Robert C. Green

The paper explores professional perspectives on the value and feasibility of lifelong genomic medicine through interviews with 52 US-based professionals.

This study explores health care professionals' views on the timing of returning genomic newborn screening results, finding parental choice is most endorsed but requires support for disclosure to children.

The paper examines motivations and outcomes of elective genomic sequencing across research, clinical, and commercial contexts, finding most participants found results medically important but few shared them with healthcare providers.

The paper estimates penetrance of disease variants in genes prioritized for genomic newborn screening using adult biobank data, finding higher penetrance than ICD codes suggest.

The paper develops a comprehensive list of genetic conditions for genomic newborn screening, including 777 conditions associated with 743 genes, for implementation across multiple U.S. states and territories.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

The paper investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

The paper explores parental perspectives on genomic sequencing research participation among under-represented racial and ethnic groups, finding high interest but identifying barriers to enrollment.

This paper proposes a fetal actionable findings list for genomic sequencing in pregnancy to identify clinically relevant incidental variants.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

The paper analyzes genetic, ancestral, and geospatial data from a biobank to explore their relationships with health outcomes in a historically diverse population.

The paper presents a framework to evaluate the clinical utility of multi-gene, multi-disease panel testing by incorporating variant frequency, penetrance estimates, and disutilities, while accounting for uncertainty in penetrance.

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

This study identifies blood-based biomarkers associated with Alzheimer's disease genetic risk variants in a large cohort of healthy adults, showing effects from early adulthood and sex-specific patterns.

The paper evaluates unanticipated monogenic disease risks found in newborns through genomic screening, finding most risks are actionable and lead to medical interventions.

This study evaluates the feasibility and costs of returning actionable genomic results from a biobank, finding high false positive rates in genotyping and significant under-testing among at-risk individuals.

Neptune delivers structured genomic data to EHR systems through customizable reports and variant reanalysis, supporting over 15,000 clinical reports.

This paper proposes a framework for automated gene selection in genomic screening, creating gene lists with strong disease associations and demonstrating their utility in healthy genome screening.

The paper investigates the association between age-related mosaic chromosomal alterations and infection risk in 767,891 individuals without blood cancer, finding increased infection susceptibility linked to mCAs.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.