Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

Body Mass Index as an Example of a Negative Confounder: Evidence and Solutions

Background: Adequate control for confounding is key to many observational study designs. Confounders are often identified based on subject matter knowledge from empirical investigations. Negative confounders, which typically generate type 2 error, i.e., false nulls, can be elusive. Such confounders can be identified comprehensively by using Mendelian randomization (MR) to search the wealth of publicly available data systematically. Here, to demonstrate the concept, we examined whether a common positive confounder, body mass index (BMI), is also a negative confounder of any common physiological exposures on health outcomes, overall and specifically by sex. Methods: We used an MR study, based on the largest overall and sex-specific genome-wide association studies of BMI (i.e., from the Genetic Investigation of ANthropometric Traits and the UK Biobank) and of relevant exposures likely affected by BMI, to assess, overall and sex-specifically, whether BMI is a negative confounder potentially obscuring effects of harmful physiological exposures. Inverse variance weighting was the main method. We assessed sex differences using a z-test. Results: BMI was a potential negative confounder for apolipoprotein B and total testosterone in men, and for both sexes regarding low-density lipoprotein cholesterol, choline, linoleic acid, polyunsaturated fatty acids, and cholesterol. Conclusions: Using BMI as an illustrative example, we demonstrate that negative confounding is an easily overlooked bias. Given negative confounding is not always obvious or known, using MR systematically to identify potential negative confounders in relevant studies may be helpful.

Quantile-specific confounding: correction for subtle population stratification via quantile regression

Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK biobank and the SardiNIA/ProgeNIA project for demonstration.

Multiancestry Genome-Wide Association Study of Early Childhood Caries

Early childhood caries (ECC) is the most common noncommunicable childhood disease-an important health problem with known environmental and social/behavioral influences lacking consensus genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multiancestry population of U.S. preschool-age children (N = 6,103) ages 3 to 5 y participating in a community-based epidemiologic study of early childhood oral health. Calibrated examiners used International Caries Detection and Assessment System criteria to measure ECC; the primary trait was the number of primary tooth surfaces with caries experience (i.e., dmfs index). We estimated heritability and concordance rates and conducted genome-wide association analyses to estimate overall genetic effects as well as stratified by sex, household water fluoride, and dietary sugar and leveraged combined gene/gene-environment effects using 2-degree-of-freedom joint tests. Common genetic variants explained 24% of ECC phenotypic variance among unrelated individuals, while concordance rates were 0.64 (95% confidence interval [CI] = 0.42-0.79) among monozygotic twins and 0.44 (95% CI = 0.34-0.53) among first-degree relatives. Across all analyses, we identified 21 novel nonoverlapping genome-wide significant loci (P < 5 × 10-8) and 1 genome-wide significant gene (TAAR6) associated with ECC. The taste receptor activity gene set, with known roles in chemosensing, bacterial recognition, and innate immunity in the oral cavity, was strongly associated with ECC. While no locus remained significant after studywise multiple-testing correction, 3 loci were nominally significant (P < 0.05) and directionally consistent in external cohorts of 285,248 adults (rs1442369, DLGAP1 and rs74606067, RP11-856F16.2) and 18,994 children (rs71327750, SLC41A3). Meanwhile, the strongest marker known to be associated with adult caries (rs1122171, tagging the long noncoding RNA PITX1-AS1) was nominally significant (P = 0.01) and directionally consistent with ECC in our study. Taken together, the results of this study add to the genomics knowledge base for early childhood caries, offer several plausible candidates for future mechanistic studies, and underscore the importance of accounting for sex and pertinent environmental exposures in genetic investigations.

A Litmus Test for Confounding in Polygenic Scores

Polygenic scores (PGSs) are being rapidly adopted for trait prediction in the clinic and beyond. PGSs are often thought of as capturing the direct genetic effect of one's genotype on their phenotype. However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including stratification, assortative mating, and dynastic effects ("SAD effects"). Our interpretation and application of PGSs may hinge on the relative impact of SAD effects, since they may often be environmentally or culturally mediated. We developed a method that estimates the proportion of variance in a PGS (in a given sample) that is driven by direct effects, SAD effects, and their covariance. We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS-which is largely immune to SAD effects-to quantify the relative contribution of each type of effect to variance in the PGS of interest. Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pron. "Pegasus"), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects. In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is "isotropic" with respect to axes of ancestry. Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment as well as in a range of PGSs constructed using the UK Biobank. In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs. contemporary samples). Finally, we show that different approaches for adjustment for population structure in GWASs have distinct advantages with respect to mitigation of ancestry-axis-specific and isotropic SAD variance in PGS. Our study illustrates how family-based designs can be combined with standard population-based designs to guide the interpretation and application of genomic predictors.

A scalable variational inference approach for increased mixed-model association power

The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.

Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits

Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene-environment and gene-gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations.

Polygenic scores and social determinants of health: Their correlations and potential biases

The use of polygenic scores (PGS) for personalized medicine has gained momentum, along with caution to avoid accentuating health disparities. Greater ancestral diversity in genetic studies is needed, as well as close attention to the social determinants of health (SDoH).We measured the correlations between 3,030 PGS from the PGS Catalog and SDoH among participants in the Personalized Environment and Genes Study (PEGS). Correlations mainly ranged from -0.05 to 0.05, yet there was a heterogeneity of correlations across SDoH themes, with the largest amount of heterogeneity for PGS predicting body measures and smoking, as well as some common diseases. We also quantify the expected bias of PGS effect size on disease risk when strong predictors, such as SDoH, are omitted from models, emphasizing the importance of including SDoH with PGS to avoid biased estimates of PGS risk and to achieve equitable precision medicine.

