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  • Review
  • Open Access

Role of SNPs in determining QTLs for major traits in cotton

Journal of Cotton Research20192:5

https://doi.org/10.1186/s42397-019-0022-5

  • Received: 22 February 2019
  • Accepted: 30 April 2019
  • Published:

Abstract

A single nucleotide polymorphism is the simplest form of genetic variation among individuals and can induce minor changes in phenotypic, physiological and biochemical characteristics. This polymorphism induces various mutations that alter the sequence of a gene which can lead to observed changes in amino acids. Several assays have been developed for identification and validation of these markers. Each method has its own advantages and disadvantages but genotyping by sequencing is the most common and most widely used assay. These markers are also associated with several desirable traits like yield, fibre quality, boll size and genes respond to biotic and abiotic stresses in cotton. Changes in yield related traits are of interest to plant breeders. Numerous quantitative trait loci with novel functions have been identified in cotton by using these markers. This information can be used for crop improvement through molecular breeding approaches. In this review, we discuss the identification of these markers and their effects on gene function of economically important traits in cotton.

Keywords

  • Abiotic stresses
  • Biotic stresses
  • Cotton
  • Earliness
  • Genotyping by sequencing

Background

Plant breeders are interested in genetic variations because these variations are the basis of phenotypic diversity. Many traits in plants arose due to genetic variations caused by mutation and/or recombination; those traits that were useful were ‘fixed’ by natural as well as artificial selection. With advances in technology, various methods have been developed by scientists to detect and analyze the minor genetic variations whose effects cannot be seen in the phenotypes (Jang et al. 2015). A base pair is the smallest unit of inheritance in an individual and when two or more individuals differ from each other based on a nucleotide then it is called a single nucleotide polymorphism (SNP). The identification of these minor variations was the initial challenging to plant scientists. The advent of next generation DNA sequencing technologies has solved this puzzle by being able to detect new functional SNPs associated with diverse traits. This whole genome sequence data serves as a reference for the identification of polymorphism due to SNPs among the individuals of the same species (Xie et al. 2010). A lot of re-sequenced data is also available to identify the sequence diversity within crop plants. This data revealed whether changes in the genome within a species arose due to one or multiple factors (DePristo et al. 2011). Indeed the function of several genes has also been modified due to changes in a nucleotide which led to differences at the phenotypic level within plants of a species (Chung et al. 2013; Shi et al. 2015). Plant scientists have also reported several functional SNPs associated with phenotypic changes in various accessions of crop plants (Jang et al. 2015; Arruda et al. 2016). Several assays have been reported for genotyping in plants and most of these assays depend upon various molecular markers (Lateef 2015). SNP markers are the most abundant and robust ones for high throughput genotyping of plants. These markers can be found in all regions of a genome and a single gene may contain multiple SNPs (Rafalski 2002; Alkan et al. 2011). They play a significant role in determining phenotypic differences in plants, animals, humans and microbes (Moen et al. 2008; De Souza et al. 2010).

Identification of the location of a particular gene, measurement of distance among genes and their arrangement on the chromosome is called genetic mapping (Semagn et al. 2006). Genetic maps play an important role for the identification of quantitative trait loci (QTLs) (Ganal et al. 2009; Poland et al. 2012). The co-dominant, abundant and cost-effective nature of identifying SNPs made them ideal for construction of genetic maps in plant species. Genetic maps based on SNPs have been developed in several crop species such as cotton (Byers et al. 2012), rice (Xie et al. 2010), maize (Buckler et al. 2009), soybean (Akond et al. 2013) and Brassica (Li et al. 2009). Likewise, genome wide association study (GWAS) using SNP markers is a useful tool to develop genome wide haplotypes (Yano et al. 2016) and to detect natural diversity in cotton (Huang et al. 2017) and other crops (Aranzana et al. 2005; Yu and Buckler 2006; Poland and Rife 2012; Pasam et al. 2012). Identifying patterns among SNPs is a good method to study the evolution of a species at the genomic level to understand the history of a population as well as genetic variation among individuals and the role of selection pressure in inducing variation (Morin et al. 2004). SNPs also provide information about evolution of the modern genome by comparing the sequences of various species (Lu et al. 2013). Phylogenetic analysis of diploid cotton species using SNP markers revealed that A1 and A2 genomes are 98% similar (Shaheen et al. 2016).

