Predicting the phenotype from genotype data on individual and pooled segregants

Miha Svetelšek (2014) Predicting the phenotype from genotype data on individual and pooled segregants. EngD thesis.

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Abstract

We have modeled the relationship between genotype and phenotype using data on thirty yeast S. cerevisiae samples. Using prior knowledge, we have determined mutations of individual nucleotides and related genes with which it is possible to build a good prediction model for the phenotype. The constructed models allow us to determine the location of important mutations in the genome (SNVs), to rank samples based on phenotype, and to determine signi_cant genotypes or parental origin, which is connected to the observed phenotype. Evaluation of these models shows that the phenotype can be predicted very reliably with linear regression. The phenotype can be predicted relatively well from data on two starting parents and the _rst pool of segregants. We also show the relation between the number of samples used to build a predictive model and its predictive error.

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