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Recent genome-wide association studies identified the angiotensin-converting enzyme gene (ACE) as an Alzheimer's disease (AD) risk locus. However, the pathogenic mechanism by which ACE causes AD is unknown. Using whole-genome sequencing, we identified rare ACE coding variants in AD families and investigated one, ACE1 R1279Q, in knockin (KI) mice. Similar to AD, ACE1 was increased in neurons, but not microglia or astrocytes, of KI brains, which became elevated further with age. Angiotensin II (angII) and angII receptor AT1R signaling were also increased in KI brains. Autosomal dominant neurodegeneration and neuroinflammation occurred with aging in KI hippocampus, which were absent in the cortex and cerebellum. Female KI mice exhibited greater hippocampal electroencephalograph disruption and memory impairment compared to males. ACE variant effects were more pronounced in female KI mice, suggesting a mechanism for higher AD risk in women. Hippocampal neurodegeneration was completely rescued by treatment with brain-penetrant drugs that inhibit ACE1 and AT1R. Although ACE variant-induced neurodegeneration did not depend on β-amyloid (Aβ) pathology, amyloidosis in 5XFAD mice crossed to KI mice accelerated neurodegeneration and neuroinflammation, whereas Aβ deposition was unchanged. KI mice had normal blood pressure and cerebrovascular functions. Our findings strongly suggest that increased ACE1/angII signaling causes aging-dependent, Aβ-accelerated selective hippocampal neuron vulnerability and female susceptibility, hallmarks of AD that have hitherto been enigmatic. We conclude that repurposed brain-penetrant ACE inhibitors and AT1R blockers may protect against AD.

It is currently thought that life-long blood cell production is driven by the action of a small number of multipotent haematopoietic stem cells. Evidence supporting this view has been largely acquired through the use of functional assays involving transplantation. However, whether these mechanisms also govern native non-transplant haematopoiesis is entirely unclear. Here we have established a novel experimental model in mice where cells can be uniquely and genetically labelled in situ to address this question. Using this approach, we have performed longitudinal analyses of clonal dynamics in adult mice that reveal unprecedented features of native haematopoiesis. In contrast to what occurs following transplantation, steady-state blood production is maintained by the successive recruitment of thousands of clones, each with a minimal contribution to mature progeny. Our results demonstrate that a large number of long-lived progenitors, rather than classically defined haematopoietic stem cells, are the main drivers of steady-state haematopoiesis during most of adulthood. Our results also have implications for understanding the cellular origin of haematopoietic disease.

Message from the ISCB: The Bioinformatics Open Source Conference (BOSC) is a yearly meeting organized by the Open Bioinformatics Foundation (OBF), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. BOSC has been run since 2000 as a two-day Special Interest Group (SIG) before the annual ISMB conference. The 17th annual BOSC (http://www.open-bio.org/wiki/BOSC_2016) took place in Orlando, Florida in July 2016. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community. The conference brought together nearly 100 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, and open and reproducible science.

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research.

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

Background The impact of raltegravir-resistant HIV-1 minority variants (MVs) on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs. Methods A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naive patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser. Results Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P

Objective: To evaluate the utility of targeted exome sequencing for the molecular diagnosis of mitochondrial disorders, which exhibit marked phenotypic and genetic heterogeneity. Methods: We considered a diverse set of 102 patients with suspected mitochondrial disorders based on clinical, biochemical, and/or molecular findings, and whose disease ranged from mild to severe, with varying age at onset. We sequenced the mitochondrial genome (mtDNA) and the exons of 1,598 nuclear-encoded genes implicated in mitochondrial biology, mitochondrial disease, or monogenic disorders with phenotypic overlap. We prioritized variants likely to underlie disease and established molecular diagnoses in accordance with current clinical genetic guidelines. Results: Targeted exome sequencing yielded molecular diagnoses in established disease loci in 22% of cases, including 17 of 18 (94%) with prior molecular diagnoses and 5 of 84 (6%) without. The 5 new diagnoses implicated 2 genes associated with canonical mitochondrial disorders ( NDUFV1 , POLG2 ), and 3 genes known to underlie other neurologic disorders ( DPYD , KARS , WFS1 ), underscoring the phenotypic and biochemical overlap with other inborn errors. We prioritized variants in an additional 26 patients, including recessive, X-linked, and mtDNA variants that were enriched 2-fold over background and await further support of pathogenicity. In one case, we modeled patient mutations in yeast to provide evidence that recessive mutations in ATP5A1 can underlie combined respiratory chain deficiency. Conclusion: The results demonstrate that targeted exome sequencing is an effective alternative to the sequential testing of mtDNA and individual nuclear genes as part of the investigation of mitochondrial disease. Our study underscores the ongoing challenge of variant interpretation in the clinical setting.

