Genetic Epidemiology

Mendelian Randomization (MR) is a popular tool for inferring causal relationships between traits using genetic variants as instrumental variables. These methods have been extended to also determine the direction of causality. However, causal direction cannot be inferred from a statistical test or estimation procedure (i.e. from data alone) without further assumptions and the methods operating characteristics and relative performances are not well understood. We conducted a comprehensive simulation study to illustrate this issue by evaluating type I error and power of 17 summary-based MR methods for inferring the effect direction. These methods fall within three methodological families: MR Steiger, Causal Direction (CD), and bidirectional MR approaches, with scenarios ranging across combinations of horizontal pleiotropy, unmeasured confounding, measurement error, longitudinal feedback, and varying sample sizes. While most methods achieved sufficient power levels under the alternative hypothesis in most scenarios, we found that every method was susceptible to inferring the wrong causal direction or under powered, and no method consistently maintained both correct type 1 error control and high power. In our applications, we evaluated the effect direction between the trait pairs body mass index (BMI) and major depressive disorder (MDD) and between BMI and asthma. To help researchers to evaluate the 17 methods to infer the effect direction and consider these challenges in their own data, we have developed MRdirection, an R package that runs the simulation studies examining the 17 directional MR methods across different user-defined scenarios. Our study, together with the accompanying R package, provides researchers with a tool for examining directional MR methods given different underlying assumptions.

Mendelian randomization (MR) is widely used to infer causal relationships using genetic variants as instrumental variables, yet the selection of genetic instruments is not always given sufficient attention. Many MR studies rely on default linkage disequilibrium (LD) clumping parameters (r2 <0.001, 10,000 kb), as implemented in commonly used tools, without assessment of their suitability for specific exposures. We investigated whether this approach yields optimal instruments or whether a more pragmatic strategy yields stronger instruments. Using UK Biobank data, we examined three distinct exposure types-circulating amino acids, body mass index (BMI), and major depressive disorder (MDD). For each phenotype, we systematically varied LD clumping thresholds (r2 and genomic distance) and evaluated each instrument via both their average strength (F-statistic) and total strength (R2). Across all phenotypes, optimal instruments differed from default parameters and varied by exposure. For amino acids and BMI, more stringent LD thresholds (r2=0.00001) combined with larger clumping windows improved instrument strength, whereas for MDD, a highly polygenic, binary trait, smaller windows with stringent r2 maximized variance explained while maintaining F-statistics above the desired threshold (>10). Notably, increasing the number of SNPs did not consistently improve instrument quality, highlighting a trade-off between instrument strength and potential pleiotropy. We demonstrate that universal reliance on default LD clumping parameters can lead to suboptimal instruments. We propose a pragmatic framework for instrument selection based on empirical evaluation of strength metrics, improving the robustness and transparency of MR analyses across different exposure types.

Population stratification is a major source of inflated false positive rates in genome wide association studies. However, relatively few studies have examined its impact on gene-gene interaction detection, despite the importance of epistasis for understanding the genetic architecture of complex traits. In this study, we identify scenarios under which population stratification can inflate the interaction test statistics. Through analytical derivations and simulation studies, we show that this inflation is not adequately controlled by including principal components as covariates in the regression model. We then propose an alternative approach that effectively controls the inflation of false-positive rates for interaction test statistics due to population stratification by using single nucleotide polymorphism-by-population structure interaction as an additional covariate term in the regression model.

Background: Current Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results. Methods: LynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature. Results: In two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing. Discussion: LynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.

Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine learning methods have been increasingly applied to construct PRSs directly from individual-level data, with the aim of improving predictive performance over standard PRSs through their ability to model non-additive genetic effects. However, their superiority across studies has been inconsistent, and the conditions under which they provide meaningful improvements remain unclear. We combined theoretical analysis, simulations and a real-world application to investigate when two widely used nonlinear machine learning methods, random forest and XGBoost, outperform standard PRSs. Theoretical analysis showed that standard PRSs can implicitly capture part of the genetic variance attributable to nonadditive genetic effects through their contributions to marginal SNP effects, thereby losing less information than commonly assumed. Although nonlinear models have a higher theoretical potential, their greater flexibility incurs a bias-variance trade-off that can limit predictive gains at finite sample sizes. Simulations showed that XGBoost outperformed the standard PRS only when the genetic architecture involves a sufficiently large proportion of interaction genetic variance concentrated across relatively few interaction effects and large training samples were available. Random forest consistently underperformed the standard PRS. In an application to ischemic heart disease prediction using UK Biobank data, XGBoost showed no meaningful improvement in predictive performance over the standard PRS, whereas random forest again performed worse. Together, these findings suggest that nonlinear machine learning do not uniformly outperform standard PRSs; rather, their relative performance depends jointly on genetic architecture and training sample size. Our study helps to reconcile the inconsistent results reported across previous studies and provides a framework for identifying settings in which more complex PRS models are likely to be beneficial.

