Dementia is a major public health challenge, and Apolipoprotein E (APOE) ε4 is strongly associated with all-cause dementia, particularly Alzheimer’s disease (AD). We aim to quantify the overall contribution of modifiable lifestyle, adiposity, socioeconomic status (SES), and health conditions occurring before dementia, to the association between ε4 genotype and the development of all-cause dementia, with AD examined as a major subtype. A population cohort study of 181,006 white UK Biobank participants aged ≥ 55 years at baseline was conducted, to examine the associations between APOE ε4 and all-cause dementia, and specifically AD, including modification and mediation role of lifestyle factors, adiposity, SES, and health conditions occurring before dementia. All risk factors, except for high alcohol intake, low diet quality, and phenotypic obesity, were associated with higher risk of all-cause dementia. The interaction contributions of lifestyle, adiposity, SES, and health conditions occurring before dementia varied by sex and dementia type. Low educational attainment had the strongest interaction effects with the association of APOE ε4 carriers and AD/all-cause dementia (up to 32.1

BackgroundThe link between cardiometabolic disease and mental illness has been well established but remains incompletely explained. One hypothesis suggests that circadian rhythm dysregulation links cardiometabolic disease and mental illnesses.BMAL1is a circadian rhythm regulatory gene. Human genetic studies have implicatedBMAL1in depression, schizophrenia, bipolar disorder as well as body mass index, blood pressure and lipid levels.ObjectiveWe investigated theBMAL1locus genetic variants for associations with both cardiometabolic and mental illness.MethodsGenetic and phenotypic data from UK Biobank (~500 000 participants) of White British, African-Caribbean, South Asian, white European and Multiple ancestries were used. Regression analyses using Plink 1.09 was used to identify significant associations, with Bonferroni multiple testing correction. Multiple ancestry meta-analyses using METAL software was used to investigate trans-ancestry consistency in genetic effects.FindingsWe identified associations for body mass index, anhedonia, diastolic and systolic blood pressure, waist-hip ratio, major depressive disorder, neuroticism and risk-taking. Meta-analyses indicated that there are ancestry-wide and ancestry-specific effects on cardiometabolic, mental illness and related traits.ConclusionsOur results suggest that the associations for mental illness (and related traits) and those for cardiometabolic traits are distinct rather than shared and that these associations were consistent across ancestry groups.Clinical implicationsFurther investigation into the tissue-specific roles ofBMAL1is required to fully understand the clinical impact of these findings.

This document contains Supplementary Figures 1-6 and Supplementary Table 1. Supplementary Figure 1. Sequencing reads of EGFR-KDD in index patient with lung adenocarcinoma. Supplementary Figure 2. cDNA sequence of EGFR-KDD. Supplementary Figure 3. Sequencing reads identifying EGFR-KDD in a lung adenocarcinoma tumor from The Cancer Genome Atlas. Supplementary Figure 4. Autophosphorylation of endogenous EGFR-KDD in A1235 cells. Supplementary Figure 5. Colony formation of NR6 cells expressing EGFR variants. Supplementary Figure 6. Efficacy of EGFR TKIs in endogenous and ectopic models of the EGFR-KDD. Supplementary Table 1. Results of MTT curve fitting from Prism.

<p>Details for variant processing, analysis of TCGA data, and the FM mutation hotspot caller.</p>

This document contains Supplementary Methods, Supplementary Figure Legends, and Supplementary Table Legend.

This file contains supplemental tables S1-S7. The data contained are as follows: (Table S1) Gene lists on the FoundationOne assay, (Table S2) Sample counts per disease, (Table S3) Foundation Medicine sample counts compared to TCGA sample counts, (Table S4) Genes with altered frequencies between local and metastatic disease, (Table S5) Merged MutationAssessor and PolyPhen-2 outputs, (Table S6) Enrichment of NOTCH1 alterations across diseases, (Table S7) Curated list of tumor suppressor genes on the FoundationOne assay.

