**1. Introduction**

Strawberry is one of the temperate climate fruit species which can be grown almost anywhere in the world from Ecuador to Siberia within wide ecological limits due to its high adaptability. Strawberry is loved by everyone and has a great market advantage in fresh and industrial consumption [1]. Strawberry, which is one of 23 species in the Rosaceae family, including apple, cherry, and rose species, is in the *Fragaria* genus [2]. The most common commercially grown strawberries are octoploid (2*n* = 8*x* = 56) and haploid chromosome number (*n* = 7) [3] and constitute a large part of the world's production [1]. The estimated genome size of the Camasora strawberry cultivar which belongs to the *Fragaria* x *ananassa* Duchesne species has been reported as 813.4 Mb [4]. Strawberry production in the world is increasing day by day. Strawberry contains important mineral substances and vitamins for human health and nutrition, such as salicylic acid, calcium, iron, and phosphorus. In addition, strawberry is rich in antioxidants and phenolic compounds for human health. These health benefits play an important role in the rapid increase of its production [5]. Many studies reported that strawberry consumption has positive effects on human health, preventing aging, Alzheimer's, obesity, and cancer diseases, especially heart diseases [6–8].

In the strawberry breeding programs, cultivars should be productive throughout the whole season, tolerant to diseases post harvesting, resistant to long transportation, with high fruit firmness and high fruit quality are the main breeding objectives for many years and many studies have been carried out intensively in European countries, especially in the USA, and Asian countries such as China [9, 10]. However, recently, some breeding programs have been carried out in strawberries covering topics such as adaptation to arid conditions or climate change like GOODBERRY supported by the European Union's Horizon 2020 research and innovation program [10]. Breeding programs should use several strawberry genetic resources or different populations and also focus on the development of new genotypes or lines resistant or tolerant to different abiotic stress conditions as breeding criteria. Selection of promising lines in strawberry breeding will be possible not only with traditional breeding methods but also with biotechnological approaches in strawberry breeding. However, identifying major and minor QTL regions associated with such complex traits that are severely affected by environmental conditions and controlled by multiple genes is a very difficult and time-consuming task in strawberries due to its open pollinated and highly heterozygous nature.

Many agriculturally important traits, such as yield and quality, tolerance to environmental stresses, and resistance to certain diseases, are controlled by polygenes, which complicates the breeding process as phenotypic traits only partially reflect the genetic influence of the individuals. These complex traits are defined as quantitative traits (multifactorial traits or polygenic), and specific regions in the genome of genes associated with a trait are described as quantitative trait loci (QTLs). Detection of QTL region or a gene in the plant genome is a complex process. QTL analysis can simply be expressed as the determination of the relationship between phenotype and genotypic data. All the loci are used to divide the population used in mapping into different genotypic groups based on the presence or absence of a particular locus and to determine whether there are significant differences between groups related to that specific trait [11, 12]. Depending on the marker system and population types, if there is a significant difference between the phenotypic means (2 or 3) of the groups, it indicates that the locus has a QTL controlling the relevant trait [13]. QTL mapping also measures the relative effects of alleles on traits. It also provides the basis for marker-assisted selection (MAS), which not only determines the physical location of QTLs on the genome but also shortens breeding time [14]. QTLs determined from several different locations are called more stable QTL (sQTL) and they are preferred for MAS due to their reliability. A genetically diverse and well-segregated population is required for QTL mapping and determining their position in the genome. QTL analysis is performed by determining the correlation between the phenotypic and genetic data with help of different algorithms [15]. The first necessary condition in QTL mapping is intra-species or interspecies populations acquired F2, recombinant inbred lines (RILs), backcross 1 (BC1), double haploid lines (DHLs), closely isogenic lines (NILs), and full-sib F1 (pseudo-testcross) lines [16].

#### *Quantitative Trait Loci Associated with Agronomical Traits in Strawberry DOI: http://dx.doi.org/10.5772/intechopen.108311*

A set of QTLs determined in the same region or confidence interval (CI) region for more than one trait is defined as a set of QTLs or overlapping QTLs (cQTLs) [17]. cQTLs are potentially important QTLs that control multiple traits. The QTL is classified as a major QTL if it accounts for 10% or more of the total phenotypic variation in the population, and as a minor QTL if it accounts for 10% or less. Many mapping definitions such as linkage, association, NIL, F2, or BC can be used to detect QTLs. Classical biparental linkage mapping involves the crossing of parents with the contrasting phenotype for a trait [18]. QTL analysis uses the linkage between two loci and recombination in biparental populations, while association mapping (AM) uses variations generated by landraces and genotypes found in natural populations. The populations used in association mapping are divided into different groups based on recombination, such as biparental populations. In other words, it describes the correlation of traits and markers in more than one biparental population. Thus, an associated locus in the AM means that it has a similar effect on many individuals in the population [19].

Thanks to the advancements in next-generation sequencing technologies and the reduction of sequencing costs, high-density sequencing can be easily performed and big data can be obtained for the detection of more variants. Higher density linkage maps can be constructed by using the obtained big genotypic data and can increase the chance of detecting rare loci. As a result, these advances greatly contributed to the identification of QTL regions in the genome. High-density genetic maps are constructed using genotyping by sequencing (GBS) data and have been used to successfully identify QTLs related to phenological, biochemical fruit traits, and pathogen resistance or tolerance in many different species. In addition, QTLs that control abiotic stress factors such as cold, heat, salinity, frost, and drought can also be more accurately detected with the help of high-density genetic linkage maps. The purpose of this section is to review the current literature on the identification and evaluation of the genomic positions of QTLs that control phenological, morphological and biochemical fruit quality traits, and disease tolerance traits in strawberries.