Introduction to Mendelian randomization

Mendelian randomization (MR), i.e. instrumental variable analysis using genetic instruments, is an approach that incorporates population genetics to improve causal inference. Given that genetics are randomly allocated at conception, this resembles the randomization process in randomized controlled trials and hence is more resistant to unobserved confounding compared to conventional observational studies (e.g. cohort studies). The seminar paper briefly described the origin of MR and its underlying assumptions (relevance, independence, and exclusion restriction). This was followed by introducing one sample MR designs (in which instrument-exposure and instrument-outcome associations are derived from the same sample) and one sample MR design (in which instrument-exposure and instrument-outcome associations are derived from different samples). The seminar paper then summarized key aspects of MR studies, such as instrument selection, data sources for conducting MR studies, and statistical analyses. Variations of MR design were also introduced, such as how this design can inform the effect of drug targets (drug target MR). The STROBE-MR checklist and relevant MR guidelines were introduced. The seminar paper concluded by discussing the credibility crisis of MR studies.

Inflammatory bowel disease and risk of ophthalmic inflammation-related diseases: a two-sample Mendelian randomization study

AIM

To investigate the causal effect of inflammatory bowel disease (IBD) on ocular inflammation using Mendelian randomization (MR) analysis.

METHODS

Genetic instruments associated with inflammatory bowel disease (IBD), ulcerative colitis (UC), and Crohn's disease (CD) were derived from the largest genome-wide association studies (GWAS) published to date. The FinnGen research project was utilized to identify genetic risk variants associated with conjunctivitis, keratitis, iridocyclitis, chorioretinitis, episcleritis, and optic neuritis. All participants were of European ancestry. Three methods which included inverse variance weighting (IVW), weighted median (WM), and MR-Egger regression were performed to estimate the causal association in this study. IVW took the inverse variance of each study as the weight to calculate the weighted average of effect sizes, to summarize the effect sizes of multiple independent studies, which could provide the most precise estimated results. IVW was used as the primary outcome, while WM and MR-Egger were used to improve the estimation of IVW.

RESULTS

A nominal causal effect of genetically predicted IBD on risk of non-infectious conjunctivitis, keratitis, iridocyclitis, and optic neuritis, but not on chorioretinitis or episcleritis. After Bonferroni correction, the results showed that genetically predicted UC was significantly associated with an increased risk of iridocyclitis (IVW: OR, 1.17; 95%CI, 1.10-1.24, P=2.54×10-7). CD was significantly associated with conjunctivitis (IVW: OR, 1.05; 95%CI, 1.03-1.08, P=3.20×10-5), keratitis (IVW: OR, 1.06; 95%CI, 1.02-1.09; P=1.13×10-3), and iridocyclitis (IVW: OR, 1.09; 95%CI, 1.04-1.14; P=1.43×10-4).

CONCLUSION

IBD causally poses a risk of inflammation of conjunctiva, cornea, Iris-ciliary body complex, and optic neuritis. CD is more closely associated with the eye inflammation than UC. These impliy that the relationship of IBD and different parts of the eye structure are different, and provide novel evidence linking based on the association of the gut-eye axis.

Association between venous leg ulcers and knee osteoarthritis: A Mendelian randomization study

To gain a clearer understanding of the relationship between venous leg ulcers (VLUs) and knee osteoarthritis (KOA), we performed a two-sample, bidirectional Mendelian randomization (MR) analysis in this study. The present MR was carried out using summary data from publicly available genome-wide association studies. After filtering single-nucleotide polymorphism (SNP), we applied a variety of MR methods including inverse variance weighted (IVW), MR egger, weighted mode, and weighted median. IVW analysis revealed that the genetic association between VLUs and KOA was not significant (β = -0.017; SE 0.039; p = 0.658). In agreement with the IVW analysis, the findings of the weighted median estimator (β = -0.017; SE 0.052, p = 0.751), MR egger (β = 0.057; SE 0.084; p = 0.513), and weighted mode (β = 0.060; SE 0.078; p = 0.456) indicated the absence of a significant genetic association between VLUs and KOA. Furthermore, reverse causality analysis suggested a lack of genetic relationship between KOA and VLUs. In conclusion, the present MR study does not suggest a causal relationship or reverse causal relationship between VLUs and KOA.

Mendelian Randomization Analysis of the Causal Effect of Cigarette Smoking on Hospital Costs

INTRODUCTION

Knowledge of the impact of smoking on health care costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error.

AIMS AND METHODS

We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome-wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300 045 individuals (mean age: 57 years at baseline, range 39-72 years) in the UK Biobank. We followed individuals up for a mean of 6 years.

RESULTS

Genetic liability to initiate smoking (ever vs. never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias.

CONCLUSIONS

Our findings have implications for the potential cost-effectiveness of smoking interventions.

IMPLICATIONS

We report the first Mendelian Randomization analysis of the causal effect of smoking on health care costs. Using two smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of antismoking interventions and to understand the scale of externalities associated with this behavior.

Mendelian randomization analysis of maternal coffee consumption during pregnancy on offspring neurodevelopmental difficulties in the Norwegian Mother, Father and Child Cohort Study (MoBa)

BACKGROUND

Previous observational epidemiological studies have suggested that coffee consumption during pregnancy may affect fetal neurodevelopment. However, results are inconsistent and may represent correlational rather than causal relationships. The present study investigated whether maternal coffee consumption was observationally associated and causally related to offspring childhood neurodevelopmental difficulties (NDs) in the Norwegian Mother, Father and Child Cohort Study.

