Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels

• Published in: Briefings in bioinformatics Vol. 25; no. 1
• Main Authors: Shi, Mingyang, Tanikawa, Chizu, Munter, Hans Markus, Akiyama, Masato, Koyama, Satoshi, Tomizuka, Kohei, Matsuda, Koichi, Lathrop, Gregory Mark, Terao, Chikashi, Koido, Masaru, Kamatani, Yoichiro
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2024; Oxford University Press
• Subjects: Bias; Deviation; Dosage; Gene Frequency; Genome-wide association studies; Genome-Wide Association Study; Genotype; Genotype & phenotype; Genotypes; Humans; Markov chains; Panels; Polymorphism, Single Nucleotide; Precision medicine; Problem Solving Protocol; East Asian; TOPMed reference panel; template switching rate; under-represented population; genotype imputation; imputation quality metric
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbad509

Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.