Stanford Advisors

Contact

  • Academic
    jcmiao@stanford.edu
    University - Scholar Department: Genetics Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5120

All Publications

  • Understanding nature and nurture: Statistical and AI innovations uncover how genes and environment shape human health. Science (New York, N.Y.) Miao, J. 2025; 390 (6773): 584

    Abstract

    Statistical and AI innovations uncover how genes and environment shape human health.

    View details for DOI 10.1126/science.aeb2636

    View details for PubMedID 41196967

  • Deciphering the impact of genomic variation on function. Nature 2024; 633 (8028): 47-57

    Abstract

    Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

    View details for DOI 10.1038/s41586-024-07510-0

    View details for PubMedID 39232149

    View details for PubMedCentralID 7405896

  • Decomposing heritability and genetic covariance by direct and indirect effect paths PLOS GENETICS Song, J., Zou, Y., Wu, Y., Miao, J., Yu, Z., Fletcher, J. M., Lu, Q. 2023; 19 (1): e1010620

    Abstract

    Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

    View details for DOI 10.1371/journal.pgen.1010620

    View details for Web of Science ID 000937238100001

    View details for PubMedID 36689559

    View details for PubMedCentralID PMC9894552