Stephen S. Rich

The paper introduces the Proteomic Sex Index (PSI), a continuous measure of plasma proteome alignment with chromosomal sex, showing its association with health and disease traits.

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

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

The paper examines short-term, long-term, and genetic factors influencing human plasma proteome variability using longitudinal cohort data.

The paper identifies genetic drivers of protein change over time using longitudinal proteomics data from MESA, UK Biobank, and CARDIA, finding that baseline adjustment improves replication but may introduce biases.

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

The paper analyzes expression and splicing quantitative trait loci (e/sQTL) across multiple tissues in the TOPMed dataset and finds that secondary e/sQTL signals are still discoverable, with many GWAS signals colocalizing with these e/sQTL.

This paper develops a machine learning framework to impute cross-platform proteomic data, improving replication of epidemiological findings and protein quality scoring.

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 investigates the association between mosaic loss of Y chromosome (mLOY) and lung function, emphysema, and epigenetic aging in men across multiple cohorts.

The paper integrates genomics and transcriptomics data from human heart samples to identify regulatory mechanisms linking genetic variation to dilated cardiomyopathy.

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

The paper identifies plasma proteins associated with clonal hematopoiesis and links them to cardiovascular disease through proteomic and genetic analyses.

The paper introduces SECRET, a method to develop polygenic scores for traits with limited data, showing improved prediction power over existing methods.

This paper develops ancestry-specific and combined HLA-focused genetic risk scores for type 1 diabetes and evaluates their performance across diverse populations.

The paper analyzes circulating metabolites using genome-wide association data from the TOPMed program, identifying 1,778 loci and highlighting sex-specific genetic contributions.

This study identifies novel genetic signals associated with severe obesity through GWAS and shows strong genetic overlap with BMI, highlighting shared comorbidities and lifestyle interactions.

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 identifies FNDC1 as a key regulator of vascular calcification through the FNDC1-NAMPT-NAD axis, showing its role in both microvascular and macrovascular diseases and its potential as a therapeutic target.

This study uses whole-genome sequence data from diverse participants to identify novel genetic loci associated with brain structural volumes, highlighting the importance of diverse cohorts in uncovering genetic architecture.

This study shows that deleterious mitochondrial DNA mutations increase over time and are associated with higher mortality risk, suggesting positive selection rather than genetic drift.

This paper develops a method to improve Mendelian randomization analysis by integrating GWAS results across ancestries, enhancing accuracy and power in discovering causal genes for autoimmune diseases.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

The paper identifies genetic drivers and clinical consequences of mosaic chromosomal alterations in 1 million individuals, linking specific aut-mCAs to disease risk and therapeutic targets.

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 conducts an epigenome-wide association study (EWAS) meta-analysis of BMI in African American adults, identifying 208 significant CpGs and 54 DMRs associated with BMI, including novel and replicated findings.

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

The paper refines a gene-physical activity interaction affecting HDL-cholesterol by analyzing lipoprotein subfractions and exploring sex differences across multiple cohorts.

The paper conducts proteome-wide and transcriptome-wide association studies for blood lipids, finding high-level consistency between proteome and transcriptome-lipid associations across tissues.