A genome-wide genealogical framework for statistical and population genetic analysis

用于统计和群体遗传分析的全基因组谱系框架

基本信息

  • 批准号:
    10658562
  • 负责人:
  • 金额:
    $ 56.21万
  • 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:
    2023-09-01 至 2027-06-30
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Genetic studies have improved our understanding of disease etiology and treatment. However, there are at least two shortcomings preventing current studies from reaching their potential in elucidating the genetic architecture of complex traits for all humans. First, current genetic studies largely ignore the genetic relationships among individuals in a study. Many of these relationships may be distant, but nonetheless can be connected on genealogical trees at every position of the genome through a coalescent process. The collection of such (unobserved) trees is encoded by the ancestral recombination graph (ARG). Second, genetic studies are generally biased towards relatively homogeneous, continental, populations such as European or East Asian populations, in part due to a lack of methods tailored towards admixed populations. In this proposal we aim to develop new methods to address both shortcomings. Our framework leverages recent breakthroughs that allow, for the first time, accurate and scalable estimation of ARGs. In Aim 1 we will leverage a new estimator of relatedness based on the ARG that can retain more information of relatedness from incomplete genetic data (e.g. array genotype data) compared to the current standard estimator for relatedness. We will use this estimator to estimate trait heritability and cross-population genetic correlation of complex traits and diseases in humans, as well as to correct for confounding due to population structure in genome-wide association studies. In Aim 2, we will develop an association-testing framework that uses the ARG to identify trait-associated genomic regions and prioritize trait-associated haplotypes. This principled approach can naturally account for allelic heterogeneity and has the potential to improve the power of association studies through lowered multiple testing burden, which is particularly important for understudied populations where recruitment of participants is more challenging. Finally, in Aim 3 we will develop a population genetic framework that uses ARGs to model the admixture history of a population. Using this model, we will develop new ways to detect genes that have responded to recent selection and identify complex traits that have evolved under different kinds of phenotypic selection. Importantly, our framework will address these evolutionary questions in each ancestral component of the admixed population. Throughout each Aim we will benchmark our methods with extensive simulations. We will also evaluate our methods empirically using large- scale real-world human genetic data. Finally, we will apply our methods to genotyping and sequencing data from admixed populations to discover new loci associated with human diseases and/or experienced natural selection in the past. In summary, we will mine the wealth of information from the ARG and address fundamental population- and human-genetic questions, particularly in understudied and admixed populations.
项目总结

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Charleston Chiang其他文献

Charleston Chiang的其他文献

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{{ truncateString('Charleston Chiang', 18)}}的其他基金

Leveraging the Evolutionary History to Improve Identification of Trait-Associated Alleles and Risk Stratification Models in Native Hawaiians
利用进化历史来改进夏威夷原住民性状相关等位基因的识别和风险分层模型
  • 批准号:
    10689017
  • 财政年份:
    2022
  • 资助金额:
    $ 56.21万
  • 项目类别:
Leveraging the Evolutionary History to Improve Identification of Trait-Associated Alleles and Risk Stratification Models in Native Hawaiians
利用进化历史来改进夏威夷原住民性状相关等位基因的识别和风险分层模型
  • 批准号:
    10365815
  • 财政年份:
    2022
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations - diversity supplement
阐明和解释不同群体复杂性状遗传结构的进化框架 - 多样性补充
  • 批准号:
    10539156
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations
阐明和解释不同人群复杂性状遗传结构的进化框架
  • 批准号:
    10624515
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations
阐明和解释不同人群复杂性状遗传结构的进化框架
  • 批准号:
    10640193
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations
阐明和解释不同人群复杂性状遗传结构的进化框架
  • 批准号:
    10458746
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations
阐明和解释不同人群复杂性状遗传结构的进化框架
  • 批准号:
    10727037
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
An evolutionary framework to elucidate and interpret the genetic architecture of complex traits in diverse populations
阐明和解释不同人群复杂性状遗传结构的进化框架
  • 批准号:
    10275367
  • 财政年份:
    2021
  • 资助金额:
    $ 56.21万
  • 项目类别:
Using whole genomes to study demography and mapping power of a population isolate
使用全基因组研究人口统计学和群体隔离的绘图能力
  • 批准号:
    8527468
  • 财政年份:
    2013
  • 资助金额:
    $ 56.21万
  • 项目类别:

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  • 批准号:
    10818088
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  • 财政年份:
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    2023
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用于识别癌症驱动因素的马赛克拷贝数改变的混合图谱
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Genetic & Social Determinants of Health: Center for Admixture Science and Technology
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