Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia

There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic data ownership, enabled by blockchain technology, as one approach for managing these priorities. We conducted an online survey, applying a mixed methods approach to collect quantitative (using scale questions) and qualitative data (using open-ended questions). We explored the views of 117 genomic professionals (clinical geneticists, genetic counsellors, bioinformaticians, and researchers) towards patient data ownership in Australia. Data analysis revealed most professionals agreed that patients have rights to data ownership. However, there is a need for a clearer understanding of the nature and implications of data ownership in this context as genomic data often is subject to collective ownership (e.g., with family members and laboratories). This research finds that while the majority of genomic professionals acknowledge the desire for patient data ownership, bioinformaticians and researchers expressed more favourable views than clinical geneticists and genetic counsellors, suggesting that their views on this issue may be shaped by how closely they interact with patients as part of their professional duties. This research also confirms that stronger health system infrastructure is a prerequisite for enabling patient data ownership, which needs to be underpinned by appropriate digital infrastructure (e.g., central vs. decentralised data storage), patient identity ownership (e.g., limited vs. self-sovereign identity), and policy at both federal and state levels.

PEPS: Polygenic Epistatic Phenotype Simulation

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

An Approach for Generating Realistic Australian Synthetic Healthcare Data

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning

Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries.

The inequity of targeted cystic fibrosis reproductive carrier screening tests in Australia

OBJECTIVE

European and Australian guidelines for cystic fibrosis (CF) reproductive carrier screening recommend testing a small number of high frequency CF causing variants, rather than comprehensive CFTR sequencing. The study objective was to determine variant detection rates of commercially available targeted reproductive carrier screening tests in Australia.

METHODS

Next-generation DNA sequencing of the CFTR gene was performed on 2552 individuals from a whole population sample to identify CF causing variants. The variant detection rates of two commercially available Australian reproductive carrier screening tests, which target 50 or 175 CF causing variants, in this population were calculated. The ethnicity of individuals was determined using principal component analysis.

RESULTS

Variant detection rates of the tests for 50 and 175 CF causing variants were 88.2% and 90.8%, respectively. No CF causing variants in individuals of East Asian ethnicity (n = 3) were detected by either test, while >86.6% (n = 69) of CF causing variants in Europeans would be identified by either test.

CONCLUSIONS

Reproductive carrier screening tests for a targeted set of high frequency CF variants are unable to detect approximately 10% of CF variants in a multiethnic Australian population, and individuals of East Asian ethnicity are disproportionally affected by this test limitation.

Life expectancy in multimorbid older adults: Why it matters for preventive care

Multimorbidity is highly prevalent among older adults and associated with a shorter life expectancy. Many guidelines recommend tailoring preventive care of multimorbid people according to life expectancy. Indeed, there is a time lag between a preventive care intervention and the expected potential benefit, and patients with a relatively short life expectancy might not have the time to benefit from the preventive care intervention. Further, both patients and health care providers tend to overestimate benefits and underestimate risks of interventions. It is therefore necessary to have a valid index for mortality prediction in multimorbid patients, but there is no life expectancy estimator designed and recommended for this population. The paper describes the development and internal validation of a new life expectancy estimator. In this presentation, we focus on the importance of life expectancy estimation in multimorbid older adults: Why does it matter in this population? What is the time lag to benefit of a preventive intervention, e.g., cancer screening? What is the state in this field, in research and clinical practice? How could tailoring preventive care to life expectancy improve patient outcomes?

Data-driven platform for identifying variants of interest in COVID-19 virus

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We provide an automated way to identify emerging variants of concern using viral genome and patient-outcome data.

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We assembled 10,000 sample-strong case-control dataset and identified 117 single nucleotide variants (SNV) associated with adverse patient outcomes.

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We observe co-evolution of protective and pathogenic interactions between the spike, nsp14, and N region with either orf3a or nsp3.

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Structural modelling reveals mutation clusters in the Zn binding domain of nsp14 suggesting ongoing adaptation to the human host.

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Our approach identified Variants Being Monitored (VBM) a week before they were flagged by Health Organizations and offers a clade-independent function-orientated grouping.

New SARS-CoV-2 variants emerge as part of the virus’ adaptation to the human host. The Health Organizations are monitoring newly emerging variants with suspected impact on disease or vaccination efficacy as Variants Being Monitored (VBM), like Delta and Omicron. Genetic changes (SNVs) compared to the Wuhan variant characterize VBMs with current emphasis on the spike protein and lineage markers. However, monitoring VBMs in such a way might miss SNVs with functional effect on disease.

Here we introduce a lineage-agnostic genome-wide approach to identify SNVs associated with disease. We curated a case-control dataset of 10,520 samples and identified 117 SNVs significantly associated with adverse patient outcome. While 40% (47) SNV are already monitored and 36% (43) are in the spike protein, we also identified 70 new SNVs that are associated with disease outcome. 31 of these are disease-worsening and predominantly located in the 3′-5′ exonuclease (NSP14) with structural modelling revealing a concise cluster in the Zn binding domain that has known host-immune modulating function. Furthermore, we generate clade-independent VBM groupings by identifying interacting SNVs (epistasis). We find 37 sets of higher-order epistatic interactions joining 5 genomic regions (nsp3, nsp14, Spike S1, ORF3a, N). Structural modelling of these regions provides insights into potential mechanistic pathways of increased virulence as well as orthogonal methods of validation.

