Genetic diversity and population structure of Heteropogon contortus L. germplasm collected from diverse agro-climatic regions in India and development of a core germplasm set

Heteropogon contortus, an important constituent of major grasslands of India, Australia and many countries in Africa, Asia and the Americas, is important for pasture and grassland productivity. Hence genetic improvement of the grass needs attention. A genetic variability study, including development of a core subset, was carried out by evaluating 235 accessions collected from different agro-ecological zones of India. The study, based on 16 metric and 14 non-metric traits along with 8 nutritional parameters, indicated that considerable genetic variability existed among the germplasm and selection could result in identification of suitable types for target environments. Clustering and subclustering was performed to select 35 accessions to form a core subset. The statistical analysis indicated that the core subset captured almost all the variability present in the entire germplasm. The study will help researchers to focus future studies on this core subset in developing genetic improvement programs.

Comparison of optimization methods for core subset selection from a large collection of Mexican wheat landraces characterized by SNP markers

Core subset selection from collections hosted by seed banks, grow in importance as the number of accessions and genetic marker information rapidly increases. A data set of 20,526 single-nucleotide polymorphism (SNP) markers characterizing 7986 Mexican creole wheat landraces, was used to test 11 methods for core subset selection, through optimization criteria containing average genetic distance and genetic diversity. Allele richness was used as an additional criterion to qualify the generated core subsets. Three replications with random samples of 1500 SNP loci, each comprising a maximum of 3000 alleles, were used to perform the method evaluations through four different objective functions. The LR greedy search (LR) and LR with random first pair (LRSemi) were consistently best across all assays for maximizing the objective functions, and they performed well even for criteria not included in those functions. The Tukey's HSD (honest significant difference) multiple comparisons grouped those methods together with the sequential forward selection (SFS) and SFS with random first pair (SFSSemi) strategies as the top set of approaches. All of them are simple heuristic maximization algorithms, and outperformed two more sophisticated optimization approaches: parallel mixed replica exchange and replica exchange Monte Carlo. For their efficiency to optimize the objective functions and computing speed, the LRSemi and SFSSemi methods demonstrated to be good alternatives for core subset selection from large collections of highly homozygous accessions characterized by many biallelic markers.

Genetic diversity of safflower (Carthamus tinctorius L.) germplasm as revealed by SSR markers

Safflower is a multi-purpose oilseed crop, primarily known for good quality oil containing highest polyunsaturated fatty acid content (>80%) among edible oils. In this study, a core subset of 148 safflower accessions representing 15 countries, predominantly of Indian origin, was evaluated for agronomic traits and characterized for genetic diversity, population structure and linkage disequilibrium (LD) using 44 simple sequence repeat (SSR) loci across 11 linkage groups to enable its utilization in breeding and genetic mapping purposes. The collection had substantial variation for seed yield-related traits. SSR allelic variation was low as indicated by average number of alleles (3.6) per locus, gene diversity (0.314) and polymorphism information content (0.284). Cluster analysis (neighbour-joining tree) revealed five major genotypic groups with very low bootstrap support. STRUCTURE analysis showed recognizable population structure; based on membership coefficients ( ≥ 0.75), 52% accessions were classified into four populations (K= 4) and the remaining 48% accessions into admixture group. High Fst values (0.30–0.48) suggested that the populations were substantially differentiated. Analysis of molecular variance results showed that maximum of genetic variation (85%) was explained between individuals within the population suggesting that the population structure was only weak. About 1.9% of SSR locus pairs were in LD, which appeared to be low. High phenotypic variation, mild population structure and low level of LD among unlinked loci suggested that the core subset can be explored for association mapping of seed yield components in safflower.

An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT

Objective Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is the emergent international health terminology standard for encoding clinical information in electronic health records. The CORE Problem List Subset was created to facilitate the terminology’s implementation. This study evaluates the CORE Subset’s coverage and examines its growth pattern as source datasets are being incorporated. Methods Coverage of frequently used terms and the corresponding usage of the covered terms were assessed by “leave-one-out” analysis of the eight datasets constituting the current CORE Subset. The growth pattern was studied using a retrospective experiment, growing the Subset one dataset at a time and examining the relationship between the size of the starting subset and the coverage of frequently used terms in the incoming dataset. Linear regression was used to model that relationship. Results On average, the CORE Subset covered 80.3% of the frequently used terms of the left-out dataset, and the covered terms accounted for 83.7% of term usage. There was a significant positive correlation between the CORE Subset’s size and the coverage of the frequently used terms in an incoming dataset. This implies that the CORE Subset will grow at a progressively slower pace as it gets bigger. Conclusion The CORE Problem List Subset is a useful resource for the implementation of Systematized Nomenclature of Medicine Clinical Terms in electronic health records. It offers good coverage of frequently used terms, which account for a high proportion of term usage. If future datasets are incorporated into the CORE Subset, it is likely that its size will remain small and manageable.