Detection of SNPs in plants

Several techniques have been reported for the detection of SNPs in crop plants. Genotyping by sequencing (GBS) has been widely used for the identification of SNPs because of its low cost, rare chances of error and lower DNA purification requirement (Davey et al. 2011). The first step to identify SNPs from GBS is the isolation of genomic DNA. After quantification, the DNA is digested with a restriction enzyme. The choice of restriction enzyme is very important. Two restriction enzymes can be used for double digestion. Methylation sensitive restriction enzymes can also be used for analysis of methylated DNA. Digested DNA is then ligated with adaptors tagged by specific end sequences for polymerase chain reaction (PCR) amplification and sequencing. Various bioinformatic analyses are carried out on sequencing data in order to identify SNPs. These SNPs are further experimentally verified for their functional annotation (Elshire et al. 2011). A disadvantage of GBS is that some important regions of the genome may be missing from genomic libraries because the selected restriction enzymes did not cut in those regions. Another drawback of GBS is potential errors during sequencing (Kim et al. 2016).

The restriction-site associated DNA sequencing (RAD-seq) technique is used for discovery of SNPs when a reference genome is not available (Andrews et al. 2016). With this technique, a P1 barcoded adapter is ligated to short DNA fragments generated after DNA digestion with restriction enzymes. Adapter-ligated fragments of different samples are combined and DNA is sheared. Then, P2 adapter primers are ligated to the DNA for amplification of these fragments and to produce sequencing libraries (Bergey et al. 2013). This technique is independent of a reference genome and relatively inexpensive. The degree of genome coverage can also be adjusted (Reitzel et al. 2013). This method requires high quality DNA and loss of sheared restriction sites may occur due to sequence polymorphism (Suchan et al. 2016). Another technique developed for large scale SNP based genotyping is specific locus amplified fragment sequencing (SLAF-seq). In this method, DNA sample is first digested with MseI and then digested with AluI. The resulting fragments are amplified by PCR, adapters are added and fragments are purified to obtain sequence libraries (Sun et al. 2013). This low cost method is useful for sequence based genotyping of large populations but it does not cover the whole genome (Ma et al. 2015). Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a sequencing tool that is used to investigate gene expression, i.e., transcription factors (Johnson et al. 2007). This tool has been characterized as robust because it profiles protein-DNA interaction in vivo on a genome-wide scale. It has enabled breakthroughs in transcriptional regulatory networks in Saccharomyces cerevisiae and human DNA regulatory sequences (Song et al. 2016). This protocol has great potential but is challenging to perform in plants due to necessary vigorous disruption of cell walls, presence of phenolic compounds and polysaccharides, and limited selection of quality antibodies that give a strong signal.

Reporting of SNPs/QTLs in cotton

Fibre quality and yield traits

Cotton is an important fibre and oilseed crop in tropical, sub-tropical and temperate regions of the world. It is widely grown on an area of 33.4 million hectares with production of 121.4 million bales annually (Johnson et al. 2018). Among 50 species of cotton, the allotetraploid species Gossypium hirsutum (also known as upland cotton) is the most widely grown (Sekmen et al. 2014). Cotton fibres and linters are the ultimate product of this crop that determine its price in an international market (Bradow et al. 1997). Staple length, strength, fineness and uniformity ratio are main parameters which are used to estimate fibre quality. Yield of seed cotton is a complex attribute that depends upon various parameters like boll weight, number of bolls per plant and lint percentage (Tang et al. 1996). Several SNPs and SNP-QTLs have been reported for yield and fibre related traits. Potential SNPs reported in cotton for all traits discussed here are summarized in Table 1. The cotton 63 K SNP array was used to identify 71 QTLs for fibre quality traits strongly linked with SNP markers. These QTLs are comprised of seven pleiotropic QTL clusters, 19 e-QTLs, five hotspots and nine novel QTLs (Li et al. 2016). The linkage mapping, chromosomal localization and phylogenomic characterization of six MYB genes were carried out in four tetraploid cotton species via SNP markers. These MYB genes are actively involved in fibre development. The amplicon cloning and sequencing method of genotyping was used to detect 108 SNPs for these genes. It was determined that all six MYB genes evolved independently and exhibited significant variation in the D genome as compared with the A genome (An et al. 2008). Keerio and colleagues used 107 introgression lines derived from an interspecific cross of G. hirsutum and G. tomentosum for QTL mapping. They used the SLAF-seq method to obtain SNP markers. In this study, 74 QTLs and five clusters were found that were related to various fibre quality parameters (Keerio et al. 2018). Islam and co-workers have detected and validated 5 617 SNPs in upland cotton using GBS (Islam et al. 2015). These researchers have also reported 6 071 SNPs and 86 QTLs for the GhRBB1_A07 gene. The experiment revealed the potential role of this gene in determining quality of cotton fibres. To identify this gene, they used a multi-parent advanced generation inter-cross (MAGIC) population which was developed through random mating of diverse G. hirsutum parents (Islam et al. 2016a).
Table 1