Clade-independent monitoring of functionally interacting (epistasis, co-evolution) SNVs detected emerging VBM a week before they were flagged by Health Organizations and in conjunction with structural modelling provides faster, mechanistic insight into emerging strains to guide public health interventions.

Isling: a tool for detecting integration of wild-type viruses and clinical vectors

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

Fast and accurate exhaustive higher-order epistasis search with BitEpi

Complex genetic diseases may be modulated by a large number of epistatic interactions affecting a polygenic phenotype. Identifying these interactions is difficult due to computational complexity, especially in the case of higher-order interactions where more than two genomic variants are involved. In this paper, we present BitEpi, a fast and accurate method to test all possible combinations of up to four bi-allelic variants (i.e. Single Nucleotide Variant or SNV for short). BitEpi introduces a novel bitwise algorithm that is 1.7 and 56 times faster for 3-SNV and 4-SNV search, than established software. The novel entropy statistic used in BitEpi is 44% more accurate to identify interactive SNVs, incorporating a p-value-based significance testing. We demonstrate BitEpi on real world data of 4900 samples and 87,000 SNPs. We also present EpiExplorer to visualize the potentially large number of individual and interacting SNVs in an interactive Cytoscape graph. EpiExplorer uses various visual elements to facilitate the discovery of true biological events in a complex polygenic environment.

INSIDER: alignment-free detection of foreign DNA sequences

External DNA sequences can be inserted into an organism's genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

Genetic Analysis of Tryptophan Metabolism Genes in Sporadic Amyotrophic Lateral Sclerosis

The essential amino acid tryptophan (TRP) is the initiating metabolite of the kynurenine pathway (KP), which can be upregulated by inflammatory conditions in cells. Neuroinflammation-triggered activation of the KP and excessive production of the KP metabolite quinolinic acid are common features of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). In addition to its role in the KP, genes involved in TRP metabolism, including its incorporation into proteins, and synthesis of the neurotransmitter serotonin, have also been genetically and functionally linked to these diseases. ALS is a late onset neurodegenerative disease that is classified as familial or sporadic, depending on the presence or absence of a family history of the disease. Heritability estimates support a genetic basis for all ALS, including the sporadic form of the disease. However, the genetic basis of sporadic ALS (SALS) is complex, with the presence of multiple gene variants acting to increase disease susceptibility and is further complicated by interaction with potential environmental factors. We aimed to determine the genetic contribution of 18 genes involved in TRP metabolism, including protein synthesis, serotonin synthesis and the KP, by interrogating whole-genome sequencing data from 614 Australian sporadic ALS cases. Five genes in the KP (AFMID, CCBL1, GOT2, KYNU, HAAO) were found to have either novel protein-altering variants, and/or a burden of rare protein-altering variants in SALS cases compared to controls. Four genes involved in TRP metabolism for protein synthesis (WARS) and serotonin synthesis (TPH1, TPH2, MAOA) were also found to carry novel variants and/or gene burden. These variants may represent ALS risk factors that act to alter the KP and lead to neuroinflammation. These findings provide further evidence for the role of TRP metabolism, the KP and neuroinflammation in ALS disease pathobiology.

Zoledronic acid reduces the rate of clinical fractures after surgical repair of a hip fracture regardless of the Pretreatment bone mineral density

In patients with surgical repair of a low-trauma hip fracture, zoledronic acid (ZA) reduced the risk of subsequent fractures regardless of pretreatment femoral neck and total hip bone mineral density (BMD).

INTRODUCTION

Zoledronic acid reduces the risk of subsequent fractures after repair of a hip fracture. It is still unclear whether the benefits in fracture reduction with ZA depend upon hip bone mineral density at the time of fracture.

METHODS

We preformed additional post hoc analyses of data from the HORIZON Recurrent Fracture Trial to determine if ZA treatment reduced the risk of new clinical fractures regardless of pretreatment BMD. We modeled femoral neck and total hip BMD as both continuous and dichotomous variables (BMD T-score above and below -2.5).

RESULTS

There are no evidence that baseline femoral neck and total hip BMD modified the anti-fracture efficacy of ZA when pretreatment BMD was analyzed as a continuous or a dichotomous variable (interaction p-values > 0.20). The clinical fracture efficacy of ZA was similar among patients with pretreatment femoral neck BMD values above and below -2.5 (relative hazards = 0.60 and 0.67, respectively, interaction p-value = 0.95). A similar result was obtained using pretreatment total hip BMD values (relative hazards = 0.72 and 0.57, respectively, interaction p-value = 0.41).

CONCLUSION

There data should provide more comfort in prescribing ZA after surgical repair of a hip fracture, regardless of pretreatment BMD.

GOANA: A Universal High-Throughput Web Service for Assessing and Comparing the Outcome and Efficiency of Genome Editing Experiments

The increased development of functionally diverse and highly specialized genome editors has created the need for comparative analytics tools that are able to profile the mutational outcomes, particularly rare and complex outcomes, to assess the editor's applicability to different domains. To address this need, we have developed Generalizable On-target activity ANAlyzer (GOANA), a high-throughput web-based software for determining editing efficiency and cataloguing rare outcomes from next-generation sequencing data. GOANA calculates mutation frequency and outcomes relative to a supplied control sample. It is scalable to thousands of target sites across the entire genome and is 4,000% faster than CRISPResso2. Mutations are reported on a "per-read" level rather than individually, enabling the identification of co-occurring mutations. GOANA is editor agnostic and can be applied to data generated from any targeted editing experiment, including base editors. Requiring only that control and treated reads are aligned to the same reference, GOANA can handle data from any library preparation method, including pooled amplicon and whole-genome sequencing. As a proof of principle, we analyze two large data sets of CRISPR-Cas9 and CRISPR-Cas12a editing, demonstrating the power of GOANA and highlighting several key differences between the two enzymes. GOANA is available for use at https://gt-scan.csiro.au/goana/ and as a command line tool from https://github.com/BauerLab/GOANA.

