Levels of sociability are continuously distributed in the general population, and decreased sociability represents an early manifestation of several brain disorders. Here, we investigated the genetic underpinnings of sociability in the population.

Main question of our research: 1. Are there common genetic variants that are associated with sociability in the general population? 2. Are genetic variants that are associated with sociability also associated with neuropsychiatric disorders?

Type of data uploaded in this repository: The UK Biobank project (see https://www.ukbiobank.ac.uk/) is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. The raw data that this project is based on comes from the publically available UK Biobank set, which is very large and is therefore not provided here. Here we only provide the results from our analysis, that is also described here: https://www.biorxiv.org/content/10.1101/781195v2 and currently in revision in a scientific journal. In the dataset you will find the association of 9327396 genetic variants with the phenotype sociability. This dataset is not applicable to be opened with Excel, and can best be opened on a cluster computer or using specfic software.

Subjects The UK Biobank (UKBB) is a major population-based cohort from the United Kingdom that includes individuals aged between 37 and 73 years. We constructed a sociability measure based on the the aggregation of scores per participant on four questions from the UKBB database that link to sociability, including (1) a question about the frequency of friend/family visits, (2) a question on the number and type of social venues that are visited, (3) a question about worrying after social embarrassment and (4) a question about feeling lonely, leading to a sociability score ranging from 0-4. Participants were excluded if they had somatic problems that could be related to social withdrawal (BMI < 15 or BMI > 40, narcolepsy (all the time), stroke, severe tinnitus, deafness or brain-related cancers) or if they answered that they had “No friends/family outside household” or “Do not know” or “Prefer not to answer” to any of the questions.

SNP genotyping and quality control Details about the available genome-wide genotyping data for UKBB participants have been reported previously (PMID: 30305743). We used third-release genotyping data (see https://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=100319). Briefly, 49,950 participants were genotyped using the UK BiLEVE Axiom Array and 438,427 participants were genotyped using UK Biobank Axiom Array. Genotypes were imputed into the dataset using the Haplotype Reference Consortium (HRC), and the UK10K haplotype resource. To account for ethnicity, we included only those individuals that identified themselves as "white" by self-report and plotted the Principal Components (PC) provided by the UKBB, excluding individuals considered to be outliers according to PCs 1 and 2. Genetic relatedness calculated with KING kinship and provided by the UKBB (https://kenhanscombe.github.io/ukbtools/articles/explore-ukb-data.html ; http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf) was used to identify first and second-degree relatives. Subsequently ´families´ (i.e. clusters of related individuals above an IBD>0.125 threshold) were created and only one individual from each of these created ‘families’ was included in the analysis. If self-reported sex and SNP-based sex differed, individuals were excluded from further analysis. Single nucleotide polymorphisms (SNPs) with minor allele frequency <0.005, Hardy-Weinberg equilibrium test P value<1e−6, missing genotype rate >0.05, and imputation quality of INFO <0.8 were excluded. In the current study, all analyses are based on 342,461 participants of European ancestry for which both genotype data and sociability scores were available.

Genome-wide association analysis Genome-wide association analysis with the imputed marker dosages was performed in PLINK1.9, using a linear regression model with the sociability measure as the dependent variable and including sex, age, 10 first PCs, assessment center, and genotype batch as covariates. SNPs were considered significantly associated if they had p-value < 5e-8. Associated loci were considered independent of each other at r2 0.6 and lead SNPs were classified as the SNP with the smallest association p-value and at r2 0.1, using a 250kb window. The summary statistics come from the plink2 linear regression analysis.

https://www.nature.com/articles/s41588-018-0321-7

Lung function is an important indicator of respiratory health and mortality. Measures of lung function show irreversible airway obstruction in chronic obstructive pulmonary disease (COPD), a progressive condition affecting 900,000 people in the UK. Smoking is a strong risk factor for COPD but not all smokers are equally susceptible. Genetic approaches to understanding the mechanisms underlying the maintenance of good lungs aim to reveal previously unknown molecular targets for drug development and to facilitate stratified approaches to treatment and care. This project aims to detect rare genetic variants associated with lung function. Once discovered, such variants tend to exert a large effect on disease risk and provide a means to translate findings from genetic studies of lung function to clinical relevant research and development. The proposed study leverages the power of Uk Biobank and respiratory genomics to advance understanding of lung function and COPD.

