; Ying Huang; David A Hinds; Ulrike Peters; Mary Pettinger; David R Cox; Erica Beilharz; Rowan T Chlebowski; Jacques E Rossouw; Bette Caan; Dennis G Ballinger
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-10-27       Impact factor: 4.254

Review 10.  Observational studies, clinical trials, and the women's health initiative.

Authors:  Ross L Prentice
Journal:  Lifetime Data Anal       Date:  2007-10-18       Impact factor: 1.588

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