METHODS

The observational relationships between maternal/paternal coffee consumption (before and during pregnancy) and offspring NDs were assessed using linear regression analyses (N = 58694 mother-child duos; N = 22 576 father-child duos). To investigate potential causal relationships, individual-level (N = 46 245 mother-child duos) and two-sample Mendelian randomization (MR) analyses were conducted using genetic variants previously associated with coffee consumption as instrumental variables.

RESULTS

We observed positive associations between maternal coffee consumption and offspring difficulties with social-communication/behavioral flexibility, and inattention/hyperactive-impulsive behavior (multiple testing corrected p < 0.005). Paternal coffee consumption (negative control) was not observationally associated with the outcomes. After adjusting for potential confounders (smoking, alcohol, education and income), the maternal associations attenuated to the null. MR analyses suggested that increased maternal coffee consumption was causally associated with social-communication difficulties (individual-level: beta = 0.128, se = 0.043, p = 0.003; two-sample: beta = 0.348, se = 0.141, p = 0.010). However, individual-level MR analyses that modelled potential pleiotropic pathways found the effect diminished (beta = 0.088, se = 0.049, p = 0.071). Individual-level MR analyses yielded similar estimates (heterogeneity p = 0.619) for the causal effect of coffee consumption on social communication difficulties in maternal coffee consumers (beta = 0.153, se = 0.071, p = 0.032) and non-consumers (beta = 0.107, se = 0.134, p = 0.424).

CONCLUSIONS

Together, our results provide little evidence for a causal effect of maternal coffee consumption on offspring NDs.

Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies

Polygenic risk scores (PRSs) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of the PRS with environmental factors is critical for interpreting and applying PRSs in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using the PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the 2 models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared with standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis, for gene-environment correlations. Finally, using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and the PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Regionally enriched rare deleterious exonic variants in the UK and Ireland

It is unclear how patterns of regional genetic differentiation in the UK and Ireland might impact the protein-coding fraction of the genome. We exploit UK Biobank (UKB) and Viking Genes whole exome sequencing data to study regional genetic differentiation across the UK and Ireland in protein coding genes, encompassing 44,696 unrelated individuals from 20 regions of origin. We demonstrate substantial exonic differentiation among Shetlanders, Orcadians, individuals with full or partial Ashkenazi Jewish ancestry and in several mainland regions (particularly north and south Wales, southeast Scotland and Ireland). With stringent filtering criteria, we find 67 regionally enriched (≥5-fold) variants likely to have adverse biomedical consequences in homozygous individuals. Here, we show that regional genetic variation across the UK and Ireland should be considered in the design of genetic studies and may inform effective genetic screening and counselling.

Mendelian randomization studies of periodontitis: Understanding benefits and natural limitations in an applied context

Mendelian randomization (MR) is a flexible analytical tool that has been widely applied to strengthen causal inference in observational epidemiology and is now gaining attention in many areas including periodontal research. The interpretation of results drawn from MR is based on a series of assumptions, which can be unrealistic or difficult to meet faithfully in some settings. However, we argue that with care, this does not necessarily prevent valuable deployment of the approach. We argue that clarity of presentation as well as careful assessment of specific analytical conditions is a fundamental part of all MR analyses. To that end, awareness of its limitations should also guide the design of MR investigations and the presentation of results rather than rule out its use altogether. Notably, considerations similar to those known to be important in conventional epidemiological settings apply to MR. While MR studies are valuable in their contrast to other study limitations, the application of this technique must be carefully cross-examined. Specific considerations include possible confounders, recruitment strategy and phenotypic measurement and differential analysis properties across studies. In the case of periodontal research, current MR applications are limited by the available evidence base for genetic contributions to periodontitis; however, this sets a specific scene for the strategic use of MR and shines light on a need for greater research emphasis on the genetics of the condition and intermediaries. This article provides a perspective on the uses and inherent limitations of MR studies and the importance of adhering to basic epidemiological principles when designing them.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover highly polygenic signals of genome-wide SDS for both viability and fecundity. Selection coefficients of [Formula: see text] may be typical. Despite its ubiquity, SDS may impose a mortality load of less than 1%. An interesting life-history tradeoff emerges: Alleles that increase viability more strongly in females than males tend to increase fecundity more strongly in males than in females. Finally, we find marginal evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome.

Proteomic Mendelian randomization to identify protein biomarkers of telomere length

Shortening of telomere length (TL) is correlated with many age-related disorders and is a hallmark of biological aging. This study used proteome-wide Mendelian randomization to identify the protein biomarkers associated with telomere length. Protein quantitative trait loci (pQTL) were derived from two studies, the deCODE Health study (4907 plasma proteins) and the UK Biobank Pharma Proteomics Project (2923 plasma proteins). Summary data from genome-wide association studies (GWAS) for TL were obtained from the UK Biobank (472,174 cases) and GWAS Catalog (418,401 cases). The association between proteins and TL was further assessed using colocalization and summary data-based Mendelian randomization (SMR) analyses. The protein-protein network, druggability assessment, and phenome-wide MR were used to further evaluate the potential biological effects, druggability, and safety of the target proteins. Proteome-wide MR analysis identified 22 plasma proteins that were causally associated with telomere length. Five of these proteins (APOE, SPRED2, MAX, RALY, and PSMB1) had the highest evidence of association with TL and should be prioritized. This study revealed telomere length-related protein biomarkers, providing new insights into the development of new treatment targets for chronic diseases and anti-aging intervention strategies.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

Thyroid function variations within the reference range and cognitive function: A two-sample Mendelian randomization study

BACKGROUND

The causal relationship between thyroid function variations within the reference range and cognitive function remains unknown. We aimed to explore this causal relationship using a Mendelian randomization (MR) approach.