Reliable detection of structural variation (SV) is playing an increasingly important role in cancer diagnostics and treatments. We introduce a best practice integrative workflow for detecting large structural variations (SVs) such as deletions, duplications, inversions, fusions and translocations from standard DNA re-sequencing techniques, annotating and prioritising for clinically actionable events. While established RNA-Seq fusion workflows exist to detect and filter fusion events in tumors detecting large structural changes from exome- and WGS data remains challenging. Current DNA-based SV callers predict a large percentage of false positive events, and the resulting event lists are not prioritised for validation. To streamline follow-up studies we automate running multiple structural variant callers (Manta, WHAM, Lumpy, MetaSV), identifying potentially disruptive large scale events. We evaluate the integrated workflow sensitivity and specificity against the ICGC-TCGA DREAM Mutation Calling Challenge data set, optimise filtering criteria based on quality and supporting information, and annotate the filtered calls using known cancer related alterations from CIViC and publicly available gene lists from AstraZeneca. This enables us to prioritise known and potentially interesting events over intergenic events or non-whole exon deletions. We show validation results from test data and clinically actionable inversions, duplications and fusion events from cancer cell lines. Our approach is based on a two tier approach of first focusing on known events that yield fusion transcripts such as the TACC3-FGFR3 tandem duplication and ALK-EML4 inversion, and secondarily on events in oncogenes and tumor suppressors of interest. While whole genome sequencing is the preferred approach to detecting these events, we also show how by design or by the nature of the breakpoint being close enough to exons, hybrid capture data can also be surprisingly useful for SV inference. Citation Format: Miika Ahdesmäki, Brad Chapman, Sally Luke, Hedley Carr, Daniel Stetson, Oliver Hofmann, Justin Johnson. Validated structural variant detection with prioritisation of known cancer related changes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3604.

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with breast cancer risk. However, the underlying genetic structure in these regions is not fully understood and it is likely that the index GWAS signal originates from one or more yet unidentified causal variants within the region. We used next-generation sequencing to characterize 12 GWAS-discovered breast cancer loci in a total of 2,300 breast cancer cases and 2,300 controls across four ethnic populations. Study subjects were women from the Nurses’ Health Study (NHS) and the Multiethnic Cohort (MEC). Region intervals spanned between 46kb and 973kb and we hybrid-captured and sequenced a total of 5.5Mb. On average, we were able to capture 82% of the non-repetitive sequence in the targeted regions, and the average fraction of captured bases sequenced with a depth %gt20x was more than 95%. After single nucleotide variant (SNV) calling and quality control, a total of 138,898 SNVs remained for analysis. 87.4% of those had a minor allele frequency less than 0.5% and 50.2% were private mutations. Across all regions, a total of 81 SNVs showed evidence for association with breast cancer (P&lt0.001) but we did not find unequivocal association evidence supporting a causal role for any individual SNV. However, in two of the regions (2q35 and 11q13), we were able to refine the GWAS signal and identify a high posterior probability (&gt90%) set including less than 15 SNVs. We also conducted burden tests of rare variants (MAF&lt0.005) but did not identify any significant associations after adjusting for number of tests. However, rare variants in FGFR2 and a second gene at 10q26 were nominally associated with breast cancer (P&lt0.05). We are currently imputing existing breast cancer GWAS using this sequencing data as a reference panel which will triple our current sample size. These results stress the importance of large study samples when attempting to refine GWAS signals and illustrate the challenges facing post-GWAS fine-mapping studies. Citation Format: Sara Lindström, Brad Chapman, Gary Chen, Constance Chen, Akweley Ablorh, Loic Le Marchand, Brian Henderson, Oliver Hofmann, Daniel Mirel, Christopher Haiman, Peter Kraft. Deep targeted sequencing of 12 breast cancer loci in 4,700 women across four different ethnicities. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4578. doi:10.1158/1538-7445.AM2013-4578

Background Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. Methods Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. Results After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) 0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. Conclusions This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.