Nov 20, 2019

BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing

Bioinformatics
Likun HuangWeiren Wu

Abstract

Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved. In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated. The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. Supplementary data are available at Bioinformatics online.

  • References20
  • Citations2

References

Mentioned in this Paper

Licensing Factor
Positioning Attribute
Determination Aspects
Nucleic Acid Sequencing
Sequence Analysis
Deep Sequencing
Mapping, Vessel
Regression Analysis
Statistical Technique
Analysis

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