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The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.In this Review, van Rheenen et al. outline how improved methodologies have enabled genetic correlations to be estimated for almost any trait pair. Genetic correlations can improve our understanding of the shared biology and causal relationships between traits.

Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

The gabapentinoids, gabapentin, and pregabalin, target the α2δ subunits of voltage-gated calcium channels. Initially licensed for pain and seizures, they have become widely prescribed drugs. Many of these uses are off-label for psychiatric indications, and there is increasing concern about their safety, so it is particularly important to have good evidence to justify this usage. We conducted a systematic review and meta-analysis of the evidence for three of their common psychiatric uses: bipolar disorder, anxiety, and insomnia. Fifty-five double-blind randomised controlled trials (RCTs) and 15 open-label studies were identified. For bipolar disorder, four double-blind RCTs investigating gabapentin, and no double-blind RCTs investigating pregabalin, were identified. A quantitative synthesis could not be performed due to heterogeneity in the study population, design and outcome measures. Across the anxiety spectrum, a consistent but not universal effect in favour of gabapentinoids compared to placebo was seen (standardised mean difference [SMD] ranging between -2.25 and -0.25). Notably, pregabalin (SMD -0.55, 95% CI -0.92 to -0.18) and gabapentin (SMD -0.92, 95% CI -1.32 to -0.52) were more effective than placebo in reducing preoperative anxiety. In insomnia, results were inconclusive. We conclude that there is moderate evidence of the efficacy of gabapentinoids in anxiety states, but minimal evidence in bipolar disorder and insomnia and they should be used for these disorders only with strong justification. This recommendation applies despite the attractive pharmacological and genetic rationale for targeting voltage-gated calcium channels.

Sex chromosome aneuploidies are a common group of disorders that are characterised by an abnormal number of X or Y chromosomes. However, many individuals with these disorders are not diagnosed, despite established groups of core features that include aberrant brain development and function. Clinical presentations often include characteristic profiles of intellectual ability, motor impairments, and rates of neurological and psychiatric disorders that are higher than those of the general population. Advances in genetics and neuroimaging have substantially expanded knowledge of potential mechanisms that underlie these phenotypes, including a putative dose effect of sex chromosome genes on neuroanatomical structures and cognitive abilities. Continuing attention to emerging trends in research of sex chromosome aneuploidies is important for clinicians because it informs appropriate management of these common genetic disorders. Furthermore, improved understanding of underlying neurobiological processes has much potential to elucidate sex-related factors associated with neurological and psychiatric disease in general.

Aortic aneurysm is a chronic aortic disease affected by many factors. Although it is generally asymptomatic, it poses a significant threat to human life due to a high risk of rupture. Because of its strong concealment, it is difficult to diagnose the disease in the early stage. At present, there are no effective drugs for the treatment of aneurysms. Surgical intervention and endovascular treatment are the only therapies. Although current studies have discovered that inflammatory responses as well as the production and activation of various proteases promote aortic aneurysm, the specific mechanisms remain unclear. Researchers are further exploring the pathogenesis of aneurysms to find new targets for diagnosis and treatment. To better understand aortic aneurysm, this review elaborates on the discovery history of aortic aneurysm, main classification and clinical manifestations, related molecular mechanisms, clinical cohort studies and animal models, with the ultimate goal of providing insights into the treatment of this devastating disease. The underlying problem with aneurysm disease is weakening of the aortic wall, leading to progressive dilation. If not treated in time, the aortic aneurysm eventually ruptures. An aortic aneurysm is a local enlargement of an artery caused by a weakening of the aortic wall. The disease is usually asymptomatic but leads to high mortality due to the risk of artery rupture.

In a phase 1–2 trial involving patients with advanced pancreatic cancer, sotorasib (a KRAS G12C inhibitor) resulted in a response in 21% of the patients and a median progression-free survival of 4 months.