Characterization of reported SNPs/QTLs in cotton for various traits of economic interest, stresses and plant architecture

No.

Cotton species

Source of genotyping

Genotyping method

No. of Experimental Years/Locations/Environments

Traits

No. of QTLs/QTNs/ SNPs discovered

Chromosomal location of major SNPs/QTLs

Ref.

Yield and fibre quality traits

1

G. hirsutum

188 F8 RILs

Cotton 63 K SNP array

2 Locations

Fibre quality and yield

71 QTLs, 16 Stable QTLs

05, 09, 10, 14, 19, 20

Li et al. 2016

2

G. hirsutum

277 F2:3 population

GBS

3 Years

Fibre quality and yield

5 178 SNPs, 110 QTLs

17, 26

Diouf et al. 2018

3

G. hirsutum

231 RILs population

Cotton SNP 80 K array

4 Locations,

Fibre quality and yield

256 QTLs, 57 Stable QTLs

Liu et al. 2018

4 Years

4

G. hirsutum

169 accessions

Cotton SNP 80 K array

2 Years,

Fibre quality traits

342 QTNs

A01, A06, A07, D01, D12, D13

Li et al. 2018a

2 Locations

5

G. hirsutum

201 accessions

Cotton 63 K SNP array

4 Years,

Fibre quality and yield

23 254 SNPs

24

Handi et al. 2017

5 Environments,

3 Locations

6

G. hirsutum

F2:3 lines

RAD-seq

1

Fibre quality and yield

27 QTLs, 9 366 SNPs

Wang et al. 2015b

7

G. hirsutum

178 RILs

RAD-seq

6 Years,

Fibre quality and yield

134 QTLs

Wang et al. 2015a

2 Locations

8

G. hirsutum

355 accessions

SLAF-seq

4 Environments

Fibre quality traits

33 SNPs

D07

Su et al. 2016b

9

G. hirsutum

180 RILs

SLAF-seq

3 Locations,

Fibre quality

6 254 SNPs, 95 QTLs

Ali et al. 2018

2 Years

10

G. hirsutum

196 F6:8 RILs

Cotton SNP 63 k array

5 Years,

Fibre strength

63 QTLs

Zhang et al. 2017

6 Locations

11

G. hirsutum

196 F6:8 RILs

SLAF-seq

11 Environments,

Fibre quality

37 QTLs

25

Zhang et al. 2015

6 Locations

12

G. hirsutum

180 RILs

Cotton SNP 80 K array

3 Environments

Fibre quality

12 116 SNPs,

Tan et al. 2018

104 QTLs

13

G. hirsutum

555 RILs and 11 parents

GBS

4 Years,

Fibre quality

6 071 SNPs,

A07

Islam et al. 2016a

2 Locations

 

86 QTLs

14

G. hirsutum

98 F5:7 RILs

GBS

2 Years,

Fibre quality and agronomic traits

412 SNPs,

08, 23

Gore et al. 2014

2 Locations

28 QTLs

15

G. hirsutum

419 accessions

GBS

12 Environments

Fibre related traits

3 665 030 total SNPs,

A07, A10, D03, D11

Ma et al. 2018

7 383 unique SNPs

16

G. hirsutum

196 F6:8 RILs

SLAF-seq

11 Environments,

boll weight

5 521 SNPs,

A03, A04

Zhang et al. 2016b

6 Locations

146 QTLs

17

G. hirsutum

375 F3 lines

Mapping by sequencing (MBS)

