This $1,205,737.49 Project Grant awarded by the National Human Genome Research Institute (NHGRI) under the Human Genome Research program (CFDA 93.172) supports the development of scalable computational methods and software tools to enable better analysis and interpretation of large genomic datasets. The key objectives are to: Develop a novel maximum likelihood estimation framework and optimization techniques to significantly accelerate phylogenetic analysis and enable the use of more complex...
This Project Grant award of $200,000 was provided by the National Science Foundation (NSF) under the Biological Sciences (CFDA 47.074) program. The funding supports research to improve the efficiency of whole genome sequencing (WGS) data analysis through two main objectives: Optimizing the Genotype Representation Graph (GRG) data structure for modern parallel computing architectures, particularly GPUs, to handle the dynamic nature of genomic computations. Additionally, a matrix abstraction of...
This three-year Project Grant from the National Science Foundation's Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program ($600,000) will support the development of new computational methods and software for analyzing collections of genomic variants. Specifically, the grantee, Carnegie Mellon University, will create algorithms and open-source software tools to more accurately construct, compare, and assess the complexity of genome...
This $200,000 Project Grant award from the National Human Genome Research Institute (NHGRI) under the Human Genome Research program (CFDA 93.172) aims to develop novel transfer learning algorithms to improve the prediction accuracy of polygenic scores (PGS) for ancestry groups underrepresented in genomics research. The project, led by the collaborative partnership of Henry Ford Health and Michigan State University Health Sciences, has three main objectives: 1) Develop and benchmark novel...
This $400,000 Project Grant award from the National Human Genome Research Institute (NHGRI) under CFDA 93.172 "Human Genome Research" will support a research program at Brigham & Women's Hospital to develop advanced computational methods for accurately identifying and genotyping structural variants within large genetic biobank datasets. The key objectives of the work are to: 1) create haplotype-informed algorithms to improve detection sensitivity and genotyping accuracy for...
The National Human Genome Research Institute (NHGRI) awarded a $457,920 Project Grant (CFDA #93.172 - Human Genome Research) to The Broad Institute, Inc. to develop a deep reinforcement learning framework for haplotype assembly. Haplotype assembly is critical for understanding human population genetics and disease, as it reconstructs the combination of alleles on maternally and paternally inherited chromosome copies. The Broad Institute, a renowned biomedical and genomic research organization,...
This Project Grant award from the National Human Genome Research Institute (NHGRI), under the Human Genome Research federal grant program (CFDA 93.172), provides $757,157 to the University of California, San Diego (UC San Diego) to develop complementary experimental and computational strategies for high-throughput characterization of the impact of genetic variants. The key objectives are to: 1) Develop a method called SCBE-SEQ that combines genome editing with single-cell sequencing to...
This federal Project Grant award from the National Human Genome Research Institute (NHGRI) under the Human Genome Research program (CFDA 93.172) provides $666,831 to The Leland Stanford Junior University to develop new techniques for using rare protein-coding variants, including loss-of-function mutations, deletions, and duplications, in burden tests to estimate the magnitude and direction of effect of each gene on a phenotype of interest. The project aims to improve the accuracy of these...
This Project Grant award from the National Human Genome Research Institute (CFDA 93.172 - Human Genome Research) provides $361,069 to The Johns Hopkins University to develop new analytical methods and software tools for unsupervised analysis of multi-sample single cell genomic data. The project aims to address three key challenges: inferring temporal progression of sample phenotype from cross-sectional data, integrating data across samples and modalities, and enabling comparative analysis of...
This $138,428 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of computationally tractable structured hierarchical models to identify complex genetic associations hidden from current methods. The grantee, J. David Gladstone Institutes, will analyze genomic and clinical trait data using machine learning to build predictive models for individualized disease risk assessment and personalized...
