Genetic Epidemiology最新文献

Harmonizing osteoporosis-related data across multiple data sets is essential for improving the accuracy and generalizability of bone mineral density (BMD) assessments. This study developed a harmonization framework to standardize phenotypic and genomic variables across three major US osteoporosis data sets: GDBF, GWAS, and NHANES. We standardized key phenotypic variables (BMD, body mass index (BMI), age, sex, and race/ethnicity) using cohort-specific data dictionaries and applied multiple imputations by chained equations (MICEs) to manage missing data. Genomic data were harmonized using principal component analysis (PCA)-based batch effect corrections. Residual regression methods were applied to standardize BMD values. The effectiveness of harmonization on BMD prediction was evaluated using generalized estimating equations (GEEs) and mixed-effects models. Post-harmonization, inter-study variability in BMI was significantly reduced (Ω² = 0.0028), and BMD associations with covariates remained consistent across data sets. Harmonized models showed improved predictive performance, with explained variance in BMD increasing (R² = 0.14%). PCA confirmed the effective alignment of genetic data, reducing batch effects and improving cross-study compatibility. This study demonstrates the feasibility and effectiveness of harmonizing phenotypic and genomic data for osteoporosis research. The harmonization framework enhances BMD prediction accuracy, supports more inclusive osteoporosis risk assessment, and improves the integration of multi-cohort data sets for future research. These findings highlight the potential of data harmonization in advancing precision medicine for osteoporosis prevention and management.

A longstanding aim of developmental psychology and epidemiology is to understand the causal effects of parental phenotypes on offspring outcomes. Traditional approaches often fail to account for confounding and reverse causation. We evaluate the use of Mendelian randomisation with non-inherited variants (MR-NIV) to address these limitations. MR-NIV leverages non-inherited genetic variants to instrument the parental phenotype independent of the offspring's genotype. We used Directed Acyclic Graphs and simulations to validate MR-NIV and explore robustness to assortative mating. In contrast to an alternative MR method which adjusts the parental genotype for offspring genotype, MR-NIV can be robust to assortative mating when used without trio data. In settings without trio data, MR-NIV outperformed the adjustment method. The adjustment method outperformed MR-NIV in settings with trio data. Applying MR-NIV to the Avon Longitudinal Study of Parents and Children, we assessed the causal effect of parental smoking on offspring smoking initiation at age 16. Results were consistent with observational studies, suggesting a meaningful increase in the risk of offspring smoking due to parental smoking. However, larger sample sizes will be necessary to provide a precise answer. MR-NIV offers a promising extension of Mendelian randomisation for studying the developmental environment.

Accurate prediction of disease risk and other complex traits across different populations is essential for clinical and research purposes. However, genetic differences among ancestries, such as allelic frequencies and genetic architecture, can affect the performance of polygenic risk score (PGS) methods in cross-ancestry prediction. To address this issue, we conducted a formal test of seven polygenic prediction methods applicable across ancestries for five traits (BMI, standing height, LDL-, HDL- and total-cholesterol) from the UK Biobank dataset. We demonstrate that, GBLUP and PRS-CSx outperformed other methods for highly polygenic traits like height and BMI. In contrast, PRSice and PolyPred performed best for less polygenic traits like cholesterol, with PRS-CSx being comparable with larger sample sizes. We also observed that utilizing concordant SNPs, which have the same effect direction across diverse ancestries, can improve the accuracy of cross-ancestry PGS models. Furthermore, we found that the transferability of PGS across ancestries varied depending on the trait. Understanding the strengths and limitations of different methods and approaches is important for future methodological development and improvement, enabling better interpretation and application of PGS results in clinical and research settings.

Mediation analysis is a pivotal tool for elucidating the indirect effect of an environmental factor or treatment on disease through potentially high-dimensional omics data, such as gene expression profiles. However, traditional mediation analysis methods tailored for binary outcomes often rely on the rare disease assumption in logistic regression and provide inadequate measures of total mediation effect when multiple mediators have effects in different directions. In this paper, we develop a MEdiation analysis framework in LOgistic regression for high-Dimensional mediators and a binarY outcome (MELODY). It leverages a second-moment-based measure analogous to the $��\; ��R^2��$ for linear models to quantify the total mediation effect. We also develop a variable selection procedure for high-dimensional data to reduce bias introduced by non-mediators. Our comprehensive simulations demonstrate the superior performance of MELODY in scenarios with non-rare disease binary outcomes and high-dimensional mediators. We apply MELODY to the Framingham Heart Study of over 5000 individuals to analyze the mediation effects of metabolomics and transcriptomics data on the pathways from sex to incident coronary heart disease.

Genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with complex traits and diseases, including Alzheimer's disease (AD). However, traditional GWAS approaches often focus on European populations, which may lead to loss of power and limit the generalizability of findings across diverse ancestries. On the other hand, LS-Imputation, a nonparametric trait imputation method, leverages GWAS summary statistics and genotype data to impute missing traits, which can then be used for GWAS and other downstream analyses. Although LS-Imputation has been applied successfully to European populations, its performance in non-European populations would be hindered by smaller sample sizes, leading to reduced imputation accuracy. To address these limitations, we propose two novel variants of LS-Imputation-LS-Imputation-Combined and LS-Imputation-Transfer—designed to integrate multi-ancestry GWAS data and enhance imputation performance. LS-Imputation-Combined optimally combines GWAS summary statistics from multiple ancestries, while LS-Imputation-Transfer sequentially refines imputed trait values across ancestries using stochastic gradient descent. We evaluate these methods using data from the UK Biobank and the Alzheimer's Disease Sequencing Project (ADSP), first applying them to high-density lipoprotein (HDL) cholesterol levels as a proof-of-concept before focusing on imputing AD status in Black individuals for genetic association analysis. Our results demonstrate that integrating multi-ancestry GWAS data improves trait imputation accuracy, with LS-Imputation-Transfer achieving the highest performance.

The process by which genes are transmitted from parent to child provides a source of randomization preceding all other factors that may causally influence any particular child phenotype. Because of this, it is natural to consider genetic transmission as a source of experimental randomization. In this work, we show how parent–child trio data can be leveraged to identify causal genetic loci by modeling the randomization during genetic transmission. We develop a new test, the transmission mean test (TMT), together with its unbiased estimator of the average causal effect, and derive its causal properties within the potential outcomes framework. We also prove that the transmission disequilibrium test (TDT) is a test of causality as a complementary case of the TMT for the affected-only design. The TMT and the TDT differ in the types of traits that they can handle and the study designs for which they are appropriate. The TMT handles arbitrarily distributed traits and is appropriate when trios are randomly sampled; the TDT handles dichotomous traits and is appropriate when sampling is based on a child's trait status. We compare the transmission-based methods with established approaches for genotype–phenotype analyses to clarify conditions appropriate for each method, what conclusions can be drawn by each one, and how these methods can be used together.

Variant set association tests (VSAT), especially those incorporating rare variants via variant collapse, are invaluable in genetic studies. However, unlike Manhattan plots for single-variant tests, VSAT statistics lack intrinsic genomic coordinates, hindering visual interpretation. To overcome this, we developed the Archipelago method, which assigns a meaningful genomic coordinate to VSAT P values so that both set-level and individual variant associations can be visualised together. This results in an intuitive and information rich illustration akin to an Archipelago of clustered islands, enhancing the understanding of both collective and individual impacts of variants. We conducted three validation studies spanning simulated and real datasets across small and biobank-scale cohorts, from 504 individuals up to 490,640 UK Biobank participants. We integrated single-variant genome-wide association studies (GWAS) with gene- and protein pathway-level rare-variant collapse. These studies included the 1KG GWAS cohort, the Pan-UK Biobank GWAS with DeepRVAT WES gene-level study, and the UKBB WGS gene-level UTR collapsing PheWAS. The Archipelago plot is applicable in any genetic association study that uses variant collapse to evaluate both individual variants and variant sets, and its customisability facilitates clear communication of complex genetic data. By integrating at least two dimensions of genetic data into a single visualisation, VSAT results can be easily read and aid in identification of potential causal variants in variant sets such as protein pathways.

Rare variant association analysis, which assesses the aggregate effect of rare damaging variants within a gene, is a powerful strategy for advancing knowledge of human biology. Numerous models have been proposed to identify damaging coding variants, with the most recent ones employing deep learning and large language models (LLMs) to predict the impact of changes in coding sequences. Here, we use newly available proteomics data on 2898 proteins across 46,665 individuals to evaluate and refine LLM predictors of damaging variants. Using one of these refined models, we evaluate the association between rare damaging variants and human phenotypes at 241 positive control gene-trait pairs. Among these gene-trait pairs, our proteomics-guided model outperforms an ensemble of conventional approaches including PolyPhen2, MutationTaster, SIFT, and LRT, as well as newer machine learning approaches for identifying damaging missense variants, such as CADD, ESM-1v, ESM-1b, and AlphaMissense. When attempting to recover known associations by correctly separating damaging singleton missense variants from other singleton variants, our approach recapitulates 36.5% of gene-trait pairs with known associations, exceeding all the alternatives we considered. Furthermore, when we apply our model to 10 example traits from the UK Biobank, we identify 177 gene–trait associations—again exceeding all other approaches. Our results demonstrate that summary statistics from large-scale human proteomics data enable evaluation and refinement of coding variant classification LLMs, improving discovery potential in human genetic studies.