METHODS

Summary statistics of a thyroid function genome-wide association study (GWAS) were obtained from the ThyroidOmics consortium, including reference range thyroid stimulating hormone (TSH) (N = 54,288) and reference range free thyroxine (FT4) (N = 49,269). GWAS summary statistics on cognitive function were obtained from the Social Science Genetic Association Consortium (SSGAC) and the UK Biobank, including cognitive performance (N = 257,841), prospective memory (N = 152,605), reaction time (N = 459,523), and fluid intelligence (N = 149,051). The primary method used was inverse-variance weighted (IVW), supplemented with weighted median, Mr-Egger regression, and MR-Pleiotropy Residual Sum and Outlier. Several sensitivity analyses were conducted to identify heterogeneity and pleiotropy.

RESULTS

An increase in genetically associated TSH within the reference range was suggestively associated with a decline in cognitive performance (β = -0.019; 95%CI: -0.034 to -0.003; P = 0.017) and significantly associated with longer reaction time (β = 0.016; 95 % CI: 0.005 to 0.027; P = 0.004). Genetically associated FT4 levels within the reference range had a significant negative relationship with reaction time (β = -0.030; 95%CI:-0.044 to -0.015; P = 4.85 × 10-5). These findings remained robust in the sensitivity analyses.

CONCLUSIONS

Low thyroid function within the reference range may have a negative effect on cognitive function, but further research is needed to fully understand the nature of this relationship.

LIMITATIONS

This study only used GWAS data from individuals of European descent, so the findings may not apply to other ethnic groups.

No evidence for causal effects of C-reactive protein (CRP) on chronic pain conditions: a Mendelian randomization study

Objective

We conducted a Mendelian randomization (MR) study to examine causal associations of C-reactive protein (CRP) with (1) spinal pain; (2) extent of multisite chronic pain; and (3) chronic widespread musculoskeletal pain.

Design

Two-sample MR study.

Setting/Subjects

We used summary statistics from publicly available genome-wide association studies (GWAS) conducted in multiple cohorts and biobanks. Genetic instrumental variables were taken from an exposure GWAS of CRP (n=204,402). Outcome GWASs examined spinal pain (n=1,028,947), extent of multisite chronic pain defined as the number of locations with chronic pain (n=387,649), and chronic widespread pain (n=249,843).

Methods

We examined MR evidence for causal associations using inverse-variance weighted (IVW) analysis and sensitivity analyses using other methods. We calculated odds ratios (ORs), 95% confidence intervals (95% CIs), and p-values, using a Bonferroni correction (p<0.0166) to account for 3 primary comparisons.

Results

Greater serum CRP (mg/L) was not significantly causally associated with spinal pain (OR=1.04, 95% CI 1.00-1.08; p=0.07) in IVW analysis. Greater serum CRP also showed no significant causal association with extent of multisite chronic pain in IVW analysis (beta coefficient= 0.014, standard error=0.011; p=0.19). CRP also showed no significant causal association with chronic widespread pain in IVW analysis (OR=1.00, 95% CI 1.00-1.00; p=0.75). All secondary and sensitivity analyses also showed no significant associations.

Conclusions

This MR study found no causal association of CRP on spinal pain, the extent of chronic pain, or chronic widespread pain. Future studies examining mechanistic biomarkers for pain conditions should consider other candidates besides CRP.

Causal relationship and shared genes between air pollutants and amyotrophic lateral sclerosis: A large-scale genetic analysis

OBJECTIVE

Air pollutants have been reported to have a potential relationship with amyotrophic lateral sclerosis (ALS). The causality and underlying mechanism remained unknown despite several existing observational studies. We aimed to investigate the potential causality between air pollutants (PM2.5, NOX, and NO2) and the risk of ALS and elucidate the underlying mechanisms associated with this relationship.

METHODS

The data utilized in our study were obtained from publicly available genome-wide association study data sets, in which single nucleotide polymorphisms (SNPs) were employed as the instrumental variantswith three principles. Two-sample Mendelian randomization and transcriptome-wide association (TWAS) analyses were conducted to evaluate the effects of air pollutants on ALS and identify genes associated with both pollutants and ALS, followed by regulatory network prediction.

RESULTS

We observed that exposure to a high level of PM2.5 (OR: 2.40 [95% CI: 1.26-4.57], p = 7.46E-3) and NOx (OR: 2.35 [95% CI: 1.32-4.17], p = 3.65E-3) genetically increased the incidence of ALS in MR analysis, while the effects of NO2 showed a similar trend but without sufficient significance. In the TWAS analysis, TMEM175 and USP35 turned out to be the genes shared between PM2.5 and ALS in the same direction.

CONCLUSION

Higher exposure to PM2.5 and NOX might causally increase the risk of ALS. Avoiding exposure to air pollutants and air cleaning might be necessary for ALS prevention.

Testing for differences in polygenic scores in the presence of confounding

Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals' phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification.

C-reactive protein and cognitive impairment: A bidirectional Mendelian randomization study

OBJECTIVES

While C-reactive protein (CRP) has been solidly linked as a risk factor for cognitive impairment, observational research alone cannot definitively demonstrate a causal relationship. This study therefore sought to determine whether there was an association between CRP and the development of cognitive impairment.

