Comparison of methods for estimating the nucleotide substitution matrix

10.1186/1471-2105-9-511BMC Bioinformatics9-BBMI

Genome-Wide Association Study Identifies Candidate Loci Underlying Agronomic Traits in a Middle American Diversity Panel of Common Bean

Common bean (Phaseolus vulgaris L.) breeding programs aim to improve both agronomic and seed characteristics traits. However, the genetic architecture of the many traits that affect common bean production are not completely understood. Genome-wide association studies (GWAS) provide an experimental approach to identify genomic regions where important candidate genes are located. A panel of 280 modern bean genotypes from race Mesoamerica, referred to as the Middle American Diversity Panel (MDP), were grown in four US locations, and a GWAS using \u3e150,000 single-nucleotide polymorphisms (SNPs) (minor allele frequency [MAF] ≥ 5%) was conducted for six agronomic traits. The degree of inter- and intrachromosomal linkage disequilibrium (LD) was estimated after accounting for population structure and relatedness. The LD varied between chromosomes for the entire MDP and among race Mesoamerica and Durango–Jalisco genotypes within the panel. The LD patterns reflected the breeding history of common bean. Genome-wide association studies led to the discovery of new and known genomic regions affecting the agronomic traits at the entire population, race, and location levels. We observed strong colocalized signals in a narrow genomic interval for three interrelated traits: growth habit, lodging, and canopy height. Overall, this study detected ~30 candidate genes based on a priori and candidate gene search strategies centered on the 100-kb region surrounding a significant SNP. These results provide a framework from which further research can begin to understand the actual genes controlling important agronomic production traits in common bean

Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator

The rapid accumulation of whole-genome data has renewed interest in the study of genomic rearrangements. Comparative genomics, evolutionary biology, and cancer research all require models and algorithms to elucidate the mechanisms, history, and consequences of these rearrangements. However, even simple models lead to NP-hard problems, particularly in the area of phylogenetic analysis. Current approaches are limited to small collections of genomes and low-resolution data (typically a few hundred syntenic blocks). Moreover, whereas phylogenetic analyses from sequence data are deemed incomplete unless bootstrapping scores (a measure of confidence) are given for each tree edge, no equivalent to bootstrapping exists for rearrangement-based phylogenetic analysis. We describe a fast and accurate algorithm for rearrangement analysis that scales up, in both time and accuracy, to modern high-resolution genomic data. We also describe a novel approach to estimate the robustness of results-an equivalent to the bootstrapping analysis used in sequence-based phylogenetic reconstruction. We present the results of extensive testing on both simulated and real data showing that our algorithm returns very accurate results, while scaling linearly with the size of the genomes and cubically with their number. We also present extensive experimental results showing that our approach to robustness testing provides excellent estimates of confidence, which, moreover, can be tuned to trade off thresholds between false positives and false negatives. Together, these two novel approaches enable us to attack heretofore intractable problems, such as phylogenetic inference for high-resolution vertebrate genomes, as we demonstrate on a set of six vertebrate genomes with 8,380 syntenic blocks. A copy of the software is available on demand