Background The study of non-hospitalised COVID-19 cases provides a context for improved understanding of the immune response to existing and new infections. Population-based cohorts provide a unique opportunity to do this in relation to rich longitudinal pre- and pan-pandemic data. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1990–1992 and has subsequently followed participants for over 30 years. Methods A study comprising three clinic visits was implemented, in response to the COVID-19 pandemic, amongst ALSPAC participants to measure SARS-CoV-2 specific humoral and cellular responses longitudinally. Here we present data from the first clinic in December 2020 before the start of the UK vaccination campaign and examine associations with a set of exemplar pre- and pan-pandemic health factors. Results We observed humoral and cellular memory immune responses to SARS-CoV-2 infection in mild cases of COVID-19 up to 9 months post-infection. Symptomatic infection elicited a memory immune response of greater magnitude, though there was variation in response in both asymptomatic and symptomatic individuals. We examined health factors associated with severe COVID-19 and found that cardio-metabolomic, respiratory and immune-related health factors associate with a memory immune response of higher magnitude. For example, in older participants (mean age 58 years), higher BMI was associated with an immune memory response of greater magnitude, particularly with anti-S and anti-N binding antibodies. Conclusions We set out to illustrate the use of cohort studies to deliver detailed immunological data and to provide example analyses of how life course health factors can be examined in relation to the immune response following a widespread and novel infection. We expanded this assessment to include longitudinally assessed traits, opening up the potential for the more common use of longitudinal population studies for the better understanding the aetiology of infection outcome.

Background The study of non-hospitalised COVID-19 cases provides a context for improved understanding of the immune response to existing and new infections. Population-based cohorts provide a unique opportunity to do this in relation to rich longitudinal pre- and pan-pandemic data. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1990–1992 and has subsequently followed participants for over 30 years. Methods A study comprising three clinic visits was implemented, in response to the COVID-19 pandemic, amongst ALSPAC participants to measure SARS-CoV-2 specific humoral and cellular responses longitudinally. Here we present data from the first clinic in December 2020 before the start of the UK vaccination campaign and examine associations with a set of exemplar pre- and pan-pandemic health factors. Results We observed humoral and cellular memory immune responses to SARS-CoV-2 infection in mild cases of COVID-19 up to 9 months post-infection. Symptomatic infection elicited a memory immune response of greater magnitude, though there was variation in response in both asymptomatic and symptomatic individuals. We examined health factors associated with severe COVID-19 and found that cardio-metabolomic, respiratory and immune-related health factors associate with a memory immune response of higher magnitude. For example, in older participants (mean age 58 years), higher BMI was associated with an immune memory response of greater magnitude, particularly with anti-S and anti-N binding antibodies. Conclusions We set out to illustrate the use of cohort studies to deliver detailed immunological data and to provide example analyses of how life course health factors can be examined in relation to the immune response following a widespread and novel infection. We expanded this assessment to include longitudinally assessed traits, opening up the potential for the more common use of longitudinal population studies for the better understanding the aetiology of infection outcome.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

BACKGROUND:Immunological memory to vaccination and viral infection involves the coordinated action of B and T cells; thus, integrated analysis of these 2 components is critical for understanding their respective contributions to protection against breakthrough infections (BIs) after vaccination. METHODS:We investigated cellular and humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and/or vaccination in 300 adult participants from the Avon Longitudinal Study of Parents and Children (ALSPAC). Participants were grouped by those with (cases) and without (controls) a history of SARS-CoV-2 infection. To provide a quantitative correlate for protection against BI in the 8-month period after the study, Youden index thresholds were calculated for all immune measures analyzed. RESULTS:The magnitude of antibody and T-cell responses following the second vaccine dose was associated with protection against BI in participants with a history of SARS-CoV-2 infection (cases), but not in infection-naive controls. Over 8 months of follow-up, 2 threshold combinations provided the best performance for protection against BI in cases: (i) anti-spike immunoglobulin G (IgG) (≥666.4 binding antibody units [BAU]/mL) combined with anti-nucleocapsid pan-immunoglobulin (pan-Ig) (≥0.1332 BAU/mL) and (ii) spike 1-specific T cells (≥195.6 spot-forming units/106 peripheral blood mononuclear cells) combined with anti-N pan-Ig (≥0.1332 BAU/mL). Both combinations offered 100% specificity for detecting cases without BI, with sensitivities of 83.3% and 72.2%, respectively. CONCLUSIONS:Collectively, these results suggest that hybrid B- and T-cell immunity offers superior protection from BI after coronavirus disease 2019 (COVID-19) vaccination, and this finding has implications for designing next-generation COVID-19 vaccines that are capable of eliciting immunity to a broader repertoire of SARS-CoV-2 proteins.

Background 'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry ( N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056). Results We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci ( r 2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null. Conclusion Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

Summary Infections can lead to persistent or long-term symptoms and diseases such as shingles after varicella zoster, cancers after human papillomavirus, or rheumatic fever after streptococcal infections 1, 2 . Similarly, infection by SARS-CoV-2 can result in Long COVID, a condition characterized by symptoms of fatigue and pulmonary and cognitive dysfunction 3–5 . The biological mechanisms that contribute to the development of Long COVID remain to be clarified. We leveraged the COVID-19 Host Genetics Initiative 6, 7 to perform a genome-wide association study for Long COVID including up to 6,450 Long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We identified the first genome-wide significant association for Long COVID at the FOXP4 locus. FOXP4 has been previously associated with COVID-19 severity 6 , lung function 8 , and cancers 9 , suggesting a broader role for lung function in the pathophysiology of Long COVID. While we identify COVID-19 severity as a causal risk factor for Long COVID, the impact of the genetic risk factor located in the FOXP4 locus could not be solely explained by its association to severe COVID-19. Our findings further support the role of pulmonary dysfunction and COVID-19 severity in the development of Long COVID.