Establishing causality in nutrition research is challenging. While randomised controlled trials (RCTs) provide robust evidence, long-term dietary intervention studies with disease endpoints are often impractical. Short-term RCTs can instead identify intermediate traits that may lie on the causal pathway between diet and disease. Mendelian randomisation (MR) is an epidemiological approach that uses genetic variants as proxies for modifiable exposures to estimate the effects of lifelong differences in exposure on disease risk. However, the utility of MR is limited for complex dietary patterns because genetic variants typically reflect biological mechanisms rather than specific diets. We propose a two-step framework integrating dietary RCTs with MR to infer potential lifetime effects of dietary interventions. First, RCT data identify molecular traits altered by an intervention. Second, MR evaluates whether these traits are associated with long-term disease risk. We demonstrate this framework using the Diabetes Remission Clinical Trial (DiRECT), which measured circulating proteins and diabetes remission. Using protein data alone, 216 of 4,601 proteins changed following the intervention (step 1), and 10 were associated with diabetes risk using MR (step 2). We then compared these MR estimates with observed protein-remission associations from DiRECT. The broad agreement between the two (r{approx}-0.645, R2=0.416) supports this framework as a useful approach for estimating long-term effects of dietary interventions.

Surrogate endpoints, or measures used in place of the true outcome of interest, have relevance across multiple disease areas. The Prentice Criteria, proposed in 1989, assess surrogacy by evaluating how the treatment's effect on the true outcome operates through the potential surrogate. Using the A4 Study of solanezumab, we evaluate multiple formulations of the Prentice Criteria using causal mediation. We estimated direct and indirect effects of solanezumab on cognitive decline through cerebral amyloid across different, but reasonable, measures of exposure, mediator, outcome, and covariate adjustment. Unsurprisingly given that solanezumab did not show benefit, estimated indirect effects were close to zero. These results provide little evidence of meaningful mediation for memory and global cognition, but precision varied substantially. Confidence interval widths varied by up to a factor of 17 across specifications. Causal mediation analysis of individual-level randomized trial data may contribute to quantitative surrogate endpoint evaluation, but our findings indicate this is only the case when analytic choices are biologically justified, prespecified, and interpreted with attention to uncertainty.

Unmeasured confounding threatens causal inference and replicability in observational multi-omic studies across variable environments. Genetic instrumental variables (Mendelian randomization) and negative-control calibration each address complementary sources of unmeasured confounding, yet no existing framework unifies them for omics-scale mediation analysis. We introduce ICONIC, an R package that embeds genetic instruments and negative controls within a proximal causal inference framework for total-effect and mediation analysis. ICONIC implements eight estimators spanning five confounding-control strategies, supports continuous, binary, and time-to-event outcomes, and provides extensive diagnostics including sensitivity analyses that map estimator performance across plausible assumptions. Ground-truth benchmarks are calibrated to real-omics covariance structures via a hybrid generative model (GAN + feature-level Gaussian copula) rather than parametric simulation, and a companion planning tool predicts performance gains from collecting additional omic data. We demonstrate ICONIC in two case studies: identifying placental transcriptomic mediators of gestational diabetes on birth weight (n = 164), and tumor-expression mediators of smoking intensity on lung cancer survival (n = 494). Notably, ICONIC's diagnostics recommended different estimation strategies across the two scenarios, reflecting differences in the likely influence of unmeasured confounding. ICONIC is freely available at https://github.com/sbresnahan/iconic/.

