Using linkage analysis of large pedigrees to guide association analyses

Area of Science:

• Genetics
• Genomic sequencing
• Complex disease research

Background:

• Genome-wide association studies (GWAS) have identified common variants contributing to familial disease aggregation.
• Decreasing sequencing costs drive interest in less common variants, but whole-genome sequencing in large cohorts remains expensive.
• Prioritizing samples and genomic regions for sequencing is crucial for cost-effective genetic discovery.

Purpose of the Study:

• To identify genomic regions for deep sequencing in large multiplex families using linkage analysis.
• To evaluate the efficiency of linkage-guided sequencing for detecting trait-associated alleles in complex diseases.

Main Methods:

• Incorporated linkage analysis into the search for quantitative trait (Q1)-associated alleles.
• Compared power and efficiency of whole-exome sequencing versus linkage-guided sequencing in large multiplex families.
• Sequenced targeted regions with high logarithm of odds (LOD) peaks.

Main Results:

• Both whole-exome and linkage-guided sequencing showed low power overall.
• Sequencing only high LOD peak regions identified fewer associated single-nucleotide polymorphisms compared to whole-exome sequencing.
• Linkage analysis enabled detection of 52% of associated susceptibility loci while examining only 2.5% of the exome.

Conclusions:

• Linkage analysis from large multiplex families can significantly enhance the efficiency of sequencing for detecting trait-associated alleles.
• This targeted approach offers a cost-effective strategy for complex disease genetic studies.