Is it rare or common?

Area of Science:

• Genetics
• Bioinformatics
• Statistical genomics

Background:

• Genome-wide association studies (GWAS) frequently identify significant signals with unclear etiological origins.
• Distinguishing between common and rare variants underlying these GWAS signals is crucial for understanding disease mechanisms.

Purpose of the Study:

• To develop and apply a Bayesian method for inferring the proportion of common versus rare variants contributing to GWAS signals.
• To compute posterior probabilities for different variant configurations (common and/or rare) within a genomic region.

Main Methods:

• Utilized single nucleotide polymorphism (SNP) data in a case-control cohort.
• Employed an extension of coalescent trees, ancestral recombination graphs (ARGs), to model sample genealogical history.
• Leveraged linkage disequilibrium between SNPs and causal variants to infer variant type.

Main Results:

• The developed Bayesian approach can predict the number of common or rare variants associated with a GWAS signal.
• Applied the method to candidate gene sequencing data from a German nonsyndromic cleft lip/palate study, demonstrating its practical utility.

Conclusions:

• The method provides a robust framework for dissecting the genetic architecture of complex diseases identified by GWAS.
• This approach aids in prioritizing follow-up studies by differentiating the roles of common and rare variants in disease etiology.