Background: Polygenic scores (PGSs) are increasingly used to investigate the genetic architecture of complex traits. In genetics, family study designs are often used to adjust for confounders such as population structure and shared environment. However, family studies may also be particularly vulnerable to to non-random ascertainment, for example when individual case status affects the probability of inclusion, leading to differential representation of sibling pairs. In sibling samples, PGS associations can be decomposed into within-family and between-family components, where the within-family estimate captures associations between sibling differences in PGS and differences in outcome, thereby providing an estimate that is less affected by shared familial confounding. In this study, we examined the impact of non-random sampling on estimated genetic effects in family-based studies using both simulations and real-world data. Further, we leveraged the iPSYCH study design to estimate within-family PGS effects for six common mental health outcomes and whether accounting for these can improve prediction accuracy. Methods: We conducted simulations and applied the same framework to real-world data to evaluate the impact of selection bias on within- and between-family PGS estimates. Selection bias was modelled through differential sampling of sibling pairs based on case status, and inverse probability weighting (IPW) was applied to adjust for known heterogeneous inclusion probabilities. Analyses were replicated in the iPSYCH cohort using registry-based sampling weights and PGSs for six major psychiatric disorders. Predictive performance of models was assessed using five-fold cross-validation. Results: In simulation studies, biased sampling led to deviations in estimated PGS effects, with greater distortion observed for between-family components. IPW adjustment reduced the discrepancy between estimates obtained from biased and true underlying data. In the iPSYCH cohort, between-family estimates from unweighted models were larger than within-family estimates across traits. IPW weighted attenuated several of these estimates. Prediction analyses comparing models using total PGS versus decomposed within- and between-family components showed minimal differences in area under the curve and scaled R2 in the iPSYCH data, while modest gains were observed in selected simulation scenarios. Conclusions: Non-random ascertainment distorts effect estimates in family-based models, with particular sensitivity when estimating between-family effects. Incorporating IPWs derived from known or estimable inclusion probabilities can reduce this bias. Our findings highlight the importance of accounting for selection bias in family studies when estimating genetic effects

It is desirable to quantify the prediction accuracy of polygenic scores (PGS) for disease on the scale of liability and adjusted for case-control ascertainment in the test sample, because that allows comparison across prevalence and ascertainment. Previous expressions have focused in their derivation and implementation on linear regression on the observed 0-1 scale followed by a transformation of the coefficient of determination (R2) to the scale of liability, adjusted for ascertainment. Yet most statistical analyses with empirical data use logistic regression. The differences in scale have led to confusion and incorrect transformations in the literature. Here we provide a new derivation and simple equation, validated by simulation, that allows a direct transformation from the empirical results from logistic regression to the scale of liability.

Background: Cardiovascular, kidney, and metabolic conditions have each been associated with hearing outcomes, but little is known about whether cardiovascular-kidney-metabolic (CKM) syndrome staging tracks objective hearing sensitivity in older adults. Methods: We analyzed NHANES 2017-March 2020 pre-pandemic data for adults aged 70-79 years. CKM syndrome Stages 0-4 were operationalized from ascertainable domains of the 2026 AHA/ACC/ADA/ASN guideline, including HbA1c and design-valid fasting plasma glucose criteria for dysglycemia. The outcome was bilateral pure-tone average at 0.5, 1, 2, and 4 kHz (PTA4). Survey-weighted models adjusted for age, sex, race/ethnicity, education, smoking, and occupational and nonoccupational noise exposure. Results: The primary CKM syndrome stage analysis included 285 participants (Stage 0, n=1; Stage 1, n=18; Stage 2, n=103; Stage 3, n=65; Stage 4, n=98). The five-stage omnibus Wald test was statistically significant (F(4,25)=21.405, p=9.01e-8), but Stage 0 contained only one participant and the adjusted means were nonmonotonic (9.49, 24.3, 31.0, 29.0, and 33.4 dB HL). None of the planned Stage 2-4 contrasts remained significant after Holm adjustment, and a Stage 2-4-only robustness analysis did not support overall heterogeneity (p=0.197). Waist circumference was the only one of nine continuous CKM-related measures retained after the nine-test Holm adjustment: bilateral PTA4 was 1.57 dB HL higher per 10 cm greater waist circumference (95% CI, 0.74-2.39; adjusted p=0.00547). A post-hoc spline diagnostic did not show clear evidence of nonlinearity (p=0.128). Five-stage component-by-stage interaction models were not estimable because Stage 0 contained a single participant; among Stages 2-4, none of six primary interaction tests met the Holm-adjusted threshold. Conclusions: The primary hypothesis of progressively poorer hearing with advancing CKM syndrome stage was not supported. Although the five-stage omnibus test was statistically significant, its interpretation was constrained by the single-participant Stage 0 category; analyses restricted to Stages 2-4 did not support a progressive stage pattern. Waist circumference showed a small secondary association with PTA4 after multiplicity adjustment, but its individual-level clinical significance remains uncertain.