Sleep duration and bone health measures in older men

The associations between objective measures of sleep duration and bone outcomes in older men are unknown. No consistent, significant association was identified between sleep duration and bone mineral density (BMD) in the current analysis. However, future research should determine if vitamin D status modifies this relationship.

INTRODUCTION

Prior studies, predominantly in women, reported that long and short self-reported sleep duration are associated with lower BMD. Associations between actigraphy-determined sleep duration and BMD or bone turnover markers (BTMs) in older men are unknown.

METHODS

Men in The Osteoporotic Fractures in Men (MrOS) Study with wrist actigraphy and concurrent BMD assessment but without comorbidities affecting bone health were included. Sleep duration was considered as a continuous (N = 1926) and dichotomized variable where men were classified as getting the recommended (7-8 h/night; N = 478) or short (< 6 h/night; N = 577) sleep. The cross-sectional association between BMD, BTMs, and sleep duration was examined using a t test or linear regression, where appropriate, in unadjusted and adjusted models.

RESULTS

There were no clinically or statistically significant differences in BMD at the L-spine, total hip, or femoral neck between men getting the recommended vs. short sleep duration, using actigraphy or self-reported sleep duration (all p ≥ 0.07). When sleep duration was considered as a continuous variable, femoral neck BMD was higher in men with longer self-reported sleep duration (β = 0.006 ±0.003, p = 0.02), but this was not significant after further adjustment. In men with low 25OHD (< 20 ng/mL), longer actigraphy-determined sleep duration was associated with higher total hip BMD (β = 0.016 ± 0.008; p = 0.04). Sleep duration and BTMs were not associated.

CONCLUSION

Sleep duration was not associated with hip or L-spine BMD or BTMs in older men. Future research should determine if vitamin D status or other factors modify this relationship.

Interoperable medical data: The missing link for understanding COVID-19

Being able to link clinical outcomes to SARS‐CoV‐2 virus strains is a critical component of understanding COVID‐19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the ‘Fast Healthcare Interoperable Resource’ (FHIR) implementation guide, we introduce an ontology‐based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data‐driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID (‘Global Initiative on Sharing All Influenza Data’), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing

The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly.

Host Porphobilinogen Deaminase Deficiency Confers Malaria Resistance in Plasmodium chabaudi but Not in Plasmodium berghei or Plasmodium falciparum During Intraerythrocytic Growth

An important component in host resistance to malaria infection are inherited mutations that give rise to abnormalities and deficiencies in erythrocyte proteins and enzymes. Understanding how such mutations confer protection against the disease may be useful for developing new treatment strategies. A mouse ENU-induced mutagenesis screen for novel malaria resistance-conferring mutations identified a novel non-sense mutation in the gene encoding porphobilinogen deaminase (PBGD) in mice, denoted here as PbgdMRI58155. Heterozygote PbgdMRI58155 mice exhibited ~50% reduction in cellular PBGD activity in both mature erythrocytes and reticulocytes, although enzyme activity was ~10 times higher in reticulocytes than erythrocytes. When challenged with blood-stage P. chabaudi, which preferentially infects erythrocytes, heterozygote mice showed a modest but significant resistance to infection, including reduced parasite growth. A series of assays conducted to investigate the mechanism of resistance indicated that mutant erythrocyte invasion by P. chabaudi was normal, but that following intraerythrocytic establishment a significantly greater proportions of parasites died and therefore, affected their ability to propagate. The Plasmodium resistance phenotype was not recapitulated in Pbgd-deficient mice infected with P. berghei, which prefers reticulocytes, or when P. falciparum was cultured in erythrocytes from patients with acute intermittent porphyria (AIP), which had modest (20-50%) reduced levels of PBGD. Furthermore, the growth of Pbgd-null P. falciparum and Pbgd-null P. berghei parasites, which grew at the same rate as their wild-type counterparts in normal cells, were not affected by the PBGD-deficient background of the AIP erythrocytes or Pbgd-deficient mice. Our results confirm the dispensability of parasite PBGD for P. berghei infection and intraerythrocytic growth of P. falciparum, but for the first time identify a requirement for host erythrocyte PBGD by P. chabaudi during in vivo blood stage infection.

VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data

BACKGROUND

Many traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions.

FINDINGS

We have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples.

CONCLUSIONS

Compared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.

Supporting pandemic response using genomics and bioinformatics: A case study on the emergent SARS-CoV-2 outbreak

Pre‐clinical responses to fast‐moving infectious disease outbreaks heavily depend on choosing the best isolates for animal models that inform diagnostics, vaccines and treatments. Current approaches are driven by practical considerations (e.g. first available virus isolate) rather than a detailed analysis of the characteristics of the virus strain chosen, which can lead to animal models that are not representative of the circulating or emerging clusters. Here, we suggest a combination of epidemiological, experimental and bioinformatic considerations when choosing virus strains for animal model generation. We discuss the currently chosen SARS‐CoV‐2 strains for international coronavirus disease (COVID‐19) models in the context of their phylogeny as well as in a novel alignment‐free bioinformatic approach. Unlike phylogenetic trees, which focus on individual shared mutations, this new approach assesses genome‐wide co‐developing functionalities and hence offers a more fluid view of the ‘cloud of variances’ that RNA viruses are prone to accumulate. This joint approach concludes that while the current animal models cover the existing viral strains adequately, there is substantial evolutionary activity that is likely not considered by the current models. Based on insights from the non‐discrete alignment‐free approach and experimental observations, we suggest isolates for future animal models.

