A novel segregation distortion in intraspecific population of Asian cotton (Gossypium arboretum L.) detected by molecular markers

SSR-Linkage map of interspecific populations derived from Gossypium trilobum and Gossypium thurberi and determination of genes harbored within the segregating distortion regions

Wild cotton species have significant agronomic traits that can be introgressed into elite cultivated varieties. The use of a genetic map is important in exploring, identification and mining genes which carry significant traits. In this study, 188 F₂mapping individuals were developed from Gossypium thurberi (female) and Gossypium trilobum (male), and were genotyped by using simple sequence repeat (SSR) markers. A total of 12,560 simple sequence repeat (SSR) markers, developed by Southwest University, thus coded SWU were screened out of which only 994 were found to be polymorphic, and 849 markers were linked in all the 13 chromosomes. The map had a length of 1,012.458 cM with an average marker distance of 1.193 cM. Segregation distortion regions (SDRs) were observed on Chr01, Chr02, Chr06, Chr07 Chr09, Chr10 and Chr11 with a large proportion of the SDR regions segregating towards the heterozygous allele. There was good syntenic block formation that revealed good collinearity between the genetic and physical map of G. raimondii, compared to the Dt_sub genome of the G. hirsutum and G. barbadense. A total of 2,496 genes were mined within the SSR related regions. The proteins encoding the mined genes within the SDR had varied physiochemical properties; their molecular weights ranged from 6.586 to 252.737 kDa, charge range of -39.5 to 52, grand hydropathy value (GRAVY) of -1.177 to 0.936 and isoelectric (pI) value of 4.087 to 12.206. The low GRAVY values detected showed that the proteins encoding these genes were hydrophilic in nature, a property common among the stress responsive genes. The RNA sequence analysis revealed more of the genes were highly upregulated in various stages of fiber development for instance; Gorai.002G241300 was highly up regulated at 5, 10, 20 and 25 day post anthesis (DPA). Validation through RT-qPCR further revealed that these genes mined within the SDR regions might be playing a significant role under fiber development stages, therefore we infer that Gorai.007G347600 (TFCA), Gorai.012G141600 (FOLB1), Gorai.006G024500 (NMD3), Gorai.002G229900 (LST8) and Gorai.002G235200 (NSA2) are significantly important in fiber development and in turn the quality, and further researches needed to be done to elucidate their exact roles in the fiber development process. The construction of the genetic map between the two wild species paves away for the mapping of quantitative trait loci (QTLs) since the average distance between the markers is small, and mining of genes on the SSR regions will provide an insight in identifying key genes that can be introgressed into the cultivated cotton cultivars.

Genome Wide SSR High Density Genetic Map Construction from an Interspecific Cross of Gossypium hirsutum × Gossypium tomentosum

A high density genetic map was constructed using F2 population derived from an interspecific cross of G. hirsutum × G. tomentosum. The map consisted of 3093 marker loci distributed across all the 26 chromosomes and covered 4365.3 cM of cotton genome with an average inter-marker distance of 1.48 cM. The maximum length of chromosome was 218.38 cM and the minimum was 122.09 cM with an average length of 167.90 cM. A sub-genome covers more genetic distance (2189.01 cM) with an average inter loci distance of 1.53 cM than D sub-genome which covers a length of 2176.29 cM with an average distance of 1.43 cM. There were 716 distorted loci in the map accounting for 23.14% and most distorted loci were distributed on D sub-genome (25.06%), which were more than on A sub-genome (21.23%). In our map 49 segregation hotspots (SDR) were distributed across the genome with more on D sub-genome as compared to A genome. Two post-polyploidization reciprocal translocations of "A2/A3 and A4/A5" were suggested by seven pairs of duplicate loci. The map constructed through these studies is one of the three densest genetic maps in cotton however; this is the first dense genome wide SSR interspecific genetic map between G. hirsutum and G. tomentosum.

Identification of new heading date determinants in wheat 5B chromosome

BACKGROUND

Variability of heading date may assist in wheat adaptation to local environments. Thereafter, discovery of new heading date determinants is important for cereal improvement. In this study we used common wheat cultivar Chinese Spring (CS) and the substitution line of CS with 5B chromosome from T. dicoccoides (CS-5Bdic), different in their heading date by two weeks, to detect determinants of heading date on 5B chromosome.

RESULTS

The possible influence of the VRN-B1 gene, the most powerful regulator of flowering, located on 5B chromosome, to differences in heading time between CS and CS-5Bdic was studied. The sequencing of this gene from CS-5Bdic showed that an insertion of a nucleotide triplet produced an additional amino acid in the corresponding protein. No changes in the transcription levels of each homoeologous VRN-1 loci were found in CS-5Bdic by comparison with CS. To ascertain the loci determining heading date difference, a set of 116 recombinant inbred 5В chromosomal lines as a result of hybridization of CS with CS-5Bdic were developed and their heading dates were estimated. Using the Illumina Infinium 15 k Wheat platform, 379 5B-specific polymorphic markers were detected and a genetic map with 82 skeletal markers was constructed. Phenotype (heading date) - genotype association analysis revealed seventy eight markers in pericentromeric region of 5B chromosome significantly associated with heading date variation. Based on this estimation and synteny with model crop genomes we identified the three best candidate genes: WRKY, ERF/AP2 and FHY3/FAR1.