Background: Hypercholesterolemia is a prevalent cardiovascular (CVD) risk factor among Hispanics/Latinos; however, rates of cholesterol awareness and treatment are low in this population. Using a polygenic risk score (PRS) associated with low-density lipoprotein cholesterol (LDL) levels may help identify those who would benefit most from statin therapy. Previous LDL-PRS have been developed but they have not been properly evaluated for use in a highly diverse Hispanic/Latino population. Methods: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a prospective study that enrolled 16,415 Hispanic/Latino adults aged 18-74 years from four U.S. communities in 2008-2011. We assessed the association of twenty PRS with baseline LDL levels and LDL control among 11,669 HCHS/SOL participants with complete data and consent to conduct genetic research. Weighted PRS were calculated using effect estimates obtained from the PGS Catalog. Multivariable linear regression analysis was used to derive effect estimates (betas (&beta), 95% confidence intervals (CI)) for the association between each PRS, modeled continuously and by quintiles, with baseline LDL levels. Multivariable logistic regression was used to derive odds ratios and 95% CI for the association between each PRS and LDL control in statin users. Models were adjusted for predefined confounders and accounted for survey weights. Results: A PRS developed with PolyFun-pred, using European GWAS summary statistics, provided the largest incremental improvement for predicting LDL levels for the full sample (&Delta R2 0.114), Caribbean background (&Delta R2 0.096), majority African ancestry (&Delta R2 0.098), and majority Amerindian ancestry subgroups (&Delta R2 0.123). PRSCSx-EUR, using multiple ancestry GWAS and weights, improved PRS performance most for the Mainland background (&Delta R2 0.090) and majority European ancestry (&Delta R2 0.061) subgroups. For every one standard deviation increase across the 20 PRS asssesed, LDL increase varied between 3 and 14mg/dL. Among 1,323 statin users, the odds of LDL control were greatly reduced with increasing PRS but differed between PRS and Hispanic/Latino background groups and genetic ancestry groups. Conclusion: A polygenic risk score prioritizing functional annotations demonstrated superior performance for predicting LDL levels and LDL control across Hispanic/Latino subgroups. These results emphasize that further development of PRS is needed for improved risk prediction before clinical application in diverse populations.

Motivation: Horizontal pleiotropy remains a major challenge for Mendelian randomization (MR), particularly as increasingly large genome-wide association studies identify many genetic instruments for complex traits. Existing pleiotropy-robust methods rely on assumptions regarding the proportion or distribution of valid instruments that may be violated when pleiotropy is widespread. Results: We developed CaPri, a Bayesian MR framework that jointly estimates causal and pleiotropic effects. CaPri allows a potentially arbitrary proportion of instruments to contribute to a shared directional pleiotropic component and estimates the posterior probability and magnitude of the causal effect, instrument-specific posterior inclusion probabilities for pleiotropy, and the shared pleiotropic effect. Across simulations, CaPri maintained appropriate false positive control and accurate causal effect estimation regardless of the proportion of pleiotropic instruments, while accurately identifying pleiotropic instruments and estimating their shared effect. Performance remained robust across varying causal and pleiotropic effect sizes and exposure-outcome confounding, although sufficiently large outcome GWAS sample sizes were important for stable inference and limited prior dependence. In real data analyses, CaPri reported low posterior probabilities of causality across negative controls and strong support for established positive control relationships. Application to alcohol consumption and coronary artery disease further demonstrated its ability to identify potentially meaningful pleiotropic instruments. CaPri provides a complementary framework for strengthening causal inference and triangulation across MR methods with different identifying assumptions. Availability and implementation: CaPri is available at https://github.com/OuRen0104/CaPri.

