On statistical inference for selective genotyping

Charles-Elie Rabier (1, 2, 3)

1 LSP - Laboratoire de Statistique et Probabilités
2 SAGA - Station d'Amélioration Génétique des Animaux
3 University of Wisconsin-Madison

In Quantitative Trait Locus detection, selective genotyping is a way to reduce costs due to genotyping : only individuals with extreme phenotypes are genotyped. We focus here on statistical inference for selective genotyping. We propose different statistical tests suitable for selective genotyping and we compare their performances in a very large framework. We prove that the non extreme phenotypes (i.e. the phenotypes for which the genotypes are missing) don't bring any information for statistical inference. We also prove that we have to genotype symmetrically, that is to say the same percentage of large and small phenotypes whatever the proportions of the two genotypes in the population. Same results are obtained in the case of a selective genotyping with two correlated phenotypes.

Mots clés: Selective genotyping; Asymptotic Relative Efficiency; Hypothesis testing; Asymptotic properties of tests; Quantitative Trait Locus detection

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Charles-Elie Rabier. On statistical inference for selective genotyping. Journal of Statistical Planning and Inference, 2014, ⟨10.1016/j.jspi.2013.11.010⟩. ⟨hal-00658583v3⟩