Published November 5, 2020 | Version Published
Journal Article Open

A faster implementation of association mapping from k-mers

  • 1. ROR icon Bangladesh University of Engineering and Technology
  • 2. ROR icon United International University
  • 3. ROR icon California Institute of Technology

Abstract

Association mapping is the process of linking phenotypes with genotypes. In genome wide association studies (GWAS), individuals are first genotyped using microarrays or by aligning sequenced reads to reference genomes. However, both these approaches rely on reference genomes which limits their application to organisms with no or incomplete reference genomes. To address this, reference free association mapping methods have been developed. Here we present the protocol of an alignment free method for association studies which is based on counting k-mers in sequenced reads, testing for associations between k-mers and the phenotype of interest, and local assembly of the k-mers of statistical significance. The method can map associations of categorical phenotypes to sequence and structural variations without requiring prior sequencing of reference genomes.

Additional Information

© 2020 Copyright Mehrab et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). Lior Pachter, and Atif Rahman were funded in part by NIH R21 HG006583. This paper describes protocol of a method originally presented in the paper "Association mapping from sequencing reads using k-mers" by Atif Rahman, Ingileif Hallgrímsdóttir, Michael Eisen and Lior Pachter, and extended in "A faster implementation of association mapping from k-mers" by Zakaria Mehrab, Jaiaid Mobin, Ibrahim Asadullah Tahmid and Atif Rahman. The authors declare no competing interests.

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Identifiers

Eprint ID
108356
Resolver ID
CaltechAUTHORS:20210309-074448590

Funding

NIH
R21 HG006583

Dates

Created
2021-03-10
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field

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