Considerations on Study Designs Using the Extreme Sibpairs Methods Under Multilocus Oligogenic Models

Corresponding author: Chi Gu, Washington University School of Medicine, Box 8067, 600 S. Euclid Ave., St. Louis, MO 63110., gc{at}wubios.wustl.edu (E-mail)

Communicating editor: G. A. CHURCHILL

Several issues pertinent to study designs employing extreme sibpairs (ESP) methods to detect complex oligogenic quantitative trait loci (QTL) are investigated in the setting of genome-wide multipoint scans. We demonstrate that when stringent -levels are imposed (e.g., = 0.00022 as recommended by Landers and Kruglyak), the power to detect a susceptibility locus could drop from 83.6% under a one-locus model down to a hopeless 22.8% under a two-locus model of the same heritability and gene frequency (p = 0.1). We introduce the notion of joint power that is the power to detect linkage to at least one location over a given panel of markers across a genomic region and describe the effect of several design factors on such joint power in a multipoint scan. Moreover, power of analysis conditional on the IBD sharings of ESPs at a known/detected locus is examined and shown to increase substantively (to 93.3% under the previous two-locus model) in detecting novel trait loci. We conclude that with such remedies, the ESP design continues to be a relatively powerful design for mapping oligogenic QTL. However, when the effect of individual contributing loci becomes less tractable, especially when their contributions are "asymmetric," deliberation on balancing two types of statistical errors and a careful examination of possible contributions from multiple genetic factors and/or interaction effects are a must in designing an efficient study.