Detection of parent-of-origin effects using general pedigree data.

Area of Science:

• Genetics
• Epigenetics
• Statistical genomics

Background:

• Genomic imprinting, an epigenetic phenomenon, influences complex traits by parent-of-origin effects.
• Existing methods like parental-asymmetry test (PAT) are limited to nuclear families.
• Extended pedigrees offer potential for more powerful genetic analyses.

Purpose of the Study:

• To extend existing methods for detecting parent-of-origin effects to general pedigree data.
• To develop a robust statistic that accommodates missing genotype data in pedigrees.
• To evaluate the performance and power of the proposed methods.

Main Methods:

• Proposed the pedigree PAT (PPAT) statistic to incorporate all informative trios within pedigrees.
• Developed the Monte Carlo PPAT (MCPPAT) statistic using Monte Carlo sampling for missing genotype data.
• Conducted extensive simulations to assess method validity and power.

Main Results:

• MCPPAT is a valid test for parent-of-origin effects, even with genetic association.
• MCPPAT demonstrates significantly higher power than PAT or PPAT, particularly with missing genotype data.
• The methods were successfully applied to a rheumatoid arthritis dataset.

Conclusions:

• The proposed PPAT and MCPPAT statistics effectively extend parent-of-origin effect detection to general pedigrees.
• MCPPAT offers a powerful and flexible approach for analyzing complex traits in the presence of genomic imprinting and missing data.
• These methods enhance the utility of large pedigree resources in genetic studies.