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Genetic diversity of 51 oil-tea camellia germplasms was analyzed using the optimized inter-simple sequence repeat (ISSR)–PCR reaction system with 22 primers screened from a set of 100 ISSR primers. The results showed that 493 discernible loci with distinct electrophoretic bands were obtained, of which, 478 loci (96.78%) were polymorphic. This indicated that oil-tea germplasms possess abundant genetic diversities. By clustering analysis performed using softwares of NTSYS 2.10 and Winboot, 51 oil-tea germplasms were divided into two groups: Group I had 48 lines of Camellia oleifera Abel, while Group II had three C. oleifera Abel related species and their similarity coefficient was 0.62. Group I was further divided into Group I-1 and Group I-2, and their similarity coefficient (Gs) was 0.634. All members of Group I-1 originated from Hunan Province, while Group I-2 included the rest of Hunan lines and those originated from other regions of China. Analyzed by software POPGENE 1.32, the Shannon's information index (I*) of genetic polymorphism was 0.3852, the genetic diversity among different region populations (Ht) was 0.2537, the genetic diversity within populations (Hs) was 0.15545, the differentiation coefficient of genetic diversity among populations (Gst) was 0.3967, and the gene flow among populations (Nm*) was 0.8262. The Nei's genetic distances between the Hunan population and the populations originated from other regions of China implied that geographic isolation strongly influenced genetic differentiation among populations. Meanwhile, seedling rootstock grafting and high grafting for tree crown produced genetic variations among clonal offsprings.

The principal aim of this paper is to evaluate alterations in the geographical distributions of priority crop wild relatives (CWRs) within the Fertile Crescent Region. The analysis encompasses a comprehensive set of 441 priority CWRs. To compile species distribution information, a meticulously constructed database of occurrence records was utilized. The process involved gathering current and projected climate data from WorldClim to facilitate the evaluation. This study employed MaxEnt version 3.4.1 for species distribution modeling (SDM) considering two distinct dispersal scenarios. One scenario proposed that the CWR taxa were incapable of adapting to their preferred weather conditions, while the other scenario suggested that CWR could track suitable climates to ensure their survival. The generated maps underwent processing in DIVA-GIS version 7.5. Notably, Aegilops bicornis, a wild relative of bread wheat, and Triticum monococcum, a wild relative of wheat, are anticipated to face substantial impacts due to climate change. Specifically, the dispersal of T. monococcum is predicted to shift toward the north, where new suitable areas are expected to emerge in Northern Iraq. Conversely, Cicer bijugum, a wild relative of chickpea, Vicia grandiflora, a wild relative of common vetch, and Beta corolliflora, a wild relative of sugar beet, are projected to experience a comparatively slight impact from climate change, as their potential distribution areas are estimated to diminish. Overall, this study foresees a decline in CWRs across the Fertile Crescent, particularly noting reduced habitats for the CWRs in Turkey.

As the last decade of human genomics research begins to bear the fruit of advancements in precision medicine, it is important to ensure that genomics’ improvements in human health are distributed globally and equitably. An important step to ensuring health equity is to improve the human reference genome to capture global diversity by including a wide variety of alternative haplotypes, sequences that are not currently captured on the reference genome.We present a method that localizes 100 basepair (bp) long sequences extracted from short-read sequencing that can ultimately be used to identify what regions of the human genome non-reference sequences belong to.We extract reads that don’t align to the reference genome, and compute the population’s distribution of 100-mers found within the unmapped reads. We use genetic data from families to identify shared genetic material between siblings and match the distribution of unmapped k-mers to these inheritance patterns to determine the the most likely genomic region of a k-mer. We perform this localization with two highly interpretable methods of artificial intelligence: a computationally tractable Hidden Markov Model coupled to a Maximum Likelihood Estimator. Using a set of alternative haplotypes with known locations on the genome, we show that our algorithm is able to localize 96% of k-mers with over 90% accuracy and less than 1Mb median resolution. As the collection of sequenced human genomes grows larger and more diverse, we hope that this method can be used to improve the human reference genome, a critical step in addressing precision medicine’s diversity crisis.

The recent accumulation of information on plant genomes has enabled us to study adaptive traits at both the phenotypic and molecular levels. Genetic diversification is a consequence of the existence of a diverse set of environments. Plant breeding will accelerate the rate of micro-evolution in our changing world. To understand ongoing micro-evolutionary processes, genetic alterations in response to temperature, photoperiod, and biotic environments were investigated in wild and cultivated rice. These adaptive mechanisms were not well explained by a few major genes, suggesting that epistasis, genotype × environment (GE) interaction, and linked genes were involved in addition to genes with a small additive effect. Genetic diversity is affected both by current patterns of micro-evolutionary forces, such as gene flow and selection, and by phylogenetic history. Genealogies of agronomic genes provided insight into their history. Unexpectedly, the "Geen Revolution" gene (sd1) preexisted in the wild ancestor, showing that farmers selected it to obtain a high yield in response to altered practices in agriculture. In contrast, in the case of C, A, and wx genes, variants were generated from landraces through natural or artificial selection, suggesting that each of the genes may have its own history.