Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.06.11.448075

Analysis of the Genetic Structure and Diversity of Upland Cotton Groups in Different Planting Areas Based on SNP Markers

Zhang, Z.·Wang, J.·Gong, Z.·Liang, Y.·Ai, X.·Sang, Z.·Guo, J.·Li, X.·Zheng, J.
View original source ↗Export metadata ↓

Abstract

Genetic diversity, kinship and population genetic structure analyses of Gossypium hirsutum germplasm can provide a better understanding of the origin and evolution of G. hirsutum biodiversity. In this study, 1313331 SNP molecular markers were used to construct a phylogenetic tree of each sample using MEGAX, to perform population structure analysis by ADMIXTURE software and principal component analysis (PCA) by EIGENSOFT software, and to estimate relatedness using SPAGeDi. ADMIXTURE software divided the experimental cotton population into 16 subgroups, and the Gossypium hirsutum samples could be roughly clustered according to source place, but there were some overlapping characteristics among samples. The experimental cotton population was divided into six groups according to source to calculate the genetic diversity index (H), and the obtained value (0.306) was close to that for germplasm collected by others in China. Cluster 4 had a relatively high genetic diversity level (0.390). The degrees of genetic differentiation within the experimental cotton population groups were low (the population differentiation indexes ranged from 0.02368 to 0.10664). The genetic distance among cotton accessions varied from 0.000332651 to 0.562664014, with an average of 0.25240429. The results of this study may provide a basis for mining elite alleles and using them for subsequent association analysis.

Source connections

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhang, Z., Wang, J., Gong, Z., Liang, Y., Ai, X., Sang, Z., Guo, J., Li, X., Zheng, J.. 2021-06-12. Analysis of the Genetic Structure and Diversity of Upland Cotton Groups in Different Planting Areas Based on SNP Markers. https://doi.org/10.1101/2021.06.11.448075

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The tiger shark, Galeocerdo cuvier (Peron & Lesueur, 1822), genome reveals genomic and epigenomic signatures of shark evolution and demographic history

Cartilaginous fishes (Chondrichthyes) represent one of the oldest extant vertebrate lineages, persisting for over 450 million years, yet genomic resources remain limited for many species despite widespread conservation concern. The tiger shark, Galeocerdo cuvier (Peron & Lesueur, 1822), the sole surviving member of the family Galeocerdonidae, is a circumglobally cosmopolitan predator vital in ecosystem regulation but is experiencing population declines. Here, we generated the first chromosome-scale haplotype-phased reference genome for the tiger shark and integrated comparative and population genomics with methylomics to characterise its genome architecture and evolutionary history. This assembly presented the first evidence of male heterogamety in this species. Comparative analyses revealed lineage-specific transposable element expansions across the chondrichthyan class suggesting ongoing genome restructuring in Carcharhiniform sharks. We report the first hydroxymethylome in a chondrichthyan, higher than that of other vertebrates, hinting at novel DNA evolutionary and regulatory pathways in an early-diverging lineage of jawed vertebrates. We identified genomic signatures of prolonged population decline that correlated with historical climate fluctuations and have resulted in sustained low levels of genetic heterozygosity in this species. These findings offer new insights into shark and broader vertebrate genomic and epigenomic evolution while providing valuable resources for conservation genomics of one of the world's most widespread top-order predators in the face of accelerating environmental change.

genomics↗
bioRxiv2026

A genomic prediction framework for growth traits in black soldier fly (Hermetia illucens) breeding programmes

Background Genomic prediction has been applied in livestock, plant and aquaculture species to accelerate genetic gain. However, its application and potential still remain unexplored in farmed black soldier fly (BSF), an emerging globally important insect production species. Due to the diversity of BSF feed sources, selection strategies need to be evaluated across various feedstocks and production environments to aid in building genomic selection into BSF breeding programmes. In this study, 2,083 BSF larvae from the base population were reared on three diets (SYK: Soy-okara, BSG: brewers' spent grain and FVW: fruit and vegetable waste), and 536 from the F7 population were reared on a chicken starter feed diet. Larvae were phenotyped for four growth traits (LBW, larval body weight; LL, larval length; LW, larval width; LSA, larval surface area) and genotyped across 4,680 SNPs. Genomic prediction was evaluated using seven methods under additive (A) and additive-dominance (AD) models, considering across- and within-diet effects, genotype by diet (G x D) interactions, differences amongst reference and test generations, and SNP densities. Results Performance varied across all scenarios, with LBW showing higher predictive ability (PA) among the growth traits. Accounting for dominance generally improved PA. Multivariate models accounting for G x D performed similarly to, or marginally better than, within-diet predictions. Cross-diet prediction was more variable and generally lower than within-diet prediction, showing reduced transferability across untested nutritional environments. Reducing the SNP density from 4,680 to 3,000 SNPs maintained comparable PA; however, PA decreased below 3,000 SNPs. Across multiple generations, predictive performance declined, resulting from reduced genetic connectivity and possible genotype-by-environment (G x E) interactions between the reference and testing populations. Conclusions Overall, these results provide an application-based framework for genomic prediction in BSF in semi- or commercial production settings. Results also highlight the importance of the genetic model, representative reference populations, and appropriate marker densities for genomic selection.

genomics↗
bioRxiv2026

SPACE-Tag Enables High-Throughput Spatial Chromatin Profiling With CUT&Tag

Spatial epigenomics maps chromatin states within intact tissues, yet current methods remain limited by accessibility and scale. We developed SPACE-Tag, a spatial CUT&Tag framework that converts epigenetic marks-associated DNA into polyadenylated RNA through T7 based linear amplification, enabling direct integration with standard spatial transcriptomic workflows. Applied to the mouse brain, SPACETag resolved activating and repressive chromatin landscapes, recovered region-specific gene regulatory programs across anatomical domains, and showed strong correlation with existing bulk and single cell reference data. We generated an integrated multimodal atlas of the aging mouse brain, profiling five histone modifications and the transcriptome in parallel across the adult lifespan to establish the largest spatial multimodal dataset to date. This analysis uncovered sharp regional divergence with aging in the brain: fiber tracts showed widespread transcriptional downregulation, often without nearby chromatin state transitions, whereas the isocortex showed coordinated chromatin transcriptome changes, including an H3K4me1 peak module that increased with age, was enriched at super-enhancers and linked to upregulation of neuronal plasticity regulators. These findings highlight the power of multimodal and high throughput spatial genomics in uncovering the divergent regulatory logic that governs tissue-specific vulnerability and resilience in the aging brain.

genomics↗