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Soil-based microorganisms assume a direct and crucial role in the promotion of soil health, quality and fertility, all factors known to contribute heavily to the quality and yield of agricultural products. Cover cropping, used in both traditional and organic farming, is a particularly efficient and environmentally favorable tool for manipulating microbiome composition in agricultural soils and has had clear benefits for soil quality and crop output. Several long-term investigations have evaluated the influence of multi-mix (multiple species) cover crop treatments on soil health and microbial diversity. The present study investigated the short-term effects of a seven species multi-mix cover crop treatment on soil nutrient content and microbial diversity, compared to a single-mix cover crop treatment and control. Analysis of 16S sequencing data of isolated soil DNA revealed that the single-mix cover crop treatment decreased overall microbial abundance and diversity, whereas the control and multi-mix treatments altered the overall microbial composition in similar fluctuating trends. Furthermore, we observed significant changes in specific bacteria belonging to the phyla Acidobacteria, Actinobacteria, Planctomycetes, Proteobacteria and Verrucombicrobia for all treatments, but only the single-mix significantly decreased in abundance of the selected bacteria over time. Our findings indicate that the control and multi-mix treatments are better at maintaining overall microbial composition and diversity compared to the single-mix. Further study is required to elucidate the specific difference between the treatment effect of the multi-mix treatment and the control, given that their microbial composition changes over time were similar but they diverge into two populations of unique bacterial types by the end of this short-term study. [ABSTRACT FROM AUTHOR]

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers. [ABSTRACT FROM AUTHOR]