Abstract

Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.

Data Description

This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.

As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.

Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.

Paper & Code

The original paper for this article can be accessed here:

• https://ieeexplore.ieee.org/abstract/document/10196736

To access the codes relevant for this project, please access the project GitHub Repos:

• https://github.com/AndreiRoibu/AgeMapper

If using this work, please cite it based on the above paper, or using the following BibTex:

@inproceedings{roibu2023brain,
 title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes},
 author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J},
 booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)},
 pages={17--25},
 year={2023},
 organization={IEEE},
 doi={10.1109/SDS57534.2023.00010}
}

Data Access

The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi

Funding

ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

This table shows the associations between the 27 phenotypes (Table 1) and coding load score computed from sites where the human genome reference allele is same as the annotated ancestral allele. (XLSX)

Data supporting paper 'Molecular genetic contributions to social deprivation and household income in UK Biobank'. Current Biology (2016). doi: 10.1016/j.cub.2016.09.035 ## Note re working with data ## Each of the three data files contains over seventeen million rows. Users will encounter difficulties if they attempt to view the content using Notepad++ or Microsoft Notepad. Microsoft Excel 2016 will not display all rows. These space-delimited text files contains seven columns, with a header row, which are listed in the readme file. ## Note re other copy ## The data files are identical to the files of the same name previously made available on the website of the Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE) http://www.ccace.ed.ac.uk/node/335 as the zip archive 'Hill_CB_2016.zip'.

Purpose:Cystatin C (CysC), beyond its biomarker role of renal function, has been implicated in various physical and pathological activities. However, the impact of serum CysC on cancer mortality in a general population remains unknown. We aimed to examine the associations of serum CysC concentrations with total mortality and mortality of 12 site-specific cancers.Methods:We included 241,008 participants of the UK Biobank cohort with CysC measurements who had normal creatinine-based estimated glomerular filtration rates and were free of cancer and renal diseases at baseline (2006–2010). Death information was obtained from the National Health Service death records through 28 February 2021. Multivariable Cox proportional hazards models were used to compute hazard ratios (HR) per one standard deviation increase in log-transformed CysC concentrations and 95% confidence intervals (95% CI) for mortality.Results:Over a median follow-up of 12.1 (interquartile range, 11.3–12.8) years, 5,744 cancer deaths occurred. We observed a positive association between serum CysC concentrations and total cancer mortality (HR = 1.16, 95% CI: 1.12–1.20). Specifically, participants with higher serum CysC concentrations had increased mortality due to lung cancer (HR = 1.12, 95% CI: 1.05–1.20), blood cancer (HR = 1.29, 95% CI: 1.16–1.44), brain cancer (HR = 1.19, 95% CI: 1.04–1.36), esophageal cancer (HR = 1.20, 95% CI: 1.05–1.37), breast cancer (HR = 1.18, 95% CI: 1.03–1.36), and liver cancer (HR = 1.49, 95% CI: 1.31–1.69).Conclusion:Our findings indicate that higher CysC concentrations are associated with increased mortality due to lung, blood, brain, esophageal, breast, and liver cancers. Future studies are necessary to clarify underlying mechanisms.

The Dataset is a summary statistics for discovery GWAS of 22,473 varicose veins cases and 379,183 non-varicose veins controls in UK Biobank (Ahmed W. et al., Nature Communications, 2022). Since it's under OAI, this dataset can be freely used as long it respects the ORA terms of use:

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MR, mendelian randomisation. (XLSX)

Associations between the 27 phenotypes (Table 1) and four load scores computed for four different groups of coding variants stratified by derived allele frequency: 76,185 variants with DAF∈(0,0.05); 10,552 variants with DAF∈[0.05,0.3); 5,530 variants with DAF∈[0.3,0.7); and 3,587 variants with DAF∈[0.7,1). (XLSX)

Associations between the 27 phenotypes (Table 1) and five load scores computed for five different groups of coding variants stratified by phyloP score: 54,840 variants with phyloP∈(0,2]; 19,414 variants with phyloP∈(2,4]; 10,865 variants with phyloP∈(4,6]; 7,582 variants with phyloP∈(6,8]; and 3,315 variants with phyloP∈(8,10]. (XLSX)

‘Alarm’ symptom-cancer pairs in the UK Biobank and CPRD comparison cohorts.