Importance:Cognitive impairment in depression is poorly understood. Family history of depression is a potentially useful risk marker for cognitive impairment, facilitating early identification and targeted intervention in those at highest risk, even if they do not themselves have depression. Several research cohorts have emerged recently that enable findings to be compared according to varying depths of family history phenotyping, in some cases also with genetic data, across the life span. Objective:To investigate associations between familial risk of depression and cognitive performance in 4 independent cohorts with varied depth of assessment, using both family history and genetic risk measures. Design, Setting, and Participants:This study used data from the Three Generations at High and Low Risk of Depression Followed Longitudinally (TGS) family study (data collected from 1982 to 2015) and 3 large population cohorts, including the Adolescent Brain Cognitive Development (ABCD) study (data collected from 2016 to 2021), National Longitudinal Study of Adolescent to Adult Health (Add Health; data collected from 1994 to 2018), and UK Biobank (data collected from 2006 to 2022). Children and adults with or without familial risk of depression were included. Cross-sectional analyses were conducted from March to June 2022. Exposures:Family history (across 1 or 2 prior generations) and polygenic risk of depression. Main Outcomes and Measures:Neurocognitive tests at follow-up. Regression models were adjusted for confounders and corrected for multiple comparisons. Results:A total of 57 308 participants were studied, including 87 from TGS (42 [48%] female; mean [SD] age, 19.7 [6.6] years), 10 258 from ABCD (4899 [48%] female; mean [SD] age, 12.0 [0.7] years), 1064 from Add Health (584 [49%] female; mean [SD] age, 37.8 [1.9] years), and 45 899 from UK Biobank (23 605 [51%] female; mean [SD] age, 64.0 [7.7] years). In the younger cohorts (TGS, ABCD, and Add Health), family history of depression was primarily associated with lower performance in the memory domain, and there were indications that this may be partly associated with educational and socioeconomic factors. In the older UK Biobank cohort, there were associations with processing speed, attention, and executive function, with little evidence of education or socioeconomic influences. These associations were evident even in participants who had never been depressed themselves. Effect sizes between familial risk of depression and neurocognitive test performance were largest in TGS; the largest standardized mean differences in primary analyses were -0.55 (95% CI, -1.49 to 0.38) in TGS, -0.09 (95% CI, -0.15 to -0.03) in ABCD, -0.16 (95% CI, -0.31 to -0.01) in Add Health, and -0.10 (95% CI, -0.13 to -0.06) in UK Biobank. Results were generally similar in the polygenic risk score analyses. In UK Biobank, several tasks showed statistically significant associations in the polygenic risk score analysis that were not evident in the family history models. Conclusions and Relevance:In this study, whether assessed by family history or genetic data, depression in prior generations was associated with lower cognitive performance in offspring. There are opportunities to generate hypotheses about how this arises through genetic and environmental determinants, moderators of brain development and brain aging, and potentially modifiable social and lifestyle factors across the life span.

Gene lists for Version 1 of the FoundationOne Assay (S1); Gene lists for Version 2 of the FoundationOne Assay (S2); Gene lists for Version 3 of the FoundationOne Assay (S3); Gene lists for Version 4 of the FoundationOne Assay (S4); Gene lists for Version 5 of the FoundationOne Assay (S5); Distribution of samples across different FoundationOne assays (S6); Filtered variants of unknown significance (S7).

Long tail distribution of the top 50 altered genes in pediatric sarcoma (S1); Long tail distribution of the top 50 altered genes in pediatric extracranial embryonal tumors (S2); Long tail distribution of the top 50 altered genes in pediatric brain tumors (S3); Long tail distribution of the top 50 altered genes in pediatric heme malignancies (S4); Long tail distribution of the top 50 altered genes in pediatric carcinomas (S5); Long tail distribution of the top 50 altered genes in pediatric gonadal tumors (S6); Known fusions identified in the pediatric cohort (S7); Tileplots showing the pattern of co-occurring alterations with novel MLL3 mutations (S8); Tileplot showing the pattern of co-occurring alterations with a novel PRSS1 mutation (S9); Tileplot showing the pattern of co-occurring alterations with novel DKC1 deletions (S10).

Happiness is a fundamental human affective trait, but its biological basis is not well understood. Using a novel approach, we construct LDpred-inf polygenic scores of a general happiness measure in 2 cohorts: the Adolescent Brain Cognitive Development (ABCD) cohort (N = 15,924, age range 9.23–11.8 years), the Add Health cohort (N = 9129, age range 24.5–34.7) to determine associations with several well-being and happiness measures. Additionally, we investigated associations between genetic scores for happiness and brain structure in ABCD (N = 9626, age range (8.9–11) and UK Biobank (N = 16,957, age range 45–83). We detected significant (p.FDR < 0.05) associations between higher genetic scores vs. several well-being measures (best r 2 = 0.019) in children of multiple ancestries in ABCD and small yet significant correlations with a happiness measure in European participants in Add Health (r 2 = 0.004). Additionally, we show significant associations between lower genetic scores for happiness with smaller structural brain phenotypes in a white British subsample of UK Biobank and a white sub-sample group of ABCD. We demonstrate that the genetic basis for general happiness level appears to have a consistent effect on happiness and wellbeing measures throughout the lifespan, across multiple ancestral backgrounds, and multiple brain structures.

