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基于新一代测序(NGS)标记的染色体定位对其分析六倍体小黑麦遗传变异和群体结构适用性的影响。

Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale.

机构信息

Institute of Plant Genetics, Breeding and Biotechnology, Faculty of Agrobioengineering, University of Life Sciences in Lublin, Akademicka St. 15, 20-950 Lublin, Poland.

Plant Breeding and Acclimatization Institute-National Research Institute, Radzików, 05-870 Błonie, Poland.

出版信息

Int J Mol Sci. 2024 Sep 3;25(17):9568. doi: 10.3390/ijms25179568.

Abstract

This study aimed to determine whether using DNA-based markers assigned to individual chromosomes would detect the genetic structures of 446 winter triticale forms originating from two breeding companies more effectively than using the entire pool of markers. After filtering for quality control parameters, 6380 codominant single nucleotide polymorphisms (SNPs) markers and 17,490 dominant diversity array technology (silicoDArT) markers were considered for analysis. The mean polymorphic information content (PIC) values varied depending on the chromosomes and ranged from 0.30 (2R) to 0.43 (7A) for the SNPs and from 0.28 (2A) to 0.35 (6R) for the silicoDArTs. The highest correlation of genetic distance (GD) matrices based on SNP markers was observed among the 5B-5R (0.642), 5B-7B (0.626), and 5A-5R (0.605) chromosomes. When silicoDArTs were used for the analysis, the strongest correlations were found between 5B-5R (0.732) and 2B-5B (0.718). A Bayesian analysis showed that SNPs (total marker pool) allowed for the identification of a more complex structure (K = 4, ΔK = 2460.2) than the analysis based on silicoDArTs (K = 2, ΔK = 128). Triticale lines formed into groups, ranging from two (most of the chromosomes) to four (7A) groups depending on the analyzed chromosome when SNP markers were used for analysis. Linkage disequilibrium (LD) varied among individual chromosomes, ranging from 0.031 for 1A to 0.228 for 7R.

摘要

本研究旨在确定使用分配给个别染色体的基于 DNA 的标记是否比使用整个标记池更有效地检测来自两个育种公司的 446 个冬季黑小麦品种的遗传结构。在进行质量控制参数过滤后,考虑了 6380 个共显性单核苷酸多态性(SNP)标记和 17490 个显性多样性阵列技术(silicoDArT)标记进行分析。平均多态信息含量(PIC)值因染色体而异,SNP 的范围为 0.30(2R)至 0.43(7A),silicoDArT 的范围为 0.28(2A)至 0.35(6R)。基于 SNP 标记的遗传距离(GD)矩阵的相关性最高的是 5B-5R(0.642)、5B-7B(0.626)和 5A-5R(0.605)染色体之间。当使用 silicoDArT 进行分析时,最强的相关性出现在 5B-5R(0.732)和 2B-5B(0.718)之间。贝叶斯分析表明,SNP(总标记池)比基于 silicoDArT 的分析(K = 2,ΔK = 128)更能识别复杂的结构(K = 4,ΔK = 2460.2)。当使用 SNP 标记进行分析时,黑小麦品系形成了 2 到 4 个组,具体取决于所分析的染色体,范围从两个(大多数染色体)到四个(7A)组。连锁不平衡(LD)在各个染色体之间变化,范围从 1A 的 0.031 到 7R 的 0.228。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ce6/11395606/bd836c57f553/ijms-25-09568-g001.jpg

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