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的关系。这些关系可以告诉我们有关遗传性状的个体倾向，因此在映射疾病基因的基因组位置。映射基因及其生化途径的功能可导致个性化疾病治疗。该研究计划着重于绘制影响疾病特征的基因的基因组位置，使用有关性状的数据和同源DNA序列的遗传变异的数据。统计基因映射寻找具有过度相关性和过量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，研究程序将使用其基因家谱。基因家谱是整个基因组区域的一组相关的祖先树。在基因组上的特征影响位置，我们期望家谱树上相似的性状值过多的聚类。因此，性状值的家谱聚类是映射疾病基因的基础。一个短期目标是研究谱系上不同聚类量度的统计特性。要在家谱上的聚类特征值，我们必须将其重建为参数，或作为参数或潜在的随机变量。 Cladistic方法将家谱视为参数。但是，依赖层流重构的映射方法可能存在偏见，因为它们忽略了家谱中的不确定性。一个短期目标是表征应用于移动重建的聚类方法的偏差和其他统计特性。谱系可能被视为潜在的随机变量，而不是被视为参数，并且在遗传数据的情况下从其后分布中取样。但是，当前可用的MCMC采样器依赖于近似序列分解的近似值。一个中期目标是通过使用粒子 - 划分的大都市 - 危机算法来开发更快的混合采样方法。为了降低状态空间的复杂性，我们将仅考虑部分谱系在目前的100代。关于低频因果变体的基因组位置的信息很少，很可能会从及时延续。在疾病基因的映射中，准确的表型至关重要。对于脑部疾病，三维成像测量值提供了对认知能力的客观评估，这些评估被认为比问卷分数更接近遗传影响。每个图像通常都有数百万个测量值，但是有关疾病变化的信息仅存在于子集中。这项研究的第二和长期重点是与我们的大脑成像合作者紧密合作，以开发可将可以集成到建议的基因映射方法中的个体之间的临床性特征相似性的衡量标准。