The availability of genomewide dense markers brings opportunities and challenges to breeding programs. An important question concerns the ways in which dense markers and pedigrees, together with phenotypic records, should be used to arrive at predictions of genetic values for complex traits. If a large number of markers are included in a regression model, marker-specific shrinkage of regression coefficients may be needed. For this reason, the Bayesian least absolute shrinkage and selection operator (LASSO) (BL) appears to be an interesting approach for fitting marker effects in a regression model. This article adapts the BL to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly. Connections between BL and other marker-based regression models are discussed, and the sensitivity of BL with respect to the choice of prior distributions assigned to key parameters is evaluated using simulation. The proposed model was fitted to two data sets from wheat and mouse populations, and evaluated using cross-validation methods. Results indicate that inclusion of markers in the regression further improved the predictive ability of models. An R program that implements the proposed model is freely available.

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by F_ST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features—the use of haplotype information and of the hierarchical structure of populations—significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection.

To comprehensively characterize microRNA (miRNA) expression in breast cancer, we performed the first extensive next-generation sequencing expression analysis of this disease. We sequenced small RNA from tumors with paired samples of normal and tumor-adjacent breast tissue. Our results indicate that tumor identity is achieved mainly by variation in the expression levels of a common set of miRNAs rather than by tissue-specific expression. We also report 361 new, well-supported miRNA precursors. Nearly two-thirds of these new genes were detected in other human tissues and 49% of the miRNAs were found associated with Ago2 in MCF7 cells. Ten percent of the new miRNAs are located in regions with high-level genomic amplifications in breast cancer. A new miRNA is encoded within the ERBB2/Her2 gene and amplification of this gene leads to overexpression of the new miRNA, indicating that this potent oncogene and important clinical marker may have two different biological functions. In summary, our work substantially expands the number of known miRNAs and highlights the complexity of small RNA expression in breast cancer. Cancer Res; 71(1); 78–86. ©2011 AACR.