Genotype Imputation with Millions of Reference Samples.

By Brian L. Browning, Sharon R. Browning. American Journal of Human Genetics, 2016

1,082 citations recorded by the source corpus.

We present a genotype imputation method that scales to millions of reference samples. The imputation method, based on the Li and Stephens model and implemented in Beagle v.4.1, is parallelized and memory efficient, making it well suited to multi-core computer processors. It achieves fast, accurate, and memory-efficient genotype imputation by restricting the probability model to markers that are genotyped in the target samples and by performing linear interpolation to impute ungenotyped variants. We compare Beagle v.4.1 with Impute2 and Minimac3 by using 1000 Genomes Project data, UK10K Project data, and simulated data. All three methods have similar accuracy but different memory requirements and different computation times. When imputing 10 Mb of sequence data from 50,000 reference samples, Beagle's throughput was more than 100× greater than Impute2's throughput on our computer servers. When imputing 10 Mb of sequence data from 200,000 reference samples in VCF format, Minimac3 consumed 26× more memory per computational thread and 15× more CPU time than Beagle. We demonstrate that Beagle v.4.1 scales to much larger reference panels by performing imputation from a simulated reference panel having 5 million samples and a mean marker density of one marker per four base pairs.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2ee27eff8ac0cfcf48b28dcbbfa769ec5af7f316

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Citations in the corpus

Fast two-stage phasing of large-scale sequence data. (2021, methodology): “1,30 The resulting method is computationally fast, multi-threaded, and memory efﬁcient.”
Very low-depth whole-genome sequencing in complex trait association studies (2017, result): “The complexity of the imputation and phasing algorithms used in this study is linear in the number of markers, linear in the number of target samples and quadratic in the number of reference samples (Browning and Browning, 2016), which results in a 50-fold increase in total processing time compared ”
Reference-based phasing using the Haplotype Reference Consortium panel (2016, methodology): “1 [24] but found it was too slow for this benchmark to be practical: for the smallest analysis (chromosome 20 with Nref = 15,000 and N target = 72), Beagle v4.”