The enzyme Dicer is best known for its role as a riboendonuclease in the small RNA pathway. In this canonical role, Dicer is a critical regulator of the biogenesis of microRNA and small interfering RNA, as well as a growing number of additional small RNAs derived from various sources. Emerging evidence demonstrates that Dicer's endonuclease role extends beyond the generation of small RNAs; it is also involved in processing additional endogenous and exogenous substrates, and is becoming increasingly implicated in regulating a variety of other cellular processes, outside of its endonuclease function. This review will describe the canonical and newly identified functions of Dicer.

Motivation: Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation. Results: We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard–Stephens–Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new ‘logistic factor analysis’ framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions. Availability and Implementation: A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html. Contact: jstorey@princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors, and assess models specific for ER-negative disease.

RNA interference is a powerful mechanism for sequence-specific inhibition of gene expression. It is widely known that small interfering RNAs (siRNAs) targeting the same region of a target-messenger RNA can have widely different efficacies. In efforts to better understand the siRNA features that influence knockdown efficiency, we analyzed siRNA interactions with a high-molecular weight complex in whole cell extracts prepared from two different cell lines. Using biochemical tools to study the nature of the complex, our results demonstrate that the primary siRNA-binding protein in the whole cell extracts is Dicer. We find that Dicer is capable of discriminating highly functional versus poorly functional siRNAs by recognizing the presence of 2-nt 3′ overhangs and the thermodynamic properties of 2–4 bp on both ends of effective siRNAs. Our results suggest a role for Dicer in pre-selection of effective siRNAs for handoff to Ago2. This initial selection is reflective of the overall silencing potential of an siRNA.