Selective genotyping and phenotyping strategies can reduce the cost of QTL (quantitative trait loci) experiments. We analyze selective genotyping and phenotyping strategies in the context of multi-locus models, and non-normal phenotypes. Our approach is based on calculations of the expected information of the experiment under different strategies. Our central conclusions are the following. (1) Selective genotyping is effective for detecting linked and epistatic QTL as long as no locus has a large effect. When one or more loci have large effects, the effectiveness of selective genotyping is unpredictable – it may be heightened or diminished relative to the small effects case. (2) Selective phenotyping efficiency decreases as the number of unlinked loci used for selection increases, and approaches random selection in the limit. However, when phenotyping is expensive, and a small fraction can be phenotyped, the efficiency of selective phenotyping is high compared to random sampling, even when over 10 loci are used for selection. (3) For time-to-event phenotypes such as lifetimes, which have a long right tail, right-tail selective genotyping is more effective than two-tail selective genotyping. For heavy-tailed phenotype distributions, such as the Cauchy distribution, the most extreme phenotypic individuals are not the most informative. (4) When the phenotype distribution is exponential, and a right-tail selective genotyping strategy is used, the optimal selection fraction (proportion genotyped) is less than 20%or 100% depending on genotyping cost. (5) For time-to-event phenotypes where followup cost increases with the lifetime of the individual, we derive the optimal followup time that maximizes the information content of the experiment relative to its cost. For example, when the cost of following up an individual for the average lifetime in the population is approximately equal to the fixed costs of genotyping and breeding, the optimal strategy is to follow up approximately 70% of the population.

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