Evidence for polygenic and oligogenic basis of Australian sporadic amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with phenotypic and genetic heterogeneity. Approximately 10% of cases are familial, while remaining cases are classified as sporadic. To date, >30 genes and several hundred genetic variants have been implicated in ALS.

METHODS

Seven hundred and fifty-seven sporadic ALS cases were recruited from Australian neurology clinics. Detailed clinical data and whole genome sequencing (WGS) data were available from 567 and 616 cases, respectively, of which 426 cases had both datasets available. As part of a comprehensive genetic analysis, 853 genetic variants previously reported as ALS-linked mutations or disease-associated alleles were interrogated in sporadic ALS WGS data. Statistical analyses were performed to identify correlation between clinical variables, and between phenotype and the number of ALS-implicated variants carried by an individual. Relatedness between individuals carrying identical variants was assessed using identity-by-descent analysis.

RESULTS

Forty-three ALS-implicated variants from 18 genes, including C9orf72, ATXN2, TARDBP, SOD1, SQSTM1 and SETX, were identified in Australian sporadic ALS cases. One-third of cases carried at least one variant and 6.82% carried two or more variants, implicating a potential oligogenic or polygenic basis of ALS. Relatedness was detected between two sporadic ALS cases carrying a SOD1 p.I114T mutation, and among three cases carrying a SQSTM1 p.K238E mutation. Oligogenic/polygenic sporadic ALS cases showed earlier age of onset than those with no reported variant.

CONCLUSION

We confirm phenotypic associations among ALS cases, and highlight the contribution of genetic variation to all forms of ALS.

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.

P755The impact of levothyroxine on cardiac function in older adults with subclinical hypothyroidism: a randomized clinical trial

Importance

Subclinical hypothyroidism has been associated with heart failure, but no conclusive clinical trial assessed whether treating subclinical hypothyroidism with levothyroxine has an impact on cardiac function.

Objective

To assess the impact of levothyroxine treatment on cardiac function in subclinical hypothyroidism.

Design

This is a randomized, double-blind placebo-controlled, multicenter Swiss substudy within the TRUST trial.

Participants

Participants aged ≥65 years with subclinical hypothyroidism.

Intervention

Levothyroxine to achieve TSH normalization, or placebo including mock titrations.

Main outcome measures

Primary outcomes, assessed by echocardiography at the end of the trial were the left ventricular ejection fraction (LVEF, normal defined as >50%) for systolic function and the ratio between mitral peak velocity of early filling (E) to early diastolic mitral annular velocity (e' (E/e' ratio) for diastolic function. Secondary outcomes included transmitral E and A waves, e' lateral/septal, left atrial (LA) volume index and systolic pulmonary artery pressure.

Results

Of 217 randomized Swiss participants of the TRUST trial, 185 (mean age 74.1 years, 47% women, mean TSH at baseline 6.35 ± SD 1.95 mIU/L) underwent echocardiography. After a median treatment duration of 18.4 months, the mean TSH among participants randomized to levothyroxine (n=95) decreased to 3.55 mIU/L, whereas it remained elevated in the placebo group (n=89; 5.29 mIU/L). The mean LVEF was similar in both arms (adjusted between-group difference 0.4%, 95% CI −1.8% to 2.5%, P=0.72) and no significant differences were found for the E/e' ratio (adjusted between-group difference 0.4, 95% CI −0.7 to 1.4, P=0.47). In intention-to-treat and per-protocol analyses, no clinically significant differences were found for secondary diastolic function parameters: e' lateral 8 vs. 8 cm/s, P=0.54; e' septal 6 vs. 6 cm/s, P=0.75; LA volume index 34 vs. 33 ml/m2, P=0.57; E/A ratio 0.8 vs. 0.8, P=0.94; E deceleration time 225 vs. 216 ms, P=0.27, except for systolic pulmonary artery pressure (37 mm Hg in the levothyoxine group vs. 33 mm Hg in the placebo group, P=0.02 intention-to-treat and P=0.06 per protocol)

Conclusion

Treatment of subclinical hypothyroidism with levothyroxine was not associated with benefits regarding systolic and diastolic heart function in older adults with subclinical hypothyroidism.

Radiological criteria for atypical features of femur fractures: what we can learn when applied in a clinical study setting

The paper focuses on the identification of atypical fractures (AFFs). This paper examines the concordance between objective classification and expert subjective review. We believe the paper adds critical information about how to apply the American Society of Bone and Mineral Research (ASBMR) criteria to diagnose AFFs and is of high interest to the field.

INTRODUCTION

Assess American Society of Bone and Mineral Research (ASBMR) criteria for identifying atypical femoral fractures (AFFs).

METHODS

Two orthopedic surgeons independently evaluated radiographs of 372 fractures, applying ASBMR criteria. We assessed ease of applying ASBMR criteria and whether criteria-based assessment matched qualitative expert assessment.

