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Showing 1–13 of 13 results
Advanced filters: Author: Sarah M. Urbut Clear advanced filters

  • Using data from 142,238 Mass General Brigham Biobank participants, researchers explored population history and social and genetic risk factors for disease in Greater Boston. The study links genetics and context to guide equitable precision health.

    • Satoshi Koyama
    • Ying Wang
    • Pradeep Natarajan
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-15

  • Variability exists in classifying high-risk individuals across polygenic scores for complex diseases. Here the authors show that an integrative scoring approach improves high-risk classification consistency and overall performance toward more reliable clinical applications.

    • Anika Misra
    • Buu Truong
    • Pradeep Natarajan
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Coronary artery disease is the leading cause of death among adults worldwide, however current risk stratification methods lack the ability to incorporate new information throughout the life-course or to combine innate genetic risk factors with acquired lifetime risk. Here the authors introduce a multistate model to address these limitations.

    • Sarah M. Urbut
    • Ming Wai Yeung
    • Pradeep Natarajan
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • A multi-ancestry genetic meta-analysis identifies 12 new loci associated with preeclampsia and gestational hypertension and proposes the integration of polygenic scores and clinical factors for disease prediction

    • Michael C. Honigberg
    • Buu Truong
    • Pradeep Natarajan
    Research
    Nature Medicine
    Volume: 29, P: 1540-1549

  • Using the GTEx data and others, a comprehensive analysis of adenosine-to-inosine RNA editing in mammals is presented; targets of the various ADAR enzymes are identified, as are several potential regulators of editing, such as AIMP2.

    • Meng How Tan
    • Qin Li
    • Jin Billy Li
    Research
    Nature
    Volume: 550, P: 249-254

  • The authors show that rare genetic variants contribute to large gene expression changes across diverse human tissues and provide an integrative method for interpretation of rare variants in individual genomes.

    • Xin Li
    • Yungil Kim
    • Stephen B. Montgomery
    ResearchOpen Access
    Nature
    Volume: 550, P: 239-243

  • Multiple transcriptome approaches, including single-cell sequencing, demonstrate that escape from X chromosome inactivation is widespread and occasionally variable between cells, chromosomes, and tissues, resulting in sex-biased expression of at least 60 genes and potentially contributing to sex-specific differences in health and disease.

    • Taru Tukiainen
    • Alexandra-Chloé Villani
    • Daniel G. MacArthur
    ResearchOpen Access
    Nature
    Volume: 550, P: 244-248

  • Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues.

    • François Aguet
    • Andrew A. Brown
    • Jingchun Zhu
    ResearchOpen Access
    Nature
    Volume: 550, P: 204-213

  • Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.

    • Alvaro N. Barbeira
    • Scott P. Dickinson
    • Hae Kyung Im
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-20

  • STAAR is a powerful rare variant association test that incorporates variant functional categories and complementary functional annotations using a dynamic weighting scheme based on annotation principal components. STAAR accounts for population structure and relatedness and is scalable for analyzing large whole-genome sequencing studies.

    • Xihao Li
    • Zilin Li
    • Xihong Lin
    Research
    Nature Genetics
    Volume: 52, P: 969-983