METHODS

This study employed bidirectional Mendelian randomization (MR) to investigate the genetic association between CRP and cognitive impairment. genome-wide association studies (GWAS) summary statistics for both were sourced from IEU Open GWAS or prior reports. Cognitive GWAS's used were on tests designed to assess cognitive performance, fluid intelligence, prospective memory, and reaction time. The MR analysis applied several methods, including inverse variance-weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode approaches, then use of MR sensitivity analyses to interrogate findings.

RESULTS

Forward MR analysis showed that genetically proxied CRP was associated with prospective memory (P = 0.009), whereas there is little evidence to support an association between CRP and other cognitive tests. Reverse MR analysis indicated a potential association between genetic proxy cognitive performance (P = 0.002) and fluid intelligence score (P = 0.019) with CRP levels. For genetically proxied CRP on prospective memory, the level of pleiotropy (P > 0.05) and no genetic variant heterogeneity (P > 0.05) made bias unlikely, and leave-one-out tests also confirmed robust associations.

CONCLUSIONS

The effect of genetically proxied CRP on prospective memory, with little evidence on other cognitive tests. The reverse MR shows some evidence of genetically proxied cognition (cognitive performance and fluid intelligence) on CRP levels.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

No genetic causal associations between periodontitis and brain atrophy or cognitive impairment: evidence from a comprehensive bidirectional Mendelian randomization study

BACKGROUND

Observational studies have explored the relationships of periodontitis with brain atrophy and cognitive impairment, but these findings are limited by reverse causation, confounders and have reported conflicting results. Our study aimed to investigate the causal associations of periodontitis with brain atrophy and cognitive impairment through a comprehensive bidirectional Mendelian randomization (MR) research.

METHODS

We incorporated two distinct genome-wide association study (GWAS) summary datasets as an exploration cohort and a replication cohort for periodontitis. Four and eight metrics were selected for the insightful evaluation of brain atrophy and cognitive impairment, respectively. The former involved cortical thickness and surface area, left and right hippocampal volumes, with the latter covering assessments of cognitive performance, fluid intelligence scores, prospective memory, and reaction time for mild cognitive impairment to Alzheimer's disease (AD), Lewy body dementia, vascular dementia and frontotemporal dementia for severe situations. Furthermore, supplementary analyses were conducted to examine the associations between the longitudinal rates of change in brain atrophy and cognitive function metrics with periodontitis. The main analysis utilized the inverse variance weighting (IVW) method and evaluated the robustness of the results through a series of sensitivity analyses. For multiple tests, associations with p-values < 0.0021 were considered statistically significant, while p-values ≥ 0.0021 and < 0.05 were regarded as suggestive of significance.

RESULTS

In the exploration cohort, forward and reverse MR results revealed no causal associations between periodontitis and brain atrophy or cognitive impairment, and only a potential causal association was found between AD and periodontitis (IVW: OR = 0.917, 95% CI from 0.845 to 0.995, P = 0.038). Results from the replication cohort similarly corroborated the absence of a causal relationship. In the supplementary analyses, the longitudinal rates of change in brain atrophy and cognitive function were also not found to have causal relationships with periodontitis.

CONCLUSIONS

The MR analyses indicated a lack of substantial evidence for a causal connection between periodontitis and both brain atrophy and cognitive impairment.

Exploring the genetic heterogeneity of Alzheimer's disease: Evidence for genetic subtypes

Background

Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins.

Objective

We investigated genetic heterogeneity in AD risk through a multi-step analysis.

Methods

We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases=2,739, controls=5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases=500, controls=470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n=399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories.

Results

PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased CSF p-tau and cognitive decline over time.

Conclusions

This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.

A causal relationship between educational attainment and risk of infectious diseases: A Mendelian randomisation study

Background

Previous observational studies have investigated the association between educational attainment and sepsis, pneumonia, and urinary tract infections (UTIs). However, their findings have been susceptible to reverse causality and confounding factors. Furthermore, no study has examined the effect of educational level on the risk of infections of the skin and subcutaneous tissue (SSTIs). Thus, we aimed to evaluate the causal relationships between educational level and the risk of four infectious diseases using Mendelian randomisation (MR) techniques.

Methods

We used univariable MR analysis to investigate the causal associations between educational attainment (years of schooling (n = 766 345) and holding college or university degree (n = 334 070)) and four infectious diseases (sepsis (n = 486 484), pneumonia (n = 486 484), UTIs (n = 463 010), and SSTIs (n = 218 792)). We included genetic instrumental variables with a genome-wide significance (P < 5 × 10-8) in the study. We used inverse variance-weighted estimation in the primary analysis and explored the stability of the results using multivariable MR analysis after adjusting for smoking, alcohol consumption, and body mass index.

Results

Genetically predicted years of schooling were associated with a reduced risk of sepsis (odds ratio (OR) = 0.763; 95% confidence interval (CI) = 0.668-0.870, P = 5.525 × 10-5), pneumonia (OR = 0.637; 95% CI = 0.577-0.702, P = 1.875 × 10-19), UTIs (OR = 0.995; 95% CI = 0.993-0.997, P = 1.229 × 10-5), and SSTIs (OR = 0.696; 95% CI = 0.605-0.801, P = 4.034 × 10-7). We observed consistent results for the correlation between qualifications and infectious diseases. These findings remained stable in the multivariable MR analyses.

Conclusions

Our findings suggest that increased educational attainment may be causally associated with a decreased risk of sepsis, pneumonia, UTIs, and SSTIs.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer

BACKGROUND

This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.

METHODS

Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.

RESULTS

Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication.

IMPLICATIONS

Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.