Objectives: Severe malaria remains a deadly disease for many young children in low-and middle-income countries. Levels of interleukin (IL)-6 have been shown to identify cases of severe malaria and associate with severity, but it is unknown if this association is causal. Methods: A single nucleotide polymorphism (SNP; rs2228145) in the IL-6 receptor was chosen as a ge-netic variant that is known to alter IL-6 signaling. We tested this, then took this forward as an instrument to perform Mendelian randomization (MR) in MalariaGEN, a large cohort study of patients with severe malaria at 11 worldwide sites. Results: In MR analyses using rs2228145, we did not identify an effect of decreased IL-6 signaling on severe malaria (odds ratio 1.14, 95% confidence interval 0.56-2.34, P = 0.713). The estimates of the as-sociation with any severe malaria subphenotype were similarly null, although with some imprecision. Further analyses using other MR approaches had similar results. Conclusion: These analyses do not support a causal role for IL-6 signaling in the development of severe malaria. This result suggests IL-6 may not be causal for severe outcomes in malaria, and that therapeutic manipulation of IL-6 is unlikely to be a suitable treatment for severe malaria. (c) 2023 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody levels can be used to assess humoral immune responses following SARS-CoV-2 infection or vaccination, and may predict risk of future infection. Higher levels of SARS-CoV-2 anti-Spike antibodies are known to be associated with increased protection against future SARS-CoV-2 infection. However, variation in antibody levels and risk factors for lower antibody levels following each round of SARS-CoV-2 vaccination have not been explored across a wide range of socio-demographic, SARS-CoV-2 infection and vaccination, and health factors within population-based cohorts. Methods: Samples were collected from 9361 individuals from TwinsUK and ALSPAC UK population-based longitudinal studies and tested for SARS-CoV-2 antibodies. Cross-sectional sampling was undertaken jointly in April-May 2021 (TwinsUK, N=4256; ALSPAC, N=4622), and in TwinsUK only in November 2021-January 2022 (N=3575). Variation in antibody levels after first, second, and third SARS-CoV-2 vaccination with health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables were analysed. Using multivariable logistic regression models, we tested associations between antibody levels following vaccination and: (1) SARS-CoV-2 infection following vaccination(s); (2) health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables. Results: Within TwinsUK, single-vaccinated individuals with the lowest 20% of anti-Spike antibody levels at initial testing had threefold greater odds of SARS-CoV-2 infection over the next 6–9 months (OR = 2.9, 95% CI: 1.4, 6.0), compared to the top 20%. In TwinsUK and ALSPAC, individuals identified as at increased risk of COVID-19 complication through the UK ‘Shielded Patient List’ had consistently greater odds (two- to fourfold) of having antibody levels in the lowest 10%. Third vaccination increased absolute antibody levels for almost all individuals, and reduced relative disparities compared with earlier vaccinations. Conclusions: These findings quantify the association between antibody level and risk of subsequent infection, and support a policy of triple vaccination for the generation of protective antibodies. Funding: Antibody testing was funded by UK Health Security Agency. The National Core Studies program is funded by COVID-19 Longitudinal Health and Wellbeing – National Core Study (LHW-NCS) HMT/UKRI/MRC ([MC_PC_20030] and [MC_PC_20059]). Related funding was also provided by the NIHR 606 (CONVALESCENCE grant [COV-LT-0009]). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. The UK Medical Research Council and Wellcome (Grant ref: [217065/Z/19/Z]) and the University of Bristol provide core support for ALSPAC.

The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.