1

MD52ne genes (fibre strength and quality)

27 SNPs,

03, 14, 24

Islam et al. 2016b

4 QTLs

18

G. hirsutum

1 genotype

Amplicon cloning and Sequencing

1

GhMyb8 gene

23 SNPs,

Hsu et al. 2008

GhMyb10 gene

44 SNPs

(Fibre quality)

19

G. hirsutum

Fibreless mutant line

Transcriptome sequencing

1

Fibre initiation

28 610 SNPs

12, 26

Ma et al. 2016

20

G. hirsutum

277 varieties

Eco-TILLING

3 Years,

Fibre quality and seed traits (GhSus genes)

24 SNPs

16, 19, 25

Zeng et al. 2016

3 Locations

21

G. hirsutum

503 accessions

Cotton 63 K SNP array

4 Locations,

Agronomic traits

324 SNPs,

Huang et al. 2017

2 Years

160 QTLs

22

G. hirsutum

355 accessions

SLAF-seq

2 Years,

Lint percentage

12 SNPs,

02, 08

Su et al. 2016a

2 Locations

2 QTLs

23

G. barbadense

2 genotypes,

Cotton 63 K SNP array

1

Fibre quality and yield

460 SNPs,

05, 06, 23

Kumar et al. 2019

185 F2,

29 QTLs

25 F1

24

G. arboreum

230 accessions,

GBS

3 Locations

Agronomic traits

17 883 108 SNPs

Du et al. 2018

G. herbaceum

13 accessions

25

G. hirsutum × G. barbadense

146 Backcross inbred lines (BILs)

RNA-Seq

5 Environments

Fibre length

703 SNPs,

05, 11, 12, 21

Li et al. 2017c

4 QTLs

26

G. hirsutum × G. tomentosum

107 BC5S5 introgression lines

SLAF-seq

3 Locations,

Fibre quality and yield

3 157 SNPs,

A01, A13, DO6

Keerio et al. 2018

2 Years

74 QTLs

27

G. hirsutum

2 lines,

Amplicon cloning and Sequencing

1

R2R3-MYB transcription factors

108 SNPs

An et al. 2008

G. tomentosum

1 line,

G. barbadense

1 line,

G. mustelinum

1 line,

G. hirsutum ×

186 RILs

G. barbadense

 

Biotic and abiotic stress tolerance

28

G. hirsutum

8 varieties

Cotton SNP 63 K array

1

Salt tolerance

7 087 SNPs,

A04, D01

Wang et al. 2016

1 282 Salinity SNPs

29

G. hirsutum

277 F2:3 population

GBS

3 Environments

Salt tolerance

5 178 SNPs,

A02, D02, A01, A05, A03, A10, D01

Diouf et al. 2017

66 QTLs

30

G. hirsutum

97 F5:9 RILs

GBS

2 Environments

Drought and salt tolerance

165 QTLs

Abdelraheem et al. 2018

31

G. hirsutum × G. tomentosum

BC2F2 population

GBS

1

Drought tolerance

10 888 SNPs

23, 25

Magwanga et al. 2018

32

G. arboreum

2 Genotypes,

Amplicon cloning and sequencing

1

MT-sHSP gene

21 SNPs

Shaheen et al. 2009

G. hirsutum

1 genotype,

(Heat stress)

G. herbaceum

1 genotype,

G. sturtianum

1 genotype,

G. aridum

1 genotype,

G. gossypoides

1 genotype,

G. laxum

1 genotype,

G. stocksii

1 genotype

33

G. hirsutum

318 accessions

GBS

3 Years,

Verticillium wilt resistance, Fibre quality and yield

1 871 401 SNPs,

A02, A13, D08

Fang et al. 2017

3 Locations

119 QTLs

34

G. hirsutum

299 accessions

SLAF-seq

2 Years

Verticillium wilt resistance

85 630 SNPs,

A06, A08

Li et al. 2017b

17 unique SNPs

35

G. hirsutum

120 accessions

Cotton SNP 63 K array

2 Environments

Verticillium wilt resistance

21 171 SNPs,

19

Zhao et al. 2017

3 cluster QTLs,

2 Major QTLs

36

G. hirsutum

196 RILs

Cotton SNP 63 K array

4 Years

Verticillium wilt Resistance

119 QTLs

Palanga et al. 2017

37

G. hirsutum

F4:5 population of 285 families

Amplicon cloning and Sequencing

1

Xanthomonas axonopodis resistance (Bacterial blight)

