A one penny imputed genome from next generation reference panels

By Brian L. Browning, Yingxiao Zhou, Sharon R. Browning. bioRxiv, 2018

1,694 citations recorded by the source corpus.

Genotype imputation is commonly performed in genome-wide association studies because it greatly increases the number of markers that can be tested for association with a trait. In general, one should perform genotype imputation using the largest reference panel that is available because the number of accurately imputed variants increases with reference panel size. However, one impediment to using larger reference panels is the increased computational cost of imputation. We present a new genotype imputation method, Beagle 5.0, which greatly reduces the computational cost of imputation from large reference panels. We compare Beagle 5.0 with Beagle 4.1, Impute4, Minimac3, and Minimac4 using 1000 Genomes Project data, Haplotype Reference Consortium data, and simulated data for 10k, 100k, 1M, and 10M reference samples. All methods produce nearly identical accuracy, but Beagle 5.0 has the lowest computation time and the best scaling of computation time with increasing reference panel size. For 10k, 100k, 1M, and 10M reference samples and 1000 phased target samples, Beagle 5.0’s computation time is 3× (10k), 12× (100k), 43× (1M), and 533× (10M) faster than the fastest alternative method. Cost data from the Amazon Elastic Compute Cloud show that Beagle 5.0 can perform genome-wide imputation from 10M reference samples into 1000 phased target samples at a cost of less than one US cent per sample. Beagle 5.0 is freely available from https://faculty.washington.edu/browning/beagle/beagle.html.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/35dffedfd47f1fea4ed76a59c8aa9810b569d6a7

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Citations in the corpus

Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, methodology): “Currently Beagle is the only tool that supports imputing with multi-allelic marker representation (Browning et al., 2018).”
Accurate, scalable and integrative haplotype estimation (2019, methodology): “For instance, SHAPEIT4-P = 4 is 1.6 to 3.6 times faster than Beagle5, 1.9 to 5.8 times faster than Eagle2, and 4.1 to 11 times faster than SHAPEIT3 when phasing from 10,000 to 400,000 individuals.”
Fast two-stage phasing of large-scale sequence data. (2021, methodology): “For each haplotype in a target individual, we obtain posterior allele probabilities at a missing or heterozygous low-frequency marker by summing the state probabilities for all reference haplotypes that have been assigned the same allele.(3,30) If the genotype is missing, we choose the allele with h”