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Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship. Furthermore, the sheer number of systematic reviews needed across the entire agriculture and food value chain, and the cost and time required to conduct them, can delay the translation of science to policy. Artificial intelligence offers the opportunity to (i) better understand the complex etiology of diet-related chronic diseases, (ii) bring more precision to our understanding of the variation among individuals in the diet-chronic disease relationship, (iii) provide new types of computed data related to the efficacy and effectiveness of nutrition/food interventions in health promotion, and (iv) automate the generation of systematic reviews that support timely decisions. These advances include the acquisition and synthesis of heterogeneous and multimodal datasets. This perspective summarizes a meeting convened at the National Academy of Sciences, Engineering, and Medicine. The purpose of the meeting was to examine the current state and future potential of artificial intelligence in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice, and guidance.

Our understanding of brain iron regulation and its disruption in disease is limited. Excess iron affects motor circuitry, contributing to Parkinson's disease (PD) risk. The molecular mechanisms regulating central iron levels, beyond a few well-known genes controlling peripheral iron, remain unclear. We generated scores based on the archetypal brain iron accumulation observed in magnetic resonance imaging scans of individuals with excessive dietary iron absorption and hemochromatosis risk. Genome-wide analysis revealed that this score is highly heritable, identifying loci associated with iron homeostasis, and driven by peripheral iron levels. Our score predicted gait abnormalities and showed a U-shaped relationship with PD risk, identifying individuals with threefold increased risk. These results establish a hormetic relationship between brain iron and PD risk, where central iron levels are strongly determined by genetics via peripheral iron. This framework combining forward and reverse genetics is a powerful study design to understand genomic drivers underlying high dimensional phenotypes.

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1-4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5-7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or fill-in missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

The development of high intensity petawatt lasers has created new possibilities for ion acceleration and nuclear fusion using solid targets. In such laser-matter interaction, multiple ion species are accelerated with broad spectra up to hundreds of MeV. To measure ion yields and for species identification, CR-39 solid-state nuclear track detectors are frequently used. However, these detectors are limited in their applicability for multi-ion spectra differentiation as standard image recognition algorithms can lead to a misinterpretation of data, there is no unique relation between track diameter and particle energy, and there are overlapping pit diameter relationships for multiple particle species. In this report, we address these issues by first developing an algorithm to overcome user bias during image processing. Second, we use calibration of the detector response for protons, carbon and helium ions (alpha particles) from 0.1 to above 10 MeV and measurements of statistical energy loss fluctuations in a forward-fitting procedure utilizing multiple, differently filtered CR-39, altogether enabling high-sensitivity, multi-species particle spectroscopy. To validate this capability, we show that inferred CR-39 spectra match Thomson parabola ion spectrometer data from the same experiment. Filtered CR-39 spectrometers were used to detect, within a background of ~ 2 × 1011 sr-1 J-1 protons and carbons, (1.3 ± 0.7) × 108 sr-1 J-1 alpha particles from laser-driven proton-boron fusion reactions.

Summary: Background: Sepsis remains a leading cause of mortality in intensive care units. Understanding the dynamics of the plasma proteome of patients with sepsis is critical for improving prognostic and therapeutic strategies. Methods: This prospective, multicentre observational cohort study included 363 patients with sepsis recruited from five university hospitals in Germany between March 2018 and April 2023. Plasma samples were collected on days 1 and 4 after sepsis diagnosis, and proteome analysis was performed using mass spectrometry. Classical statistical methods and machine learning (random forest) were employed to identify proteins associated with 30-day survival outcomes. Findings: Out of 363 patients, 224 (62%) survived, and 139 (38%) did not survive the 30-day period. Proteomic analysis revealed significant differences in 87 proteins on day 1 and 95 proteins on day 4 between survivors and non-survivors. Additionally, 63 proteins were differentially regulated between day 1 and day 4 in the two groups. The identified protein networks were primarily related to blood coagulation, immune response, and complement activation. The random forest classifier achieved an area under the receiver operating characteristic curve of 0.75 for predicting 30-day survival. The results were compared and partially validated with an external sepsis cohort. Interpretation: This study describes temporal changes in the plasma proteome associated with mortality in sepsis. These findings offer new insights into sepsis pathophysiology, emphasizing the innate immune system as an underexplored network, and may inform the development of targeted therapeutic strategies. Funding: European Regional Development Fund of the European Union. The State of North Rhine-Westphalia, Germany.

