Reducing animal sequencing redundancy by preferentially selecting animals with low-frequency haplotypes

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Whole-genome sequencing (WGS) is crucial for identifying rare and causal variants.
• Current WGS surveys often include redundant haplotype segments, complicating variant phasing.
• High-frequency haplotypes in base populations lead to inefficiencies in variant discovery.

Purpose of the Study:

• To introduce a novel algorithm, inverse weight selection (IWS), for optimizing WGS survey efficiency.
• To reduce the number of individuals needed for comprehensive haplotype coverage in WGS studies.
• To decrease the overall cost of variant discovery in population genomics.

Main Methods:

• Developed the inverse weight selection (IWS) algorithm, prioritizing individuals with cumulative rare haplotypes.
• Applied IWS to genotype data from 112,113 US Holstein bulls.
• Compared IWS efficiency against existing methods for selecting individuals to capture specific haplotype frequencies.

Main Results:

• IWS demonstrated at least 6.8% greater efficiency than previous methods in selecting individuals for WGS.
• The algorithm successfully identified the minimal set of individuals to sequence all haplotype segments ≥4% frequency in the Holstein population.
• A strategy of initially sequencing homozygous haplotype segments could reduce costs by 50% and improve variant phasing.

Conclusions:

• The IWS algorithm significantly enhances the efficiency of WGS surveys for variant discovery.
• Proposed experimental design modifications, including focusing on homozygous segments, offer substantial cost reductions.
• These advancements make identifying causal variants for traits like disease resistance and production more feasible and cost-effective.