RESULTS

There was up to 27% uncertainty about how to classify specific features. 84% of films were classified similarly for the presence of AFF according to ASBMR criteria; agreement increased to 94% after consensus meeting. Of 37 fractures categorized as AFFs based on ASBMR criteria, 23 (62.2%) were considered AFFs according to expert assessment (not relying on criteria). Only one (0.5%) femoral shaft fracture that did not meet ASBMR criteria was considered an AFF per expert assessment. The number of major ASBMR features present (four vs five) and whether there was periosteal or endosteal thickening ("beaking" or "flaring") played major roles in the discrepancies between ASBMR criteria-based and expert-based determinations.

CONCLUSIONS

ASBMR AFF criteria were useful for reviewers but several features were difficult to interpret. Expert assessments did not agree with the ASBMR classification in almost one-third of cases, but rarely identified an AFF when a femoral shaft fracture did not meet ASBMR AFF criteria. Experts identified lateral cortical transverse fracture line and associated new-bone formation along with no or minimal comminution as crucial features necessary for the definition of atypical femoral fractures.

Evaluation of computational programs to predict HLA genotypes from genomic sequencing data

Motivation

Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative.

Results

In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set of > 1000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB (HLA-VBSeq) to 0.46 GB (HLAminer) per sample.

While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the single-stranded oligodeoxynucleotide (ssODN), i.e. the repair template, to be a governing factor. Furthermore, different regions of the ssODN have variable influence, which reflects the underlying mechanism of the repair process. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or - for-the-first-time - HDR-mediated nucleotide changes.

Abstract P1-08-04: Withdrawn

This abstract was withdrawn by the authors.

Citation Format: Trabert B, Bauer DC, Brinton LA, Buist DS, Cauley JA, Dallal CM, Gierach GL, Falk RT, Hue TF, Lacey, Jr. JV, LaCroix AZ, Tice JA, Xu X. Withdrawn [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-08-04.

The Current State and Future of CRISPR-Cas9 gRNA Design Tools

Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.

High Activity Target-Site Identification Using Phenotypic Independent CRISPR-Cas9 Core Functionality

The activity of CRISPR-Cas9 target sites can be measured experimentally through phenotypic assays or mutation rate and used to build computational models to predict activity of novel target sites. However, currently published models have been reported to perform poorly in situations other than their training conditions. In this study, we hence investigate how different sources of data influence predictive power and identify the best data set for the most robust predictive model. We use the activity of 28,606 target sites and a machine learning approach to train a predictive model of CRISPR-Cas9 activity, outperforming other published methods by an average increase in accuracy of 80% for prediction of the degree of activity and 13% for classification into active and inactive categories. We find that using data sets that measure CRISPR-Cas9 activity through sequencing provides more accurate predictions of activity. Our model, dubbed TUSCAN, is highly scalable, predicting the activity of 5000 target sites in under 7 s, making it suitable for genome-wide screens. We conclude that sophisticated machine learning methods can classify binary CRISPR-Cas9 activity; however, predicting fine-scale activity scores will require larger data sets directly measuring Indel insertion rate.

Insights into the bisphosphonate holiday: a preliminary FTIRI study

Bone composition evaluated by FTIRI analysis of iliac crest biopsies from post-menopausal women treated with alendronate for 10 years, continuously or alendronate for 5 years, followed by a 5-year alendronate-holiday, only differed with the discontinued biopsies having increased cortical crystallinity and heterogeneity of acid phosphate substitution and decreased trabecular crystallinity heterogeneity.

INTRODUCTION

Bisphosphonates (BP) are the most commonly used and effective drugs to prevent fragility fractures; however, concerns exist that prolonged use may lead to adverse events. Recent recommendations suggest consideration of a BP "holiday" in individuals taking long-term BP therapy not at high risk of fracture. Data supporting or refuting this recommendation based on bone quality are limited. We hypothesized that a "holiday" of 5 years would cause no major bone compositional changes.

METHODS

We analyzed the 31 available biopsies from the FLEX-Long-term Extension of FIT (Fracture Intervention Trial) using Fourier transform infrared imaging (FTIRI). Biopsies from two groups of post-menopausal women, a "Continuously treated group" (N = 16) receiving alendronate for ~ 10 years and a "Discontinued group" (N = 15), alendronate treated for 5 years taking no antiresorptive medication during the following 5 years. Iliac crest bone biopsies were provided at 10 years.

RESULTS

Key FTIRI parameters, mineral-to-matrix ratio, carbonate-to-phosphate ratio, acid phosphate substitution, and collagen cross-link ratio as well as heterogeneity of these parameters were similar for Continuously treated and Discontinued groups in age-adjusted models. The Discontinued group had 2% greater cortical crystallinity (p = 0.01), 31% greater cortical acid phosphate heterogeneity (p = 0.02), and 24% lower trabecular crystallinity heterogeneity (p = 0.02).

CONCLUSIONS

Discontinuation of alendronate for 5 years did not affect key FTIRI parameters, supporting the hypothesis that discontinuation would have little impact on bone composition. Modest differences were observed in three parameters that are not likely to affect bone mechanical properties. These preliminary data suggest that a 5-year BP holiday is not harmful to bone composition.

Predictors of change of trabecular bone score (TBS) in older men: results from the Osteoporotic Fractures in Men (MrOS) Study

Among older men, characteristics that predict longitudinal changes in trabecular bone score (TBS) are different from characteristics that predict changes in bone mineral density (BMD). Most notably, weight loss is strongly associated with concomitant loss in BMD but with concomitant increases in TBS, when measured on Hologic densitometers.

