A high density SLAF-SNP genetic map and QTL detection for fibre quality traits in Gossypium hirsutum

Ali, Iftikhar; Teng, Zhonghua; Bai, Y.; Yang, Qing; Hao, Yongshui; Hou, Juan; Jia, Yongbin; Liu, Tian; Liu, Xueying; Tan, Zhuhua; Wang, Wenwen; Kenneth, Kiirya; Sharkh, Abdalla Yousef Ahmed; Liu, Dexin; Guo, Kai; Zhang, Jian; Liu, Dajun; Zhang, Zhengsheng

doi:10.1186/s12864-018-5294-5

Cited by 35 publications

(41 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specific length amplified fragment (SLAF) sequencing (SLAF-seq) is an improved reduced representation library (RRL) sequencing strategy that brings down the cost through genome reduction. SLAF-seq has been proved an effective method for de novo SNP discovery and high-throughput genotyping and has wide applications in genetic map construction in sesame , kiwifruit (Huang et al, 2013), soybean (Qi et al, 2014;Zhang et al, 2018), cucumber (Wei et al, 2014;Zhu et al, 2016), peanut (Hu et al, 2018), sweet osmanthus (He et al, 2017), and cotton (Zhang et al, 2016;Ali et al, 2018). In the present study, we developed an interspecific F 2 population containing 121 individuals from a cross between an eggplant cultivar and the wild relative S. linnaeanum.…”

Section: Introductionmentioning

confidence: 99%

Construction of a SNP-Based Genetic Map Using SLAF-Seq and QTL Analysis of Morphological Traits in Eggplant

Wei

Wang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Eggplant (Solanum melongena; 2n = 24) is an economically important fruit crop of the family Solanaceae that was domesticated in India and Southeast Asia. Construction of a high-resolution genetic map and map-based gene mining in eggplant have lagged behind other crops within the family such as tomato and potato. In this study, we conducted high-throughput single nucleotide polymorphism (SNP) discovery in the eggplant genome using specific length amplified fragment (SLAF) sequencing and constructed a high-density genetic map for the quantitative trait locus (QTL) analysis of multiple traits. An interspecific F 2 population of 121 individuals was developed from the cross between cultivated eggplant "1836" and the wild relative S. linnaeanum "1809." Genomic DNA extracted from parental lines and the F 2 population was subjected to high-throughput SLAF sequencing. A total of 111.74 Gb of data and 487.53 million pair-end reads were generated. A high-resolution genetic map containing 2,122 SNP markers and 12 linkage groups was developed for eggplant, which spanned 1530.75 cM, with an average distance of 0.72 cM between adjacent markers. A total of 19 QTLs were detected for stem height and fruit and leaf morphology traits of eggplant, explaining 4.08-55.23% of the phenotypic variance. These QTLs were distributed on nine linkage groups (LGs), but not on LG2, 4, and 9. The number of SNPs ranged from 2 to 11 within each QTL, and the genetic interval varied from 0.15 to 10.53 cM. Overall, the results establish a foundation for the fine mapping of complex QTLs, candidate gene identification, and marker-assisted selection of favorable alleles in eggplant breeding.

show abstract

Section: Introductionmentioning

confidence: 99%

Construction of a SNP-Based Genetic Map Using SLAF-Seq and QTL Analysis of Morphological Traits in Eggplant

Wei

Wang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Cotton genomes for diploid species (Paterson et al 2012; Wang et al 2012; Li et al 2014; Du et al 2018) and tetraploid genomes (Zhang et al 2015; Li et al 2015c; Yuan et al 2015; Liu et al 2015) had been released recently, so as recent genomics researches in cotton (Fang et al 2017a; Wang et al 2018). These genomics analysis in cotton facilitate applications of SNP markers (Ali et al 2018) and GWAS for fiber quality (Fang et al 2017b; Ma et al 2018b). It is very important to detect novel QTLs and to validate the reported QTLs using diverse populations.…”

Section: Introductionmentioning

confidence: 99%

QTL and genetic analysis controlling fiber quality traits using paternal backcross population in Upland Cotton