INFERENCE AND APPLICATION OF GRAPHS FOR GENOMIC DATA - PROJECT SUMMARY/ABSTRACT THE GENEALOGICAL STRUCTURE FOR WHOLE GENOMES CAN BE DESCRIBED THROUGH ANCESTRAL RECOMBINATION GRAPHS (ARGS). ARGS ARE SUMMARIES THAT CONTAIN ALL OF THE INFORMATION IN GENOMIC SEQUENCING DATA ABOUT PROCESSES SUCH AS DEMOGRAPHIC HISTORY, SELECTION, AND RECOMBINATION. THE PRIMARY OBJECTIVE OF THIS RESEARCH IS TO DEVELOP A SUITE OF COMPUTATIONAL TOOLS THAT USE POSTERIOR SAMPLING OF ARGS IN ORDER TO PROVIDE METHODS FOR TESTING HYPOTHESES ABOUT THE DISTRIBUTION AND EVOLUTION OF GENOMIC VARIATION, AND IN GENERAL, TO PROVIDE IMPROVED QUANTIFICATION OF MUTATION, RECOMBINATION, SELECTION, AND DEMOGRAPHIC HISTORY. THESE METHODS WILL BE FULL LIKELIHOOD/BAYESIAN METHODS THAT CAN TAKE ADVANTAGE OF THE RICH POPULATION GENETIC INFORMATION IN WHOLE-GENOME SEQUENCING DATA. WE EXPECT THE METHODS TO SCALE UP TO ALLOW POSTERIOR SAMPLING OF ARGS FROM A COALESCENCE PRIOR FOR MANY HUNDREDS, OR PERHAPS THOUSANDS, OF GENOMES. WE WILL MAKE AN OPEN-SOURCE, USER-FRIENDLY, FLEXIBLE, AND INTEGRATED PROGRAM AVAILABLE TO OTHER RESEARCHERS THAT WILL ALLOW THEM TO TEST A WIDE RANGE OF DEMOGRAPHIC AND EVOLUTIONARY HYPOTHESES ON THEIR OWN DATA. WE WILL ALSO DEVELOP ASSOCIATED METHODS FOR ANCESTRAL INFERENCE OF PAST MIGRATION AND THE GEOGRAPHIC LOCATION OF ANCESTORS OF AN INDIVIDUAL. ADDITIONALLY, WE WILL DEVELOP IMPROVED METHODS FOR QUANTIFYING SPATIOTEMPORAL PATTERNS OF NATURAL SELECTION AFFECTING THE GENOME. WE WILL APPLY THE METHODS TO MODERN AND ANCIENT DNA TO TEST HYPOTHESES ABOUT THE RELATIVE CONTRIBUTION OF DEMOGRAPHIC PROCESSES AND NATURAL SELECTION FOR SHAPING THE LANDSCAPE OF PHENOTYPIC VARIATION IN EUROPE, INCLUDING DISEASE SUSCEPTIBILITY. WE WILL ALSO USE THE METHODS TO REVISIT AN ONGOING CONTROVERSY OF THE RELATIVE IMPORTANCE OF CHANGING MUTATION PATTERNS AND CHANGING GENERATION TIMES IN SHAPING THE PATTERN OF HUMAN MUTATION VARIATION. FINALLY, WE WILL USE THE METHODS TO DEVELOP MORE ACCURATE HUMAN RECOMBINATION MAPS AND TO TEST HYPOTHESES ABOUT RECOMBINATION RATE VARIATION. IN ADDITION TO THIS, WE WILL DEVELOP NEW BAYESIAN MARKOV CHAIN MONTE CARLO METHODS FOR ESTIMATING DEVELOPMENTAL LINEAGE TREES (DLTS) USING MITOCHONDRIAL HETEROPLASMIES AND SINGLE CELL DNA SEQUENCING. WE WILL ALSO DEVELOP METHODS THAT CAN JOINTLY ANALYZE SINGLE CELL RNA SEQUENCING AND DNA SEQUENCING DATA TO MAKE JOINT MODELS OF DLTS WITH ASSOCIATED TRANSITIONS IN EXPRESSION STATE. SUCH EXPLICIT TEMPORAL MODELS OF CELL DIFFERENTIATION WILL BE CENTRAL IN THE TRANSLATIONAL ASPECTS OF CELL SPECIFIC ANALYSES, IN PARTICULAR FOR PREDICTING THE EFFECTS OF VARIOUS FORMS OF MEDICAL INTERVENTION.