INTRODUCTION

Our objective was to compare and contrast predictors of changes in TBS, total hip BMD, and lumbar spine BMD.

METHODS

Our study population was 3969 Osteoporotic Fractures in Men (MrOS) cohort participants (mean age 72.8 years) with repeat measures of TBS, lumbar spine and total hip BMD, body mass index (BMI) less than 37 kg/m², and no use of bisphosphonate or glucocorticoid medications. TBS was scored (Med-Imaps Software version 2.1) and BMD measured on Hologic densitometers.

RESULTS

One thousand four hundred forty-four men had a TBS decrease > 0.04 units (estimated least significant change for TBS), 795 men had a TBS increase > 0.04 units, and 1730 men had TBS change ≤ 0.04 units over mean follow-up of 4.6 years. Older age was not associated with TBS change, but was associated with greater decline in lumbar spine and total hip BMD. Compared to stable weight, > 10% weight loss was strongly associated with an increase in TBS [effect size = 1.24 (95% CI 1.12, 1.36)] and strongly associated with a decrease in total hip BMD [- 1.16 (95% CI - 1.19, - 1.03)]. Other predictors discordant for longitudinal changes of TBS and BMD included baseline BMI, walk speed, and ACE inhibitor use.

CONCLUSIONS

Predictors of changes in TBS are different from predictors of changes in lumbar spine and total hip BMD. At least when assessed on Hologic densitometers, weight loss is associated with subsequent declines in spine and total hip BMD but subsequent increase in TBS. Faster walk speed may protect against loss of hip BMD, but is not associated with longitudinal changes of TBS.

High dairy protein intake is associated with greater bone strength parameters at the distal radius and tibia in older men: a cross-sectional study

Dairy protein but not plant protein was associated with bone strength of the radius and tibia in older men. These results are consistent with previous results in women and support similar findings related to fracture outcomes. Bone strength differences were largely due to thickness and area of the bone cortex.

INTRODUCTION

Our objective was to determine the association of protein intake by source (dairy, non-dairy animal, plant) with bone strength and bone microarchitecture among older men.

METHODS

We used data from 1016 men (mean 84.3 years) who attended the Year 14 exam of the Osteoporotic Fractures in Men (MrOS) study, completed a food frequency questionnaire (500-5000 kcal/day), were not taking androgen or androgen agonists, and had high-resolution peripheral quantitative computed tomography (HR-pQCT) scans of the distal radius and distal or diaphyseal tibia. Protein was expressed as percentage of total energy intake (TEI); mean ± SD for TEI = 1548 ± 607 kcal/day and for total protein = 16.2 ± 2.9%TEI. We used linear regression with standardized HR-pQCT parameters as dependent variables and adjusted for age, limb length, center, education, race/ethnicity, marital status, smoking, alcohol intake, physical activity level, corticosteroids use, supplement use (calcium and vitamin D), and osteoporosis medications.

RESULTS

Higher dairy protein intake was associated with higher estimated failure load at the distal radius and distal tibia [radius effect size = 0.17 (95% CI 0.07, 0.27), tibia effect size = 0.13 (95% CI 0.03, 0.23)], while higher non-dairy animal protein was associated with higher failure load at only the distal radius. Plant protein intake was not associated with failure load at any site.

CONCLUSION

The association between protein intake and bone strength varied by source of protein. These results support a link between dairy protein intake and skeletal health, but an intervention study is needed to evaluate causality.

Potential drug-drug and drug-disease interactions in well-functioning community-dwelling older adults

WHAT IS KNOWN AND OBJECTIVE

There are few studies examining both drug-drug and drug-disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug-drug and drug-disease interactions and associated factors in community-dwelling older adults.

METHODS

This cross-sectional study included 3055 adults aged 70-79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug-drug and drug-disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use.

RESULTS

Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug-drug interactions. Nearly 10·7% of the participants had a drug-drug interaction that involved a non-prescription medication. % The most common drug-drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug-disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug-disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35-40% [drug-drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27-1·42; drug-disease interaction AOR = 1·30; CI = 1·21-1·40; and both AOR = 1·45; CI = 1·34-1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49-84% compared with those not hospitalized (drug-drug interaction AOR = 1·49, 95% CI = 1·11-2·01; drug-disease interaction AOR = 1·69, CI = 1·15-2·49; and both AOR = 1·84, CI = 1·20-2·84).

WHAT IS NEW AND CONCLUSION

Drug interactions are common among community-dwelling older adults and are associated with the number of medications and hospitalization in the previous year. Longitudinal studies are needed to evaluate the impact of drug interactions on health-related outcomes.

