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. 2024 May 20;19(5):e0303751.
doi: 10.1371/journal.pone.0303751. eCollection 2024.

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

Binbin Du  1 , Jia Wu  1 , Qingming Wang  2 , Chaoyue Sun  1 , Genlou Sun  3 , Jie Zhou  2 , Lei Zhang  2 , Qingsong Xiong  4 , Xifeng Ren  5 , Baowei Lu  1
Affiliations

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

Binbin Du et al. PLoS One. .

Abstract

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001-2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. QTL information for barley yield and yield-related traits in previous QTL studies.
(A) Population size of different population types. (B) Percentage of QTL for different yield and yield-related traits. (C) QTL distribution on 7 chromosomes. (D) Frequency distribution of QTL with different LOD scores. (E) Frequency distribution of QTL for different PVEs.
Fig 2
Fig 2. Distribution of markers on the barley consensus map in this study.
The number of markers decreases sequentially from red to green.
Fig 3
Fig 3. Basic information of MQTL in this study.
(A) The number of MQTL containing different initial QTL numbers. (B) The number of MQTL is associated with the number of different traits. (C) MQTL distribution on seven chromosomes. (D) Confidence interval comparison between initial QTLs (green bar) and MQTLs (orange bar).
Fig 4
Fig 4. Distribution of MQTL on chromosomes verified by GWAS.
The circles from inside to outside indicate the genetic map, the PVE of the initial QTL, the position of the MTA on the physical map, the high-confidence gene distribution, and the physical map, respectively.
Fig 5
Fig 5. Distribution of 25 breeder’s MQTLs affecting different yield-related traits on chromosomes.
The axes on the left indicate physical distances (Mb), and different traits were represented by squares of different colors. GMT grain morphological traits, GN grain number, SRT spike-related traits, GW grain weight, GY grain yield, GPT growth period traits, BY biomass yield, TN tiller number, PH plant height, HI harvest index, GFRT grain filling-related traits.
Fig 6
Fig 6. Expression characteristics of 17 candidate genes in 10 barley tissues.
The expression level gradually increases from blue to red.
See this image and copyright information in PMC

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