Statistical Methods to Map Disease Genes in Populations

绘制人群疾病基因图谱的统计方法

• 批准号: RGPIN-2018-04296
• 负责人: Graham, Jinko
• 金额: $ 1.46万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=677298
• 关键词: Statistical; Methods; Map; Disease; Genes

Variation in DNA sequences reflects their relationships. The relationships can tell us about individual predisposition to inherited traits, and so are of use in mapping the genomic location of disease genes. The function of the mapped genes and their biochemical pathways can lead to personalized treatments for disease. ******The research program focuses on mapping the genomic location of genes that influence disease traits, using data on traits and on genetic variation in homologous DNA sequences. Statistical gene mapping looks for genomic regions with excess relatedness and excess trait similarity. To characterize the relatedness in a sample of DNA sequences, the research program will use their gene genealogy. The gene genealogy is a set of correlated ancestral trees across the genomic region. At trait-influencing locations on the genome, we expect excess clustering of similar trait values on the genealogical tree. Genealogical clustering of trait values is thus a basis for mapping disease genes. A short-term objective is to investigate the statistical properties of different measures of clustering on the genealogy.******To cluster trait values on the genealogy, we must reconstruct it, either as a parameter or as a latent random variable. Cladistic methods view the genealogy as a parameter. However, mapping approaches that rely on cladistic reconstructions are potentially biased because they ignore uncertainty in the genealogy. A short-term goal is to characterize the bias and other statistical properties of clustering approaches applied to cladistic reconstructions. Instead of being viewed as parameters, genealogies may be viewed as latent random variables, and sampled from their posterior distribution given the genetic data. However, currently-available, MCMC samplers rely on approximations that break down for a larger number of sequences. A medium-term goal is to develop improved sampling methods with faster mixing, through the use of particle-marginal Metropolis-Hastings algorithms. To reduce the complexity of the state space, we will consider only partial genealogies going back 100 generations before present. Little information about the genomic location of low-frequency causal variants is likely to be gained from going further back in time.******In the mapping of disease genes, accurate phenotyping is critical. For brain disorders, 3-dimensional imaging measurements provide objective assessments of cognitive capacity that are thought to be closer to genetic influences than questionnaire scores. Each image typically has millions of measurements, but the information on changes due to disease is thought to reside in only a subset. A second and longer-term focus of the research is to work closely with our brain-imaging collaborators to develop clinically-meaningful measures of trait similarities between individuals that can be integrated into the proposed gene-mapping methods.

DNA序列的变异反映了它们之间的关系。这些关系可以告诉我们个体对遗传特征的易感性，因此可以用于绘制疾病基因的基因组位置。映射基因的功能及其生化途径可以导致疾病的个性化治疗。** 该研究计划的重点是利用有关性状和同源DNA序列遗传变异的数据，绘制影响疾病性状的基因的基因组位置。统计基因定位寻找具有过度相关性和过度性状相似性的基因组区域。为了表征DNA序列样本中的相关性，研究计划将使用它们的基因谱系。基因谱系是一组跨基因组区域的相关祖先树。在基因组上的性状影响位置，我们预期系谱树上相似性状值的过度聚类。因此，性状值的系谱聚类是绘制疾病基因的基础。一个短期目标是研究不同聚类度量对谱系的统计特性。**为了在系谱上聚类性状值，我们必须将其重构为参数或潜在随机变量。分支分类方法将系谱视为一个参数。然而，依赖于分支重建的映射方法是潜在的偏见，因为他们忽略了系谱的不确定性。短期目标是描述应用于分支重建的聚类方法的偏差和其他统计特性。而不是被视为参数，系谱可以被视为潜在的随机变量，并从其后验分布给定的遗传数据进行采样。然而，目前可用的MCMC采样器依赖于分解大量序列的近似。中期目标是通过使用微粒边际Metropolis-Hastings算法，开发出混合速度更快的改进采样方法。为了降低状态空间的复杂性，我们将只考虑100代之前的部分家谱。关于低频致病变异的基因组位置的信息很少，可能会从更早的时间中获得。在疾病基因定位中，精确的表型分析至关重要。对于大脑疾病，三维成像测量提供了对认知能力的客观评估，认为这比问卷评分更接近遗传影响。每幅图像通常有数百万个测量值，但有关疾病变化的信息被认为只存在于一个子集中。研究的第二个也是更长期的重点是与我们的脑成像合作者密切合作，开发出具有临床意义的个体之间特征相似性的测量方法，这些方法可以整合到拟议的基因图谱方法中。