{"id":595,"date":"2025-11-02T22:49:20","date_gmt":"2025-11-02T22:49:20","guid":{"rendered":"https:\/\/mrivas.su.domains\/gbe\/?page_id=595"},"modified":"2025-11-03T16:45:56","modified_gmt":"2025-11-03T16:45:56","slug":"595-2","status":"publish","type":"page","link":"https:\/\/mrivas.su.domains\/gbe\/595-2\/","title":{"rendered":"Methods and Tools"},"content":{"rendered":"\n

Meta-regression models<\/h2>\n\n\n\n
<\/figure>\n\n\n\n

We’ve developed a class of meta-regression models designed to integrate variant level features including constraint, pathogenicity, loss-of-function annotation, and evolutionary scores. It improves power over standard rare variant genetic analysis as is showcased in our epilepsy application.<\/p>\n\n\n\n

\n
Meta-regression Github link<\/a><\/div>\n<\/div>\n\n\n\n

Bayesian summary statistic framework<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

We’ve developed a Bayesian framework for rare variant aggregation analysis that uses a full Kronecker expansion of the prior matrices.<\/p>\n\n\n\n

\n
MRP Github link<\/a><\/div>\n<\/div>\n\n\n\n

We’ve also developed a Bayesian clustering algorithm for rare variant effect profiles. <\/p>\n\n\n\n

\n
MRPMM Github link<\/a><\/div>\n<\/div>\n\n\n\n

Penalized regression for ultrahigh-dimensional problems<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

We’ve developed algorithms for fitting penalized regression to ultrahigh-dimensional problems. <\/p>\n\n\n\n

    \n
  1. snpnet<\/a> <\/li>\n\n\n\n
  2. sparse-snpnet and snpnet-2.0<\/a> with selective inference <\/li>\n\n\n\n
  3. snpnet-Cox<\/a> <\/li>\n\n\n\n
  4. multiSnpnet<\/a> Fast Multi-Phenotype Sparse Reduced Rank Regression on Genetic Data<\/li>\n\n\n\n
  5. multiSnpnet-Cox, mrcox<\/a> Efficient Group-Sparse Lasso solver for multi-response Cox model <\/li>\n\n\n\n
  6. multiSnpnet-Cox-gpu<\/a> GPU version of multiSnpnet-Cox<\/li>\n\n\n\n
  7. multiSnpnet-Cox-tv, Coxtv<\/a> Regularized Cox proportional hazard model with time-varying covariate<\/li>\n\n\n\n
  8. regularizedMR<\/a>\u00a0Regularized Mendelian randomization<\/li>\n<\/ol>\n\n\n\n

    Causal inference methods<\/h2>\n\n\n\n

    We’ve developed methods for causal inference from population biobank data.<\/p>\n\n\n\n

      \n
    1. cGAUGE: Causal Graphical Analysis Using GEnetics<\/a>\u00a0<\/li>\n<\/ol>\n\n\n\n

      Decomposition of genetic effect methods<\/h2>\n\n\n\n

      We’ve developed methods to decompose genetic effects from large-scale human genetic studies and project them to individual risk.<\/p>\n\n\n\n

        \n
      1. DEcomposition of Genetic ASsociations<\/a> <\/li>\n\n\n\n
      2. DeGAs-risk scores<\/a>\u00a0<\/li>\n<\/ol>\n\n\n\n

        Additional polygenic risk score methods<\/h2>\n\n\n\n

        We’ve developed methods that train sex-aware polygenic risk models, and that use genetic risk from biomarkers in addition to disease risk scores. <\/p>\n\n\n\n

          \n
        1. Sex-specific Effects Mixture Model<\/a> <\/li>\n\n\n\n
        2. multiPRS<\/a>\u00a0<\/li>\n<\/ol>\n\n\n\n

          Visualization and inference engine<\/h2>\n\n\n\n

          We’ve developed Global Biobank Engine, software for visualizing genetic association results from population biobank data. <\/p>\n\n\n\n

            \n
          1. Global Biobank Engine<\/a><\/li>\n<\/ol>\n\n\n\n

            Artificial intelligence tools<\/h2>\n\n\n\n

            We’ve developed artificial intelligence tools for tumor board discussions, and interpreting rare variants from whole genome sequencing data. <\/p>\n\n\n\n

              \n
            1. Artificial intelligence tumor board dashboard<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

              Meta-regression models We’ve developed a class of meta-regression models designed to integrate variant level features including constraint, pathogenicity, loss-of-function annotation, and evolutionary scores. It improves power over standard rare variant genetic analysis as is showcased in our epilepsy application. Bayesian summary statistic framework We’ve developed a Bayesian framework for rare variant aggregation analysis that uses […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-595","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/595","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/comments?post=595"}],"version-history":[{"count":9,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/595\/revisions"}],"predecessor-version":[{"id":611,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/595\/revisions\/611"}],"wp:attachment":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/media?parent=595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}