Background Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between the labels. Recent studies for BioNER have reported state-of-the-art performance by combining deep learning-based models (e.g., bidirectional Long Short-Term Memory) and CRF. The deep learning-based models in the CRF-based methods are dedicated to estimating individual labels, whereas the relationships between connected labels are described as static numbers; thereby, it is not allowed to timely reflect the context in generating the most plausible label-label transitions for a given input sentence. Regardless, correctly segmenting entity mentions in biomedical texts is challenging because the biomedical terms are often descriptive and long compared with general terms. Therefore, limiting the label-label transitions as static numbers is a bottleneck in the performance improvement of BioNER. Results We introduce DTranNER, a novel CRF-based framework incorporating a deep learning-based label-label transition model into BioNER. DTranNER uses two separate deep learning-based networks: Unary-Network and Pairwise-Network. The former is to model the input for determining individual labels, and the latter is to explore the context of the input for describing the label-label transitions. We performed experiments on five benchmark BioNER corpora. Compared with current state-of-the-art methods, DTranNER achieves the best F1-score of 84.56% beyond 84.40% on the BioCreative II gene mention (BC2GM) corpus, the best F1-score of 91.99% beyond 91.41% on the BioCreative IV chemical and drug (BC4CHEMD) corpus, the best F1-score of 94.16% beyond 93.44% on the chemical NER, the best F1-score of 87.22% beyond 86.56% on the disease NER of the BioCreative V chemical disease relation (BC5CDR) corpus, and a near-best F1-score of 88.62% on the NCBI-Disease corpus. Conclusions Our results indicate that the incorporation of the deep learning-based label-label transition model provides distinctive contextual clues to enhance BioNER over the static transition model. We demonstrate that the proposed framework enables the dynamic transition model to adaptively explore the contextual relations between adjacent labels in a fine-grained way. We expect that our study can be a stepping stone for further prosperity of biomedical literature mining.

As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for example, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p -value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p -value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers. Linear mixed models have bias due to the assumed independence between random effects. Here, the authors describe a genome-based restricted maximum likelihood, CORE GREML, which estimates covariance between random effects. Application to UK Biobank data highlights this as an important parameter for multi-omics analyses of phenotypic variance.

35% of patients with pancreatic cancer have unresectable locally advanced disease at diagnosis. Several studies have examined systemic chemotherapy with FOLFIRINOX (leucovorin and fluorouracil plus irinotecan and oxaliplatin) in patients with locally advanced pancreatic cancer. We aimed to assess the effectiveness of FOLFIRINOX as first-line treatment in this patient population. We systematically searched Embase, MEDLINE (OvidSP), Web of Science, Scopus, PubMed Publisher, Cochrane, and Google Scholar from July 1, 1994, to July 2, 2015, for studies of treatment-naive patients of any age who received FOLFIRINOX as first-line treatment of locally advanced pancreatic cancer. Our primary outcome was overall survival. Secondary outcomes were progression-free survival; rates of grade 3 or 4 adverse events; and the proportion of patients who underwent radiotherapy or chemoradiotherapy, surgical resection after FOLFIRINOX, and R0 resection. We evaluated survival outcomes with the Kaplan–Meier method with patient-level data. Grade 3 or 4 adverse events, and the proportion of patients who underwent subsequent radiotherapy or chemoradiotherapy or resection, were pooled in a random-effects model. We included 13 studies comprising 689 patients, of whom 355 (52%) patients had locally advanced pancreatic cancer. 11 studies, comprising 315 patients with locally advanced disease, reported survival outcomes and were eligible for patient-level meta-analysis. Median overall survival from the start of FOLFIRINOX ranged from 10·0 months (95% CI 4·0–16·0) to 32·7 months (23·1–42·3) across studies with a pooled patient-level median overall survival of 24·2 months (95% CI 21·7–26·8). Median progression-free survival ranged from 3·0 months (95% CI not calculable) to 20·4 months (6·5–34·3) across studies with a patient-level median progression-free survival of 15·0 months (95% 13·8–16·2). In ten studies comprising 490 patients, 296 grade 3 or 4 adverse events were reported (60·4 events per 100 patients). No deaths were attributed to FOLFIRINOX toxicity. The proportion of patients who underwent radiotherapy or chemoradiotherapy ranged from 31% to 100% across studies. In eight studies, 154 (57%) of 271 patients received radiotherapy or chemoradiotherapy after FOLFIRINOX. The pooled proportion of patients who received any radiotherapy treatment was 63·5% (95% CI 43·3–81·6, I2 90%). The proportion of patients who underwent surgical resection for locally advanced pancreatic cancer ranged from 0% to 43%. The proportion of patients who had R0 resection of those who underwent resection ranged from 50% to 100% across studies. In 12 studies, 91 (28%) of 325 patients underwent resection after FOLFIRINOX. The pooled proportion of patients who had resection was 25·9% (95% CI 20·2–31·9, I2 24%). R0 resection was reported in 60 (74%) of 81 patients. The pooled proportion of patients who had R0 resection was 78·4% (95% CI 60·2–92·2, I2 64%). Patients with locally advanced pancreatic cancer treated with FOLFIRINOX had a median overall survival of 24·2 months—longer than that reported with gemcitabine (6–13 months). Future research should assess these promising results in a randomised controlled trial, and should establish which patients might benefit from radiotherapy or chemoradiotherapy or resection after FOLFIRINOX. None.