While up to 25% of ovarian cancer (OVCA) cases are thought to be due to inherited factors, the majority of genetic risk remains unexplained. To address this gap, we sought to identify previously undescribed OVCA risk variants through the whole exome sequencing (WES) and candidate gene analysis of 48 women with ovarian cancer and selected for high risk of genetic inheritance, yet negative for any known pathogenic variants in either BRCA1 or BRCA2. In silico SNP analysis was employed to identify suspect variants followed by validation using Sanger DNA sequencing. We identified five pathogenic variants in our sample, four of which are in two genes featured on current multi-gene panels; (RAD51D, ATM). In addition, we found a pathogenic FANCM variant (R1931*) which has been recently implicated in familial breast cancer risk. Numerous rare and predicted to be damaging variants of unknown significance were detected in genes on current commercial testing panels, most prominently in ATM (n = 6) and PALB2 (n = 5). The BRCA2 variant p.K3326*, resulting in a 93 amino acid truncation, was overrepresented in our sample (odds ratio = 4.95, p = 0.01) and coexisted in the germline of these women with other deleterious variants, suggesting a possible role as a modifier of genetic penetrance. Furthermore, we detected loss of function variants in non-panel genes involved in OVCA relevant pathways; DNA repair and cell cycle control, including CHEK1, TP53I3, REC8, HMMR, RAD52, RAD1, POLK, POLQ, and MCM4. In summary, our study implicates novel risk loci as well as highlights the clinical utility for retesting BRCA1/2 negative OVCA patients by genomic sequencing and analysis of genes in relevant pathways.

BackgroundLow complexity regions (LCRs) are a ubiquitous feature in genomes and yet their evolutionary history and functional roles are unclear. Previous studies have shown contrasting evidence in favor of both neutral and selective mechanisms of evolution for different sets of LCRs suggesting that modes of identification of these regions may play a role in our ability to discern their evolutionary history. To further investigate this issue, we used a multiple threshold approach to identify species-specific profiles of proteome complexity and, by comparing properties of these sets, determine the influence that starting parameters have on evolutionary inferences.ResultsWe find that, although qualitatively similar, quantitatively each species has a unique LCR profile which represents the frequency of these regions within each genome. Inferences based on these profiles are more accurate in comparative analyses of genome complexity as they allow to determine the relative complexity of multiple genomes as well as the type of repetitiveness that is most common in each. Based on the multiple threshold LCR sets obtained, we identified predominant evolutionary mechanisms at different complexity levels, which show neutral mechanisms acting on highly repetitive LCRs (e.g., homopolymers) and selective forces becoming more important as heterogeneity of the LCRs increases.ConclusionsOur results show how inferences based on LCRs are influenced by the parameters used to identify these regions. Sets of LCRs are heterogeneous aggregates of regions that include homo- and heteropolymers and, as such, evolve according to different mechanisms. LCR profiles provide a new way to investigate genome complexity across species and to determine the driving mechanism of their evolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0625-0) contains supplementary material, which is available to authorized users.

Background: The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging.Discussion: In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies.Conclusion: Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since “system inheritance” is defined by the genome context (gene content and genomic topology) while “parts inheritance” is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Low complexity regions (LCRs) are a common feature shared by many genomes, but their evolutionary and functional significance remains mostly unknown. At the core of the uncertainty is a poor understanding of the mechanisms that regulate their retention in genomes, whether driven by natural selection or neutral evolution. Applying a comparative approach of LCRs to multiple strains and species is a powerful approach to identify patterns of conservation in these regions. Using this method, we investigate the evolutionary history of LCRs in the genus Plasmodium based on orthologous protein coding genes shared by 11 species and strains from primate and rodent-infecting pathogens. We find multiple lines of evidence in support of natural selection as a major evolutionary force shaping the composition and conservation of LCRs through time and signatures that their evolutionary paths are species specific. Our findings add a comparative analysis perspective to the debate on the evolution of LCRs and harness the power of sequence comparisons to identify potential functionally important LCR candidates.