Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development.

INTRODUCTION: Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permits genome-wide analyses to identify rare variants contributing to AD risk. METHODS: We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family-based WGS-based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals. RESULTS: We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2. DISCUSSION: Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of the exome.

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development.

INTRODUCTION: Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permit genome-wide analyses to identify rare variants contributing to AD risk. METHODS: We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency ≤1%) in a family-based WGS-based association study of 2,247 subjects from 605 multiplex AD families, followed by replication in 1,669 unrelated individuals. RESULTS: We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, CLSTN2. DISCUSSION: Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity. These loci have not been previously associated with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of coding regions.

Aß-accelerated neurodegeneration caused by Alzheimer's-associated ACE variant R1279Q is rescued by angiotensin system inhibition in mice.

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 ß-amyloid (Aß) pathology, amyloidosis in 5XFAD mice crossed to KI mice accelerated neurodegeneration and neuroinflammation, whereas Aß deposition was unchanged. KI mice had normal blood pressure and cerebrovascular functions. Our findings strongly suggest that increased ACE1/angII signaling causes aging-dependent, Aß-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.

Identification of Novel Alzheimer's Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data.

With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer's Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (Pinter = 1.83 × 10-7), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.

Best practices for benchmarking germline small-variant calls in human genomes.

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.

Author Correction: Best practices for benchmarking germline small-variant calls in human genomes.

In the version of this article initially published online, two pairs of headings were switched with each other in Table 4: "Recall (PCR free)" was switched with "Recall (with PCR)," and "Precision (PCR free)" was switched with "Precision (with PCR)." The error has been corrected in the print, PDF and HTML versions of this article.

The 2018 Bioinformatics Open Source Conference (GCCBOSC 2018).

In 2018, the annual Bioinformatics Open Source Conference was held for the first time in conjunction with the Galaxy Community Conference, as an experiment to see if we could reach people in the bioinformatics community who aren't part of the audience attracted by ISMB. Held in June 2018 at Reed College in Portland, Oregon, GCCBOSC (Galaxy Community Conference and Bioinformatics Open Source Conference) attracted over 300 participants from around the world. The meeting started with two days of training, followed by two days of talks and poster/demo sessions (with some joint and some parallel sessions). The joint sessions included well-received keynote talks by Tracy Teal, Fernando Pérez and Lucia Peixoto, as well as a panel discussion about documentation and training. After the main meeting, many attendees stayed for up to four additional collaboration days, an extended version of the Codefests that have been held in conjunction with previous BOSCs. GCCBOSC was a successful experiment. The organizers concluded that the best way to serve the broadest community of potential BOSC attendees will be to partner some years with the International Society for Computational Biology (ISMB) and others with GCC.

The 2017 Bioinformatics Open Source Conference (BOSC).

The Bioinformatics Open Source Conference (BOSC) is a 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. The 18th annual BOSC ( http://www.open-bio.org/wiki/BOSC_2017) took place in Prague, Czech Republic in July 2017. The conference brought together nearly 250 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, open and reproducible science, and this year's theme, open data. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community, called the OBF Codefest.

Prioritisation of structural variant calls in cancer genomes.

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.

Silencing of the Drosophila ortholog of SOX5 leads to abnormal neuronal development and behavioral impairment.

SOX5 encodes a transcription factor that is expressed in multiple tissues including heart, lung and brain. Mutations in SOX5 have been previously found in patients with amyotrophic lateral sclerosis (ALS) and developmental delay, intellectual disability and dysmorphic features. To characterize the neuronal role of SOX5, we silenced the Drosophila ortholog of SOX5, Sox102F, by RNAi in various neuronal subtypes in Drosophila. Silencing of Sox102F led to misorientated and disorganized michrochaetes, neurons with shorter dendritic arborization (DA) and reduced complexity, diminished larval peristaltic contractions, loss of neuromuscular junction bouton structures, impaired olfactory perception, and severe neurodegeneration in brain. Silencing of SOX5 in human SH-SY5Y neuroblastoma cells resulted in a significant repression of WNT signaling activity and altered expression of WNT-related genes. Genetic association and meta-analyses of the results in several large family-based and case-control late-onset familial Alzheimer's disease (LOAD) samples of SOX5 variants revealed several variants that show significant association with AD disease status. In addition, analysis for rare and highly penetrate functional variants revealed four novel variants/mutations in SOX5, which taken together with functional prediction analysis, suggests a strong role of SOX5 causing AD in the carrier families. Collectively, these findings indicate that SOX5 is a novel candidate gene for LOAD with an important role in neuronal function. The genetic findings warrant further studies to identify and characterize SOX5 variants that confer risk for AD, ALS and intellectual disability.

Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities.

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. For SNVs with 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.

The 2016 Bioinformatics Open Source Conference (BOSC).

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.

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research.

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.

The 2015 Bioinformatics Open Source Conference (BOSC 2015).

The Bioinformatics Open Source Conference (BOSC) is organized by the Open Bioinformatics Foundation (OBF), a nonprofit group dedicated to promoting the practice and philosophy of open source software development and open science within the biological research community. Since its inception in 2000, BOSC has provided bioinformatics developers with a forum for communicating the results of their latest efforts to the wider research community. BOSC offers a focused environment for developers and users to interact and share ideas about standards; software development practices; practical techniques for solving bioinformatics problems; and approaches that promote open science and sharing of data, results, and software. BOSC is run as a two-day special interest group (SIG) before the annual Intelligent Systems in Molecular Biology (ISMB) conference. BOSC 2015 took place in Dublin, Ireland, and was attended by over 125 people, about half of whom were first-time attendees. Session topics included "Data Science;" "Standards and Interoperability;" "Open Science and Reproducibility;" "Translational Bioinformatics;" "Visualization;" and "Bioinformatics Open Source Project Updates". In addition to two keynote talks and dozens of shorter talks chosen from submitted abstracts, BOSC 2015 included a panel, titled "Open Source, Open Door: Increasing Diversity in the Bioinformatics Open Source Community," that provided an opportunity for open discussion about ways to increase the diversity of participants in BOSC in particular, and in open source bioinformatics in general. The complete program of BOSC 2015 is available online at http://www.open-bio.org/wiki/BOSC_2015_Schedule.

Community-driven development for computational biology at Sprints, Hackathons and Codefests.

BACKGROUND: Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. RESULTS: This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled "unconferences" (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how these are organised, how their audience is addressed, and their outreach to their respective communities. CONCLUSIONS: Hackathons, Codefests and Sprints share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a self-driven proactive environment, as well as providing an opportunity for new participants to get involved in collaborative projects.

Clonal dynamics of native haematopoiesis.

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.

Comparison of illumina and 454 deep sequencing in participants failing raltegravir-based antiretroviral therapy.

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-naïve 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<0.001). Illumina sequencing detected 2.4-fold greater nucleotide MVs and 2.9-fold greater amino acid MVs compared to 454. The only raltegravir-resistant MV detected was an E138K mutation in one participant by Illumina sequencing, but not by 454. CONCLUSIONS: In participants of A5262 with raltegravir resistance at virologic failure, baseline raltegravir-resistant MVs were rarely detected. At comparable costs to 454 sequencing, Illumina demonstrated greater depth of coverage, increased sensitivity for detecting HIV MVs, and fewer false positive variant calls.

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls.

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.