LAMARC 2.0 is freely available at http://evolution.gs.washington.edu/lamarc

Coalescent genealogy samplers attempt to estimate past qualities of a population, such as its size, growth rate, patterns of gene flow or time of divergence from another population, based on samples of molecular data. Genealogy samplers are increasingly popular because of their potential to disentangle complex population histories. In the last decade they have been widely applied to systems ranging from humans to viruses. Findings include detection of unexpected reproductive inequality in fish, new estimates of historical whale abundance, exoneration of humans for the prehistoric decline of bison and inference of a selective sweep on the human Y chromosome. This review summarizes available genealogy-sampler software, including data requirements and limitations on the use of each program.

All cancers are believed to arise by dynamic, stochastic somatic genomic evolution with genome instability, generation of diversity and selection of genomic alterations that underlie multi-stage progression to cancer. Advanced esophageal adenocarcinomas (EAs) have high levels of somatic copy number alterations. Barrett’s esophagus (BE) is a risk factor for developing EA, and somatic chromosomal alterations (SCAs) are known to occur in BE. The vast majority (~95%) of individuals with BE do not progress to EA during their lifetimes, but a small subset develop EA, many of which arise rapidly even in carefully monitored patients without visible endoscopic abnormalities at the index endoscopy. Using a well-designed, longitudinal case-cohort study, we characterized SCA as assessed by SNP arrays over space and time in 79 “progressors” with BE as they approach the diagnosis of cancer and 169 “nonprogressors” with BE who did not progress to EA over 20,425 person-months of follow-up. The genomes of nonprogressors typically had small localized deletions involving fragile sites and 9p loss/copy neutral LOH that generate little genetic diversity and remained relatively stable over prolonged follow-up. As progressors approach the diagnosis of cancer, their genomes developed chromosome instability with initial gains and losses, genomic diversity, and selection of SCAs followed by catastrophic genome doublings. Our results support a model of differential disease dynamics in which nonprogressor genomes largely remain stable over prolonged periods whereas progressor genomes evolve significantly increased SCA and diversity within four years of EA diagnosis, suggesting a window of opportunity for early detection.