Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations.

Area of Science:

• Genetics
• Population Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) utilize imputation to infer untyped genetic variants, increasing genotyping array coverage.
• Current imputation methods often yield suboptimal results for underrepresented populations due to inadequate reference panels and array designs.
• This limitation arises because selected single nucleotide polymorphisms (SNPs) may not capture population-specific haplotype structures, thus missing crucial genetic variation.

Purpose of the Study:

• To enhance the accuracy of genetic imputation in underrepresented populations.
• To develop a strategy for selecting population-specific tag SNPs and creating tailored imputation reference panels.
• To improve the capture of common genetic variation in diverse cohorts.

Main Methods:

• Sequencing the full genomes of a small subset of an underrepresented cohort.
• Selecting population-specific add-on tag SNPs based on sequenced data.
• Generating an internal, population-specific imputation reference panel.
• Evaluating imputation accuracy using a Tanzania-based cohort and the H3Africa array.

Main Results:

• Demonstrated improved imputation accuracy in the Tanzania-based cohort.
• Showcased the effectiveness of population-specific add-on tag SNPs in enhancing imputation.
• Validated the proposed approach for underrepresented populations.

Conclusions:

• The proposed method of sequencing a subset and designing population-specific SNPs significantly improves imputation accuracy.
• This strategy is effective in overcoming limitations of existing reference panels for underrepresented groups.
• Enhancing imputation accuracy is crucial for advancing genetic research in diverse global populations.