Consent for genomic sequencing: a conversation, not just a form
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Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Analysis of microbial composition and sharing in low-biomass human milk samples: a comparison of DNA isolation and sequencing techniques
Human milk microbiome studies are currently hindered by low milk bacterial/human cell ratios and often rely on 16S rRNA gene sequencing, which limits downstream analyses. Here, we aimed to find a method to study milk bacteria and assess bacterial sharing between maternal and infant microbiota. We tested four DNA isolation methods, two bacterial enrichment methods and three sequencing methods on mock communities, milk samples and negative controls. Of the four DNA isolation kits, the DNeasy PowerSoil Pro (PS) and MagMAX Total Nucleic Acid Isolation (MX) kits provided consistent 16S rRNA gene sequencing results with low contamination. Neither enrichment method substantially decreased the human metagenomic sequencing read-depth. Long-read 16S-ITS-23S rRNA gene sequencing biased the mock community composition but provided consistent results for milk samples, with little contamination. In contrast to 16S rRNA gene sequencing, 16S-ITS-23S rRNA gene sequencing of milk, infant oral, infant faecal and maternal faecal DNA from 14 mother-infant pairs provided sufficient resolution to detect significantly more frequent sharing of bacteria between related pairs compared to unrelated pairs. In conclusion, PS or MX kit-DNA isolation followed by 16S rRNA gene sequencing reliably characterises human milk microbiota, and 16S-ITS-23S rRNA gene sequencing enables studies of bacterial transmission in low-biomass samples.
Language measures correlate with other measures used to study emotion
Researchers are increasingly using language measures to study emotion, yet less is known about whether language relates to other measures often used to study emotion. Building on previous work which focuses on associations between language and self-report, we test associations between language and a broader range of measures (self-report, observer report, facial cues, vocal cues). Furthermore, we examine associations across different dictionaries (LIWC-22, NRC, Lexical Suite, ANEW, VADER) used to estimate valence (i.e., positive versus negative emotion) or discrete emotions (i.e., anger, fear, sadness) in language. Associations were tested in three large, multimodal datasets (Ns = 193–1856; average word count = 316.7–2782.8). Language consistently related to observer report and consistently related to self-report in two of the three datasets. Statistically significant associations between language and facial cues emerged for language measures of valence but not for language measures of discrete emotions. Language did not consistently show significant associations with vocal cues. Results did not tend to significantly vary across dictionaries. The current research suggests that language measures (in particular, language measures of valence) are correlated with a range of other measures used to study emotion. Therefore, researchers may wish to use language to study emotion when other measures are unavailable or impractical for their research question.
Weak ties and the value of social connections for autistic people as revealed during the COVID-19 pandemic
A diverse portfolio of social relationships matters for people’s wellbeing, including both strong, secure relationships with others (‘close ties’) and casual interactions with acquaintances and strangers (‘weak ties’). Almost all of autism research has focused on Autistic people’s close ties with friends, family and intimate partners, resulting in a radically constrained understanding of Autistic sociality. Here, we sought to understand the potential power of weak-tie interactions by drawing on 95 semi-structured interviews with Autistic young people and adults conducted during the COVID-19 pandemic. We analysed the qualitative data using reflexive thematic analysis within an essentialist framework. During the COVID-19 lockdowns, Autistic people deeply missed not only their close personal relationships but also their “incidental social contact” with acquaintances and strangers. These weak-tie interactions appear to serve similar functions for Autistic people as they do for non-autistic people, including promoting wellbeing. These findings have important implications both for future research into Autistic sociality and for the design of practical services and supports to enhance Autistic people’s opportunities to flourish.
Returning genetic risk information for hereditary cancers to participants in a population-based cohort study in Japan
Large-scale population cohort studies that collect genomic information are tasked with returning an assessment of genetic risk for hereditary cancers to participants. While several studies have applied to return identified genetic risks to participants, comprehensive surveys of participants’ understanding, feelings, and behaviors toward cancer risk remain to be conducted. Here, we report our experience and surveys of returning genetic risks to 100 carriers of pathogenic variants for hereditary cancers identified through whole genome sequencing of 50 000 individuals from the Tohoku Medical Megabank project, a population cohort study. The participants were carriers of pathogenic variants associated with either hereditary breast and ovarian cancer (n = 79, median age=41) or Lynch syndrome (n = 21, median age=62). Of these, 28% and 38% had a history of cancer, respectively. We provided information on cancer risk, heritability, and clinical actionability to the participants in person. The comprehension assessment revealed that the information was better understood by younger (under 60 years) females than by older males. Scores on the cancer worry scale were positively related to cancer experiences and general psychological distress. Seventy-one participants were followed up at Tohoku University Hospital; six females underwent risk-reducing surgery triggered by study participation and three were newly diagnosed with cancer during surveillance. Among first-degree relatives of hereditary breast and ovarian cancer carriers, participants most commonly shared the information with daughters. This study showed the benefits of returning genetic risks to the general population and will provide insights into returning genetic risks to asymptomatic pathogenic variant carriers in both clinical and research settings.
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