The Python ecosystem represents a global, data rich, technology-enabled network. By analyzing Python's dependency network, its top 14 most imported libraries and cPython (or core Python) libraries, this research finds clear evidence the Python network can be considered a problem solving network. Analysis of the contributor network of the top 14 libraries and cPython reveals emergent specialization, where experts of specific libraries are isolated and focused while other experts link these critical libraries together, optimizing both local and global information exchange efficiency. As these networks are expanded, the local efficiency drops while the density increases, representing a possible transition point between exploitation (optimizing working solutions) and exploration (finding new solutions). These results provide insight into the optimal functioning of technology-enabled social networks and may have larger implications for the effective functioning of modern organizations.

Background: UK Biobank is a prospective cohort study of around half-a-million general population participants, recruited between 2006 and 2010, with baseline studies at recruitment and multiple assessments since. From 2014 to date magnetic resonance imaging (MRI) has been pursued in a participant sub-sample, with the aim to scan around n=100k. This sub-sample are studied widely and therefore understanding their relative characteristics is important for future reports. We aimed to quantify psychological and physical health in the UK Biobank imaging sub-sample, compared with the rest of the cohort. Methods: We used t-tests and chi-squares for continuous/categorical variables respectively, to estimate average differences on a range of cognitive, mental and physical health phenotypes. We contrasted baseline values of participants who attended imaging (vs. had not), and compared their values at the imaging visit vs. baseline values of participants who were not scanned. We also tested the hypothesis that the associations of established risk factors with worse cognition would be underestimated in the (hypothesized) healthier imaging group compared with the full cohort. We tested these interactions using linear regression models.Results: On a range of cognitive, mental health, cardiometabolic, inflammatory and neurological phenotypes we found that the 47,920 participants who were scanned by January 2021 showed consistent statistically significant ‘healthy’ bias compared with the ~450,000 who were not scanned. These effect sizes were small to moderate based on Cohen’s d/Cramer’s V metrics. We found evidence of interaction, where stratified analysis demonstrated that associations of established cognitive risk factors were smaller in the imaging sub-sample compared with the full cohort. Conclusion: Of the ~100,000 participants who ultimately will undergo MRI assessment within UK Biobank, the first ~50,000 showed some ‘healthy’ bias on a range of metrics at baseline. Those differences largely remained at the subsequent imaging visit, and we provide evidence that testing associations in the imaging sub-sample alone could lead to potential underestimation of exposure/outcome estimates.

We present a genome-wide association study of a general happiness measure in 118,851 participants from the UK Biobank. Using BOLT-LMM, we identify 3 significant loci with a heritability estimate of 0.8%. Linkage disequilibrium score regression was performed on the ‘big five’ personality traits finding significant associations with lower neuroticism and higher extraversion and conscientiousness. Using a novel approach, we construct LDpred-inf polygenic risk scores in the Adolescent Brain Cognitive Development (ABCD) cohort and the Add Health cohort. We detected nominally significant associations with several well-being measures in ABCD and significant correlations with a happiness measure in Add Health. Additionally, we tested for associations with several brain regions in a white British subsample of UK Biobank finding significant associations with several brain structure and integrity phenotypes. We demonstrated a genetic basis for general happiness level and brain structure that appears to remain consistent throughout the lifespan and across multiple ancestral backgrounds. Author summary At the genetic level, there has been little investigation into whether people may have a baseline happiness level which varies from person to person. Here we perform a genetic analysis in the UK Biobank to identify three genetic loci that associate with general happiness level and preform genetic correlations of our results with the ‘Big Five’ personality traits, identifying significant correlations with neuroticism, conscientiousness and extraversion. We use the resulting summary statistics to create LDpred-inf polygenic risk scores in UK biobank identifying several brain metrics and regions associate with genetic loading for general happiness level. We also use a novel method to create LDpred-inf polygenic risk scores in two other cohorts, ABCD and Add Health. We found significant correlations with an independent happiness measure in Add Health and nominally significant correlations with several well-being measures in ABCD in both those of European Ancestry and all other ancestries found in these cohorts. We also attempted to replicate our UK Biobank MRi finding in ABCD. We conclude there is evidence that individuals have a general happiness level that is in part genetic which spans across age and ancestry.

