GWAS summary statistics

Leakage-Controlled Benchmarking of Variant Effect Predictors and Development of Gradient-Boosting Consensus Model

The paper benchmarks variant effect predictors using a leakage-controlled dataset and develops a gradient-boosting consensus model that outperforms existing tools.

MutQA: A Cross-Validated Q&A Dataset for Genetic Mutations

The paper introduces MutQA, a cross-validated Q&A dataset linking genetic mutations to their functional consequences, and demonstrates its utility in improving LLM performance.

Scaling genetic discovery through automated post-GWAS interpretation

This paper introduces PGI, a system that automates post-GWAS interpretation, achieving high recovery of findings and enabling cross-study biological evidence accumulation.

Variant effect predictors are shaping the ClinVar classifications used to train and evaluate them

The paper shows that variant effect predictors influence ClinVar classifications, creating circularity in variant classification and prediction.

A Pipeline for Predicting Variant Associations with Complex Disorders via Functional Annotations

The paper introduces a pipeline to create curated, balanced variant-trait association datasets for complex disorders using GWAS and functional annotations.

Insomnia and the genetic overlap of sleep with depression and anxiety: a genomic structural equation modelling study

This study uses genomic structural equation modelling to assess the genetic overlap between insomnia, depression, and anxiety, finding insomnia is the best-supported location of the genetic overlap.

S4-Oligo: optimizing polygenic score methods for clinical deployment

This paper introduces S4-Oligo, an optimized polygenic score method for clinical use, which matches or exceeds existing models' accuracy while using fewer SNPs.

Polygenic prediction of treatment response for precision medicine is vulnerable to collider bias

This paper shows that polygenic risk scores for CAD can create false treatment response patterns due to collider bias, not actual biological effects.

Disentangling phenotype scale, amplification, and effect heterogeneity underlying polygenic GxE architecture of complex traits

This paper introduces REGENA, a method to disentangle gene-environment interaction mechanisms, finding that half of significant GxE signals are scale-dependent and effect heterogeneity drives most remaining GxE heritability.

Accounting for gene action improves the accuracy of polygenic scores for some complex traits

This study introduces PGSa,d, a polygenic scoring framework that incorporates both additive and dominance effects, showing improved accuracy for traits with significant dominance contributions.

Understanding genetic and environmental complexity in phenotypic resemblance across relatedness in population-scale data

The paper evaluates non-linearities in phenotypic similarity versus genetic relatedness by analyzing three processes: imperfect tagging, assortative mating, and shared environments, using UK Biobank and All of Us data.

Sample overlap in genome-wide association meta-analysis: practical comparisons across significance thresholds

The paper evaluates the impact of sample overlap in GWAS meta-analysis across different significance thresholds and shows minimal impact when overlap is small.

An integrated data exploration engine of knowledge, expression and genetic association for human transposable elements

TE-Dee integrates TE knowledge, expression, and genetic associations to provide a structured resource for TE biology and AI model training.

CaPri: Bayesian Mendelian randomization for joint estimation of causal and pleiotropic effects

CaPri is a Bayesian Mendelian randomization framework that jointly estimates causal and pleiotropic effects, showing robust performance across varying pleiotropy proportions and real data applications.

Quantifying the genetic separability of disease subtypes

This paper introduces GenSep, a framework to quantify genetic separability of disease subtypes, showing current PRSs fall short of theoretical limits.

The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations

This study evaluates polygenic score prediction accuracy and cross-ancestry portability across 3,994 PGSs in diverse populations, identifying factors affecting their performance and equity.

Significance filtering induces denominator selection bias in local genetic correlation: closed-form characterisation, a gate-aware correction, and an applicability diagnostic

The paper identifies a bias in local genetic correlation estimation due to significance filtering and proposes a correction method with a diagnostic tool.

Phenotypes of ultra-rare variant carriers benchmark variant effect scores

The paper benchmarks variant effect scores using UK Biobank data on ultra-rare variant carriers, showing coding and splicing scores better predict protein abundance and traits than transcriptional regulation models.

Leveraging Shared Genetic Architecture Between Psychiatric and Other Medical Disorders Increases Power and Reveals Novel Psychiatric Risk Loci

The paper identifies shared genetic architectures between psychiatric and non-psychiatric disorders, revealing novel psychiatric risk loci through cross-trait analyses.

snpXplorer: haplotype-block representation of GWAS signals for integrated variant and cross-trait interpretation

Interpreting Convolutional Neural Networks in Population Genetics

This study investigates how convolutional neural networks in population genetics implicitly compute summary statistics and reveals their ability to efficiently approximate long-range linkage disequilibrium.

Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS): A Community Framework for Effector Gene Reporting

PEGASUS defines a community reporting standard for GWAS predicted effector genes: a metadata record, a full locus-by-gene evidence matrix, and a derived author-prioritised gene list.

Sum-h2, enabling genetic discovery for deep learning-derived phenotypes through a fast evaluation framework and arena of performance

The paper introduces sum-h2, a fast framework for evaluating genetic discovery from deep learning-derived phenotypes using heritability measures, and establishes a Genetic Discovery Arena for benchmarking.

MaveMD: A functional data resource for genomic medicine

MaveMD provides a clinical interface for MAVE data to classify variants of uncertain significance and improve genetic medicine outcomes.

Evaluating Diabetes Drug Targets for Vascular Dementia Prevention Using Drug Target Mendelian Randomisation

This study uses Mendelian randomisation to assess if diabetes drug targets affect vascular dementia risk, finding no strong evidence but a suggestive protective effect for DPP4 inhibition.

Large language model-based bibliometric evaluation of population descriptors in human genetics

This study evaluates adherence to NASEM recommendations on population descriptors in GWAS papers using a large language model-based toolkit, finding improvements over time but limited impact post-report publication.

Adult Patient Perspectives on Psychiatric Polygenic Risk Scores

This study explores adult perspectives on psychiatric polygenic risk scores through interviews, highlighting both potential benefits and concerns about their use.

From Language-Cognition Coupling to AI Foundations: An Evolutionary Perspective on 13,415 SNVs in 33 Language/Cognition Genes

This paper analyzes 13,415 SNVs in 33 language/cognition genes across species to identify evolutionary breakpoints and assess gene enrichment, finding language and cognition genes are not separable modules but part of an integrated system.

Learning Environment-Associated Marker Rankings with Attention-Based Graph Neural Networks

This paper introduces a graph neural network to estimate environment-associated marker rankings from crop trial data and evaluates its performance on maize datasets.

Comparing Cardiac Genetic Testing Pathways: Impacts on Access, Informed Choice, and Decisional Satisfaction

This study compares two genetic testing pathways for cardiac conditions, finding higher uptake and faster access in the mainstreaming model but lower informed decision rates compared to a genetics referral model.