Benjamin M. Neale

This study identifies ultra-rare damaging variants in schizophrenia risk genes and shows they are associated with earlier hospitalizations, greater childhood adversity, and poorer neuropsychological outcomes across psychotic disorders.

The paper conducts a genome-wide association study in a Chilean admixed population to identify genetic loci associated with COVID-19 severity and susceptibility, finding four new loci.

The paper identifies a founder variant in ADAP1 associated with increased risk of schizophrenia and bipolar disorder in a Colombian genetic isolate.

The paper introduces CASTIE, a method for context-dependent eQTL mapping in single-cell data, identifying regulatory variants missed by static models and linking them to disease traits.

ALLSPICE identifies heterogeneous rare variant effects across traits in gene-trait associations using summary statistics from the UK Biobank.

The paper presents a large reference panel of 515,579 genomes to improve rare-variant imputation across underrepresented populations, enhancing accuracy for variants with frequencies as low as 0.002%.

The paper identifies 253 autism-associated genes using rare variation from 62,429 individuals and finds pleiotropy across physiological systems and developmental disorders.

The paper maps context-dependent genetic regulation of cardiometabolic loci in human adipocytes using a multi-donor cell village system and single-nucleus multiome sequencing under various metabolic stress conditions.

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

This study analyzes 173 multiplex Portuguese families to identify rare genetic variants associated with schizophrenia, mood disorders, and autism, finding a CHD2 mutation linked to diverse mental illness phenotypes.

This paper introduces ReCLIP, a transformer-based framework that models protein-protein interactions at the residue level, showing high accuracy in predicting mutation effects and peptide-MHC binding.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

The paper performs large-scale GWAS and RVAS on 392,030 whole genomes from All of Us, identifying 49,863 associations and 193 novel pLoF gene-phenotype links through meta-analysis with UK Biobank.

The paper shows that European GWAS findings for serious mental illness and cognition are portable to an admixed Latin American population, associating genetic risk scores with multiple symptoms and cognitive traits.

This paper constructs phenome-derived polygenic scores and evaluates how they, along with social determinants of health, influence disease risk across different populations and diseases.

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

The paper investigates the mechanism of age-related mtDNA mutation accumulation in human blood using whole genome sequences, revealing a two-step process involving cryptic mutations and clonal expansion.

The paper identifies five genomic factors explaining most genetic variance across 14 psychiatric disorders and finds shared and disorder-specific genetic signals with distinct biological functions.

This paper introduces SVCR, a scalable variant call format that reduces file size and enables efficient analysis of large genetic datasets.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

The paper identifies non-linear genetic regulation of plasma proteins using a deep learning method, finding dominance and epistasis effects in large cohorts.

The paper identifies compound heterozygous variants in UK Biobank data and shows their phenotypic impact on common diseases using statistical phasing and mixed models.

The paper analyzes GWAS data for 27 traits to test evolutionary models, finding most traits are consistent with stabilizing selection rather than neutral evolution or directional selection.

This paper introduces GISMO and GISMO-mis metrics to quantify gene loss and missense variation across mammals, identifying essential genes and improving gene-disease associations.

SAIGE-QTL is a tool for single-cell eQTL analysis that directly maps eQTLs without pseudobulk aggregation and improves power for detecting rare variants.

This paper introduces GUIDE, a method to identify latent genetic factors from GWAS data, revealing biological pathways and environmental influences in complex traits.

The paper harmonizes diverse human genomes from multiple datasets to create a new reference panel with improved variant coverage and population structure analysis.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.