GWAS summary statistics

This study compares pooled analysis and meta-analysis for multi-ancestry GWAS, finding pooled analysis offers better statistical power while controlling population structure.

This paper provides guidance for reducing bias in case-control GWAS by addressing genetic relatedness, allelic interactions, and control genotype biases, introducing the AlleliC-GWAS tool.

This paper develops a semi-empirical Bayesian framework to calibrate weak instrumental bias in sex-specific Mendelian randomization studies, revealing significant sex differences in causal effects of sleep phenotypes on cardiovascular diseases.

This paper identifies genome-wide pleiotropic loci linking blood pressure and cognitive performance measures using longitudinal data from multiple cohorts.

A-LAVA identifies germline variants affecting metabolic pathways in cancer genomes through SNP, gene, and pathway-level analysis, improving accuracy by correcting for overlapping gene sets and reducing false positives.

MAESTRO identifies tandem paralog copy number variation as a key mechanism for stress adaptation in Pichia kudriavzevii, linking it to drug resistance and environmental tolerance.

This study finds that higher polygenic risk scores for breast cancer are associated with longer survival in patients, suggesting a link between genetic predisposition and less aggressive tumors.

The paper identifies the LRRK2 p.A419V variant as a significant risk factor for Parkinson's disease in East Asian populations and explores its association with age of onset.

This paper introduces localGEBV, a haplotype-based method that improves QTL discovery and phenotypic prediction compared to traditional GWAS approaches in barley.

The paper analyzes four immunity genes associated with severe COVID-19 and other immune-related diseases, identifying SNPs under positive selection and their association with disease severity.

IDEAL-GENOM streamlines GWAS analysis by integrating tools like PLINK and GCTA into a Python-based framework for reproducible and customizable genomic analysis.

The paper analyzes UK Biobank data to assess the association of APC I1307K variant with colorectal cancer risk in Ashkenazi and non-Ashkenazi whites, finding no significant association and highlighting the need for larger studies.

This paper improves polygenic risk scores for chronic back pain by combining multiple traits, showing better performance in clinical samples.

This study identifies 40 novel susceptibility loci for psoriatic arthritis through GWAS and fine-mapping, including new therapeutic targets and disease-relevant cell types.

The paper integrates genetic, transcriptomic, and structural data to show that Ca2+ dysregulation via ATP2B2 missense variants contributes to neuropsychiatric disorders.

SAGA is a user-friendly, automated pipeline that simplifies GWAS by integrating PLINK, GMMAT, and SAIGE, enabling non-experts to perform reproducible analyses.

This paper introduces sfFDR to enhance variant discovery by integrating related trait summary statistics, increasing power and discovering additional SNPs in GWAS.

This study identifies 21 novel genetic loci associated with keloids through a multi-ancestry GWAS, highlighting integrin signaling and fibrosis pathways.

This study identifies genetic variants associated with antipsychotic-induced lipid and BMI changes in schizophrenia patients using GWAS, offering potential for personalized treatment strategies.

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.

This study uses a multivariate GWAS approach to identify genetic loci associated with reading ability and dyslexia, finding 35 new genome-wide significant loci and estimating SNP heritability at 24%.

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 paper identifies and validates novel combinatorial genetic risk factors for endometriosis in UK and US cohorts, showing high reproducibility across diverse populations.

This study performs a genome-wide association study of T1D stratified by HLA DR3 and DR4 status, revealing genetic and biological heterogeneity in T1D progression.

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.

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

This paper identifies and validates 5q31.1 as a neuroblastoma susceptibility locus in Chinese children through GWAS and functional studies.

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.