Polygenic scores (PGSs) have progressed toward clinical utility in recent years, demonstrating odds ratios that are comparable with both lifestyle-related and monogenic risk factors for several complex diseases. However, PGS implementation relies on developing analytically and clinically valid assays. Methods for developing polygenic models (PGMs) that incorporate tens of thousands to millions of variants require either genome-wide chip genotyping combined with imputation or whole genome sequencing, limiting their portability. Additionally, variants may be in regions where the local genomic sequence makes it difficult to assay, and multi-allelic or strand ambiguous variants can make bioinformatics more complicated. Because of these challenges, attempts to develop a reliable assay for published PGMs can produce multiple variations from the same underlying model, hindering routine clinical deployment and incorporation into multi-factorial risk prediction models. In this analysis, we present an adjustment to the previously published select and shrink with summary statistics (S4) PGM method designed to optimize performance for realistic clinical use by restricting to a pre-specified number of available SNPs that are well-captured by genotyping arrays. We compare the performance of this oligogenic S4 method (S4-Oligo) to currently published PGSs as well as the full S4 model for prediction of breast, colorectal, ovarian, and prostate cancers in the All of Us Research Program, Biobank Japan, and UK Biobank. We find that S4-Oligo matches or exceeds the accuracy of published PGMs at comparable model sizes while retaining between 88% and 99% of the performance of the original S4 model when limited to 1,000 variants.

Purpose: Age-related Macular Degeneration (AMD), a degenerative disease of aging, leads to central vision loss and has a strong genetic risk. Genetic heritability, used to quantify genetic influence on a trait, has mainly focused on twin study designs but these are vulnerable to bias. Studying relatives beyond twins is necessary to bring clarity to the genetic burden of AMD and help focus the search for additional genetic risk loci. Methods: Through both single nucleotide polymorphism (SNP) and pedigree-based heritability methods, the heritability of AMD was analyzed using relationship informed analyses of families from an Amish population (n = 525). AMD status was determined using the Beckman grading scale (285 controls and 240 cases). An estimate of genetic relatedness preceded SNP heritability estimation, whereas the pedigree heritability model utilized genealogical reports. Primary models were adjusted for age, sex, and population structure. A comparison of SNP- and pedigree-based models followed heritability estimation. Sensitivity models adjusting for all possible combinations of three known strong AMD genetic risk variants were constructed. Results: SNP heritability is 55% +/- 13% (p= 9.87e-06) and the pedigree heritability is 49% +/- 18% (p= 3.06e-04). The sensitivity analyses revealed that the estimates were robust to changes in the inclusion of AMD variants as covariates. Conclusions: These heritability estimates support existing twin and SNP-based AMD heritability estimates and corroborate the substantial involvement of genetics in AMD. Adjusting for known AMD variants revealed that additional genetic contribution exists, supporting a large polygenic effect in AMD.

Background Lean mass is positively associated with bone mineral density (BMD), and this relationship may vary according to age and sex. Sedentary behavior has also been associated with BMD, although findings differ across populations and skeletal sites. However, it remains unclear whether sedentary time modifies the relationship between regional lean mass and BMD and whether such modification varies across sex and age. These relationships were explored using nationally representative U.S. data. Methods Adults aged 18-59 years from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed. Mean bilateral leg lean mass and mean bilateral leg BMD were derived from dual-energy X-ray absorptiometry measurements, and sedentary time was obtained from self-report. Survey-weighted regression models incorporated NHANES examination weights, strata, and primary sampling units. The primary model examined the four-way interaction among leg lean mass, sedentary time, sex, and continuous age, with adjustment for race/ethnicity and height. Quadratic lean-mass terms were examined in sensitivity analyses. Results The primary complete-case analysis included 2,543 adults (1,191 men and 1,352 women). Women showed a more apparent decline in both leg lean mass and leg BMD at ages 50-59 years, whereas sedentary time did not increase in parallel with these age-related changes. Leg lean mass was positively associated with leg BMD in both sexes, with modest nonlinearity, particularly among women. In the primary survey-weighted model, the four-way interaction among leg lean mass, sedentary time, sex, and age was positive (beta = 0.000111 per year, 95% CI 0.000039-0.000183; P = 0.0051), indicating increasing sex divergence in sedentary-time modification of the lean mass-BMD association with age. At younger ages, conditional interaction estimates were similar between sexes; at older ages, estimates became increasingly positive in women while remaining negative but imprecise in men. Quadratic sensitivity analyses attenuated the four-way interaction estimate by approximately 9-12% and reduced its statistical precision, while broadly preserving its direction and magnitude. Conclusions In this exploratory cross-sectional analysis, sedentary-time modification of the association between DXA-derived leg lean mass and leg BMD varied according to sex and age. The sex difference was small at younger ages and became progressively more apparent with increasing age. These findings suggest that behavioral and demographic context may contribute to variation in the muscle-bone relationship itself, although confirmation in independent and longitudinal datasets is needed.

