Power of segregation analysis for detection of major gene effects on quantitative traits

Authors

  • I. B. Borecki Ph.D.,

    Corresponding author
    1. Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
    • Division of Biostatistics, Washington University School of Medicine, Box 8067, 660 South Euclid Avenue, St. Louis, MO 63110

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  • M. A. Province,

    1. Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
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  • D. C. Rao

    1. Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
    2. Department of Psychiatry and Genetics, Washington University School of Medicine, St. Louis, Missouri
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Abstract

The power to detect major gene effects by rejection of the “no major gene” null hypothesis against a mixed model alternative was determined in random samples of nuclear families over a variety of conditions. Benchmarks have been developed using a varying number of families whose structure includes both parents and three children. Phenotypes were simulated assuming a Mendelian major gene under either recessive or dominant inheritance, with 0–30% residual polygenic heritability. Three trait prevalences—5, 10, and 20%—were considered in combination with increasing displacement between homozygous means, spanning a range of 14 to 36% of the phenotypic variance attributable to the major gene effect. All other assumptions of the traditional mixed model were adopted in the generating models. Segregation analysis was carried out on the simulated data sets, and the proportion of samples out of 200 replications in which the null hypothesis q = 0 was rejected is reported as the power. Thus, failure to detect a major gene effect in this context is solely due to sampling variation, since no other perturbations were introduced. In general, there appears to be greater power to detect dominant major gene effects as opposed to recessive ones using otherwise comparable parameter values, and the effect of varying sibship size under dominant models appears to be greater as well. The use of joint vs. conditional likelihood calculations also was evaluated; substantial drops in power were observed when using conditional likelihoods under recessive inheritance, while the differences in power appeared to be nominal under dominant inheritance. The results of this investigation are offered as a guide to assist in the design of family studies whose aim is to detect major gene effects.

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