CONCLUSIONS

We supposed that the difference in activity of WRKY, ERF/AP2 and/or FHY3/FAR1 transcription factors between CS and CS-5Bdic to be a probable reason for the observed difference in heading dates. Data obtained in this study provide a good basis for the subsequent investigation of heading time pathways in wheat.

Identification and Characterization of Segregation Distortion Loci on Cotton Chromosome 18

Segregation distortion is commonly detected via genetic mapping and this phenomenon has been reported in many species. However, the genetic causes of the segregation distortion regions in a majority of species are still unclear. To genetically dissect the SD on chromosome 18 in cotton, eight reciprocal backcross populations and two F₂ populations were developed. Eleven segregation distortion loci (SDL) were detected in these ten populations. Comparative analyses among populations revealed that SDL18.1 and SDL18.9 were consistent with male gametic competition; whereas SDL18.4 and SDL18.11 reflected female gametic selection. Similarly, other SDL could reflect zygotic selection. The surprising finding was that SDL18.8 was detected in all populations, and the direction was skewed towards heterozygotes. Consequently, zygotic selection or heterosis could represent the underlying genetic mechanism for SDL18.8. Among developed introgression lines, SDL18.8 was introgressed as a heterozygote, further substantiating that a heterozygote state was preferred under competition. Six out of 11 SDL on chromosome 18 were dependent on the cytoplasmic environment. These results indicated that different SDL showed varying responses to the cytoplasmic environment. Overall, the results provided a novel strategy to analyze the molecular mechanisms, which could be further exploited in cotton interspecific breeding programs.

The Negative Correlation between Fiber Color and Quality Traits Revealed by QTL Analysis

Naturally existing colored cotton was far from perfection due to having genetic factors for lower yield, poor fiber quality and monotonous color. These factors posed a challenge to colored cotton breeding and innovation. To identify novel quantitative trait loci (QTL) for fiber color along with understanding of correlation between fiber color and quality in colored cotton, a RIL and two F2 populations were generated from crosses among Zong128 (Brown fiber cotton) and two white fiber cotton lines which were then analyzed in four environments. Two stable and major QTLs (qLC-7-1, qFC-7-1) for fiber lint and fuzz color were detected accounting for 16.01%-59.85% of the phenotypic variation across multiple generations and environments. Meanwhile, some minor QTLs were also identified on chromosomes 5, 14, 21 and 24 providing low phenotypic variation (<5%) from only F2 populations, not from the RILs population. Especially, a multiple-effect locus for fiber color and quality has been detected between flanking markers NAU1043 and NAU3654 on chromosome 7 (A genome) over multiple environments. Of which, qLC-7-1, qFC-7-1 were responsible for positive effects and improved fiber color in offsprings. Meanwhile, the QTLs (qFL-7-1, qFU-7-1, qFF-7-1, qFE-7-1, and qFS-7-1) for fiber quality had negative effects and explained 2.19%-8.78% of the phenotypic variation. This multiple-effect locus for fiber color and quality may reveal the negative correlation between the two types of above traits, so paving the way towards cotton genetic improvement.

Association of AFLP and SSR markers with agronomic and fibre quality traits in Gossypium hirsutum L

Molecular markers linked to QTL contributing to agronomic and fibre quality traits would be useful for cotton improvement. We have attempted to tag yield and fibre quality traits with AFLP and SSR markers using F(2) and F(3) populations of a cross between two Gossypium hirsutum varieties, PS56-4 and RS2013. Out of 50 AFLP primer combinations and 177 SSR primer pairs tested, 32 AFLP and four SSR primers were chosen for genotyping F(2) individuals. Marker-trait associations were studied for eight agronomic and five fibre quality traits through simple and multiple regression analysis (MRA) using a set of 92 AFLP polymorphic loci and four SSR markers. Simple linear regression analysis (SLRA) identified 23 markers for eight different traits whereas multiple regression analysis identified 30 markers for at least one of the 13 traits. SSR marker BNL 3502 was consistently identified to be associated with fibre strength. While all the markers identified in SLRA were also detected in MRA, as many as 16 of the 30 markers were identified to be associated with respective traits in both F2 and F3 generations. The markers explained up to 41 per cent of phenotypic variation for individual traits. A number of markers were found to be associated with multiple traits suggesting clustering of QTLs for fibre quality traits in cotton.