Quantitative Comparisons between genotypes and model species
Quantitative Comparisons between genotypes and model species
批准号:
8546427
负责人:
Lauren M. MCINTYRE
金额:
$28.2万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-17 至 2016-06-30
关键词:
AffectAggressive behaviorAlcohol dependenceAlcoholismAlcoholsAllelesAllelic ImbalanceBehaviorBiological AssayBiological ModelsComplexCourtshipDataDiseaseDrosophila genusEcologyEnvironmentEquationEthanolEuphoriaEvolutionExposure toFemaleFertilityGABA ReceptorGenesGeneticGenetic PolymorphismGenetic VariationGenotypeGoalsHeadHealthHeritabilityHumanImpotenceIndividualIntoxicationLocomotionMapsMeasuresMediatingMedical GeneticsMethodologyModelingMolecularMutationNatureOutcomePathway interactionsPhenotypePopulationPredictive ValueQuality of lifeReceptor GeneRegulationResolutionSedation procedureStructureSystemTestingTranslatingUrsidae FamilyVariantalcohol exposurealcohol responsealcohol riskaldehyde dehydrogenasesbasedesignfascinategenome wide association studyhuman diseaseimprovedindexinginsightmalemen&aposs groupnovelpredictive modelingsexskillsspecies differencetherapeutic targettraittranscriptome sequencingvapor
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Identifying the genetic basis of variation in complex traits is a grand challenge. Genome-wide
association studies suggest that many alleles with small effects may be responsible for many
common diseases. When allelic effects are individually small, identification of these alleles is a
daunting task. A mutation may not cause disease in a single molecular step, but instead interact
with other mutations or cascade through intermediate molecular pathways eventually resulting
in disease traits. Identifying the networks which underlie variation in disease traits and
understanding how perturbations in these networks affect phenotypes may be a more efficient
approach to identifying therapeutic targets. We propose to test this approach by building
predictive genotype to phenotype models for complex behaviors in D. melanogaster and
D.simulans (courtship and locomotion) assayed under standard conditions and during ethanol
exposure (a model for environmental perturbations or disease states). By moving beyond
descriptive studies in single populations to causative directional models which are predictive in
novel conditions, shared and specific network structures will be identified. Networks which do
not differ between sexes, species or environmental conditions are good candidates for
comparison to other model systems or to humans. Highly conserved regulatory relationships are
most likely critical for viability or fertility. Divergence in the regulation of individual genes or
changes in network structure highlight the possible evolution of regulatory interactions among
genes and identify candidate polymorphisms, genes and networks which may contribute to the
evolution of novel traits, such as increased ethanol tolerance. Alcohol dependency affects
approximately 18 million people in the USA, impacting health and quality of life. Ethanol has
severe effects on human behavior, resulting in euphoria and sedation. In addition, intoxication
can increase aggression, cause impotence, and decrease sexual inhibition and locomotor skills.
The effects of alcohol and risk for alcoholism have a strong and complex genetic component,
but are also mediated by the environment. While there are some large effect loci (e.g. ADH,
ALDH and GABA receptor genes), much of the heritability remains unexplained. Drosophila
have similar responses to ethanol, with courtship and locomotion affected by exposure. We will
bring the power of Drosophila genetics to bear on this problem with an in depth examination of
two species and their response to ethanol. We will directly test the hypothesis that conserved
networks correspond to a higher degree of translatability of prediction of phenotypic outcomes
across species. In addition, we will determine how robust these predictions are to gene by
environment effects by exposing both populations to ethanol.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
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批准号:10133273
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资助金额:$56.33万
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财政年份:2021
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依托单位:
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批准号:10322035
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批准号:10539272
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财政年份:2021
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Allele Specific Regulation of Context Specific GRN
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批准号:10254258
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资助金额:$36.27万
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财政年份:2018
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依托单位:
Computational Core
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批准号:10180968
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资助金额:$32.48万
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财政年份:2018
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Quantitative Comparisons between genotypes and model species
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批准号:8341420
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项目类别:
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资助金额:$30.45万
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财政年份:2012
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负责人:Lauren M. MCINTYRE
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Quantitative Comparisons between genotypes and model species
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批准号:8883575
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项目类别:
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资助金额:$29.38万
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财政年份:2012
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负责人:Lauren M. MCINTYRE
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依托单位:
Quantitative Comparisons between genotypes and model species
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批准号:8678952
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项目类别:
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资助金额:$29.38万
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财政年份:2012
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负责人:Lauren M. MCINTYRE
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依托单位:
Genetic variation of allele-specific transcriptome in Drosophila
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批准号:7884921
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项目类别:
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资助金额:$38.77万
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财政年份:2009
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负责人:Lauren M. MCINTYRE
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依托单位:
Genetic variation of allele-specific transcriptome in Drosophila
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批准号:7767758
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项目类别:
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资助金额:$27.76万
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财政年份:2007
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负责人:Lauren M. MCINTYRE
-
依托单位:
Genetic variation of allele-specific transcriptome in Drosophila
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批准号:7206642
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项目类别:
-
资助金额:$28.92万
-
财政年份:2007
-
负责人:Lauren M. MCINTYRE
-
依托单位:
Genetic variation of allele-specific transcriptome in Drosophila
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批准号:7345493
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项目类别:
-
资助金额:$28.04万
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财政年份:2007
-
负责人:Lauren M. MCINTYRE
-
依托单位:
Genetic variation of allele-specific transcriptome in Drosophila
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批准号:7569984
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项目类别:
-
资助金额:$28.04万
-
财政年份:2007
-
负责人:Lauren M. MCINTYRE
-
依托单位:
Computational Core
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批准号:9767163
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项目类别:
-
资助金额:$40.67万
-
财政年份:--
-
负责人:Lauren M. MCINTYRE
-
依托单位:
海外基金