Multiple haplotype reconstruction from allele frequency data

Pelizzola M, Behr M, Li H, Munk A, Futschik A (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Nature Computational Science Nature Research

Book Volume: 1

Pages Range: 262-271

Journal Issue: 4

DOI: 10.1038/s43588-021-00056-5

Abstract

Because haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose an efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. Compared to other methods, haploSep is very fast, with linear computational complexity in the haplotype length. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.

Involved external institutions

Georg-August-Universität Göttingen DE Germany (DE) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna AT Austria (AT) University of California, Berkeley US United States (USA) (US) Johannes Kepler Universität (JKU) Linz AT Austria (AT)

How to cite

APA:

Pelizzola, M., Behr, M., Li, H., Munk, A., & Futschik, A. (2021). Multiple haplotype reconstruction from allele frequency data. Nature Computational Science, 1(4), 262-271. https://dx.doi.org/10.1038/s43588-021-00056-5

MLA:

Pelizzola, Marta, et al. "Multiple haplotype reconstruction from allele frequency data." Nature Computational Science 1.4 (2021): 262-271.

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