Baseline characteristics of the UKBB participants included in this study.

Multimodal MRI protocols for COVID-related neuroimaging with Siemens and GE 3T scanners.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Details of the different outcome measures considered.

BackgroundBoth cancer and diabetes are complex chronic diseases that have high economic costs for society. The co-occurrence of these two diseases in people is already well known. The causal effects of diabetes on the development of several malignancies have been established, but the reverse causation of these two diseases (e.g., what type of cancer can cause T2D) has been less investigated.MethodsMultiple Mendelian randomization (MR) methods, such as the inverse-variance weighted (IVW) method, weighted median method, MR-Egger, and MR pleiotropy residual sum and outlier test, were performed to evaluate the causal association of overall and eight site-specific cancers with diabetes risk using genome-wide association study summary data from different consortia, such as Finngen and UK biobank.ResultsA suggestive level of evidence was observed for the causal association between lymphoid leukaemia and diabetes by using the IVW method in MR analyses (P = 0.033), indicating that lymphoid leukaemia increased diabetes risk with an odds ratio of 1.008 (95% confidence interval, 1.001-1.014). Sensitivity analyses using MR-Egger and weighted median methods showed consistent direction of the association compared with the IVW method. Overall and seven other site-specific cancers under investigation (i.e., multiple myeloma, non-Hodgkin lymphoma, and cancer of bladder, brain, stomach, lung, and pancreas) were not causally associated with diabetes risk.ConclusionsThe causal relationship between lymphoid leukaemia and diabetes risk points to the necessity of diabetes prevention amongst leukaemia survivors as a strategy for ameliorating the associated disease burden.

BackgroundReduced bone mineral density (BMD) and osteoporosis are common in chronic liver diseases. However, the causal effect of alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD) on BMD remains uncertain.ObjectivesThis study uses a two-sample Mendelian randomization (MR) design to evaluate the genetically predicted effect of ALD and NAFLD on BMDs using summary data from publically available genome-wide association studies (GWASs).MethodsThe GWAS summary statistics of ALD (1416 cases and 213,592 controls) and NAFLD (894 cases and 217,898 controls) were obtained from the FinnGen consortium. BMDs of four sites (total body, n = 56,284; femoral neck, n = 32,735; lumbar spine, n = 28,498; forearm, n = 8143) were from the GEnetic Factors for OSteoporosis Consortium. Data for alcohol consumption (n = 112,117) and smoking (n = 33,299) and serum 25-Hydroxyvitamin D (25-OHD) level (n = 417,580) were from UK-biobank. We first performed univariate MR analysis with the Inverse Variance Weighted (IVW) method as the primary analysis to investigate the genetically predicted effect of ALD or NAFLD on BMD. Then, multivariate MR and mediation analysis were performed to identify whether the effect was mediated by alcohol consumption, smoking, or serum 25-OHD level.ResultsThe MR results suggested a robust genetically predicted effect of ALD on reduced BMD in the femoral neck (FN-BMD) (IVW beta = -0.0288; 95% CI: -0.0488, -0.00871; P = 0.00494) but not the other three sites. Serum 25-OHD level exhibited a significant mediating effect on the association between ALD and reduced FN-BMD albeit the proportion of mediation was mild (2.21%). No significant effects of NAFLD, alcohol consumption, or smoking on BMD in four sites, or reverse effect of BMD on ALD or NAFLD were detected.ConclusionOur findings confirm the genetically predicted effect of ALD on reduced FN-BMD, and highlight the importance of periodic BMD and serum 25-OHD monitoring and vitamin D supplementation as needed in patients with ALD. Future research is required to validate our results and investigate the probable underlying mechanisms.