ABSTRACTCommon SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody levels can be used to assess humoral immune responses following SARS-CoV-2 infection or vaccination, and may predict risk of future infection. Higher levels of SARS-CoV-2 anti-Spike antibodies are known to be associated with increased protection against future SARS-CoV-2 infection. However, variation in antibody levels and risk factors for lower antibody levels following each round of SARS-CoV-2 vaccination have not been explored across a wide range of socio-demographic, SARS-CoV-2 infection and vaccination, and health factors within population-based cohorts. Methods: Samples were collected from 9361 individuals from TwinsUK and ALSPAC UK population-based longitudinal studies and tested for SARS-CoV-2 antibodies. Cross-sectional sampling was undertaken jointly in April-May 2021 (TwinsUK, N=4256; ALSPAC, N=4622), and in TwinsUK only in November 2021-January 2022 (N=3575). Variation in antibody levels after first, second, and third SARS-CoV-2 vaccination with health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables were analysed. Using multivariable logistic regression models, we tested associations between antibody levels following vaccination and: (1) SARS-CoV-2 infection following vaccination(s); (2) health, socio-demographic, SARS-CoV-2 infection, and SARS-CoV-2 vaccination variables. Results: Within TwinsUK, single-vaccinated individuals with the lowest 20% of anti-Spike antibody levels at initial testing had threefold greater odds of SARS-CoV-2 infection over the next 6–9 months (OR = 2.9, 95% CI: 1.4, 6.0), compared to the top 20%. In TwinsUK and ALSPAC, individuals identified as at increased risk of COVID-19 complication through the UK ‘Shielded Patient List’ had consistently greater odds (two- to fourfold) of having antibody levels in the lowest 10%. Third vaccination increased absolute antibody levels for almost all individuals, and reduced relative disparities compared with earlier vaccinations. Conclusions: These findings quantify the association between antibody level and risk of subsequent infection, and support a policy of triple vaccination for the generation of protective antibodies. Funding: Antibody testing was funded by UK Health Security Agency. The National Core Studies program is funded by COVID-19 Longitudinal Health and Wellbeing – National Core Study (LHW-NCS) HMT/UKRI/MRC ([MC_PC_20030] and [MC_PC_20059]). Related funding was also provided by the NIHR 606 (CONVALESCENCE grant [COV-LT-0009]). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. The UK Medical Research Council and Wellcome (Grant ref: [217065/Z/19/Z]) and the University of Bristol provide core support for ALSPAC. Editor's evaluation This important study provides convincing evidence that a third SARS-CoV-2 vaccination elicits substantial increases in spike antibody responses, decreasing variability in titers observed after first and second doses, and also decreasing differences between groups at low and high-risk of low antibody responses. High antibody titers are subsequently associated with a lower incidence of infection. This paper is strong methodologically and will be of interest to clinicians and public health officials. https://doi.org/10.7554/eLife.80428.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest Vaccination against the virus that causes COVID-19 triggers the body to produce antibodies that help fight future infections. But some people generate more antibodies after vaccination than others. People with lower levels of antibodies are more likely to get COVID-19 in the future. Identifying people with low antibody levels after COVID-19 vaccination is important. It could help decide who receives priority for future vaccination. Previous studies show that people with certain health conditions produce fewer antibodies after one or two doses of a COVID-19 vaccine. For example, people with weakened immune systems. Now that third booster doses are available, it is vital to determine if they increase antibody levels for those most at risk of severe COVID-19. Cheetham et al. show that a third booster dose of a COVID-19 vaccine boosts antibodies to high levels in 90% of individuals, including those at increased risk. In the experiments, Cheetham et al. measured antibodies against the virus that causes COVID-19 in 9,361 individuals participating in two large long-term health studies in the United Kingdom. The experiments found that UK individuals advised to shield from the virus because they were at increased risk of complications had lower levels of antibodies after one or two vaccine doses than individuals without such risk factors. This difference was also seen after a third booster dose, but overall antibody levels had large increases. People who received the Oxford/AstraZeneca vaccine as their first dose also had lower antibody levels after one or two doses than those who received the Pfizer/BioNTech vaccine first. Positively, this difference in antibody levels was no longer seen after a third booster dose. Individuals with lower antibody levels after their first dose were also more likely to have a case of COVID-19 in the following months. Antibody levels were high in most individuals after the third dose. The results may help governments and public health officials identify individuals who may need extra protection after the first two vaccine doses. They also support current policies promoting booster doses of the vaccine and may support prioritizing booster doses for those at the highest risk from COVID-19 in future vaccination campaigns. Introduction Immunological responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and SARS-CoV-2 vaccination vary between individuals and over time (Shrotri et al., 2021a; Shrotri et al., 2021b; Wei et al., 2021b). Within 2–4 weeks of infection, most individuals generate detectable levels of several antibody subtypes (immunoglobulin A, M, G) directed against different domains of the virus (Nucleocapsid protein, Spike protein, receptor-binding domain of Spike), which gradually decline over time (Seow et al., 2020; Brochot et al., 2020; Wei et al., 2021a; Long et al., 2020; Gaebler