Illuminating the distribution of extreme evolutionary constraint in the human genome from fetal demise to severe developmental disorders
阐明人类基因组中从胎儿死亡到严重发育障碍的极端进化限制的分布
基本信息
- 批准号:10601318
- 负责人:
- 金额:$ 4.05万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-01-01 至 2025-12-31
- 项目状态:未结题
- 来源:
- 关键词:AtlasesAutomobile DrivingAutopsyBiologicalBirthCellsChildhoodChromatinClinicalCodeDataData SetDevelopmentDevelopmental ProcessDisciplineDiseaseDisease modelDoctor of PhilosophyEtiologyExhibitsFamily memberFellowshipFertilityFetal DeathFetusFirst Pregnancy TrimesterGene DosageGene FrequencyGeneral PopulationGenesGenetic Predisposition to DiseaseGenetic RiskGenetic TranscriptionGenetic VariationGenetic studyGenomeGenomic SegmentGenomicsHumanHuman DevelopmentHuman GenomeIndividualInheritedInstitutionInternationalLaboratoriesMaternal-fetal medicineMeasuresMentorsMentorshipMusMutationNatural SelectionsNeurodevelopmental DisorderOutcomeParentsPathway interactionsPatternPlayPoint MutationPopulationPositioning AttributePropertyPublishingRecurrenceRegulatory ElementResearchResearch SupportResourcesRisk FactorsRoleSamplingSourceStatistical ModelsStructural defectTestingTrainingUntranslated RNAVariantWorkbasecareercell typecohortcongenital anomalydevelopmental diseaseexomefetalfetal lossgene discoverygene functiongenetic architecturegenome sequencinggenome-widegenomic variationhands on researchinsertion/deletion mutationinsightlaboratory experienceloss of functionnovelprogramsprotein protein interactionpurgereproductivesexskill acquisitionstillbirthsymposiumtooltraining opportunitywhole genome
项目摘要
Abstract
Natural selection purges deleterious mutations from populations in genomic regions impacting survival or
reproductive capacity. Recent genetic studies of massive population cohorts have revealed a continuous
distribution across the human genome of this constraint on deleterious mutations. Large-scale association
studies have found mutations in strongly constrained genomic regions to be major risk factors in many childhood
developmental disorders (DDs), suggesting that highly constrained sequences are likely to play key roles in
development. However, genetic studies of fetal demise after the first trimester (FD), an extreme outcome of DDs,
have thus far been limited in scope and size. The establishment by my mentors of an international Fetal
Genomics Consortium to sequence 10,500 samples (3,500 FD cases and their family members) ascertained for
FD of suspected genetic etiology now offers an unprecedented opportunity to illuminate the most extreme
consequences of mutation across individual genes and dosage sensitive genomic segments critical for human
development. In this fellowship, I will integrate whole-genome sequencing and autopsy data from this cohort
together with data from previously established DD cohorts to discover and functionally characterize mutationally
intolerant loci in the human genome. I will first define patterns of genetic variation in FD across constraint metrics
(loss-of-function, missense, and noncoding) and genomic variation classes (point mutations, indels, structural
variants, and repeat expansions), and investigate biases in these patterns with respect to fetal sex and mutational
parent-of-origin. I will then adapt a statistical framework for disease association capable of integrating evidence
from all coding and noncoding variant classes with prioritization of constrained regions, which I will apply to
perform novel gene discovery in FD (Aim 1). I will leverage these findings to generate functional predictions of
mutationally intolerant loci by defining the biological networks of activity and the cell types in which they are likely
to operate early in development (Aim 2). Finally, I will test these functional hypotheses across DDs that do not
result in FD, including liveborn fetal structural abnormalities, neurodevelopmental disorders, and congenital
anomalies (Aim 3). In parallel with these research aims, an exceptional team of six mentors and advisors across
multiple disciplines, career stages, and institutions will provide didactic training, hands-on research support,
regular opportunities for presentation in seminars and conferences, and a variety of soft skill development
sessions that directly align with my career objectives during my PhD training. Collectively, the aims outlined in
this proposal will take advantage of unique tools and resources to yield novel insights into the etiology of the
extremes along the continuum of developmental anomalies and evolutionary constraint, and will serve as an
outstanding training opportunity for me in computational, statistical, and functional disease genomics.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
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Lily Wang其他文献
Lily Wang的其他文献
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{{ truncateString('Lily Wang', 18)}}的其他基金
New computational tools for understanding and predicting AD via age-associated DNA methylation changes
通过与年龄相关的 DNA 甲基化变化来理解和预测 AD 的新计算工具
- 批准号:
10509428 - 财政年份:2022
- 资助金额:
$ 4.05万 - 项目类别:
New statistical strategies for comprehensive analysis of epigenomewide methylation data
表观基因组甲基化数据综合分析的新统计策略
- 批准号:
9763421 - 财政年份:2018
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$ 4.05万 - 项目类别:
Integrative statistical models for pathway analysis of GWAS data
GWAS 数据路径分析的综合统计模型
- 批准号:
9187527 - 财政年份:2013
- 资助金额:
$ 4.05万 - 项目类别:
Integrative statistical models for pathway analysis of GWAS data
GWAS 数据路径分析的综合统计模型
- 批准号:
8241543 - 财政年份:2013
- 资助金额:
$ 4.05万 - 项目类别:
Integrative statistical models for pathway analysis of GWAS data
GWAS 数据路径分析的综合统计模型
- 批准号:
8654353 - 财政年份:2013
- 资助金额:
$ 4.05万 - 项目类别:
Mapping the Genetic Architecture of Complex Disease via RNA-seq and GWAS Data
通过 RNA-seq 和 GWAS 数据绘制复杂疾病的遗传结构
- 批准号:
8658841 - 财政年份:2012
- 资助金额:
$ 4.05万 - 项目类别:
Mapping the Genetic Architecture of Complex Disease via RNA-seq and GWAS Data
通过 RNA-seq 和 GWAS 数据绘制复杂疾病的遗传结构
- 批准号:
8217762 - 财政年份:2012
- 资助金额:
$ 4.05万 - 项目类别:
Understanding Genetic Basis of Dental Caries via Integrative Genomic Approaches
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8320126 - 财政年份:2011
- 资助金额:
$ 4.05万 - 项目类别:
Understanding Genetic Basis of Dental Caries via Integrative Genomic Approaches
通过综合基因组方法了解龋齿的遗传基础
- 批准号:
8176915 - 财政年份:2011
- 资助金额:
$ 4.05万 - 项目类别:
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