Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2002 Mar;58(1):163-70.
doi: 10.1111/j.0006-341x.2002.00163.x.

Two-stage designs for gene-disease association studies

Jaya M Satagopan  1 , David A Verbel, E S Venkatraman, Kenneth E Offit, Colin B Begg
Affiliations

Two-stage designs for gene-disease association studies

Jaya M Satagopan et al. Biometrics. 2002 Mar.

Abstract
in English, French

The goal of this article is to describe a two-stage design that maximizes the power to detect gene-disease associations when the principal design constraint is the total cost, represented by the total number of gene evaluations rather than the total number of individuals. In the first stage, all genes of interest are evaluated on a subset of individuals. The most promising genes are then evaluated on additional subjects in the second stage. This will eliminate wastage of resources on genes unlikely to be associated with disease based on the results of the first stage. We consider the case where the genes are correlated and the case where the genes are independent. Using simulation results, it is shown that, as a general guideline when the genes are independent or when the correlation is small, utilizing 75% of the resources in stage 1 to screen all the markers and evaluating the most promising 10% of the markers with the remaining resources provides near-optimal power for a broad range of parametric configurations. This translates to screening all the markers on approximately one quarter of the required sample size in stage 1.

Le but de cet article est de décrire une stratégie d’étude à deux étapes qui maximise la puissance de détection d’associations gène-maladie quand la principale contrainte est le coût total, représenté par le nombre total d’évaluations de gènes plutôt que le nombre total d’individus. Dans la première étape, tous les gènes d ‘intérêt sont évalués sur un sous-groupe d’individus. Les gènes les plus prometteurs sont alors évalués sur d’autres sujets dans la deuxième étape. Ceci évitera de gaspiller du matériel sur des gènes ayant une faible probabilité d’être associés à la maladie d’après les résultats de la première étape. Nous envisageons le cas où les gènes sont corrélés et celui où les gènes sont indépendants. A l’aide de simulations, nous montrons qu’en règle générale, lorsque les gènes sont indépendants ou faiblement corrélés, utiliser 75% du matériel dans l’étape 1 pour tester tous les marqueurs et ne tester que les 10% plus prometteurs avec le matériel restant apporte une puissance quasi optimale pour une large étendue de configurations paramétriques. Ceci revient à tester tous les marqueurs sur environ un quart de l’échantillon nécessaire à l’étape l.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Power of one- and two-stage designs for m = 1000, T/m = 500, and values of μ = 0.120 for increasing values of correlation between adjacent markers. The bold line shows the maximum power of the optimal two-stage design. Optimal parameters (i and j) are shown below the horizontal axis. The dotted line shows the rule-of-thumb two-stage design (when i = 0.10 and j = 0.75). The dashed line gives the power of a one-stage design. The value of μ = 0.120 corresponds to a one-stage design with 30% power and independent markers.
Figure 2.
Figure 2.
Power of one- and two-stage designs for m = 100, T/m = 100, and values of μ = 0.275 for increasing values of correlation between adjacent markers. The bold line shows the maximum power of the optimal two-stage design. Optimal parameters (i and j) are shown below the horizontal axis. The dotted line shows the rule-of-thumb two-stage design (when i = 0.10 and j = 0.75). The dashed line gives the power of a one-stage design. The value of μ = 0.275 corresponds to a one-stage design with 60% power and independent markers.
See this image and copyright information in PMC

References

    1. Boehnke M (2000). A look at linkage disequilibrium. Nature Genetics 25, 246–247. - PubMed
    1. Feller W (1966). An Introduction to Probability Theory and Its Applications, Volume 2. New York: Wiley.
    1. Ford D, Easton DF, Stratton M, et al. (1998). Genetic heterogeneity and penetrance analysis of BRCA1 and BRCA2 in breast cancer families. The Breast Cancer Linkage Consortium. American Journal of Human Genetics 62, 676–689. - PMC - PubMed
    1. Martin ER, Kaplan NL, and Weir BW (1997). Tests for linkage and association in nuclear families. American, Journal of Human Genetics 61, 439–448. - PMC - PubMed
    1. Satagopan JM, Offit K, Foulkes W, Robson ME, Wacholder S, Eng C Karp SE, and Begg CB (2001). The lifetime risks of breast cancer in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations. Cancer Epidemiology, Biomarkers, and Prevention 10, 467–473. - PubMed

Publication types