Susan S Redline

ScreenDMT identifies metabolites consistently linked to sleep disorders across three human cohorts, revealing replicable associations with insomnia, sleep duration, and daytime sleepiness.

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

This study identifies metabolites linked to insomnia symptoms and develops risk scores predicting cognitive decline in Hispanic/Latino adults.

cellSTAAR integrates single-cell ATAC-seq with whole-genome sequencing to enhance power and interpretation of rare variant association tests in non-coding regions.

This study identifies genetic associations for multiple sleep disorders and medications in over 1.6 million individuals, revealing shared and unique genetic factors across sleep traits.

The paper analyzes sleep data from 1527 pediatric PSGs across five sites to study neurodevelopmental disorders, identifying sleep architecture disruptions in Down Syndrome and developing a sleep stager and brain age prediction model.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

This study performs genome-wide association analysis on composite sleep health scores, identifying 28 novel loci and confirming 341 previously reported loci, with insights into genetic mechanisms and pathways.

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

This study identifies gene-environment interactions between excessive daytime sleepiness and genetic variants linked to obstructive sleep apnea severity, revealing novel therapeutic targets.

The paper estimates mCA fitness in the NHLBI TOPMed cohort and identifies genetic variants associated with clonal expansion rates.

The paper identifies 83 rare lncRNA variant sets associated with blood lipid levels using WGS data from the TOPMed program and validates some in UK Biobank data.

The paper predicts cognitive performance from sleep EEG features in older men, finding that certain EEG patterns are associated with cognitive ability, though this association weakens after adjusting for demographic and health factors.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

The paper identifies common genetic variants for sleep duration across multiple ancestries and finds shared genetic effects in diverse populations.

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

This paper uses pleiotropy to discover new genetic loci for OSA and provide biological hypotheses by combining colocalization tests with pleiotropy analysis.

The paper develops a sleep-based metric to quantify brain maturation using EEG data from children, showing high accuracy in predicting chronological age and detecting neurodevelopmental differences.

This study uses whole-genome sequencing to analyze mosaic chromosomal alterations across diverse ancestries, finding higher rates in certain populations and associations with germline variants.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper performs whole genome sequence analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

This study examines the impact of Mendelian sleep and circadian genetic variants in population cohorts and finds no strong associations with extreme sleep traits, though some variants show minor effects.

The paper replicates and extends findings on EEG spectral slope variation across sleep states in a large sample, identifying factors influencing slope estimates and their relationship to sleep parameters.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

This paper uses gradient boosted trees to improve polygenic risk prediction by capturing non-linear genetic effects and SNP interactions, showing significant increases in variance explained for multiple complex traits.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper integrates multi-omics data to analyze how sleep-disordered breathing traits affect gene expression, metabolites, and potential causal pathways across blood cell types.