4 SNPs

14

Xiao et al. 2010

38

G. hirsutum

364 F2.3 population

Amplicon cloning and Sequencing

1

Cotton blue disease

4 SNPs

10

Fang et al. 2010

Earliness

39

G. hirsutum

137 RILs

GBS

6 Years

Earliness

6 295 SNPs,

A06, D03

Jia et al. 2016

247 QTLs

40

G. hirsutum

355 accessions

SLAF-seq

4 Environments

Earliness

81 675 SNPs,

D01, D03

Su et al. 2016c

11 Favorable SNP alleles

41

G. hirsutum

169 accession

Cotton SNP 80 K array

2 Years,

Earliness

49 650 SNPs,

29 Significant SNPs

A06, A07, A08, D01, D02, D09

Li et al. 2018b

2 Locations

42

G. hirsutum

170 F2:3 population

GBS

1

Earliness

3 978 SNPs,

D03

Li et al. 2017a

47 QTLs

Plant architecture and related traits

43

G. hirsutum

121 genotypes

GBS

3 Environments

Plant height and fruit spur branch number

2 620 639 SNPs,

A08, D02

Wen et al. 2019

11 QTLs

44

G. hirsutum

355 accessions

SLAF-seq

2 Locations,

Plant architecture traits

93 250 SNPs,

D03

Su et al. 2018

3 Years

22 novel SNPs

45

G. hirsutum

F2 population

dCAPS

1

Short fruiting branch gene

1 SNP locus

D07

Zhang et al. 2018a

46

G. hirsutum

F2:3 lines

GBS

1

Plant height, height of fruiting branch node and number of vegetative shoot

17 QTLs

03, 04, 05, 07, 09, 17, 19, 23, 25

Qi et al. 2017

47

G. hirsutum × G. barbadense

168 F2 population

GBS

1

Nulliplex-branch gene (gb_nb1)

42 SNPs

16(D07)