A novel ENU-induced ankyrin-1 mutation impairs parasite invasion and increases erythrocyte clearance during malaria infection in mice

Genetic defects in various red blood cell (RBC) cytoskeletal proteins have been long associated with changes in susceptibility towards malaria infection. In particular, while ankyrin (Ank-1) mutations account for approximately 50% of hereditary spherocytosis (HS) cases, an association with malaria is not well-established, and conflicting evidence has been reported. We describe a novel N-ethyl-N-nitrosourea (ENU)-induced ankyrin mutation MRI61689 that gives rise to two different ankyrin transcripts: one with an introduced splice acceptor site resulting a frameshift, the other with a skipped exon. Ank-1^(MRI61689/+) mice exhibit an HS-like phenotype including reduction in mean corpuscular volume (MCV), increased osmotic fragility and reduced RBC deformability. They were also found to be resistant to rodent malaria Plasmodium chabaudi infection. Parasites in Ank-1^(MRI61689/+) erythrocytes grew normally, but red cells showed resistance to merozoite invasion. Uninfected Ank-1^(MRI61689/+) erythrocytes were also more likely to be cleared from circulation during infection; the “bystander effect”. This increased clearance is a novel resistance mechanism which was not observed in previous ankyrin mouse models. We propose that this bystander effect is due to reduced deformability of Ank-1^(MRI61689/+) erythrocytes. This paper highlights the complex roles ankyrin plays in mediating malaria resistance.

Vertebral Fractures: Clinical Importance and Management

Vertebral fractures are common and can result in acute and chronic pain, decreases in quality of life, and diminished lifespan. The identification of vertebral fractures is important because they are robust predictors of future fractures. The majority of vertebral fractures do not come to clinical attention. Numerous modalities exist for visualizing suspected vertebral fracture. Although differing definitions of vertebral fracture may present challenges in comparing data between different investigations, at least 1 in 5 men and women aged >50 years have one or more vertebral fractures. There is clinical guidance to target spine imaging to individuals with a high probability of vertebral fracture. Radiology reports of vertebral fracture need to clearly state that the patient has a "fracture," with further pertinent details such as the number, recency, and severity of vertebral fracture, each of which is associated with risk of future fractures. Patients with vertebral fracture should be considered for antifracture therapy. Physical and pharmacologic modalities of pain control and exercises or physiotherapy to maintain spinal movement and strength are important components in the care of vertebral fracture patients.

Targeted next-generation sequencing of 22 mismatch repair genes identifies Lynch syndrome families

Causative germline mutations in mismatch repair (MMR) genes can only be identified in ~50% of families with a clinical diagnosis of the inherited colorectal cancer (CRC) syndrome hereditary nonpolyposis colorectal cancer (HNPCC)/Lynch syndrome (LS). Identification of these patients are critical as they are at substantially increased risk of developing multiple primary tumors, mainly colorectal and endometrial cancer (EC), occurring at a young age. This demonstrates the need to develop new and/or more thorough mutation detection approaches. Next‐generation sequencing (NGS) was used to screen 22 genes involved in the DNA MMR pathway in constitutional DNA from 14 HNPCC and 12 sporadic EC patients, plus 2 positive controls. Several softwares were used for analysis and functional annotation. We identified 5 exonic indel variants, 42 exonic nonsynonymous single‐nucleotide variants (SNVs) and 1 intronic variant of significance. Three of these variants were class 5 (pathogenic) or class 4 (likely pathogenic), 5 were class 3 (uncertain clinical relevance) and 40 were classified as variants of unknown clinical significance. In conclusion, we have identified two LS families from the sporadic EC patients, one without a family history of cancer, supporting the notion for universal MMR screening of EC patients. In addition, we have detected three novel class 3 variants in EC cases. We have, in addition discovered a polygenic interaction which is the most likely cause of cancer development in a HNPCC patient that could explain previous inconsistent results reported on an intronic EXO1 variant.

Clinical utility of routine laboratory testing to identify possible secondary causes in older men with osteoporosis: the Osteoporotic Fractures in Men (MrOS) Study

We investigated the value of routine laboratory testing for identifying underlying causes in older men diagnosed with osteoporosis. Most osteoporotic and nonosteoporotic men had ≥1 laboratory abnormality. Few individual laboratory abnormalities were more common in osteoporotic men. The benefit of routine laboratory testing in older osteoporotic men may be low.

INTRODUCTION

To evaluate the utility of recommended laboratory testing to identify secondary causes in older men with osteoporosis, we examined prevalence of laboratory abnormalities in older men with and without osteoporosis.

METHODS

One thousand five hundred seventy-two men aged ≥65 years in the Osteoporotic Fractures in Men study completed bone mineral density (BMD) testing and a battery of laboratory measures, including serum calcium, phosphorus, alkaline phosphatase, parathyroid hormone (PTH), thyroid-stimulating hormone (TSH), 25-OH vitamin D, total testosterone, spot urine calcium/creatinine ratio, spot urine albumin/creatinine ratio, creatinine-derived estimated glomerular filtration rate, 24-h urine calcium, and 24-h urine free cortisol. Using cross-sectional analyses, we calculated prevalence ratios (PRs) and 95 % confidence intervals (CI) for the association of any and specific laboratory abnormalities with osteoporosis and the number of men with osteoporosis needed to test to identify one additional laboratory abnormality compared to testing men without osteoporosis.

RESULTS

Approximately 60 % of men had ≥1 laboratory abnormality in both men with and without osteoporosis. Among individual tests, only vitamin D insufficiency (PR, 1.13; 95 % CI, 1.05-1.22) and high alkaline phosphatase (PR, 3.05; 95 % CI, 1.52-6.11) were more likely in men with osteoporosis. Hypercortisolism and hyperthyroidism were uncommon and not significantly more frequent in men with osteoporosis. No osteoporotic men had hypercalciuria.

