Beschreibung: Many approaches for inferring adaptive molecular evolution analyze the unfolded site frequency spectrum (SFS), a vector of counts of sites with different numbers of copies of derived alleles in a sample of alleles from a population. Accurate inference of the high-copy-number elements of the SFS is difficult, however, because of misassignment of alleles as derived vs. ancestral. This is a known problem with parsimony using outgroup species. Here we show that the problem is particularly serious if there is variation in the substitution rate among sites brought about by variation in selective constraint levels. We present a new method for inferring the SFS using one or two outgroups that attempts to overcome the problem of misassignment. We show that two outgroups are required for accurate estimation of the SFS if there is substantial variation in selective constraints, which is expected to be the case for nonsynonymous sites in protein-coding genes. We apply the method to estimate unfolded SFSs for synonymous and nonsynonymous sites in a population of Drosophila melanogaster from phase 2 of the Drosophila Population Genomics Project. We use the unfolded spectra to estimate the frequency and strength of advantageous and deleterious mutations and estimate that ~50% of amino acid substitutions are positively selected but that 〈0.5% of new amino acid mutations are beneficial, with a scaled selection strength of N e s 12.

Beschreibung: Molecular heterogeneity in human breast cancer has challenged diagnosis, prognosis, and clinical treatment. It is well known that molecular subtypes of breast tumors are associated with significant differences in prognosis and survival. Assuming that the differences are attributed to subtype-specific pathways, we then suspect that there might be gene regulatory mechanisms that modulate the behavior of the pathways and their interactions. In this study, we proposed an integrated methodology, including machine learning and information theory, to explore the mechanisms. Using existing data from three large cohorts of human breast cancer populations, we have identified an ensemble of 16 master regulator genes (or MR16) that can discriminate breast tumor samples into four major subtypes. Evidence from gene expression across the three cohorts has consistently indicated that the MR16 can be divided into two groups that demonstrate subtype-specific gene expression patterns. For example, group 1 MRs, including ESR1 , FOXA1 , and GATA3 , are overexpressed in luminal A and luminal B subtypes, but lowly expressed in HER2-enriched and basal-like subtypes. In contrast, group 2 MRs, including FOXM1 , EZH2 , MYBL2 , and ZNF695 , display an opposite pattern. Furthermore, evidence from mutual information modeling has congruently indicated that the two groups of MRs either up- or down-regulate cancer driver-related genes in opposite directions. Furthermore, integration of somatic mutations with pathway changes leads to identification of canonical genomic alternations in a subtype-specific fashion. Taken together, these studies have implicated a gene regulatory program for breast tumor progression.