Chen et al. 2015

48

G. hirsutum

3 parental lines and their BC1F1 population

Amplicon cloning and sequencing

1

Virescent leaf expression

4 SNPs

20

Zhang et al. 2018b

49

G. barbadense

3 genotypes

cDNA library sequencing

2 Locations

Diversity of leaf transcriptomes

> 10 000 SNPs

Kottapalli et al. 2016

50

G. hirsutum

395 genotypes

Cotton 63 K SNP array

1

Diversity analysis

38 822 SNPs

02, 13, 15

Hinze et al. 2017

51

G. hirsutum

11 genotypes and their C5S6 RILs

GBS

1

Genetic diversity

5 617 SNPs

08, 09

Islam et al. 2015

52

G. hirsutum

2 accessions,

GR-RSC and KASP assays

1

Genetic diversity

151 000 putative SNPs

Byers et al. 2012

G. barbadense

2 accessions

53

G. hirsutum

2 genotypes,

GBS

1

Genetic diversity

25 529 SNPs

Logan-Young et al. 2015

G. barbadense

2 genotypes,

G. herbaceum

1 genotype,

G. raimondii

1 genotype

54

G. hirsutum × G. barbadense

59 interspecific F2 individuals

GBS

1

Structural variability

4 999 048 SNPs

Wang et al. 2015c

55

G. hirsutum ×

186 RILs

Golden Gate assay

1

247 SNPs

John et al. 2012

G. barbadense

56

G. hirsutum

440 accessions,

GBS

1

Haplotype distribution

10 129 SNPs

Reddy et al. 2017

G. barbadense

218 accessions

More recently, 110 QTLs and five key genes namely Gh_D12G0410, Gh_D12G0969, Gh_D12G0093, Gh_D12G0435 and Gh_D03G0889 were found to be involved in fibre development in intraspecific crosses of G. hirsutum. These QTLs were detected though the GBS approach (Diouf et al. 2018). Another research group detected 28 QTLs related to fibre quality and agronomic parameters in a recombinant inbred mapping population using the GBS approach. They found seven QTLs for fibre strength while one QTL was detected for lint yield (Gore et al. 2014). Liu et al. used 231 recombinant inbred lines (RILs) and the Cotton SNP 80 K array to identify 122 QTLs for yield related traits and 134 QTLs for fibre quality parameters. Of these QTLs, 57 were detected in multiple environments and, therefore, were named as stable QTLs. The same group has also found 348 quantitative trait nucleotides (QTNs) with 74 stable QTNs for yield and fibre related traits (2018). The research group of Su has recognized 12 SNPs and 2 highly stable QTLs for lint percentage through a GWAS of 355 accessions. They used the SLAF-seq method for genotyping these cotton lines. These SNPs could provide a source to improve lint yield though molecular breeding (Su et al. 2016a). In another study, researchers have discovered 37 QTLs on chromosome 25 in a RIL population of upland cotton using the SLAF-seq method. These QTLs were related to various fibre quality attributes (Zhang et al. 2015). In a separate report, Zhang found 63 QTLs for fibre strength, and these QTL were highly stable in nature. The researchers have used the Cotton SNP 63 K array for genotyping. This chip contains SNPs from several cotton species including G. hirsutum, G. barbadense, G. tometosum, G. mustelinum, G. armourianum and G. longicalyx (Hulse-Kemp et al. 2015; Zhang et al. 2017). SNPs were also used to construct a genetic linkage map through the SLAF-seq approach and identify QTLs for boll weight. One hundred forty-six QTLs were found in 11 environments, and 16 of these QTLs were classified as stable QTLs because they were detected in more than three environments (Zhang et al. 2016b). Resequencing of 419 upland cotton accessions lead to the discovery of 3 665 030 SNPs. These accessions were phenotyped for 13 fibre related traits in 12 different environments. GWAS revealed the association of 7 383 unique SNPs and 4 820 candidate genes for these traits (Ma et al. 2018).

Biotic and abiotic stress tolerance

The cotton plant faces various stresses during its life cycle that limit the productivity of the crop around the world. A single base pair difference between genotypes may be the underlying reason for a differential response to environmental stresses. Many studies have been conducted to evaluate whether genomic information can be used to identify SNPs and QTLs related to biotic and abiotic stress tolerance. The GBS method has been exploited to construct a high density genetic map with 10 888 SNPs from segregating populations of an interspecific cross (G. hirsutum × G. tomentosum) to detect QTLs related to drought tolerance. Thirty-four thousand four hundred two (34 402) and 32 032 genes were also mined within the Dt and At sub-genomes, respectively, to understand the genetics of drought tolerance (Magwanga et al. 2018). Abdelraheem et al. mapped QTLs for drought and salt tolerance using an RIL population derived from a cross of two diverse parental lines. A total of 165 QTLs were discovered though the GBS approach in this study, with 15 QTLs associated with tolerance to salinity and drought stresses common to two environments, i.e., greenhouse and field conditions (2018). Likewise, a high-density linkage map was also constructed using a segregating population of an intra-specific cross between salt tolerant and salt susceptible genotypes. A total of 66 QTLs and 5 178 SNP markers were identified thorough GBS for 10 salinity tolerance related traits in three different environments. Out of these QTLs, 14 were designated as stable due to their presence in more than one environment. Nine and five stable QTLs were located in the Dt and At sub-genomes, respectively, and 12 key genes were found to be involved in conferring salinity resistance at the seedling stage (Diouf et al. 2017). In another experiment, Wang et al. used salt tolerant and susceptible genotypes for mining SNPs using the Cotton 63 K SNP array. A total of 7 087 SNPs were mined, out of which 1 282 were highly related to salinity tolerance in cotton (2016). Beside salinity and drought, another major abiotic stress is high temperature, but the SNPs related to this stress are yet to be explored in cotton. Previously, 21 SNPs were reported for the mitochondrial small heat shock protein gene (MT-sHSP). These SNPs were identified through PCR amplification and sequencing of this gene derived from several cotton species (Shaheen et al. 2009).

