Multiple-testing corrections in case-control studies using identity-by-descent segments

Area of Science:

• Genetics
• Genomic analysis
• Statistical genetics

Background:

• Genome-wide association studies (GWASs) may miss complex genetic signals from multiple causal variants.
• Identity-by-descent (IBD) mapping offers complementary insights into disease genetics.

Purpose of the Study:

• To introduce a novel IBD mapping statistic and hypothesis testing framework for identifying disease risk loci.
• To develop a scalable and reproducible computational workflow for IBD mapping.
• To apply the method to discover Alzheimer's disease (AD) risk loci.

Main Methods:

• Proposed a statistic based on the difference between affected-affected and control-control IBD rates.
• Utilized a computationally efficient stochastic process approach for genome-wide significance testing controlling the family-wise error rate (FWER).
• Integrated IBD mapping with selection scans and phenotype randomization for confounding assessment.
• Developed automated workflows for haplotype phasing and local ancestry probability calling.

Main Results:

• Whole-genome simulations confirmed conservative control of the FWER.
• Identified six genome-wide significant AD risk loci in the Alzheimer's Disease Sequencing Project (ADSP) data.
• Detected signals in samples of African, European, and Amish ancestries.
• Found previously associated variants and nominated therapeutic target genes within identified loci.

Conclusions:

• The developed IBD mapping approach is a scalable and effective tool for discovering disease risk loci, particularly for complex genetic architectures.
• This method enhances the utility of large genomic consortia data for understanding disease mechanisms.
• The identified AD risk loci provide further targets for therapeutic intervention and research.