For the past few years, researchers in the Human Pangenome Reference Consortium (HPRC) have been working to catalog almost all human genomic diversity. Frazer and Schork preview an article recently published in Nature, "A draft human pangenome reference,"1 which represents the initial release of 47 fully phased diploid assemblies of genomes of individuals with diverse ancestries.

Endometriosis is a common condition associated with debilitating pelvic pain and infertility. A genome-wide association study meta-analysis, including 60,674 cases and 701,926 controls of European and East Asian descent, identified 42 genome-wide significant loci comprising 49 distinct association signals. Effect sizes were largest for stage 3/4 disease, driven by ovarian endometriosis. Identified signals explained up to 5.01% of disease variance and regulated expression or methylation of genes in endometrium and blood, many of which were associated with pain perception/maintenance (SRP14/BMF, GDAP1, MLLT10, BSN and NGF). We observed significant genetic correlations between endometriosis and 11 pain conditions, including migraine, back and multisite chronic pain (MCP), as well as inflammatory conditions, including asthma and osteoarthritis. Multitrait genetic analyses identified substantial sharing of variants associated with endometriosis and MCP/migraine. Targeted investigations of genetically regulated mechanisms shared between endometriosis and other pain conditions are needed to aid the development of new treatments and facilitate early symptomatic intervention.

Summary: Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

BACKGROUND: Increased prevalence of mental illness has been reported in clinical studies of sex chromosome aneuploidies, but accurate population-based estimates of the prevalence and clinical detection rate of sex chromosome aneuploidies and the associated risks of psychiatric disorders are needed. In this study, we provide such estimates, valid for children and young adults of the contemporary Danish population. METHODS: We used the iPSYCH2015 case-cohort dataset, which is based on a source population of single-born individuals born in Denmark between May 1, 1981, and Dec 31, 2008. The case sample comprises all individuals from the source population with a diagnosis of any index psychiatric disorder (schizophrenia spectrum disorder, bipolar disorder, major depressive disorder, autism spectrum disorder, or ADHD) by the end of follow-up (Dec 31, 2015), registered in the hospital-based Danish Psychiatric Central Research Register. The cohort consists of individuals randomly selected from the source population, and overlaps with the case sample. Biobanked blood samples for individuals in the case and cohort samples underwent genotyping and quality-control filtering, after which we analysed microarray data to detect sex chromosome aneuploidy karyotypes (45,X, 47,XXX, 47,XXY, and 47,XYY). We estimated the population-valid prevalence of these karyotypes from the cohort sample. Weighted Cox proportional hazards models were used to estimate the risks of each index psychiatric disorder associated with each sex chromosome aneuploidy karyotype, by use of date of first hospitalisation with the index disorder in the respective case group and the cohort as outcome. The clinical detection rate was determined by comparing records of clinical diagnoses of genetic conditions from the Danish National Patient Register with sex chromosome aneuploidy karyotype determined by our study. FINDINGS: The assessed sample comprised 119 481 individuals (78 726 in the case sample and 43 326 in the cohort) who had genotyped and quality-control-filtered blood samples, including 64 533 (54%) people of gonadal male sex and 54 948 (46%) of gonadal female sex. Age during follow-up ranged from 0 to 34·7 years (mean 10·9 years [SD 3·5 years]). Information on ethnicity was not available. We identified 387 (0·3%) individuals as carriers of sex chromosome aneuploidies. The overall prevalence of sex chromosome aneuploidies was 1·5 per 1000 individuals. Each sex chromosome aneuploidy karyotype was associated with an increased risk of at least one index psychiatric disorder, with hazard ratios (HRs) of 2·20 (95% CI 1·42-3·39) for 47,XXY; 2·73 (1·25-6·00) for 47,XXX; 3·56 (1·01-12·53) for 45,X; and 4·30 (2·48-7·55) for 47,XYY. All karyotypes were associated with an increased risk of ADHD (HRs ranging from 1·99 [1·24-3·19] to 6·15 [1·63-23·19]), autism spectrum disorder (2·72 [1·72-4·32] to 8·45 [2·49-28·61]), and schizophrenia spectrum disorder (1·80 [1·15-2·80] to 4·60 [1·57-13·51]). Increased risk of major depressive disorder was found for individuals with 47,XXY (1·88 [1·07-3·33]) and 47,XYY (2·65 [1·12-5·90]), and of bipolar disorder for those with 47,XXX (4·32 [1·12-16·62]). The proportion of sex chromosome aneuploidy carriers who had been clinically diagnosed was 93% for 45,X, but lower for 47,XXY (22%), 47,XXX (15%), and 47,XYY (15%). Among carriers, the risk of diagnosis of at least one index psychiatric disorder did not significantly differ between those who had and had not been clinically diagnosed with sex chromosome aneuploidies (p=0·65). INTERPRETATION: Increased risks of psychiatric disorders associated with sex chromosome aneuploidies, combined with low rates of clinical diagnosis of sex chromosome aneuploidies, compromise the adequate provision of necessary health care and counselling to affected individuals and their families, which might be helped by increased application of genetic testing in clinical settings. FUNDING: Lundbeck Foundation and National Institutes of Health.

