fine-mapping

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 fine-maps GWAS risk variants for PDAC and identifies candidate target genes through chromatin conformation capture, finding functional interactions and eQTLs at specific loci.

The paper quantifies the contribution of structural variants to complex trait heritability using MiXeRSV, finding SVs explain up to 32% of heritability in some traits.

This study shows S2F models underestimate causal variant effects, limiting their utility for fine-mapping, but top predictions are reliable.

This paper introduces CT-FM and CT-FM-SNP methods to identify causal cell types and variants for diseases, finding most SNPs affect traits via a single cell type set.

The paper introduces CTACIT to fine-map neuropsychiatric regulatory variants by integrating conservation scores with cell type-specific open chromatin data across species.

This paper applies FINEMAP and SuSiE to fine-map schizophrenia risk loci, identifying new genes with high concordance between methods and validating them using LoFI and rare variant enrichment.

HiFiMAP provides high-resolution IBD mapping at SNP level, outperforming window-based methods and detecting novel associations in UK Biobank and brain imaging data.

The paper introduces LOCATER, a genealogy-based method that improves trait association power by leveraging haplotype diversity and allelic heterogeneity, identifying novel associations and recovering known loci in a Finnish cohort.

This paper evaluates Evo 2's ability to predict variant effects in plant genes using Arabidopsis thaliana data, showing it can identify functionally impactful variants.

The paper identifies functional variants and VNTRs at the 5p15.33 locus using fine-mapping, MPRA, and CRISPRi screens, revealing their role in cancer susceptibility through antagonistic pleiotropy.

The paper develops a Flex-Seq SNP panel for red raspberry and validates it through linkage maps and QTL identification for agronomic traits.

OmiGA improves molQTL mapping in complex populations by using an efficient linear mixed model, enhancing power and reducing false positives.

This study evaluates sequence-to-omics models to improve reproducibility in genetic fine-mapping across eQTL datasets, showing reduced replication failure rates and enhanced SNP prioritization.

The paper identifies a significant underrepresentation of South Asian ancestry breast cancer risk variants in ClinVar, highlighting a knowledge gap for this population.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data by allowing causal effect sizes to vary across ancestries, achieving higher power and lower computational costs.

SuSiE 2.0 improves genetic fine-mapping and phenotype prediction with faster performance, better calibration, and new methods like SuSiE-ash.

The paper functionally measures allele-specific regulatory element activity across five human genomes to fine map causal variants in eQTLs and chromatin QTLs.

SAFE-LD estimates linkage disequilibrium from GWAS summary statistics using pseudo-genotypes that preserve in-sample LD while protecting privacy.

This paper introduces GPN-Star, a phylogeny-informed genomic language model that improves variant effect prediction across species and evolutionary timescales.

The paper identifies rare pathogenic variants with higher carrier rates in the Beauce region of Quebec due to a regional founder effect, highlighting the need for fine-scale population structure studies.

Sniff improves causal variant prioritization in GWAS by integrating Borzoi-predicted variant effects with functional annotations, identifying more fine-mapped variants with higher posterior inclusion probabilities.

DNACipher predicts variant effects across cell types and assays, identifying imVars at most GWAS loci with support from experimental data.

The paper applies the MR-Horse method to reduce selection bias in GWAS of disease progression by accounting for genetic correlation and pleiotropy, showing reduced bias and controlled type 1 error rates.

This study identifies the genetic basis of silver iridescence polymorphism in Mormon Fritillary butterflies, linking it to the optix gene and showing evidence of selection and introgression.

The paper conducts a GWAS in European beech to identify genetic markers associated with drought stress traits, finding significant SNPs linked to stomatal density and water use efficiency.

The paper fine maps CAD loci in UK Biobank whole genome sequencing data using PolyFun and ML tools, showing improved sensitivity over traditional methods and demonstrating cost and speed benefits of cloud-based analysis.

ESM-Effect is a fine-tuning framework for predicting mutation functional effects, achieving state-of-the-art performance and faster training than multi-modal methods.

This paper improves fine-mapping by estimating effect sizes and regional heritability using FINEMAP, showing it captures more heritability than BOLT and provides better precision.

The paper uses ENCODE-derived regulatory elements to analyze rare variants in WGS data, identifying 1,987 significant associations across 100 traits with high replication rates.