{"id":178419,"date":"2025-09-24T11:19:10","date_gmt":"2025-09-24T11:19:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/us\/178419\/"},"modified":"2025-09-24T11:19:10","modified_gmt":"2025-09-24T11:19:10","slug":"advances-in-haplotype-phasing-and-genotype-imputation","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/us\/178419\/","title":{"rendered":"Advances in haplotype phasing and genotype imputation"},"content":{"rendered":"

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Browning, S. R., Waples, R. K. & Browning, B. L. Fast, accurate local ancestry inference with FLARE. Am. J. Hum. Genet. 110, 326\u2013335 (2023).<\/p>\n

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Fuchsberger, C., Abecasis, G. R. & Hinds, D. A. minimac2: faster genotype imputation. Bioinformatics 31, 782\u2013784 (2015). This paper introduces the minimac2 imputation method (minimac3 and minimac4 are not associated with any publications).<\/p>\n

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Browning, B. L., Zhou, Y. & Browning, S. R. A one-penny imputed genome from next-generation reference panels. Am. J. Hum. Genet. 103, 338\u2013348 (2018). This paper introduces the Beagle5 imputation method.<\/p>\n

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Rubinacci, S., Delaneau, O. & Marchini, J. Genotype imputation using the positional burrows wheeler transform. PLoS Genet. 16, e1009049 (2020). This paper introduces the IMPUTE5 imputation method.<\/p>\n

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Sun, Q. et al. Analyses of biomarker traits in diverse UK Biobank participants identify associations missed by European-centric analysis strategies. J. Hum. Genet. 67, 87\u201393 (2022).<\/p>\n

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Huerta-Chagoya, A. et al. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes. Diabetologia 66, 1273\u20131288 (2023).<\/p>\n

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Qin, Z. S., Niu, T. & Liu, J. S. Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. Am. J. Hum. Genet. 71, 1242\u20131247 (2002).<\/p>\n

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Stephens, M. & Scheet, P. Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am. J. Hum. Genet. 76, 449\u2013462 (2005).<\/p>\n

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Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629\u2013644 (2006).<\/p>\n

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Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084\u20131097 (2007).<\/p>\n

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Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40, 1068\u20131075 (2008).<\/p>\n

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Halperin, E. & Karp, R. M. Perfect phylogeny and haplotype assignment. In Proc. 8th Annual International Conference on Computational Molecular Biology 10\u201319 (ACM, 2004).<\/p>\n

Halperin, E. & Eskin, E. Haplotype reconstruction from genotype data using imperfect phylogeny. Bioinformatics 20, 1842\u20131849 (2004).<\/p>\n

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Li, N. & Stephens, M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165, 2213\u20132233 (2003).<\/p>\n

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Fearnhead, P. & Donnelly, P. Estimating recombination rates from population genetic data. Genetics 159, 1299\u20131318 (2001).<\/p>\n

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Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443\u20131448 (2016). This paper introduces the Eagle2 phasing method, where PBWT is applied to improve computational efficiency.<\/p>\n

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Hofmeister, R. J., Ribeiro, D. M., Rubinacci, S. & Delaneau, O. Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank. Nat. Genet. 55, 1243\u20131249 (2023). This paper introduces the SHAPEIT5 phasing method, where singletons are explicitly considered.<\/p>\n

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Browning, B. L., Tian, X., Zhou, Y. & Browning, S. R. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 108, 1880\u20131890 (2021). This paper introduces the Beagle5 phasing method (which is different from the Beagle5 imputation publication), where a two-stage phasing strategy is proposed separately for common and rare variants.<\/p>\n

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Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290\u2013299 (2021). This paper introduces the TOPMed reference panel containing haplotypes from diverse populations, which is more suitable for imputation of global populations compared with previous reference panels, including 1000\u2009Genomes and HRC.<\/p>\n

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Feng, Y.-C. A. et al. Taiwan Biobank: a rich biomedical research database of the Taiwanese population. Cell Genom. 2, 100197 (2022).<\/p>\n

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Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284\u20131287 (2016). This paper introduces the Michigan imputation server, exemplary in promoting broader usage of reference panels and public servers without accessing individual genotypes contributing to the panels.<\/p>\n

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Al Bkhetan, Z., Zobel, J., Kowalczyk, A., Verspoor, K. & Goudey, B. Exploring effective approaches for haplotype block phasing. BMC Bioinform. 20, 540 (2019).<\/p>\n


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Al Bkhetan, Z., Chana, G., Ramamohanarao, K., Verspoor, K. & Goudey, B. Evaluation of consensus strategies for haplotype phasing. Brief. Bioinform. 22, bbaa280 (2021).<\/p>\n

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Wertenbroek, R., Hofmeister, R. J., Xenarios, I., Thoma, Y. & Delaneau, O. Improving population scale statistical phasing with whole-genome sequencing data. PLoS Genet. 20, e1011092 (2024). This paper introduces a method to correct phasing errors leveraging raw sequencing.<\/p>\n

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Sun, Q. et al. MagicalRsq: machine-learning-based genotype imputation quality calibration. Am. J. Hum. Genet. 109, 1986\u20131997 (2022). This paper introduces a framework to recalculate imputation quality metric for post-imputation quality control, especially for low-frequency and rare variants where the state-of-the-art imputation quality metric (for example, Rsq) performs less well.<\/p>\n

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Sun, Q. et al. MagicalRsq-X: a cross-cohort transferable genotype imputation quality metric. Am. J. Hum. Genet. 111, 990\u2013995 (2024).<\/p>\n

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Aleknonyt\u0117-Resch, M., Szymczak, S., Freitag-Wolf, S., Dempfle, A. & Krawczak, M. Genotype imputation in case-only studies of gene-environment interaction: validity and power. Hum. Genet. 140, 1217\u20131228 (2021).<\/p>\n

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Sun, Q. et al. Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients. HGG Adv. 3, 100090 (2022).<\/p>\n

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Liu, E. Y. et al. Genotype imputation of metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the women\u2019s health initiative. Genet. Epidemiol. 36, 107\u2013117 (2012).<\/p>\n

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Sengupta, D. et al. Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations. Cell Genom. 3, 100332 (2023).<\/p>\n

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