Niall J. Lennon

The paper reports on the All of Us multi-omics cohort, identifying 182,246 QTLs and demonstrating rare variant effects across proteomic and transcriptomic data.

The Rare Genomes Project provides remote genomic sequencing to rare disease families, achieving a 23.3% diagnostic solve rate and highlighting the role of research in overcoming clinical diagnostic barriers.

Himito is a graph-based toolkit for analyzing mitochondrial genomes using long reads, achieving superior performance in assembly and variant calling and revealing age-related patterns in the All of Us dataset.

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%.

This paper introduces a high-throughput single-cell RNA isoform sequencing method using sequencing-by-expansion (SBX) and the CUDLL algorithm, achieving high accuracy and enabling isoform-level analysis across cell types.

This study evaluates the diagnostic yield of short-read and long-read sequencing in rare diseases, finding limited added value from long-read sequencing despite increased variant discovery.

The paper analyzes long-read sequencing data from the All of Us Research Program to identify structural variants and their associations with diseases in a large cohort of Black or African American participants.

This study shows that whole-genome sequencing can replace chromosomal microarray and Fragile X testing with higher accuracy and broader coverage for genetic disorders.

Celeste provides a scalable cloud infrastructure for population-scale sequencing projects, including a variant-calling pipeline optimized for clinical variant detection.

The paper uses long-read sequencing data to analyze STR variability across 2,645 individuals, identifying disease-associated STR loci with elevated allelic variability and introducing the PLVI metric.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

blend-seq improves variant discovery by combining short and low-coverage long reads, enhancing SNP detection and structural variant recall while maintaining precision.

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.

The paper identifies a common flanking variant linked to increased meiotic stability at the FGF14-SCA27B repeat locus.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

Open flow cell on a circular wafer with mostly natural nucleotides and no reversible terminators: about 300 bp reads at $1/Gb, 99.6% SNP accuracy on reference human genomes.

This study uses EHR data to identify gene-phenotype associations and develop phenotype risk scores for variant interpretation in hereditary cancer genes.

The paper describes the process of obtaining an FDA IDE for returning genomic results via WGS in the All of Us Research Program and presents validation results.

The paper validates genetic sex using a 96-SNP PCR panel to track samples in clinical testing and identify inconsistencies.

The paper designs and tests a polygenic score report for coronary artery disease, identifying user preferences for visual elements, risk communication, and personalized resources.

The paper characterizes index swapping on Illumina sequencers and proposes dual indexing to mitigate its effects, showing varying swap rates across methods and sequencers.

The paper uses pseudotemporal ordering to analyze single-cell data and reveals dynamics and regulators of cell fate decisions.