Genome Resequencing Reveals Genetic Variation between the Parents of An Elite Hybrid Upland Cotton

Abstract

Cotton is one of the most important economic crops worldwide. As the global demands rising, cotton yield improvement is the most important goal of cotton breeding. Hybrids have great potential for increasing yield, however, the genetic mechanism of hybrids is still not clear. To investigate the genetic basis of cotton hybrids, we resequenced 9053 and sGK9708 with 62.13x coverage depth, the parents of the elite hybrid cotton CCRI63 that has obvious heterosis in lint percentage (LP) and boll weight (BW). Based on the cotton reference genome (TM-1), 1,287,661 single nucleotide polymorphisms (SNPs) and 152,479 insertions/deletions (InDels) were identified in 9053, and 1,482,784 SNPs and 152,985 InDels in sGK9708. Among them, 8649 SNPs and 629 InDels in the gene coding regions showed polymorphism between parents. Moreover, these variations involved 5092 genes, and 3835 of these genes were divided into 10 clusters based on the gene expression profiles. The genes in Cluster 3 and 7 were specifically expressed in the ovule and fiber development stage, suggesting that they might relate to LP and BW. We further co-localized the polymorphic SNPs and InDels with the reported quantitative trait loci (QTLs) of LP and BW, and identified 68 genes containing the polymorphic SNPs or InDels within these QTL intervals and as being related to fiber development. This suggested that the outstanding traits of CCRI63 such as LP and BW might be generated by accumulating the favorable variations from the parents. The results generated herein provide a genetic basis for cotton hybrids and genetic markers for marker-assisted selection breeding of cotton.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

“…The availability of a genetic map with higher resolution and better genome coverage will represent a valuable resource for QTL mapping and provide useful information for understanding of disease resistance mechanisms in cotton. G. hirsutum L. is characterized by high yield and wide adaptability [ 57 , 58 ], while G. barbadense L. is highly resistant to diseases [ 59 ]. It is therefore representative of many traits to construct a consensus map of G. barbadense L. and G. hirsutum L. maps.…”

Development of molecular resources for the genetic improvement of noug (Guizotia abyssinica (L.f) Cass): a mini review

“…Different types of molecular marker systems are widely applied to oilseed crops for genotyping and breeding efforts. For instance, the genetic diversity of some major oilseed crops was studied and assisted the breeding efforts using RAPD in Caster bean (Gajera et al 2010), AFLP in Safflower (Zhang et al 2006), RFLP in Sunflower (Gentzbittel et al 1994), ISSR in groundnut (Mondal et al 2009), SSR, and/or EST-SSR in Soybean (Liu et al 2010) and SNPs in Cotton (Song et al 2018). The progress in the exploitation of genetic diversity and utilization of the genetic gains on major oilseed crops shifted the interest also to minor-oilseed crops.…”

“…Linkage and quantitative trait loci (QTL) mapping help to identify the relative location of various genetic markers present in the chromosome of crops (Singh et al 2015) and to identify regions of the genome contributing to variation in desirable traits. High-density genetic maps are particularly important for genome assembly and precise mapping of interest agronomic traits (genes) for marker-assisted selection (Song et al 2018). This approach has been applied in oilseed crops like identification of QTL markers contributing to plant growth, oil yield, and fatty acid composition in Jatropha (King et al 2015), SSR based genetic mapping and identification of QTLs in Sesame (Wang et al 2017), high-density genetic map construction, and identification of QTLs controlling oleic acid and linoleic acid in peanut using SSR and specific length amplified fragment sequencing (SLAFseq markers (Hu et al 2018) and mapping of quantitative trait loci for yield-related traits in peanut using high-resolution SNPs markers (Liang et al 2018).…”