ObjectiveWhether fasting insulin (FI) plays a role in cancer risk remains unclear. This study aimed to investigate the association between FI and cancer risk and to explore its potential mediator role in the association between type 2 diabetes mellitus (T2DM) and cancer.MethodsTwo-sample Mendelian randomization (TSMR) analysis was performed to evaluate the effect of FI on overall and 14 site-specific cancers using genome-wide association study (GWAS) summary-level data from Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) and consortia of 14 site-specific cancers. The primary MR approach was conducted by using the random-effect inverse-variance weighted (IVW) method, and sensitivity analyses were implemented by adopting weighted-median, weighted-mode, MR-Egger, and MR-PRESSO tests. Polygenic risk score analysis was executed by using individual-level data from UK Biobank to validate the findings from TSMR analyses. Multivariable Mendelian randomization (MVMR) was carried out to estimate the mediation effect of FI on the association between T2DM and cancer.ResultsTSMR study suggested that genetically determined high FI levels were associated with increased risk of colorectal cancer (odds ratio (OR) = 1.87, 95% CI: 1.23–2.84, p = 0.003) and endometrial cancer (OR = 1.89, 95% CI: 1.08–3.01, p = 0.008), but not associated with overall cancer risk or the other 12 studied cancer sites. Polygenic risk score analysis successfully replicated the association between genetic liability to high FI levels and the increased risk of colorectal and endometrial cancers. MVMR and MR mediation analyses detected an intermediary effect of FI and quantified that FI mediated 21.3% of the association between T2DM and endometrial cancer.ConclusionsThis study demonstrated that FI levels are associated with the risk of colorectal and endometrial cancers, and FI was found to play an intermediary role in the association between T2DM and endometrial cancer. The associations between FI and other cancers need to be further studied.

BackgroundSepsis is a severe systemic infection that can result in organ dysfunction and mortality. Dyslipidemia emerges as a key player in the intricate web of sepsis pathogenesis. Yet, the causal relationship between blood lipid profiles and sepsis risk remains uncertain. This study aims to investigate the association between genetically predicted lipid traits, drug targets, and sepsis.MethodsThe UK Biobank’s Genome-wide association studies (GWAS) produced data on lipid and apolipoprotein characteristics. Four independent GWAS datasets were used to generate the sepsis statistics. The study utilized the two-sample Mendelian randomization (MR) approach, which incorporates multivariable (MVMR) models, to assess the correlations between sepsis risk and lipid-related parameters. To gain further insight, expression quantitative trait loci (eQTL) data were used to investigate the significant drug targets for lipid-lowering.ResultsIncreasing ApoA-1 levels was associated with a diminished risk of sepsis (under 75) (OR 0.927, 95% CI 0.861–0.999; p = 0.047). This inverse correlation persevered even after performing multivariable MR. Elevated levels of HDL-C were associated with a decreased risk of sepsis (under 75) (OR 0.897, 95% CI 0.838–0.960; P = 0.002) and incidence of sepsis (OR 0.883, 95% CI 0.820–0.951; P = 0.001), which was consistent across sensitivity analyses. Furthermore, a decrease in total cholesterol exhibited a causal effect on sepsis in multivariable MR (OR 0.779, 95% CI 0.642–0.944; P = 0.01). The genetic variants related to lowering LDL-C, located near the HMGCR and LDLR genes, were predicted to elevate the risk of sepsis. Moreover, genetic mimicry near the ANGPTL3 and LPL gene suggested that reducing the activity of ANGPTL3 and LPL (mimicking antisense anti-ANGPTL3 and LPL agents) was forecasted to decrease sepsis risk.ConclusionGenetically inferred elevated ApoA-1, total cholesterol, and HDL-C manifest a protective effect against sepsis. Within the 9 lipid-lowering drug targets investigated ANGPTL3 and LPL exhibit potential as candidate drug targets for sepsis.