Hua

et al. 2019

Preprint

View full text Add to dashboard Cite

In present study, F14 recombinant inbred line (RIL) population was backcrossed to paternal parent for a paternal backcross (BC/P) population, deriving from one Upland cotton hybrid. Three repetitive BC/P field trials and one BC/M field trial were performed including both two BC populations and the original RIL population. Totally, for fiber quality traits, 24 novel QTLs were detected and 13 QTLs validated previous results. And 19 quantitative trait loci (QTL) in BC/P populations explained 5.01% - 22.09% of phenotype variation (PV). Among the 19 QTLs, three QTLs were detected simultaneously in BC/M population. The present study provided novel alleles of male parent for fiber quality traits with positive genetic effects. Particularly, qFS-Chr3-1 controlling fiber strength explained 22.09% of PV in BC/P population, which increased 0.48 cN/tex for fiber strength. A total of seven, two, eight, two and six QTLs explained over 10.00% of PV for fiber length, fiber uniformity, fiber strength, fiber elongation and fiber micronaire, respectively. In the RIL population, six common QTLs detected in more than one environment such as qFL-Chr1-2, qFS-Chr5-1, qFS-Chr9-1, qFS-Chr21-1, qFM-Chr9-1 and qFM-Chr9-2. Two common QTLs of qFE-Chr2-2 (TMB2386-SWU12343) and qFM-Chr9-1 (NAU2873-CGR6771) explained 22.42% and 21.91% of PV. In addition, a total of 142 and 46 epistatic QTLs and QTL × environments (E-QTLs and QQEs) were identified in RIL-P and BC/P populations, respectively.

show abstract

“…However, the disadvantages of previous genetic maps, such as low marker density, asymmetric distribution of mapped markers, and unavailability of reference genomes for upland cotton, hindered the above-mentioned applications of the QTL detection results (Deschamps et al 2012;Jamshed et al 2016;Yang et al 2015). Due to the rapid development of highthroughput sequencing technologies, the reduction of sequencing cost, and the establishment of the reference genome of upland cotton (TM-1), a number of highdensity genetic maps have been constructed by single nucleotide polymorphism (SNP) markers including genotyping by sequencing (GBS) (Diouf et al 2018;Qi et al 2017), restriction-site associated DNA sequencing (RAD-Seq) (Hegarty et al 2013;Kundu et al 2015;Wang et al 2017), specific locus-amplified fragment sequencing (SLAF-seq) (Ali et al 2018;Zhang et al 2016), CottonSNP63K array (Hulse-Kemp et al 2015;Li et al 2016;Li et al 2018a;Zhang et al 2016), and Cot-tonSNP80K array (Cai et al 2017;Tan et al 2018;Liu et al 2018;Zou et al 2018). These high-density genetic maps significantly improved QTL detection accuracy (Ma et al 2019a;Su et al 2018;Jia et al 2016).…”

Section: Introductionmentioning

confidence: 99%

QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

et al. 2020

View full text Add to dashboard Cite

Background: Plant height (PH) and fruit branch number (FBN) are important traits for improving yield and mechanical harvesting of cotton. In order to identify genes of PH and FBN in cotton germplasms to develop superior cultivars, quantitative trait loci (QTLs) for these traits were detected based on the phenotypic evaluation data in nine environments across four locations and 4 years and a previously reported genetic linkage map of an recombinant inbred line (RIL) population of upland cotton. Results: In total, 53 QTLs of PH and FBN, were identified on 21 chromosomes of the cotton genome except chromosomes c02, c09-c11, and c22. For PH, 27 QTLs explaining 3.81%-8.54% proportions of phenotypic variance were identified on 18 chromosomes except c02, c08-c12, c15, and c22. For FBN, 26 QTLs explaining 3.23%-11.00% proportions of phenotypic variance were identified on 16 chromosomes except c02-c03, c06, c09-c11, c17, c22-c23, and c25. Eight QTLs were simultaneously identified in at least two environments. Three QTL clusters containing seven QTLs were identified on three chromosomes (c01, c18 and c21). Eleven QTLs were the same as previously reported ones, while the rest were newly identified. Conclusions: The QTLs and QTL clusters identified in the current study will be helpful to further understand the genetic mechanism of PH and FBN development of cotton and will enhance the development of excellent cultivars for mechanical managements in cotton production.

show abstract

A high density SLAF-SNP genetic map and QTL detection for fibre quality traits in Gossypium hirsutum

Cited by 35 publications

References 66 publications

Construction of a SNP-Based Genetic Map Using SLAF-Seq and QTL Analysis of Morphological Traits in Eggplant

Construction of a SNP-Based Genetic Map Using SLAF-Seq and QTL Analysis of Morphological Traits in Eggplant

QTL and genetic analysis controlling fiber quality traits using paternal backcross population in Upland Cotton

QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

Contact Info

Product

Resources

About