TY - JOUR

T1 - A linkage strategy for detection of human quantitative-trait loci. II. Optimization of study designs based on extreme sib pairs and generalized relative risk ratios

AU - Gu, Chi

AU - Rao, D. C.

N1 - Funding Information:
This work was supported, in part, by NIH and NIMH grants GM28719 and MH31302, respectively. We thank the two anonymous reviewers for their many helpful comments, which improved this article.

PY - 1997/7

Y1 - 1997/7

N2 - We are concerned here with practical issues in the application of extreme sib-pair (ESP) methods to quantitative traits. Two important factors- namely, the way extreme trait values are defined and the proportions in which different types of ESPs are pooled, in the analysis-are shown to determine the power and the cost effectiveness of a study design. We found that, in general, combining reasonable numbers of both extremely discordant and extremely concordant sib pairs that were available in the sample is more powerful and more cost effective than pursuing only a single type of ESP. We also found that dividing trait values with a less extreme threshold at one end or at both ends of the trait distribution leads to more cost-effective designs. The notion of generalized relative risk ratios (the λ method, as described in the first part of this series of two articles) is used to calculate the power and sample size for various choices of polychotomization of trait values and for the combination of different types of ESPs. A balance then can be struck among these choices, to attain an optimum design.

AB - We are concerned here with practical issues in the application of extreme sib-pair (ESP) methods to quantitative traits. Two important factors- namely, the way extreme trait values are defined and the proportions in which different types of ESPs are pooled, in the analysis-are shown to determine the power and the cost effectiveness of a study design. We found that, in general, combining reasonable numbers of both extremely discordant and extremely concordant sib pairs that were available in the sample is more powerful and more cost effective than pursuing only a single type of ESP. We also found that dividing trait values with a less extreme threshold at one end or at both ends of the trait distribution leads to more cost-effective designs. The notion of generalized relative risk ratios (the λ method, as described in the first part of this series of two articles) is used to calculate the power and sample size for various choices of polychotomization of trait values and for the combination of different types of ESPs. A balance then can be struck among these choices, to attain an optimum design.

UR - http://www.scopus.com/inward/record.url?scp=0030873885&partnerID=8YFLogxK

U2 - 10.1086/513909

DO - 10.1086/513909

M3 - Article

C2 - 9246002

AN - SCOPUS:0030873885

VL - 61

SP - 211

EP - 222

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

SN - 0002-9297

IS - 1

ER -