Correcting for cryptic relatedness in population-based association studies of continuous traits

Area of Science:

• Genetics
• Statistical Genetics
• Population Genetics

Background:

• Cryptic relatedness is a known confounder in population-based association studies (PBAS).
• The impact of cryptic relatedness on the performance of association tests for continuous traits requires further investigation.
• Understanding these effects is crucial for accurate genetic association findings.

Purpose of the Study:

• To investigate the effects of cryptic relatedness on the power and type I error rates of simple association tests (SAT).
• To evaluate the performance of extended likelihood ratio tests (ELRT) in controlling for cryptic relatedness in PBAS.
• To compare ELRT with genomic control (GC) for managing cryptic relatedness.

Main Methods:

• Simulated related samples using biased sampling and inbreeding models.
• Evaluated simple association tests (SAT) without relatedness correction.
• Applied extended likelihood ratio tests (ELRT) and genomic control (GC) to account for cryptic relatedness.

Main Results:

• Cryptic relatedness reduced power and increased type I error rates for SAT in both biased sampling and inbreeding scenarios.
• The impact of cryptic relatedness was less pronounced in biased sampling compared to inbreeding.
• ELRT demonstrated superior power and type I error control compared to SAT and GC across various conditions.
• Inbred populations with uncorrected cryptic relatedness led to excessive false positives with SAT.

Conclusions:

• Ignoring cryptic relatedness can lead to spurious associations in PBAS.
• ELRT offers a novel and effective method for controlling cryptic relatedness in PBAS of human continuous traits.
• Accurate control of cryptic relatedness is essential for reliable genetic association studies.