Abstract Understanding local adaptation has become a key research area given the ongoing climate challenge and the concomitant requirement to conserve genetic resources. Perennial plants, such as forest trees, are good models to study local adaptation given their wide geographic distribution, largely outcrossing mating systems, and demographic histories. We evaluated signatures of local adaptation in European aspen (Populus tremula) across Europe by means of whole-genome resequencing of a collection of 411 individual trees. We dissected admixture patterns between aspen lineages and observed a strong genomic mosaicism in Scandinavian trees, evidencing different colonization trajectories into the peninsula from Russia, Central and Western Europe. As a consequence of the secondary contacts between populations after the last glacial maximum, we detected an adaptive introgression event in a genome region of ∼500 kb in chromosome 10, harboring a large-effect locus that has previously been shown to contribute to adaptation to the short growing seasons characteristic of Northern Scandinavia. Demographic simulations and ancestry inference suggest an Eastern origin—probably Russian—of the adaptive Nordic allele which nowadays is present in a homozygous state at the north of Scandinavia. The strength of introgression and positive selection signatures in this region is a unique feature in the genome. Furthermore, we detected signals of balancing selection, shared across regional populations, that highlight the importance of standing variation as a primary source of alleles that facilitate local adaptation. Our results, therefore, emphasize the importance of migration–selection balance underlying the genetic architecture of key adaptive quantitative traits.

AbstractIntroductionThe aim of this study was to determine risk of being SARS‐CoV‐2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. MethodsWe used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS‐CoV‐2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without.ResultsOf 401,268 participants in total, 13,331 tested positive for SARS‐CoV‐2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS‐CoV‐2 positive (risk ratio‐RR 1.35; 95% confidence interval‐CI 1.24–1.47) and severe infection (RR 1.30; 95% CI 1.04–1.59), compared to those without. The excess risk of being tested positive for SARS‐CoV‐2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes.ConclusionThe totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes.

Understanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

Background and purpose: Previous studies testing associations between polygenic risk for late-onset Alzheimer’s disease (LOAD-PGR) and brain magnetic resonance imaging (MRI) measures have been limited by small samples and inconsistent consideration of potential confounders. This study investigates whether higher LOAD-PGR is associated with differences in structural brain imaging and cognitive values in a relatively large sample of non-demented, generally healthy adults (UK Biobank). Method: Summary statistics were used to create PGR scores for n=32,790 participants using LDpred. Outcomes included 12 structural MRI volumes and 6 concurrent cognitive measures. Models were adjusted for age, sex, body mass index, genotyping chip, 8 principal components, lifetime smoking, apolipoprotein (APOE) e4 genotype and socioeconomic deprivation. We tested for statistical interactions between APOE e4 allele dose and LOAD-PGR vs. all outcomes. Results: In fully adjusted models, LOAD-PGR was associated with worse fluid intelligence (standardised beta [β] = -0.080 per LOAD-PGR standard deviation, p = 0.002), matrix completion (β = -0.102, p = 0.003), smaller left hippocampal total (β = -0.118, p = 0.002) and body (β = -0.069, p = 0.002) volumes, but not other hippocampal subdivisions. There were no significant APOE x LOAD-PGR score interactions for any outcomes in fully adjusted models. Discussion: This is the largest study to date investigating LOAD-PGR and non-demented structural brain MRI and cognition phenotypes. LOAD-PGR was associated with smaller hippocampal volumes and aspects of cognitive ability in healthy adults, and could supplement APOE status in risk stratification of cognitive impairment/LOAD.

Objective: Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM software to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease. ConclusionS: These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression.

Individuals with severe mental illness have an increased risk of cardiometabolic diseases compared to the general population. Shared risk factors and medication effects explain part of this excess risk; however, there is growing evidence to suggest that shared biology (including genetic variation) is likely to contribute to comorbidity between mental and physical illness. Contactins are a family of genes involved in development of the nervous system and implicated, though genome-wide association studies, in a wide range of psychological, psychiatric and cardiometabolic conditions. Contactins are plausible candidates for shared pathology between mental and physical health. We used data from UK Biobank to systematically assess how genetic variation in contactin genes was associated with a wide range of psychological, psychiatric and cardiometabolic conditions. We also investigated whether associations for cardiometabolic and psychological traits represented the same or distinct signals and how the genetic variation might influence the measured traits. We identified: A novel genetic association between variation in CNTN1 and current smoking; two independent signals in CNTN4 for BMI; and demonstrated that associations between CNTN5 and neuroticism were distinct from those between CNTN5 and blood pressure/HbA1c. There was no evidence that the contactin genes contributed to shared aetiology between physical and mental illness