Soybean plant height QTL mapping and meta-analysis for mining candidate genes

2017 ◽  
Vol 136 (5) ◽  
pp. 688-698 ◽  
Author(s):  
Zhengong Yin ◽  
Huidong Qi ◽  
Qingshan Chen ◽  
Zhanguo Zhang ◽  
Hongwei Jiang ◽  
...  
2021 ◽  
Vol 12 ◽  
Author(s):  
Heng Chen ◽  
Xiangwen Pan ◽  
Feifei Wang ◽  
Changkai Liu ◽  
Xue Wang ◽  
...  

Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.


2018 ◽  
Vol 32 (4) ◽  
pp. 915-922
Author(s):  
Zhengong Yin ◽  
Huidong Qi ◽  
Xinrui Mao ◽  
Jingxin Wang ◽  
Zhenbang Hu ◽  
...  

2010 ◽  
Vol 36 (5) ◽  
pp. 771-778 ◽  
Author(s):  
Zhuo-Kun LI ◽  
Quan-Gang XIE ◽  
Zhan-Ling ZHU ◽  
Jin-Liang LIU ◽  
Shu-Xiao HAN ◽  
...  
Keyword(s):  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shenping Zhou ◽  
Rongrong Ding ◽  
Fanming Meng ◽  
Xingwang Wang ◽  
Zhanwei Zhuang ◽  
...  

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.


2017 ◽  
Vol 32 (2) ◽  
pp. 135-140 ◽  
Author(s):  
M. Ryan Miller ◽  
Jason K. Norsworthy

AbstractTo address recent concerns related to auxin herbicide drift onto soybean, a study was developed to understand the susceptibility of the reproductive stage of soybean to a new auxin herbicide compared with dicamba. Florpyrauxifen-benzyl is under development as the second herbicide in a new structural class of synthetic auxins, the arylpicolinates. Field studies were conducted to (1) evaluate and compare reproductive soybean injury and yield following applications of florpyrauxifen-benzyl or dicamba across various concentrations and reproductive growth stages and (2) determine whether low-rate applications of florpyrauxifen-benzyl or dicamba to soybean in reproductive stages would have similar effect on the progeny of the affected plants. Soybean were treated with 0, 1/20, or 1/160, of the 1X rate of florpyrauxifen-benzyl (30 g ai ha−1) or dicamba (560 g ae ha−1) at R1, R2, R3, R4, or R5 growth stage. Soybean plant height and yield was reduced from 1/20X dicamba across all reproductive stages. High drift rates (1/20X) of florpyrauxifen-benzyl also reduced soybean plant height >25% and yield across R1 to R4 stages. Germination, stand, plant height, and yield of the offspring of soybean plants treated with dicamba and florpyrauxifen-benzyl were significantly affected. Dicamba applied at a rate of 1/20X at R4 and R5 resulted in 20% and 35% yield reduction for the offspring, respectively. A similar reduction occurred from florpyrauxifen-benzyl applied at R4 and R5 at the 1/20X rate, resulting in 15% to 24% yield reduction for the offspring, respectively. Based on these findings, it is suggested that growers use caution when applying these herbicides in the vicinity of reproductive soybean.


2016 ◽  
Vol 7 ◽  
Author(s):  
Danna Liang ◽  
Minyang Chen ◽  
Xiaohua Qi ◽  
Qiang Xu ◽  
Fucai Zhou ◽  
...  

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0193072
Author(s):  
Weiqiang Zhang ◽  
Zhi Li ◽  
Hui Fang ◽  
Mingcai Zhang ◽  
Liusheng Duan

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