CONCLUSIONS

Though most of these older men had ≥1 laboratory abnormality, few routinely recommended individual tests were more common in men with osteoporosis than in those without osteoporosis. Possibly excepting vitamin D and alkaline phosphatase, benefit of routine laboratory testing to identify possible secondary causes in older osteoporotic men appears low. Results may not be generalizable to younger men or to older men in whom history and exam findings raise clinical suspicion for a secondary cause of osteoporosis.

Genome-wide analysis of chemically induced mutations in mouse in phenotype-driven screens

Background

N-ethyl-N-nitrosourea (ENU) mutagen has become the method of choice for inducing random mutations for forward genetics applications. However, distinguishing induced mutations from sequencing errors or sporadic mutations is difficult, which has hampered surveys of potential biases in the methodology in the past. Addressing this issue, we created a large cohort of mice with biological replicates enabling the confident calling of induced mutations, which in turn allowed us to conduct a comprehensive analysis of potential biases in mutation properties and genomic location.

Results

In the exome sequencing data we observe the known preference of ENU to cause \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$A:T\Rightarrow G:C$\end{document}A:T⇒G:C transitions in longer genes. Mutations were frequently clustered and inherited in blocks hampering attempts to pinpoint individual causative mutations by genome analysis only. Furthermore, ENU mutations were biased towards areas in the genome that are accessible in testis, potentially limiting the scope of forward genetic approaches to only 1–10 % of the genome.

Conclusion

ENU provides a powerful tool for exploring the genome-phenome relationship, however forward genetic applications that require the mutation to be passed on through the germ line may be limited to explore only genes that are accessible in testis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2073-4) contains supplementary material, which is available to authorized users.

Gut permeability, its interaction with gut microflora and effects on metabolic health are mediated by the lymphatics system, liver and bile acid

There is evidence to link obesity (and metabolic syndrome) with alterations in gut permeability and microbiota. The underlying mechanisms have been questioned and have prompted this review. We propose that the gut barrier function is a primary driver in maintaining metabolic health with poor health being linked to ‘gut leakiness'. This review will highlight changes in intestinal permeability and how it may change gut microflora and subsequently affect metabolic health by influencing the functioning of major bodily organs/organ systems: the lymphatic system, liver and pancreas. We also discuss the likelihood that metabolic syndrome undergoes a cyclic worsening facilitated by an increase in intestinal permeability leading to gut dysbiosis, culminating in ongoing poor health leading to further exacerbated gut leakiness.

Cpipe: a shared variant detection pipeline designed for diagnostic settings

The benefits of implementing high throughput sequencing in the clinic are quickly becoming apparent. However, few freely available bioinformatics pipelines have been built from the ground up with clinical genomics in mind. Here we present Cpipe, a pipeline designed specifically for clinical genetic disease diagnostics. Cpipe was developed by the Melbourne Genomics Health Alliance, an Australian initiative to promote common approaches to genomics across healthcare institutions. As such, Cpipe has been designed to provide fast, effective and reproducible analysis, while also being highly flexible and customisable to meet the individual needs of diverse clinical settings. Cpipe is being shared with the clinical sequencing community as an open source project and is available at http://cpipeline.org.

Arterialized venous bicarbonate is associated with lower bone mineral density and an increased rate of bone loss in older men and women

CONTEXT

Higher dietary net acid loads have been associated with increased bone resorption, reduced bone mineral density (BMD), and increased fracture risk.

OBJECTIVE

The objective was to compare bicarbonate (HCO3) measured in arterialized venous blood samples to skeletal outcomes.

DESIGN

Arterialized venous samples collected from participants in the Health, Aging and Body Composition (Health ABC) Study were compared to BMD and rate of bone loss.

SETTING

The setting was a community-based observational cohort.

PARTICIPANTS

A total of 2287 men and women age 74 ± 3 years participated.

INTERVENTION

Arterialized venous blood was obtained at the year 3 study visit and analyzed for pH and pCO2. HCO3 was determined using the Henderson-Hasselbalch equation.

MAIN OUTCOME MEASURE

BMD was measured at the hip by dual-energy x-ray absorptiometry at the year 1 (baseline) and year 3 study visits.

RESULTS

Plasma HCO3 was positively associated with BMD at both year 1 (P = .001) and year 3 (P = .001) in models adjusted for age, race, sex, clinic site, smoking, weight, and estimated glomerular filtration rate. Plasma HCO3 was inversely associated with rate of bone loss at the total hip over the 2.1 ± 0.3 (mean ± SD) years between the two bone density measurements (P < .001). Across quartiles of plasma HCO3, the rate of change in BMD over the 2.1 years ranged from a loss of 0.72%/y in the lowest quartile to a gain of 0.15%/y in the highest quartile of HCO3.

CONCLUSIONS

Arterialized plasma HCO3 was associated positively with cross-sectional BMD and inversely with the rate of bone loss, implying that systemic acid-base status is an important determinant of skeletal health during aging. Ongoing bone loss was linearly related to arterialized HCO3, even after adjustment for age and renal function. Further research in this area may have major public health implications because reducing dietary net acid load is possible through dietary intervention or through supplementation with alkaline potassium compounds.

Mutation analysis of MATR3 in Australian familial amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that arises from the progressive degeneration of the motor neurons. Recently, mutations in the matrin 3 (MATR3) gene were described in both ALS and autosomal dominant distal myopathy with vocal cord and pharyngeal weakness. We sought to determine the prevalence of MATR3 mutations in Australian familial ALS (n = 106) using whole exome sequencing. No mutations were identified, indicating that MATR3 mutations are not a common cause of ALS in Australian familial cases with predominately European ancestry.