et al., 2021). Anti-Nucleocapsid antibodies are generated following infection but not by any current SARS-CoV-2 vaccines, while anti-Spike antibodies are generated following infection or vaccination. Levels of anti-Spike antibodies correlate with SARS-CoV-2-neutralising anti-receptor-binding domain antibody titre (Perkmann et al., 2021). A similar profile of antibody induction with subsequent waning is observed after vaccination against SARS-CoV-2 (Shrotri et al., 2021a; Shrotri et al., 2021b; Matsunaga et al., 2022; Pegu et al., 2021). Waning of antibody levels are likely correlated with observed reductions in vaccine effectiveness over time (Pouwels et al., 2021; UKHSA, 2021; Lopez Bernal et al., 2021). Reduced antibody neutralising activity and vaccine effectiveness have been observed for variants of concern in comparison to ancestral SARS-CoV-2 (UKHSA, 2021; Collie et al., 2022; Yu et al., 2022; Nemet et al., 2022; Cheng et al., 2022). Anti-Spike antibody levels have been found to be inversely proportional to risk of future infection, and so identified as a correlate of protection (Goldblatt et al., 2022a; Perry et al., 2022; Gilbert et al., 2022; Goldblatt et al., 2022b; Feng et al., 2021; Dimeglio et al., 2022a; Dimeglio et al., 2022b; Khoury et al., 2021). Goldblatt et al. estimated protective thresholds of 154 (95% CI: 42, 559) and 171 (95% CI: 57, 519) BAU/mL for wild-type and alpha variant SARS-CoV-2 respectively and an initial estimate range of 36–490 BAU/ml for delta variant (Goldblatt et al., 2022b), while Feng et al., 2021 estimated 80% vaccine effectiveness against alpha variant for levels above 264 (95% CI: 108, 806) BAU/mL. Dimeglio et al. estimated much higher levels of more than 6000 BAU/mL were needed for protection against omicron variant BA.1, while no relationship was found between infection and antibody level for BA.2 (Dimeglio et al., 2022b). Several clinical variables contribute to variation in antibody response following vaccination. Lower antibody levels following both first and second vaccinations have been observed in individuals with particular comorbidities (including cancer, renal disease, and hepatic disease; Parry et al., 2021; Monin et al., 2021; Kearns et al., 2021), individuals using immunosuppressant medications (Shrotri et al., 2021a; Shrotri et al., 2021b; Parry et al., 2021; Monin et al., 2021), and individuals identified from electronic health records data as of moderate or high risk of COVID-19 complications (according to UK government prior ‘Shielded Patient List’ criteria of conditions, ongoing treatments, and medications) (Shrotri et al., 2021a; Shrotri et al., 2021b; GOV.UK, 2022b). Studies testing for associations between antibody response and non-clinical factors such as socio-demographics have been more limited. Here, the use of longitudinal studies, with broader catalogues of bio-social data, are well suited to such investigations. Here, we aimed to examine variables associated with variation in post-vaccination antibody levels, and the subsequent likelihood of post-vaccination infection. We measured the antibody levels of participants from two population-based longitudinal cohorts during the time of the UK vaccination roll-out: TwinsUK (in April-May 2021 and November 2021-January 2022) (Verdi et al., 2019) and Avon Longitudinal Study of Parents and Children (ALSPAC) (Fraser et al., 2013; Boyd et al., 2013) (April-May 2021 only). We aimed firstly to assess the relationship between anti-Spike antibody levels (identified as a correlate of protection against infection), measured after first or second vaccination in April-May 2021, and an outcome of subsequent post-vaccination infection over the following 6–9 months (identified through further serological evidence and/or self-reported COVID-19 from data collected in TwinsUK between November 2021 and January 2022). Secondly, we used peri-pandemic and historical data to investigate associations with an outcome of having relatively low antibody levels following first, second (ALSPAC and TwinsUK), or third (TwinsUK only) vaccination, for multiple socio-demographic, physical and mental health characteristics, prior SARS-CoV-2 infection, and vaccination history. Finally, we used twin-pair analysis within TwinsUK to probe genetic and environmental contributions to antibody level variation. Methods Study participants TwinsUK is a UK-based national registry of monozygotic (MZ) and dizygotic (DZ) twins, with over 15,000 twins registered since 1992 (Verdi et al., 2019). ALSPAC is a prospective population-based cohort of pregnant women with expected delivery dates between April 1991 and December 1992 who lived in Bristol, UK, and the nearby surrounding area; with follow-up of these women and their partners (collectively known as Generation 0, G0), and their children (Generation 1, G1), ever since (Fraser et al., 2013; Boyd et al., 2013). The initial cohort consisted of 14,541 pregnancies, with 13988 children alive at 1 year, and was later expanded when children were approximately age 7, to give a total of 15454 pregnancies, with 14,901 children alive at 1 year. Analyses herein were carried out solely with G0 participants due to low rates of vaccination among the G1 children generation at the time of initial serology. During the COVID-19 pandemic, participants from both cohorts were invited to complete cohort-specific questionnaires and to submit blood samples via post for SARS-CoV-2 antibody testing. In the first round of coordinated testing in TwinsUK and ALSPAC, participants submitted samples in April and May 2021. This first testing round is referred to throughout as Q2 testing (from calendar year quarter 2 start date). Participants of TwinsUK were later invited for a second round of antibody testing with the same assay, with samples collected from November 2021 to January 2022. This round of antibody testing is referred to throughout as Q4 testing (from quarter 4 start date). Further details of COVID-19 questionnaires and antibody testing are given in following sections. Inclusion and exclusion criteria were as follows. Individuals with unknown vaccination status at time of antibody testing were excluded from all