Background and aimsNAFLD is the most common chronic liver disease in children. Large pediatric studies identifying single nucleotide polymorphisms (SNPs) associated with risk and histologic severity of NAFLD are limited. Study aims included investigating SNPs associated with risk for NAFLD using family trios and association of candidate alleles with histologic severity.Approach and resultsChildren with biopsy-confirmed NAFLD were enrolled from the NASH Clinical Research Network. The Expert Pathology Committee reviewed liver histology. Genotyping was conducted with allele-specific primers for 60 candidate SNPs. Parents were enrolled for trio analysis. To assess risk for NAFLD, the transmission disequilibrium test was conducted in trios. Among cases, regression analysis assessed associations with histologic severity. A total of 822 children with NAFLD had mean age 13.2 years (SD 2.7) and mean ALT 101 U/L (SD 90). PNPLA3 (rs738409) demonstrated the strongest risk ( p = 2.24 × 10 -14 ) for NAFLD. Among children with NAFLD, stratifying by PNPLA3 s738409 genotype, the variant genotype associated with steatosis ( p = 0.005), lobular ( p = 0.03) and portal inflammation ( p = 0.002). Steatosis grade associated with TM6SF2 ( p = 0.0009), GCKR ( p = 0.0032), PNPLA3 rs738409 ( p = 0.0053), and MTTP ( p = 0.0051). Fibrosis stage associated with PARVB rs6006473 ( p = 0.0001), NR1I2 ( p = 0.0021), ADIPOR2 ( p = 0.0038), and OXTR ( p = 0.0065). PNPLA3 rs738409 ( p = 0.0002) associated with borderline zone 1 NASH.ConclusionsThis study demonstrated disease-associated SNPs in children with NAFLD. In particular, rs6006473 was highly associated with severity of fibrosis. These hypothesis-generating results support future mechanistic studies of development of adverse outcomes such as fibrosis and generation of therapeutic targets for NAFLD in children.

BackgroundAPOE is the largest genetic risk factor for Alzheimer's disease (AD), but there is a substantial polygenic component. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk across molecular processes and pathways that contribute to heterogeneity of disease presentation.ObjectiveWe examined polygenic risk impacting specific AD-associated pathways and its relationship with clinical status and biomarkers of amyloid, tau, and neurodegeneration (A/T/N).MethodsWe analyzed data from 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied pathway analysis and clustering to identify AD-associated "pathway clusters" and construct pathway-specific PRSs (excluding the APOE region). We tested associations with diagnostic status, abnormal levels of amyloid and ptau, and hippocampal volume.ResultsThirteen pathway clusters were identified, and eight pathway-specific PRSs were significantly associated with AD diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau-positivity was also associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs.ConclusionsPathway PRS may contribute to understanding separable disease processes, but do not add significant power for predictive purposes. These findings demonstrate that AD-phenotypes may be preferentially associated with risk in specific pathways, and defining genetic risk along multiple dimensions may clarify etiological heterogeneity in AD. This approach to delineate pathway-specific PRS can be used to study other complex diseases.

The observation of genetic correlations between disparate human traits has been interpreted as evidence of widespread pleiotropy. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation. We observe that xAM affects many phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2=74%). We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates and that previously reported genetic correlation estimates between some pairs of psychiatric disorders are congruent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

Abstract Standard single‐cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single‐cell physiology. Here, a scPatch‐Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild‐type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single‐cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC‐neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch‐proteomics technique creates a new proteogenetic‐therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single‐cells.