Among biotic stresses, Verticillium wilt is one of the major threats to cotton production in the USA, China and Turkey (Baytar et al. 2017). This disease causes significant reduction in yield, and the pathogen can survive for several years in the soil (Zhang et al. 2016a). GWAS revealed 17 SNPs related to Verticillium wilt resistance through the SLAF-seq method of genotyping. These SNPs were stable in three different environments. QTL analysis also revealed that CG02 (a disease resistance protein belonging to the TIR-NBS-LRR class) seems to be responsible for resistance to Verticillium dahlia (Li et al. 2017b). Likewise, Zhao et al. used the Cotton SNP 63 K array to detect SNPs and QTLs related to this disease in two different environments. The results revealed the presence of 21 171 SNPs across 120 accessions of G. hirsutum. Three clustered QTLs, two major QTLs, 12 functional genes and six mRNAs conferring resistance against Verticillium were also detected (2017). In another research report, genomic analysis of many accessions through GBS revealed three trait loci involved in Verticillium wilt resistance. A candidate gene (Gh_D06G0687) was also reported that conferred resistance to this pathogen by encoding an NB-ARC domain (Fang et al. 2017). Cotton blue disease is one of the major diseases of cotton in Brazil, and it is transmitted through aphids (Silva et al. 2008). Haplotype mapping of a large segregating population through amplicon cloning and sequencing using specific SSR primers revealed that resistance was conferred by four SNPs (Fang et al. 2010). Another four SNP markers were discovered through haplotype mapping that were highly associated with resistance to bacterial blight disease (Xanthomonas axonopodis pv. Malvacearum) (Xiao et al. 2010). Aside from these diseases, the productivity of cotton is also affected by cotton leaf curl virus, root rot and cotton mosaic virus. Moreover, a huge number of pest insects are associated with this crop, but no SNPs linked to these biotic stresses have been reported in the literature to our knowledge. Therefore, it is important for molecular plant breeders to explore SNPs related to these biological threats in order to understand the basis of genetic resistance.

Earliness

Early maturity is an important feature which is essential if growing more than one crop per year or to escape from late season environmental stresses. An early maturing genotype also requires less irrigation as well as less fertilizer and chemical inputs (Bednarz and Nichols 2005; Cober et al. 2010; Akter et al. 2019). One study was conducted to detect SNPs related to early maturity in upland cotton using 137 RILs. Sequence based genotyping revealed that 6 295 SNPs and 247 QTLs were associated with six morphological traits related to earliness. These QTLs were deemed highly stable due to their identification in six consecutive years, i.e., 2010 to 2015 (Jia et al. 2016). In another project, the SLAF-seq genotyping strategy was used to identify SNPs related to six earliness linked traits from 355 G. hirsutum accessions grown in four different environments. A total of 81 675 SNPs and 11 highly favorable SNP alleles were discovered. GWAS also revealed a potential candidate gene (CotAD_01947) that was associated with early maturity (Su et al. 2016c). More recently, a GWAS was conducted to identify SNPs and genes associated with four earliness related traits. A total of 49 650 SNPs were discovered using the cotton SNP 80 K array, and 29 SNPs were highly associated with early maturity. In addition, two potential candidate genes (Gh_D01G0340 and Gh_D01G0341) were also related to earliness (Li et al. 2018b). Likewise, the GBS method has been used to construct a high-density genetic linkage map to discover QTLs related to this trait. The linkage map was comprised of 3 978 SNPs, and 47 QTLs were detected. These QTLs were associated with six earliness qualities. A study of an early maturing cultivar revealed two highly expressed potential candidate genes (i.e., Gh_D03G0885 and Gh_D03G0922) (Li et al. 2017a).

Plant architecture and other important traits

A combination of traits are desirable to increase productivity of the cotton crop. Plant architecture is an important factor that determines suitability of cotton genotypes for mechanical picking and as well as to improve yield (Song and Zhang 2009). This complex multigenic trait has been given less importance in cotton as comparing with wheat and rice where deployment of dwarfing genes led to the Green Revolution. To investigate the genetic basis of plant architecture, a GWAS experiment was conducted with 121 upland cotton genotypes. The researchers identified 2 620 639 SNPs, 11 QTLs and 5 candidate genes for two plant architecture traits, i.e., fruit spur branch number and plant height. The cotton accessions were genotyped with the whole genome resequencing approach and phenotyped in multiple environments (Wen et al. 2019). In another study, 93 250 SNPs for five plant architecture traits were found in 355 Chinese upland cotton accessions using the SLAF-Seq method. GWAS revealed 22 highly associated SNPs and 21 candidate genes for these traits (Su et al. 2018). Molecular analysis of the short fruiting branch gene was carried out in an F2 population between two parents, one with short fruiting branches and the other with long fruiting branches. One SNP locus (SNP_GH1570) was found to be highly associated with short fruiting branches when using derived cleaved amplified polymorphic sequences (dCAPS). It was concluded that this SNP maker was useful for selection of cotton plants with short fruiting branches (Zhang et al. 2018a). A separate study revealed the presence of 17 QTLs associated with plant height, height of fruiting branch node and number of vegetative shoots. These QTLs were located on nine different chromosomes and were detected through the GBS method (Qi et al. 2017).

