Selective genotyping and phenotyping strategies in a complex trait context.

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Quantitative trait locus (QTL) studies aim to identify genes influencing complex traits.
• Selective genotyping and phenotyping reduce costs but their efficiency is debated for complex traits.
• Existing research often simplifies trait architecture and error distributions.

Purpose of the Study:

• To evaluate the performance of selective genotyping and phenotyping strategies for complex traits.
• To determine optimal strategies for non-normally distributed phenotypes and time-to-event data.
• To provide an information-theoretic framework for experimental design in QTL studies.

Main Methods:

• Utilized an information-theoretic perspective to analyze experimental design.
• Simulated complex trait scenarios with multiple loci, linkage, and epistasis.
• Investigated the impact of non-normal phenotype distributions and varying follow-up times.

Main Results:

• Selective strategies' efficiency is highly dependent on trait architecture and phenotype distribution.
• Non-normal phenotypes and complex genetic architectures necessitate careful strategy selection.
• Optimal follow-up time for time-to-event phenotypes is influenced by genetic effects and sample size.

Conclusions:

• Selective genotyping and phenotyping can be efficient for complex traits but require careful consideration of genetic architecture and phenotype distribution.
• Recommendations are provided for optimizing experimental designs in complex trait QTL studies.
• The information-theoretic approach offers a robust framework for evaluating QTL study designs.