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机器学习方法如何帮助在 GBS 数据中找到假定的黑麦蜡基因。

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

机构信息

Department of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71-434 Szczecin, Poland.

Institute of Plant Genetics, Breeding and Biotechnology, University of Life Sciences in Lublin, ul. Akademicka, 20-950 Lublin, Poland.

出版信息

Int J Mol Sci. 2020 Oct 12;21(20):7501. doi: 10.3390/ijms21207501.

DOI:10.3390/ijms21207501
PMID:33053706
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7593958/
Abstract

The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter.

摘要

标准的遗传图谱方法通过机器学习 (ML) 得到补充,以确定与表皮蜡形成(灰白色表型)相关的黑麦基因的位置。使用 DArTseq(基于测序的多样性阵列技术)对 180 多株双亲亲本 F 群体进行了基因型分析。最大似然 (MLH) 算法(JoinMap 5.0)和三种 ML 算法:逻辑回归 (LR)、随机森林和极端梯度提升树 (XGBoost) 用于选择与编码蜡层的基因紧密连锁的标记。从品种 Karlikovaja Zelenostebelnaja 中衍生出的、使植物呈现非灰白色外观的等位基因被定位在 2R 染色体上,这是首次关于该定位的报道。与使用 ML 方法检测到的蜡质分离紧密连锁的 DArT-Silico 3585843 的 DNA 序列被指出是控制所研究性状的候选基因之一。该假定基因编码 ABCG11 转运蛋白。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/4186284ab9d3/ijms-21-07501-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/b24a489ebd3a/ijms-21-07501-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/d227104267f3/ijms-21-07501-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/6627089e0171/ijms-21-07501-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/00f129ede4bb/ijms-21-07501-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/4186284ab9d3/ijms-21-07501-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/b24a489ebd3a/ijms-21-07501-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/d227104267f3/ijms-21-07501-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/6627089e0171/ijms-21-07501-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/00f129ede4bb/ijms-21-07501-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5822/7593958/4186284ab9d3/ijms-21-07501-g005.jpg

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