analyses. For descriptive analysis of antibody levels versus time since vaccination, all individuals with known vaccination status were included. For analysis of variables associated with low antibody levels, individuals sampled fewer than 28 days since first vaccination, or fewer than 14 days since second or third vaccination, were excluded (these thresholds were chosen to allow sufficient time for an immunological response after each vaccine dose, based on previous studies Shrotri et al., 2021a; Shrotri et al., 2021b), while individuals with 77 days or more since first vaccination were excluded in case of misclassification due to unreported further vaccination (based on 11–12 week spacing between doses for majority of adults in the UK). In addition to the above criteria, for analysis of variables associated with post-vaccination infection within TwinsUK, individuals must have participated in Q2 antibody testing followed by either Q4 antibody testing and/or concurrent COVID-19 questionnaire. Questionnaires administered during the COVID-19 pandemic TwinsUK COVID-19 questionnaires were administered in April-May 2020 (Suthahar et al., 2021), July-August 2020, October-December 2020, April-July 2021 (approximating first round of antibody testing, Q2), and November 2021-February 2022 (approximating second round of antibody testing, Q4). ALSPAC COVID-19 questionnaires were administered in April-May 2020 (Northstone et al., 2020a), May-July 2020 (Northstone et al., 2020b), October 2020 (Northstone et al., 2021), November 2020-March 2021 (approximating first round of antibody testing, Q2) (Smith et al., 2021), and July-December 2021. Details of variables collected through cohort-specific pandemic questionnaires are provided in Supplementary file 1. Questions included self-reported SARS-CoV-2 infection and symptoms, results of SARS-CoV-2 testing, and vaccination status (date, dose number, manufacturer/brand) once the UK SARS-CoV-2 vaccination programme had commenced (8 December 2020). Questions made no distinction between pre-planned third vaccination for high-risk individuals and third vaccination given as part of the wider community ‘booster’ campaign – as such we refer to third vaccination or triple-vaccinated individuals throughout. By virtue of the national vaccination roll-out policy (tiered by age and at-risk status), at Q2 participants may have received nought, one, or two vaccination doses; by Q4 some individuals had received a third dose. As questionnaires were cohort-specific, assessed variables were not completely uniform (both question wording and collected data). Details for comparison are shown in Supplementary file 1. SARS-CoV-2 antibody testing Q2 testing in TwinsUK and ALSPAC occurred along with an additional nine UK-based longitudinal studies who collected samples in unison as part of the UK National Core Studies Longitudinal Health & Wellbeing (NCS-LH&W) programme (University College London, 2022). Additional cohort-specific details and results for ALSPAC and Extended Cohort for E-health, Environment and DNA (EXCEED) are provided elsewhere (Lee et al., 2021; Major-Smith et al., 2021). Data availability in cohorts limited analysis to TwinsUK and ALSPAC. For TwinsUK antibody testing in Q2 and Q4, invitation criteria were based on availability of email addresses and/or completion of previous COVID-19 questionnaires. ALSPAC invitation criteria are given in detail elsewhere (Major-Smith et al., 2021). For both cohorts, participants received fingerprick blood sample collection kits via post. Blood sample collection was self-administered. Samples were sent via post to either Pura Diagnostics or Eurofins County Pathology (partner laboratories of Thriva Ltd), who assayed samples and shared results with TwinsUK and ALSPAC. Quantitative IgG anti-Spike SARS-CoV-2 antibody levels and qualitative IgG anti-Nucleocapsid antibody status were assayed using CE-marked capillary blood Roche Elecsys Anti-SARS-CoV-2 immunoassays (Roche, 2021). Quantitative anti-Spike results were given in units per millilitre (U/mL), with a quantitative range of 0.4–250 U/mL for Q2 testing. For Q4 testing, samples were diluted to give an expanded quantitative range of 0.4–25000 U/mL, allowing quantitative discernment for higher levels at this timepoint. Tests had a positive threshold of 0.8 U/mL. One U/mL is equivalent to 1 unit of WHO standardised unit, binding antibody units per millilitre (BAU/mL) (WHO international standard: 20/136 NIBSC, 2020). Thus, we have quoted results in BAU/mL to aid comparison across studies. Anti-Nucleocapsid results were qualitative, with a positive result for a value greater than a cut-off unit = 1. Additional antibody testing was also undertaken in-house for TwinsUK samples between April 2020 and April 2021. Quantitative enzyme-linked immunosorbent assay (ELISA) testing anti-Nucleocapsid and anti-Spike antibody levels were performed using previously published methods (Seow et al., 2020). These data were used to determine serology-based infection status prior to Q2 antibody testing. Identification of SARS-CoV-2 infection Assessment of prior SARS-CoV-2 infection, at time of antibody testing Prior SARS-CoV-2 infection was classified with three distinct variables derived from self-reported questionnaire data or serological testing. ‘SARS-CoV-2 infection status (self-reported)’: derived solely from self-reported COVID-19 infection and testing questionnaire data. The classification was primarily derived from responses to ‘Do you think that you have or have had COVID-19?’ in prior questionnaires. Classification options are given below: Confirmed case: ‘Yes, confirmed by a positive test’. Suspected case: ‘Yes, suspected by a doctor but not tested’. Suspected case: ‘Yes, my own suspicions’. Unsure (TwinsUK only): ‘Unsure’. No: ‘No’. In TwinsUK questionnaires only, individuals were also asked to self-report any positive COVID-19 tests. Infection status of individuals who self-reported a positive test was classified as a confirmed case, irrespective of their answer to the question above. ‘SARS-CoV-2 infection status (serology-based)’: derived from laboratory serological testing (Q2 [TwinsUK and ALSPAC], Q4 [TwinsUK only], and/or other within-cohort testing [TwinsUK only]), informed by self-reported vaccination status. We followed Centers for Disease Control and Prevention guidance on interpretation of anti-Nucleocapsid and anti-Spike results while accounting for vaccination status (CDC, 2021) as follows: Evidence of SARS-CoV-2 infection: A positive anti-Nucleocapsid result at any time or a positive anti-Spike result prior to vaccination. No evidence of SARS-CoV-2 infection: Negative anti-Nucleocapsid and anti-Spike result prior to vaccination, or negative anti-Nucleocapsid and positive anti-Spike result following vaccination (anti-Spike antibody assumed to be generated by vaccination). ‘Anti-Nucleocapsid antibody status’: derived solely from laboratory serological testing (from Q2 or Q4 testing only). The classification was as follows: Positive: Positive anti-Nucleocapsid test result at Q2 or Q4 testing. Negative: Negative anti-Nucleocapsid test result at Q2 or Q4 testing. From these variables, distinct measures of the proportion of individuals with evidence of prior SARS-CoV-2 infection, or ‘natural infection’, at time of Q2 and Q4 testing were quantified within both cohorts. Thus, ‘SARS-CoV-2 infection status (self-reported)’ and ‘SARS-CoV-2 infection status (serology-based)’ variables identify individuals with any history of SARS-CoV-2 infection (who are not necessarily seropositive for anti-Nucleocapsid antibodies at time of testing), while ‘Anti-Nucleocapsid antibody status’ assesses the contemporaneous level of infection-induced antibody response. Assessment of post-vaccination SARS-CoV-2 infection For analysis of variables associated with post-vaccination SARS-CoV-2 infection (performed within TwinsUK only), individuals with post-vaccination SARS-CoV-2 infections were identified using the following criteria: A ‘suspected case’ or ‘confirmed case’ from ‘SARS-CoV-2 infection status (self-reported)’ variable at Q4 testing, with symptoms commencing after first vaccination. Infection and vaccination dates obtained from COVID-19 questionnaires. A ‘confirmed case’ from ‘SARS-CoV-2 infection status (self-reported)’ variable at Q4 testing, with a self-reported positive antigen test dated after first vaccination. Infection and vaccination dates obtained from COVID-19 questionnaires. A positive SARS-CoV-2 anti-Nucleocapsid result at Q4 testing after previous negative anti-Nucleocapsid results up to and including Q2, for individuals vaccinated at least once at Q2. The approximate date of infection is unknown for individuals who meet this criterion only. Individuals meeting one or more of these criteria were considered as having post-vaccination infection. Individuals who did not meet any of these criteria were considered as controls (i.e., no post-vaccination infection). Individuals must have participated in TwinsUK Q4 antibody testing and/or concurrent COVID-19 questionnaire for post-vaccination infection to be determinable and for inclusion as controls or cases. Phenotypic data list Variables from antibody testing and pandemic questionnaire data were supplemented with pre-pandemic socio-demographic and health variables for TwinsUK and ALSPAC analyses (details in Supplementary file 1). A full list of variables considered in analyses is given in Table 1. Table 1 Phenotypic variables used in analyses. Variables marked with an asterisk were outcome variables in logistic regression analyses; all other variables were adjustment or exposure variables. Variables only available in TwinsUK are notated as [TUK], and those only in ALSPAC as [ALSPAC]. Variable groupVariableAntibody levelsAnti-Spike level*Socio-demographicAgeSexEthnicityLocal area deprivation (index of multiple deprivation, IMD [using national IMD rank decile/quintile]) (GOV.UK, 2019; Northern Ireland Statistics and Research Agency, 2017; Gov.scot, 2020; GOV.WALES, 2021)Rural-urban classification [TUK] (GOV.UK, 2022a)Highest educational attainmentEmployment statusCOVID-19 infectionSARS-CoV-2 infection status (self-reported)SARS-CoV-2 infection status (serology-based)Anti-Nucleocapsid antibody statusPost-vaccination SARS-CoV-2 infection [TUK]*COVID-19 vaccinationBrand/manufacturer of first/second/third vaccinationNumber of weeks between first/second/third vaccination and antibody samplingHealth indicatorsBody mass indexFrailty index [TUK] (derived following Searle et al., 2008)Frailty (PRISMA-7 assessment; Raîche et al., 2008) [ALSPAC]Self-reported advised as on ‘Shielded Patient List’Self-rated health (5-point scale from ‘poor’ to ‘excellent’)Prescribed immunosuppressant medication [TUK]Self-reported immunocompromised [ALSPAC]Anxiety (hospital anxiety and depression assessment scale (HADS) [TUK] Zigmond and Snaith, 1983, or 7-item generalised anxiety disorder scale (GAD-7) [ALSPAC] Spitzer et al., 2006, assessment)Depression (HADS [TUK] or short mood and feelings questionnaire (SMFQ) [ALSPAC] Turner et al., 2014 assessment)Number of comorbidities from: anxiety/depression, diabetes, cancer, hypertension, heart diseaseIndividual comorbiditiesAnxietyArthritis (any) [TUK]AsthmaAtrial fibrillation [TUK]Cancer (any)DepressionDiabetes (any)Heart diseaseHigh cholesterol [TUK]HypertensionLung diseaseOsteoporosis [TUK]Rheumatoid arthritis [TUK]Stroke [TUK]Comorbidity domainsCardiac disease [TUK]Cardiac risk factors [TUK]Neurological diseaseSubjective memory impairment [TUK] Statistical analyses Descriptive analysis of antibody levels after first, second, and third vaccination Median, interquartile range, 10th and 5th percentile antibody levels were produced for univariate splits of adjustment and exposure variables listed in Table 1. Differences in median antibody levels (per Results) were tested using a two-sided Mann-Whitney U-test (Mann and Whitney, 1947). Trend in median antibody level versus number of weeks post-vaccination was tested using the Mann-Kendall trend test (Mann, 1945; Kendall, 1975). Association between SARS-CoV-2 infection and socio-demographic variables Associations between SARS-CoV-2 infection, quantified from SARS-CoV-2 infection status (self-reported), SARS-CoV-2 infection status (serology-based), and anti-Nucleocapsid antibody status, and age, sex, ethnicity, local