Imputation Performance in Latin American Populations: Improving Rare Variants Representation With the Inclusion of...

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Genome-Wide Association Studies (GWAS) utilize genotype imputation for enhanced statistical power and fine-mapping.
• Limited Native American genome representation in current imputation panels hinders the discovery of relevant disease variants in Latin American populations.
• Genomic database diversity is crucial for equitable biomedical research.

Purpose of the Study:

• To assess the impact of incorporating Native American genomes on genotype imputation performance in Latin American individuals.
• To quantify the improvement in statistical power and imputation accuracy for association studies.
• To determine the number of Native American genomes required for equitable imputation performance.

Main Methods:

• Expansion of the 1,000 Genomes reference panel (1KGP) with 134 Native American genomes (1KGP + NAT).
• Evaluation of imputation performance in Latin American individuals of mixed ancestry.
• Simulations to project imputation performance with increased Native American genome representation.

Main Results:

• The 1KGP + NAT panel increased the number of single nucleotide polymorphisms (SNPs) above the GWAS quality threshold, boosting statistical power.
• Imputation accuracy improved, especially for low-frequency variants within Native American ancestry segments.
• Simulations indicated a need for at least 3,000 Native American genomes to match European ancestry imputation performance.

Conclusions:

• The addition of Native American genomes significantly enhances imputation performance for Latin American populations.
• This work addresses the underrepresentation of Native American diversity in genomic reference panels.
• The findings support efforts to improve global equity in genomic research and disease variant discovery.