Creating an advanced multi-ancestral resource and tools for short tandem repeat analysis in the AOURP researcher workbench
Creating an advanced multi-ancestral resource and tools for short tandem repeat analysis in the AOURP researcher workbench
批准号:
10798717
负责人:
Matthew Christopher Danzi
金额:
$42.21万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-15 至 2025-08-31
关键词:
AccelerationAffectAfrican AmericanAll of Us Research ProgramAllelesApplications GrantsAreaBioinformaticsBiologyBiomedical ResearchCatalogsCollectionComplexDataData AnalysesDatabasesDevelopmentDiseaseEnvironmentEyeFutureGene FrequencyGeneral PopulationGeneticGenetic VariationGenomeGenomicsGenotypeHealthHealth BenefitHeritabilityHeterogeneityHumanHuman GenomeImageIndividualLeadershipLearningLengthLinkMachine LearningMapsMeasuresMinisatellite RepeatsModelingNatureNervous SystemParticipantPathogenicityPatientsPatternPersonsPhenotypePilot ProjectsPlayPrevalencePrincipal InvestigatorPublicationsPublishingQualifyingResearch PersonnelResourcesRoleSamplingShort Tandem RepeatSignal TransductionSingle Nucleotide PolymorphismSoftware ToolsStandardizationStructureTandem Repeat SequencesTechnologyTrainingVariantWorkanalytical toolcohortdata curationdata resourcedisease phenotypeempowermentgenetic analysisguided inquiryhealth recordimprovedlarge datasetsmembernext generationnovelnovel therapeuticssoftware developmenttoolwhole genomeworking group
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Title
Creating an advanced multi-ancestral resource and tools for short tandem repeat analysis in the
AoURP researcher workbench
Abstract
The AoURP researcher workbench provides an unparalleled opportunity to study multi-ancestral
human genome variation at scale with the promise of benefitting health and diseases for all
people in the USA. A particular type of variation, only recently amenable to bioinformatic
analysis due to breakthroughs in software development and long-read sequencing, are tandem
repeats (TRs). TRs, when unstable and expanded, have been linked to disease and it is widely
expected that they will play a much larger role for health and disease going forward. We have
been developing TR resources, machine learning based tools (RExPRT), and discovered novel
disease-causing TRs published in Nature Genetics and NEJM in the past 5 years. The co-PI’s
of this proposal recently worked as part of the AoURP Long Reads Working Group to create a
call set of over 3.5 billion TR alleles from 1,027 AoURP participants sequenced with PacBio HiFi
reads using the newly developed TRGT tool. This grant application proposes to characterize
this recently established AoURP data resource by developing novel analytical tools to unearth
interrelated patterns of tandem repeat length, motif, and flanking variation in unprecedented
detail. In addition to this long-read based data resource, we will also characterize the TRs in the
larger cohort of >250,000 Illumina short-read whole genomes produced by the AoURP, though
in lesser detail, using tools such as ExpansionHunter. The product of this work will be available
to all workbench users in the form of normative databases, tools, notebooks, and scripts to
accelerate the study of TRs in the context of health records data. We believe this work will
enable prioritization of disease-causing TR loci and lead to a better understanding of TR biology
which will be vital to the development of new therapeutics.
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