Causal mediation analysis is widely used to identify biological pathways linking exposures to outcomes, but most methods assume homogeneous mediation effects across individuals. In high-dimensional omics settings, this assumption can mask important heterogeneity driven by demographic, genetic, or environmental factors. We propose the M-high-learner, a flexible framework for detecting heterogeneous mediation effects with high-dimensional mediators. The method identifies mediators with subgroup-specific indirect effects while distinguishing them from null or homogeneous signals and controlling the type I error rate. It is computationally efficient, scalable, and yields interpretable sub-types. Simulation studies show that the proposed approach achieves high power while maintaining accurate error control. Applications to the Framingham Heart Study and the Multi-Ethnic Study of Atherosclerosis reveal that the mediation role of gene expression in sexs effect on high-density lipoprotein varies across subgroups defined by body mass index and age. Our framework provides a practical tool for uncovering heterogeneous biological mechanisms in high-dimensional genomic studies. Author SummaryBiological processes linking risk factors to disease often differ across individuals, but many existing methods assume these processes are the same for everyone. This can hide important differences between groups. We developed a powerful method to identify when these pathways vary across subgroups using large-scale molecular data. Our approach detects differences in how intermediate biological factors contribute to outcomes in populations defined by characteristics such as age and body mass index. Applying our method to population studies, we found that some biological pathways operate differently across groups, suggesting that key mechanisms may be missed when differences are ignored. Our work provides a tool to better understand how disease-related processes vary across individuals, which may support more targeted and personalized approaches to health research.

MotivationIdentifying the mechanisms by which genetic variants affect the molecular response to an applied treatment is important across multiple biological fields, and an effective approach to this end is interaction molecular QTL mapping. However, the statistical models commonly used to detect such gene-by-treatment interactions (GxT) are non-trivially misspecified, and this can lead to decreased power. ResultsWe developed an R software package, DetectGxT, that uses nonlinear regression to more accurately model the relationship between the genotype and the transformed molecular count phenotypes. It also optionally models donor or polygenic random effects. Simulations show that nonlinear regression can increase the power to detect interactions. In existing interaction expression QTL mapping data from primary human neural progenitor cells, nonlinear and linear regression approaches identified overlapping but distinct sets of gene-SNP pairs with significant GxT interactions. Overall, our results suggest an advantage of nonlinear regression over linear regression in detecting GxT interactions on molecular phenotypes. AvailabilityThe DetectGxT software is available at https://github.com/yharigaya/detectgxt. Contactmilove@email.unc.edu, william.valdar@unc.edu

Observational studies have identified associations between circulating inflammatory markers (CIMs) and mammographic density (MD) phenotypes, but whether these associations reflect causal effects remains unclear. We utilized two-sample Mendelian randomization (MR) to assess relationships between 60 CIMs and MD phenotypes in individuals of European genetic ancestry by examining associations between genetically predicted CIM concentrations and three MD phenotypes (dense area, DA: N = 13,965; non-dense area, NDA: N = 14,036; percent density, PMD: N = 17,841). Primary analyses were conducted using the inverse variance weighted MR method. No CIM-MD phenotype associations reached statistical significance after correction for multiple testing. At a nominal significance level of p < 0.05, we observed suggestive evidence for eight associations represented by six unique CIMs (MD phenotype/direction of association): CCL23 (DA/+), CD40 (NDA/-), GDNF (DA/+ & PMD/+), MMP-10 (DA/+ & PMD/+), TRANCE (DA/-), and TWEAK (NDA/+). In multivariable MR analyses accounting for body mass index (BMI), we observed six nominal associations represented by six unique CIMs, although most instruments demonstrated weak conditional instrument strength after inclusion of BMI. Overall, our comprehensive analysis of 60 CIMs provides limited evidence supporting broad causal effects of circulating inflammatory markers contributing to variation in MD phenotypes.

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.