HIGHLIGHTS

The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

An analysis of the accuracy of retrospective birth location recall using sibling data

Many surveys ask participants to retrospectively record their location of birth. This paper examines the accuracy of such data in the UK Biobank using a sample of full siblings. Comparison of reported birth locations for siblings with different age gaps allows us to estimate the probabilities of household moves and of misreported birth locations. Our first contribution is to show that there are inaccuracies in retrospective birth location data, showing a sizeable probability of misreporting, with 28% of birth coordinates, 16% of local districts and 6% of counties of birth being incorrectly reported. Our second contribution is to show that such error can lead to substantial attenuation bias when investigating the impacts of location-based exposures, especially when there is little spatial correlation and limited time variation in the exposure variable. Sibling fixed effect models are shown to be particularly vulnerable to the attenuation bias. Our third contribution is to highlight possible solutions to the attenuation bias and sensitivity analyses to the reporting error.

The risk of venous thromboembolism in oral contraceptive users: the role of genetic factors-a prospective cohort study of 240,000 women in the UK Biobank

BACKGROUND

More than 150 million women worldwide use oral contraceptives. Women with inherited thrombophilia and carriers of certain thrombophilia gene variants, such as factor V Leiden and the prothrombin, are at an increased risk for venous thromboembolism, especially when combined with oral contraceptive use. Venous thromboembolism is a complex disorder involving many genetic risk factors, and recently, polygenic risk scores have been proposed to capture a significant proportion of the genetic risk of venous thromboembolism.

OBJECTIVE

The aim of this study was to estimate the risk for developing venous thromboembolism when initiating oral contraceptive use (first 2 years) and during continued use among women with a high genetic liability.

STUDY DESIGN

We used a prospective study design in which 244,420 participants from the UK Biobank were followed from birth. The effect of oral contraceptive use during the first 2 years and in the remaining years of oral contraceptive use on the risk of developing venous thromboembolism was estimated using a Cox regression with a time-dependent exposure variable. Women were stratified according to their polygenic risk scores and whether they were carriers of factor V Leiden and/or prothrombin variants.

RESULTS

When genetic risk was not considered, an increased risk for venous thromboembolism was observed during the first 2 years of oral contraceptive use (hazard ratio, 3.09; 95% confidence interval, 3.00-3.20) but not during continued use (hazard ratio, 0.92; 95% confidence interval, 0.80-1.05). However, when genetic risk was considered, women in the highest polygenic risk score category had a more pronounced risk of developing a venous thromboembolism during the first 2 years of oral contraceptive use (hazard ratio, 6.35; 95% confidence interval, 4.98-8.09), and a high risk was also observed among factor V Leiden (hazard ratio, 5.73; 95% confidence interval, 5.31-6.17) and prothrombin variant carriers (hazard ratio, 5.23; 95% confidence interval, 4.67 - 5.87). A high polygenic risk score in combination with being a factor V Leiden and prothrombin variant carrier conferred the highest risk for developing a venous thromboembolism during the first 2 years of oral contraceptive use (hazard ratio, 14.8; 95% confidence interval, 9.28-23.6). Women with a high genetic liability also had an increased risk during continued use but it was less pronounced, and the highest risk was conferred to carriers of both factor V Leiden and the prothrombin variant (hazard ratio, 4.93; 95% confidence interval, 3.16-7.7).

CONCLUSION

Evaluating polygenic risk can identify additional venous thromboembolism risk that is not captured in the commonly investigated genes for inherited thrombophilia. Our results indicate that oral contraceptive use is associated with an increased risk for developing a venous thromboembolism, particularly among women with a high genetic predisposition, and that oral contraceptive use dramatically increases the risk thereof short after initiation of use, which decreases with continued use. This suggests that the polygenic risk score could be used to identify women who are at high risk for developing a venous thromboembolism and advise them on alternative methods of contraception.

Association of educational attainment with hypertension and type-2 diabetes: A Mendelian randomization study

BACKGROUNDDue to the long time interval between exposure and outcome, it is difficult to infer the causal relationship between educational attainment (EA) and common chronic diseases. Therefore, we utilized Mendelian randomization (MR) to predict the causal relationships of EA with hypertension and type-2 diabetes (T2DM). METHODSA two-sample MR analysis was conducted using genome-wide association studies (GWASs) combined with inferential measurements. A GWAS meta-analysis including 1,131,881 European individuals was used to identify instruments for EA. Hypertension and T2DM data were obtained from a Finnish database. MR analyses were performed using inverse-variance weighted meta-analysis (IVW), weighted median regression, MR‒Egger regression, simple mode regression, weighted mode regression and the MR-Pleiotropy RESidual Sum and Outlier test. Sensitivity analyses were further performed using the leave-one-out method to test the robustness of our findings. RESULTSUsing the MR approach, our results showed that EA was significantly associated with a reduced risk of hypertension (OR = 0.63; P = 2.94 × 10-47; [95% CI: 0.59, 0.67]) and type-2 diabetes (OR = 0.59; P = 1.25 × 10-16; [95% CI: 0.52, 0.67]). CONCLUSIONSThis study showed that EA is causally linked to the risk of chronic diseases, including high blood pressure and T2DM.

Life course plasma metabolomic signatures of genetic liability to Alzheimer's disease

Mechanisms through which most known Alzheimer's disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically.

The causal effect of air pollution on the risk of essential hypertension: a Mendelian randomization study

BACKGROUND

Air pollution poses a major threat to human health by causing various illnesses, such as cardiovascular diseases. While plenty of research indicates a correlation between air pollution and hypertension, a definitive answer has yet to be found.

