In many studies, related individuals are phenotyped in order to infer how their genotype contributes to their phenotype, through the estimation of parameters such as breeding values or locus effects. When it is not possible to phenotype all the individuals, it is important to properly sample the population to improve the precision of the statistical analysis. This article studies how to optimize such sampling designs for pedigrees and association studies. Two sampling methods are developed, stratified sampling and D optimality. It is found that it is important to take account of mutation when sampling pedigrees with many generations: as the size of mutation effects increases, optimized designs sample more individuals in late generations. Optimized designs for association studies tend to improve the joint estimation of breeding values and locus effects, all the more as sample size is low and the genetic architecture of the trait is simple. When the trait is determined by few loci, they are reminiscent of classical experimental designs for regression models and tend to select homozygous individuals. When the trait is determined by many loci, locus effects may be difficult to estimate, even if an optimized design is used.

中文翻译：

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谱系和关联研究抽样设计的优化

在许多研究中，通过估计育种值或基因座效应等参数，对相关个体进行表型分析，以推断其基因型如何影响其表型。当不可能对所有个体进行表型分析时，重要的是对人群进行适当抽样以提高统计分析的精度。本文研究如何为系谱和关联研究优化此类抽样设计。开发了两种抽样方法，分层抽样和D最优性。人们发现，在对多代系谱进行采样时考虑突变很重要：随着突变效应的大小增加，优化设计会在后代中采样更多的个体。关联研究的优化设计往往会改善育种值和基因座效应的联合估计，尤其是样本量小且性状的遗传结构简单时。当性状由少数基因座决定时，它们让人想起回归模型的经典实验设计，并倾向于选择纯合子个体。当性状由许多基因座决定时，即使使用优化设计，基因座效应也可能难以估计。