Genetic Associations and Epidemiology

This paper estimates gene conversion rates using multi-individual identity-by-descent inference and finds higher correlation with PRDM9 binding enrichment than crossover maps.

The paper introduces the Causal Pivot (CP) method to detect causal genetic variants in complex diseases using structural causal models and applies it to UK Biobank data for three diseases.

fSuSiE improves fine-mapping of QTLs by modeling spatial genetic effects on molecular traits, achieving higher resolution in identifying causal variants and their molecular targets.

This study uses genome-wide meta-regression to analyze social behavior across different contexts, ages, and reporters, identifying six genetic loci and showing modest heritability.

This study models the genomic architecture of adiposity and anthropometrics across the lifespan, identifying shared and distinct genetic factors and their associations with health outcomes.

The paper uses nanoparticle enriched mass spectrometry proteomics in British South Asians to identify novel variant-protein-disease mechanisms through integrative analysis of exome sequencing and proteomic data.

LDAK-KVIK is a mixed-model association analysis tool that outperforms existing methods in power and efficiency for quantitative and binary phenotypes.

This study identifies 38 COPD GWAS loci with evidence of sQTLs, showing that sQTL analysis in whole blood and lung tissue provides novel insights into COPD mechanisms.

This study identifies shared genetic loci between psychiatric disorders and cortical brain structure using genome-wide association data from over 700,000 individuals.

The paper improves brain age estimation using an adversarial convolution network and identifies BAG-associated genes and regulatory networks linked to brain disorders.

This study introduces VAE-PRS, a deep learning model that improves polygenic prediction for blood cell traits by capturing interaction effects and outperforming traditional methods in 14 out of 16 traits.

The paper introduces LOCATER, a method to test genealogical trees for association with traits, showing improved power over traditional methods in detecting allelic heterogeneity.

This study identifies genetic risk mechanisms for six cancers by analyzing isoform-level expression data, finding more associations and greater heritability explained compared to gene-level analysis.

This paper introduces a computational pipeline for genotyping TOMM40 poly-T polymorphisms from whole-genome sequencing data, achieving high accuracy and consistency with PCR-based genotypes.

This paper develops and validates polygenic scores for within-family disease risk prediction, showing strong performance across ancestries and demonstrating their clinical utility in embryo screening.

The paper infers parent-of-origin effects in ~265,000 individuals using a novel method, identifying over 30 novel POEs and confirming known associations, supporting the parental conflict hypothesis.

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

This study identifies genetic variants associated with cervical spinal cord morphology and links them to neurological and systemic diseases.

The paper performs metabolomics exome and genome-wide association studies in Amish adults, identifying genetic variants linked to metabolites and cardiometabolic traits.

The paper presents a pipeline for analyzing image-based complex traits using AI phenotyping, combined-GWAS, and federated learning, applied to human facial data to identify genetic loci associated with facial shape.

This study identifies plasma proteins associated with insulin sensitivity across diverse populations and shows their response to interventions, with some proteins suggesting causal relationships and therapeutic targets.

This study evaluates SNP imputation accuracy for forensic trait prediction, finding that SNP selection, MAF, and reference panel quality impact imputation performance and trait prediction accuracy.

This paper identifies childhood phenotypes associated with genetic risk for elevated CRP using a PheWAS in the ABCD Study, finding associations with weight, eating, and screen time that are largely independent of BMI.

The paper develops an imputation panel integrating structural variants for Alzheimer's disease and related dementias, improving genotype imputation and identifying new genetic associations.

The paper develops sex-specific transcriptome-based polygenic risk scores for depression and evaluates their associations with brain structure and symptoms in adolescents.

This paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

The paper analyzes SNP heritability across eQTL networks to show heritability clusters in tissue-specific modules and local hubs, which influence complex trait evolution.

CREST-GV prioritizes disease-relevant cell types by analyzing genetic variants intersecting open chromatin regions across hundreds of cell types.

The paper introduces SME, a fast method for detecting genome-wide epistasis in large biobank studies by focusing on functionally relevant genomic regions.

FLOW-MR estimates causal effects of time-varying risk factors using GWAS summary statistics, revealing BMI's childhood protective effect on breast cancer and evolving impacts on stroke risk.