A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the "missing heritability" problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Genome sequence for the blue-flowered Andean shrub Iochroma cyaneum reveals extensive discordance across the berry clade of Solanaceae.

The tomato (Solanum lycopersicum L.) family, Solanaceae, is a model clade for a wide range of applied and basic research questions. Currently, reference-quality genomes are available for over 30 species from seven genera, and these include numerous crops as well as wild species [e.g., Jaltomata sinuosa (Miers) Mione and Nicotiana attenuata Torr. ex S. Watson]. Here we present the genome of the showy-flowered Andean shrub Iochroma cyaneum (Lindl.) M. L. Green, a woody lineage from the tomatillo (Physalis philadelphica Lam.) subfamily Physalideae. The assembled size of the genome (2.7 Gb) is more similar in size to pepper (Capsicum annuum L.) (2.6 Gb) than to other sequenced diploid members of the berry clade of Solanaceae [e.g., potato (Solanum tuberosum L.), tomato, and Jaltomata]. Our assembly recovers 92% of the conserved orthologous set, suggesting a nearly complete genome for this species. Most of the genomic content is repetitive (69%), with Gypsy elements alone accounting for 52% of the genome. Despite the large amount of repetitive content, most of the 12 I. cyaneum chromosomes are highly syntenic with tomato. Bayesian concordance analysis provides strong support for the berry clade, including I. cyaneum, but reveals extensive discordance along the backbone, with placement of chili pepper and Jaltomata being highly variable across gene trees. The I. cyaneum genome contributes to a growing wealth of genomic resources in Solanaceae and underscores the need for expanded sampling of diverse berry genomes to dissect major morphological transitions.