area deprivation, and rural-urban classification were tested using the chi-square test of independence. Logistic regression analyses Within TwinsUK only, univariable and multivariable logistic regression were used to test associations between an outcome of post-vaccination SARS-CoV-2 infection and exposure variables related to: Q2 anti-Spike antibody levels; socio-demographics; COVID-19 infection; COVID-19 vaccination. In TwinsUK and ALSPAC, multivariable logistic regression was also performed to test associations between the outcome of low anti-Spike antibody levels (as defined below) after each round of vaccination (after first and second vaccinations for both TwinsUK and ALSPAC, and after third vaccination for TwinsUK only) and all exposure variables previously listed. Each model included the outcome variable, a single exposure variable of interest, and a set of adjustment variables. Individual exposure variables of interest were tested in sequence, fitting a separate logistic regression model for each combination of outcome, adjustment, and exposure variables. Only individuals with complete data for the given model were included. For each categorical variable within logistic regression models, reference categories were chosen based on the normative, modal, maximum or minimum value/category, as appropriate (reference categories given in Supplementary file 1). Within TwinsUK models only, the HC3 estimator of logistic regression coefficient standard errors was used to account for heteroskedasticity (which biases conventional standard errors in analysis of related twin-pairs; Hayes and Cai, 2007; statsmodels, 2021; Farbmacher and Kögel, 2017). (Two-sided) p-values were corrected for multiple testing using the Benjamini/Hochberg p-value adjustment (Benjamini and Hochberg, 1995). An outcome of post-vaccination SARS-CoV-2 infection was identified using the criteria previously described. An a priori outcome of ‘low anti-Spike antibody levels’ was defined relatively within each group stratified by vaccination status (single-, double-, triple-vaccinated within TwinsUK, and single-, double-vaccinated within ALSPAC) and assigned to individuals in the lowest 10% of anti-Spike antibody levels. As such the anti-Spike threshold value used to define low levels varied between models. Most double-vaccinated individuals at Q2 testing had antibody levels above the upper assay limit of 250 BAU/mL (TwinsUK: 92%, ALSPAC: 92%). Thus, a threshold of <250 BAU/mL was used instead of 10% to identify low antibody levels after second vaccination at Q2 testing, corresponding to the lowest 8% in both TwinsUK and ALSPAC. In total, for each exposure variable, there were four TwinsUK models and two ALSPAC models. Multivariable models testing association between post-vaccination SARS-CoV-2 infection and anti-Spike antibody levels used the following sets of adjustment variables: (1) number of weeks since most recent vaccination; (2) age, sex, number of weeks since most recent vaccination. Multivariable models testing association between post-vaccination SARS-CoV-2 infection and socio-demographic variables used the following sets of adjustment variables: (1) age; (2) age, SARS-CoV-2 infection status (serology-based); (3) age, sex, SARS-CoV-2 infection status (serology-based). Multivariable models testing associations with low anti-Spike antibody levels used the following set of adjustment variables: age, sex, most recent vaccine received, and number of weeks since most recent vaccination. Adjustment variables were chosen based on relatively large effects observed in preliminary descriptive analysis. Twin-pair analyses To assess the relationship between zygosity and relatedness on variation in antibody levels between pairs of individuals after third vaccination within TwinsUK, antibody level differences were calculated for all pairs of MZ and DZ twins, and within all combinations of non-related pairs. The difference between the resulting median pair differences within MZ, DZ, and non-related pairs were tested using the two-sided Mann-Whitney U-test. For variables associated with low antibody levels (from logistic regression analyses), within-twin-pair associations with unadjusted anti-Spike antibody levels after

Introduction Heme-oxygenase 1 (HMOX1) is a critical stress response gene that catalyzes the multistep oxidation of heme. A GT(n) repeat of variable length in the promoter in has been associated with a wide range of human diseases, including infections. This paper aims to summarise and systematically review associations between the length of the HMOX1 GT(n) promoter and infectious disease in humans. Methods A search using relevant terms was performed in PubMED and EMBASE through to 15/01/21 identifying all research that studied an association between the HMOX1 GT(n) repeat polymorphism and the incidence and/or outcome of any human infectious disease. Citations were screened for additional studies. Potential studies were screened for inclusion by two authors. Data was extracted on allele frequency, genotype, strength of association, mechanism of genotyping, and potential biases. A narrative review was performed across each type of infection. Results 1,533 studies were identified in the search, and one via citation screening. Sixteen studies were ultimately included, seven in malaria, three in HIV, three in sepsis, and one each in pneumonia, hepatitis C, and acute respiratory distress syndrome (ARDS). Sample sizes for nearly all studies were small (biggest study, n = 1,646). Allelic definition was different across all included studies. All studies were at some risk of bias. In malaria, three studies suggested that longer alleles were associated with reduced risk of severe malaria, particularly malaria-induced renal dysfunction, with four studies identifying a null association. In sepsis, two studies suggested an association with longer alleles and better outcomes. Conclusions Despite the importance of HMOX1 in survival from infection, and the association between repeat length and gene expression, the clinical data supporting an association between repeat length and incidence and/or outcome of infection remain inconclusive.