A nulliplex-branch mutant was developed to explore the position of flowers on the cotton plant. This mutant line exhibits flowers which arise directly from leaf axils on the main stem, without a fruiting branch, i.e., monopodial and sympodial branches. This trait is desirable so planting densities can be increased without using chemicals to regulate plant growth (Du et al. 1996). To discover the molecular basis of the nulliplex-branch mutant, a genetic map was constructed from a G. hirsutum by G. barbadense interspecific population. The map was comprised of 11 805 SNP markers which were identified through next generation sequencing. The analysis revealed that 42 SNPs were associated with gb_nb1, a recessive gene that controls the nulliplex-branch trait (Chen et al. 2015). Virescent leaves in cotton are characterized by their yellowish appearance at early stages of plant growth. This abnormality is due to a recessive gene, v1. Sequence analysis of wild and mutant alleles showed the differences in four SNPs at sequence positions 426, 450, 709 and 1 082. It was further revealed that the SNP at position 1 082 caused a point mutation that resulted in synthesis of arginine instead of lysine in mutant polypeptides (Zhang et al. 2018b). In another study, genetic diversity for leaf transcriptomes was identified in G. barbadense. Through a cDNA library sequencing technique, researchers have found more than 10 000 SNPs associated with various traits in three Egyptian cotton cultivars (Kottapalli et al. 2016). Likewise, many SNP markers were also identified using the GBS approach. These SNPs were considered as a source of variation for various agronomic and biochemical traits in cotton (Logan-Young et al. 2015).

Conclusions

The study of SNPs opens new horizons for plant biotechnologists to improve various features of a crop plant; a single SNP has the potential to alter the expression of a gene by inducing changes in its amino acid sequence. SNPs identified in coding regions of genes have gained more attention from molecular plant breeders as comparing with those found in non-coding regions. Various assays have been exploited using these markers to detect genetic variability in the genomes of field crops. Plant researchers have utilized these markers successfully in cotton and other crops for improvement and development of tolerance to biotic and abiotic stresses, fibre quality and yield in order to enhance profitability for farmers.

Abbreviations

dCAPS: 

Derived cleaved amplified polymorphic sequences

GBS: 

Genotyping by sequencing

GWAS: 

Genome wide association study

NGS: 

Next generation sequencing

PCR: 

Polymerase chain reaction

QTL: 

Quantitative trait loci

QTN: 

Quantitative trait nucleotides

RAD-seq: 

Restriction-site associated DNA sequencing

RILs: 

Recombinant inbred lines

SLAF-seq: 

Specific locus amplified fragment sequencing

SNP: 

Single nucleotide polymorphism

Declarations

Acknowledgements

The authors are highly grateful to reviewers for critical review and also thankful to all of collaborators for giving productive contribution for preparing this review article.

Funding

Not applicable.

Availability of data and materials

Not applicable.

Authors’ contributions

Majeed S and Azhar MT has collected the literature and wrote this draft, Rana IA, Atif RM, Ali Z and Hinze L have reviewed and edited this article for publication. All authors read and approved the final manuscript.

Authors’ information

Not applicable.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All the authors and co-authors are agreed to submit the review article in BMC Journal of Cotton Research.

Competing interests

The authors declare that they have no competing interests.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan
(2)
Centre of Advanced Studies in Agriculture and Food Security/Center of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, Pakistan
(3)
Institute of Plant Breeding and Biotechnology, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan
(4)
US Department of Agriculture, Agricultural Research Service, College Station, TX, USA

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