Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization.

Development of efficient analytical techniques is required for effective interpretation of biological data to take novel hypotheses and finding the critical predictive patterns. Machine Learning algorithms provide a novel opportunity for development of low-cost and practical solutions in biology. In this study, we proposed a new integrated analytical approach using supervised machine learning algorithms and microsatellites data of worldwide vitis populations. A total of 1378 wild (V. vinifera spp. sylvestris) and cultivated (V. vinifera spp. sativa) accessions of grapevine were investigated using 20 microsatellite markers. Data cleaning, feature selection, and supervised machine learning classification models vis, Naive Bayes, Support Vector Machine (SVM) and Tree Induction methods were implied to find most indicative and diagnostic alleles to represent wild/cultivated and originated geography of each population. Our combined approaches showed microsatellite markers with the highest differentiating capacity and proved efficiency for our pipeline of classification and prediction of vitis accessions. Moreover, our study proposed the best combination of markers for better distinguishing of populations, which can be exploited in future germplasm conservation and breeding programs.

Molecular insights into the genetic diversity and population structure of Artemisia annua L. as revealed by insertional polymorphisms.

The knowledge about the level of genetic diversity and population structure in natural populations of Artemisia annua L. is a primary step in breeding programs for development of new cultivars with higher artemisinin level and better quality of secondary metabolites composition. We used PCR-based "retrotransposon-microsatellite amplified polymorphisms" (REMAPs) to study insertional polymorphism in A. annua genome to assess genetic variability and population structure in a collection of 118 accessions collected from north and northwest of Iran. Twenty-five primer combinations of 10 retrotransposon and seven ISSR primers amplified a total of 693 clear and unambiguous fragments in the studied accessions. The average number of bands, polymorphic bands, polymorphism, effective number of alleles, Shannon's information index and expected heterozygosity were 27.72, 24.76, 88.14%, 1.47, 0.42 and 0.28, respectively. The analysis of molecular variance revealed high genetic variation present within sampled geographical regions. Distance-based cluster analysis assigned the studied accessions into four clusters according to their geographical origin, which were also confirmed by principal coordinate analysis. In model-based Bayesian clustering, the maximum value of Δk was obtained when the collection of 118 assayed A. annua accessions assigned into two subgroups (K = 2). The results showed the high genetic variation in the collection of Iranian sweet wormwood which revealed by REMAP markers indicating the reliability and efficiency of this marker system for analysis of genetic diversity and population structure of A. annua. Supplementary Information: The online version contains supplementary material available at 10.1007/s40415-022-00860-x.

Preliminary evidence for associations between molecular markers and quantitative traits in a set of bread wheat (Triticum aestivum L.) cultivars and breeding lines.

The identification of polymorphic markers associated with various quantitative traits allows us to test their performance for the exploitation of the extensive quantitative variation maintained in gene banks. In the current study, a set of 97 wheat germplasm accessions including 48 cultivars and 49 breeding lines were evaluated for 18 agronomic traits. The accessions were also genotyped with 23 ISSR, nine IRAP and 20 REMAP markers, generating a total of 658 clear and scorable bands, 86% of which were polymorphic. Both neighbor-joining dendrogram and Bayesian analysis of clustering of individuals revealed that the accessions could be divided into four genetically distinct groups, indicating the presence of a population structure in current wheat germplasm. Associations between molecular markers and 18 agronomic traits were analyzed using the mixed linear model (MLM) approach. A total of 94 loci were found to be significantly associated with agronomic traits (P≤0.01). The highest number of bands significantly associated with the 18 traits varied from 11 for number of spikelets spike-1 (NSS) to two for grain yield in row (GRY). Loci ISSR16-9 and REMAP13-10 were associated with three different traits. The results of the current study provide useful information about the performance of retrotransposon-based and ISSR molecular markers that could be helpful in selecting potentially elite gene bank samples for wheat-breeding programs.