METHODS

Our analyses were performed using the Genome-wide association study (GWAS) of exposure to air pollutants from UKB (PM2.5, PM10, NO2, and NOX; n = 423,796 to 456,380), essential hypertension from FinnGen (42,857 cases and 162,837 controls) and from UKB (54,358 cases and 408,652 controls) as a validated cohort. Univariable and multivariable Mendelian randomization (MR) were conducted to investigate the causal relationship between air pollutants and essential hypertension. Body mass index (BMI), alcohol intake frequency, and the number of cigarettes previously smoked daily were included in multivariable MRs (MVMRs) as potential mediators/confounders.

RESULTS

Our findings suggested that higher levels of both PM2.5 (OR [95%CI] per 1 SD increase in predicted exposure = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) and PM10 (OR [95%CI] = 1.24 [1.02-1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02-1.06], p = 7.58E-05 from UKB) were linked to an increased risk for essential hypertension. Even though we used MVMR to adjust for the impacts of smoking and drinking on the relationship between PM2.5 exposure and essential hypertension risks, our findings suggested that although there was a direct positive connection between them, it is not present after adjusting BMI (OR [95%CI] = 1.05 [0.87-1.27], p = 6.17E-01). Based on the study, higher exposure to PM2.5 and PM10 increases the chances of developing essential hypertension, and this influence could occur through mediation by BMI.

CONCLUSION

Exposure to both PM2.5 and PM10 is thought to have a causal relationship with essential hypertension. Those impacted by substantial levels of air pollution require more significant consideration for their cardiovascular health.

PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment

Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

Prospective study design and data analysis in UK Biobank

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Harnessing population diversity: in search of tools of the trade

Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed the advent of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual's position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain-behavior relationships depend on human subgroups.

Causal relationship between sleep traits and cognitive impairment: A Mendelian randomization study

OBJECTIVE

Observational studies had demonstrated a link between sleep disturbances and cognitive decline. Here, we aimed to investigate the causal association between genetically predicted sleep traits and cognitive impairment using Mendelian randomization (MR).

METHODS

Using strict criteria, we selected genetic variants from European ancestry Genome-wide association studies (GWAS) from the Sleep Disorders Knowledge Portal and UK Biobank as instrumental variables for several sleep traits, including insomnia, sleep duration, daytime sleepiness, daytime napping, and chronotype. Summary statistics related to cognitive impairment were derived from five different GWAS, including the Social Science Genetic Association Consortium. The role of self-reported sleep trait phenotypes in the etiology of cognitive impairment was explored using inverse-variance weighted (IVW) tests, MR-Egger tests, and weighted medians, and sensitivity analyses were conducted to ensure robustness.

RESULTS

In the main IVW analysis, sleep duration (reaction time: β = -0.05, 95% CI -0.07 to -0.04, p = 1.93×10-12 ), daytime sleepiness (average cortical thickness: β = -0.12, 95% CI -0.22 to -0.02, p = 0.023), and daytime napping (fluid intelligence: β = -0.47, 95% CI -0.87 to -0.07, p = 0.021; hippocampal volume in Alzheimer's disease: β = -0.99, 95% CI -1.64 to -0.35, p = 0.002) were significantly negatively correlated with cognitive performance. However, any effects of insomnia and chronotype on cognitive impairment were not determined.

CONCLUSIONS

Our findings highlighted that focusing on sleep behaviors or distinct sleep patterns-particularly sleep duration, daytime sleepiness, and daytime napping, was a promising approach for preventing cognitive impairment. This study also shed light on risk factors for and potential early markers of cognitive impairment risk factors.

Repurposing Antihypertensive and Statin Medications for Spinal Pain: A Mendelian Randomization Study

STUDY DESIGN

Mendelian randomization (MR) study.

OBJECTIVE

To examine whether antihypertensive medications (beta-blockers, calcium channel blockers, and angiotensin-converting enzyme inhibitors) and statins can be repurposed to prevent or treat spinal pain (back or neck pain).

SUMMARY OF BACKGROUND DATA

Observational studies and a recent MR study have found associations between elevated blood pressure and a greater risk of back pain. Observational studies have found associations between hyperlipidemia and statin use and greater risk of back pain. No prior MR studies have examined the effects of antihypertensives or statins on spinal pain.

MATERIALS AND METHODS

This was a two-sample MR study using publicly available summary statistics from large-scale genome-wide association studies (GWAS). Sample sizes in exposure GWASs were n=757,601 (systolic blood pressure) and n=173,082 (low-density lipoprotein cholesterol), and n=1,028,947 for the outcome GWAS of spinal pain defined as health care seeking for any spinal pain-related diagnosis. Genes and cis-acting variants were identified as proxies for the drug targets of interest. MR analyses used inverse-variance weighted meta-analysis. The threshold for statistical significance after correction for multiple testing was P <0.0125.

RESULTS

No statistically significant associations of these medications with spinal pain were found. However, findings were suggestive of a protective effect of beta-blockers on spinal pain risk (odds ratio [OR] 0.84, 95% confidence interval [CI] 0.72-0.98; P =0.03), and calcium channel blockers on greater spinal pain risk (OR 1.12, 95% CI 1.02-1.24; P =0.02).

CONCLUSIONS

A protective effect of beta-blockers on spinal pain was suggested in the current study, consistent with findings from observational studies of various other pain phenotypes. The detrimental effect of calcium channel blockers on spinal pain suggested in the current study must be interpreted in the context of conflicting directions of effect on nonspinal pain phenotypes in other observational studies.This Mendelian randomization study examined whether antihypertensive medications (beta-blockers, calcium channel blockers, and angiotensin-converting enzyme inhibitors) and statins can be repurposed to prevent or treat spinal.This was a two-sample MR study using publicly available summary statistics from large-scale genome-wide association studies ranging size from 173,082 to 1,028,947 adults.While no statistically significant associations were found, a protective effect of beta-blockers on spinal pain was suggested (odds ratio [OR] 0.84, 95% confidence interval [CI] 0.72 to 0.98; p= 0.03), as was a detrimental effect of calcium channel blockers on spinal pain (OR 1.12, 95% CI 1.02 to 1.24; p= 0.02).

Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is the leading cause of dementia. Currently, there are no effective disease-modifying treatments for AD. Mendelian randomisation (MR) has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for AD and analyse their pathophysiological mechanisms and potential side effects.

METHODS

A two-sample MR integrating the identified druggable genes was performed to estimate the causal effects of blood and brain druggable expression quantitative trait loci (eQTLs) on AD. A repeat study was conducted using different blood and brain eQTL data sources to validate the identified genes. Using AD markers with available genome-wide association studies data, we evaluated the causal relationship between established AD markers to explore possible mechanisms. Finally, the potential side effects of the druggable genes for AD treatment were assessed using a phenome-wide MR.

RESULTS

Overall, 5883 unique druggable genes were aggregated; 33 unique potential druggable genes for AD were identified in at least one dataset (brain or blood), and 5 were validated in a different dataset. Among them, three prior druggable genes (epoxide hydrolase 2 (EPHX2), SERPINB1 and SIGLEC11) reached significant levels in both blood and brain tissues. EPHX2 may mediate the pathogenesis of AD by affecting the entire hippocampal volume. Further phenome-wide MR analysis revealed no potential side effects of treatments targeting EPHX2, SERPINB1 or SIGLEC11.

CONCLUSIONS

This study provides genetic evidence supporting the potential therapeutic benefits of targeting the three druggable genes for AD treatment, which will be useful for prioritising AD drug development.

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders

C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600-14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study

BACKGROUND

Previous Mendelian randomization (MR) studies using population samples (population MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. MR using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis.

METHODS

Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72 932 siblings from UK Biobank and the Norwegian HUNT study, and summary-level data from a within-sibship Genome-wide Association Study including >140 000 individuals.

RESULTS

Both population and within-sibship MR estimates provided evidence that educational attainment decreased BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect.

CONCLUSIONS

These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independently of potential demographic and family-level confounders.

Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925-1989

Major changes in the educational distribution of the population and in institutions over the past century have affected the societal barriers to educational attainment. These changes can possibly result in stronger genetic associations. Using genetically informed, population-representative Finnish surveys linked to administrative registers, we investigated the polygenic associations and intergenerational transmission of education for those born between 1925 and 1989. First, we found that a polygenic index (PGI) designed to capture genetic predisposition to education strongly increased the predictiveness of educational attainment in pre-1950s cohorts, particularly among women. When decomposing the total contribution of PGI across different educational transitions, the transition between the basic and academic secondary tracks was the most important. This transition accounted for 60-80% of the total PGI-education association among most cohorts. The transition between academic secondary and higher tertiary levels increased its contribution across cohorts. Second, for cohorts born between 1955 and 1984, we observed that one eighth of the association between parental and one's own education is explained by the PGI. There was also an increase in the intergenerational correlation of education among these cohorts, which was partly explained by an increasing association between family education of origin and the PGI.

Polygenic scores for social science: Clarification, consensus, and controversy

In this response, I focus on clarifying my arguments, highlighting consensus, and addressing competing views about the utility of polygenic scores (PGSs) for social science. I also discuss an assortment of expansions to my arguments and suggest alternative approaches. I conclude by reiterating the need for caution and appropriate scientific skepticism.

The social stratification of population as a mechanism of downward causation

This commentary expands on Burt's concept of downward causation to include any association between genomic variants and a given outcome that is forged through social practices rather than biochemical pathways. It proposes the social stratification of population, through which endogamy over a period of generations produces allele frequency differences between socioeconomic strata, as a mechanism of downward causation.

Testing of two SNP array-based genealogy algorithms using extended Han Chinese pedigrees and recommendations for improved performances in forensic practice

Distant genetic relatives can be linked to a crime scene sample by computing identity-by-state (IBS) and identity-by-descent (IBD) shared by individuals. To test the methods of genetic genealogy estimation and optimal the parameters for forensic investigation, a family-based genetic genealogy analysis was performed using a dataset of 262 Han Chinese individuals from 11 families. The dataset covered relative pairs from 1st- to 14th degrees. But the 7th-degree relative is the most distant kinship to be fully investigated, and each individual has ∼200 relatives within the 7th degree. The KING algorithm by calculating IBS and IBD statistics can correctly discriminate the first-degree relationships of monozygotic twin, parent-offspring and full sibling. The inferred relationship was reliable within the fifth-degree, false positive rate <1.8%. The IBD segment algorithm, GERMLINE + ERSA, could provide reliable inference result prolonged to eighth degree. Analysis of IBD segments produced obviously false negative estimations (<27.4%) rather than false positives (0%) within the eighth-degree inferences. We studied different minimum IBD segment threshold settings (changed from >0 to 6 cM); the inferred results did not make much difference. In distant relative analysis, genetically undetectable relationships begin to occur from the sixth degree (second cousin once removed), which means the offspring after seven meiotic divisions may share no ancestor IBD segment at all. Application of KING and GERMLINE + ERSA worked complementarily to ensure accurate inference from first degree to eighth degree. Using simulated low call rate data, the KING algorithm shows better tolerance to marker decrease compared with the GERMLINE + ERSA segment algorithm.