Likun Huang1,2, Weiqi Tang3, Suhong Bu1,2, Weiren Wu1,2. 1. Fujian Key Laboratory of Crop Breeding by Design, Fuzhou, Fujian 350002. 2. Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002. 3. Institute of Oceanography, Marine Biotechnology Center, Minjiang University, Fuzhou, Fujian 350108, China.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Gregory Vogel; Kyle E LaPlant; Michael Mazourek; Michael A Gore; Christine D Smart Journal: Theor Appl Genet Date: 2021-01-03 Impact factor: 5.699
Authors: Khin Mar Thi; Yan Zheng; Ei Ei Khine; Ei Ei Nyein; Min Htay Wai Lin; Khin Than Oo; Win Win New; Moe Zin Zi Thet; Moe Moe Khaing; Myat Myat Moe; San San Aye; Weiren Wu Journal: Breed Sci Date: 2020-10-28 Impact factor: 2.086