Power and sample sizes for linkage with extreme sampling under an oligogenic model for quantitative traits

Area of Science:

• Genetics
• Statistical Genetics
• Quantitative Genetics

Background:

• Extreme sampling of sibling pairs enhances statistical power for detecting quantitative trait loci (QTLs) with normally distributed residuals.
• Oligogenic models, where multiple genes contribute additively, present unique challenges for locus detection.

Purpose of the Study:

• To analytically investigate the efficiency of various extreme sampling strategies in detecting individual loci within an oligogenic model.
• To compare the performance of extreme concordant high (ECH), extreme concordant low (ECL), and extreme discordant (ED) sampling methods.

Main Methods:

• Utilized a test statistic based on identical-by-descent (IBD) statuses of independent sibling pairs.
• Analyzed an oligogenic model where the joint effect of genes is the sum of individual locus effects.
• Considered nonepistatic effects of two QTLs with varying displacement magnitudes.

Main Results:

• Extreme discordant (ED) sampling demonstrated the highest efficiency across most scenarios, particularly concerning allele frequency and mode of inheritance.
• The power to detect a locus with a smaller effect did not consistently increase with more extreme sampling.
• Differences between ECH and ECL sampling were found to be arbitrary, influenced by the direction of displacement effects.

Conclusions:

• Extreme discordant sampling is generally the most effective strategy for detecting QTLs in oligogenic models.
• The choice of sampling strategy impacts the power to detect individual loci, especially those with smaller effects.
• Further exploration of combined sampling strategies may offer additional insights.