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: $ 2.91万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765761
• 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序列的变化反映了它们之间的关系。这种关系可以告诉我们个体对遗传特征的易感性，因此在绘制疾病基因的基因组位置时很有用。绘制的基因的功能及其生化途径可以导致疾病的个性化治疗。该研究项目的重点是绘制影响疾病性状的基因的基因组位置，利用性状数据和同源DNA序列的遗传变异。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。为了描述DNA序列样本的亲缘关系，研究项目将使用他们的基因谱系。基因谱系是整个基因组区域的一组相关祖先树。在基因组上的性状影响位置，我们期望在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。短期目标是研究不同的聚类方法在家谱上的统计特性。为了在谱系上聚类特征值，我们必须将其作为参数或作为潜在随机变量进行重构。谱系分析方法将家谱视为参数。然而，依赖于支系重建的制图方法有潜在的偏差，因为它们忽略了家谱中的不确定性。短期目标是描述用于枝系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，家谱可以被视为潜在的随机变量，并从他们的后验分布抽样给定的遗传数据。然而，目前可用的MCMC采样器依赖于分解大量序列的近似值。中期目标是通过使用粒子边缘Metropolis-Hastings算法，开发更快混合的改进采样方法。为了降低状态空间的复杂性，我们将只考虑100代之前的部分谱系。关于低频率因果变异的基因组位置的信息很少可能从更早的时间回溯中获得。在疾病基因的定位中，准确的表型是至关重要的。对于脑部疾病，三维成像测量提供了认知能力的客观评估，被认为比问卷得分更接近遗传影响。每张图像通常有数百万个测量值，但由于疾病引起的变化信息被认为只存在于一个子集中。研究的第二个长期重点是与我们的脑成像合作者密切合作，开发具有临床意义的个体特征相似性测量方法，这些方法可以整合到拟议的基因定位方法中。