In this longitudinal observational study, we measured urinary glucose concentration, body composition and volume status (bioimpedance spectroscopy) and plasma renin and aldosterone concentrations in n = 22 kidney transplant recipients (KTRs) initiating on SGLT2I at baseline (BL), and after 1 week and 1, 3, and 6 months. Estimated glomerular filtration rate (eGFR) decreased by −2 mL/min/1.73 m2 (IQR −10–0) after 1 week and remained stable thereafter. Urinary glucose concentration was 10 (3–24) g/g creatinine after 1 week and correlated with eGFR (r2 = 0.273; p = 0.057). SGLT2I did not affect HbA1c, fasting blood glucose, body weight, fat or lean mass. SGLT2I decreased fluid overload dependent on baseline overhydration (OH, r2 = 0.54, p = 0.0003) without occurrence of dehydration. Plasma aldosterone increased at day 7, while plasma renin did not change significantly. In conclusion, SGLT2I corrected fluid overload in patients with elevated overhydration at baseline, while in euvolemic KTRs fluid status remained stable without reduction of body water below the reference range, thus promoting the safety of SGLT2I therapy in patients following kidney transplantation. Glucosuria, together with effects of SGLT2I on blood glucose control and body weight, is attenuated in KTRs dependent on eGFR.

For more than half a century, Denmark has maintained population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system. We leverage this resource to reconstruct the genealogy of the entire nation based on all individuals legally residing in Denmark since 1968. We cross-reference 6,691,426 individuals with nationwide health care registers to estimate heritability and genetic correlations of 10 broad diagnostic categories involving all major organs and systems. Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134]). The average genetic correlation of each of the 10 diagnostic categories with the other nine was highest for gastrointestinal conditions (average rg = 0.567, 95% CI = [0.566, 0.567]) and lowest for urogenital conditions (average rg = 0.386, 95% CI = [0.385, 0.388]). Mental, pulmonary, gastrointestinal, and neurological conditions had similar genetic correlation profiles.

Abstract Nature provides a rich source of information for the design of novel materials; yet there remain significant challenges in the design and manufacture of materials that replicate the form, function, and sustainability of biological solutions. Here, we identify key challenges and promising approaches to the development of materials informed by biology. These challenges fall into two main areas; the first relates to harnessing biological information for materials innovation, including key differences between biological and synthetic materials, and the relationship between structure and function. We propose an approach to materials innovation that capitalizes on biodiversity, together with high-throughput characterization of biological material architectures and properties, linked to environmental and ecological context. The second area relates to the design and manufacture of bioinformed materials, including the physical scale of material architectures and manufacturing scale up. We suggest ways to address these challenges and promising prospects for a bioinformed approach to materials innovation.

Abstract The development of high intensity petawatt lasers has created new possibilities for ion acceleration and nuclear fusion using solid targets. In such laser-matter interaction, multiple ion species are accelerated with broad spectra up to hundreds of MeV. To measure ion yields and for species identification, CR-39 solid-state nuclear track detectors are frequently used. However, these detectors are limited in their applicability for multi-ion spectra differentiation as standard image recognition algorithms can lead to a misinterpretation of data, there is no unique relation between track diameter and particle energy, and there are overlapping pit diameter relationships for multiple particle species. In this report, we address these issues by first developing an algorithm to overcome user bias during image processing. Second, we use calibration of the detector response for protons, carbon and helium ions (alpha particles) from 0.1 to above 10 MeV and measurements of statistical energy loss fluctuations in a forward-fitting procedure utilizing multiple, differently filtered CR-39, altogether enabling high-sensitivity, multi-species particle spectroscopy. To validate this capability, we show that inferred CR-39 spectra match Thomson parabola ion spectrometer data from the same experiment. Filtered CR-39 spectrometers were used to detect, within a background of ~ 2 × 1011 sr−1 J−1 protons and carbons, (1.3 ± 0.7) × 108 sr−1 J−1 alpha particles from laser-driven proton-boron fusion reactions.

Abstract Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary inter-individually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context.

Gene variants associated with longevity are also associated with protection against cognitive decline, dementia and Alzheimer's disease, suggesting that common physiologic pathways act at the interface of longevity and cognitive function. To test the hypothesis that variants in genes implicated in cognitive function may promote exceptional longevity, we performed a comprehensive 3-stage study to identify functional longevity-associated variants in ~700 candidate genes in up to 450 centenarians and 500 controls by target capture sequencing analysis. We found an enrichment of longevity-associated genes in the nPKC and NF-κB signaling pathways by gene-based association analyses. Functional analysis of the top three gene variants (NFKBIA, CLU, PRKCH) suggests that non-coding variants modulate the expression of cognate genes, thereby reducing signaling through the nPKC and NF-κB. This matches genetic studies in multiple model organisms, suggesting that the evolutionary conservation of reduced PKC and NF-κB signaling pathways in exceptional longevity may include humans.