Iron deficiency is associated with a substantial burden of morbidity. However, supplementation of iron has been linked to increased rates of serious infection in randomised trials of children in sub-Saharan Africa. Randomised trials in other settings have been inconclusive and it is unknown if changes in levels of iron biomarkers – a mark of setpoint changes in iron homeostasis - are linked to sepsis in these other settings. We used genetic variants associated with levels of iron biomarkers as instrumental variables in a Mendelian randomisation (MR) analysis to test the hypothesis that increasing levels of iron biomarkers increase the risk of sepsis. In observational and MR analyses we found that increases in iron biomarkers increase the risk of sepsis. In stratified analyses, we show that this risk may be larger in those with iron deficiency and/or anaemia. Taken together, results here suggest a required caution in supplementation of iron and underline the role of iron homeostasis in severe infection.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementFHs time was funded by the GW4-CAT Wellcome Doctoral Fellowship Scheme. UK Biobank was funded by the Wellcome Trust, the Medical Research Council, the NIHR, and a variety of other charities (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/our-funding). FinnGen is a public-private partnership (https://www.finngen.fi/en/access_results) funded by multiple instititions across Finland. We want to acknowledge the participants and investigators of the FinnGen study. PGs time was funded by the Ser Cymru programme.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UK Biobank received ethical approval from the Research Ethics Committee (REC reference for UK Biobank is 11/NW/0382). This study was performed under application number 52643.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAccess to individual data for UK Biobank is via application to the independent data access committee. For this paper, summary outcome GWAS are available at the OpenGWAS repository (https://gwas.mrcieu.ac.uk/datasets/ieu-b-5066/), and summary exposure data are available at deCODE.genetics website (https://www.decode.com/summarydata/), and therefore MR results can be replicated using the TwoSampleMR package.

OBJECTIVES:Observational studies report mixed findings regarding the association between vitamin D and juvenile idiopathic arthritis (JIA) incidence or activity; however, such studies are susceptible to considerable bias. Because low vitamin D levels are common within the general population and easily corrected, there is potential public health benefit in identifying a causal association between vitamin D insufficiency and JIA incidence. To limit bias due to confounding and reverse causation, we examined the causal effect of the major circulating form of vitamin D, 25-hydroxy vitamin D (25-[OH]D), on JIA incidence using Mendelian randomization (MR). METHODS:In this 2-sample MR analysis, we used summary level data from the largest and most recent genome-wide association study of 25-(OH)D levels (sample size 443,734), alongside summary data from 2 JIA genetic studies (sample sizes 15,872 and 12,501), all from European populations. To test and account for potential bias due to pleiotropy, we employed multiple MR methods and sensitivity analyses. RESULTS:We found no evidence of a causal relationship between genetically predicted 25-(OH)D levels and JIA incidence (odds ratio 1.00 [95% confidence interval (95% CI) 0.76, 1.33] per SD increase in standardized natural-log transformed 25-[OH]D levels). This estimate was consistent across all methods tested. Additionally, there was no evidence that genetically predicted JIA causally influences 25-(OH)D levels (-0.002 SD change in standardized natural-log transformed 25-[OH]D levels per doubling odds in genetically predicted JIA [95% CI -0.006, 0.002]). CONCLUSION:Given the lack of a causal relationship between 25-(OH)D levels and JIA, population level vitamin D supplementation is unlikely to reduce JIA incidence.

Introduction Meta-analyses implicate immune dysfunction in depression confirming increased levels of circulating immune proteins (e.g., cytokines) in depression cases compared to controls. White blood cells (WBC) both produce and are influenced by cytokines, and play key roles in orchestrating innate and adaptive immune responses, but their role in depression remains unclear. Therefore, a systematic review of studies of various WBC subsets in depression is required for a greater understanding of the nature of immune dysfunction in this illness. Methods We searched PubMed and PsycINFO databases (inception to 5 th April 2022) and conducted a systematic review and meta-analysis of identified studies comparing absolute count and/or relative percentage of flow cytometry-derived WBC subsets between depression cases and controls. Selected studies were quality assessed. Random-effect meta-analysis was performed. Results Thirty-three studies were included and 27 studies ( n = 2277) were meta-analysed. We report an increase in mean absolute counts of WBC (seven studies; standardised mean difference [SMD] = 1.07; 95% CI, 0.61–1.53; P < 0.01; I 2 = 64%), granulocytes (two studies; SMD = 2.07; 95% CI, 1.45–2.68; P < 0.01; I 2 = 0%), neutrophils (four studies; SMD = 0.91; 95% CI, 0.23–1.58; P < 0.01; I 2 = 82%), monocytes (seven studies; SMD = 0.60; 95% CI, 0.19–1.01; P < 0.01; I 2 = 66%), CD4 + helper T cells (11 studies; SMD = 0.30; 95% CI, 0.15–0.45; P < 0.01; I 2 = 0%), natural killer cells (11 studies; SMD = 1.23; 95% CI, 0.38–2.08; P < 0.01; I 2 = 95%), B cells (10 studies; SMD = 0.30; 95% CI, 0.03–0.57; P = 0.03; I 2 = 56%), and activated T cells (eight studies; SMD = 0.45; 95% CI, 0.24–0.66; P < 0.01; I 2 = 0%) in depression, compared to controls. Fewer studies reported relative percentage, indicating increased neutrophils and decreased total lymphocytes, Th1, and Th2 cells in depression. Conclusions Depression is characterised by widespread alterations in circulating myeloid and lymphoid cells, consistent with dysfunction in both innate and adaptive immunity. Immune cells could be useful biomarkers for illness subtyping and patient stratification in future immunotherapy trials of depression, along with cytokines, other biomarkers, and clinical measures.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.