GWAS summary statistics

This study shows that excluding low-frequency variants and indels in fine-mapping leads to false discoveries and introduces SuSiE-OUT to address this issue.

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

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.

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

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

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

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.

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

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

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.

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.

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.

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

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

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.

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

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.

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

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.

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

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.

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.

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

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.

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.

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

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.

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