Stanford Advisors

All Publications

  • Quantifying concordant genetic effects of de novo mutations on multiple disorders ELIFE Guo, H., Hou, L., Shi, Y., Jin, S., Zeng, X., Li, B., Lifton, R. P., Brueckner, M., Zhao, H., Lu, Q. 2022; 11


    Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.

    View details for DOI 10.7554/eLife.75551

    View details for Web of Science ID 000867699200001

    View details for PubMedID 35666111

    View details for PubMedCentralID PMC9217133

  • Minimal sigma-field for flexible sufficient dimension reduction ELECTRONIC JOURNAL OF STATISTICS Guo, H., Hou, L., Zhu, Y. 2022; 16 (1): 1997-2032

    View details for DOI 10.1214/22-EJS1999

    View details for Web of Science ID 000825293500038

  • Detecting local genetic correlations with scan statistics NATURE COMMUNICATIONS Guo, H., Li, J. J., Lu, Q., Hou, L. 2021; 12 (1): 2033


    Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

    View details for DOI 10.1038/s41467-021-22334-6

    View details for Web of Science ID 000636772600020

    View details for PubMedID 33795679

    View details for PubMedCentralID PMC8016883