Statistical Methods to Map Disease Genes in Populations

DNA序列的变化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，因此在绘制疾病基因的基因组位置图方面也是有用的。绘制的基因及其生化途径的功能可以导致疾病的个性化治疗。 该研究计划的重点是绘制影响疾病特征的基因的基因组位置，使用有关特征的数据和同源DNA序列中的遗传变异。统计基因图谱寻找具有过度亲缘关系和过度特征相似性的基因组区域。为了表征DNA序列样本中的相关性，该研究计划将使用它们的基因谱系。基因谱系是一组跨越基因组区域的相互关联的祖先树。在基因组上影响性状的位置，我们预计在系谱树上相似的性状值会过度聚集。因此，性状值的系谱聚类法是绘制疾病基因图谱的基础。一个短期目标是调查家谱上不同聚类法的统计特性。 要在系谱上聚类性状值，我们必须将其重建为参数或潜在随机变量。分支方法将家谱视为一个参数。然而，依赖于分支重建的作图方法可能是有偏见的，因为它们忽略了谱系中的不确定性。一个短期目标是表征应用于分支重建的聚类法的偏差和其他统计特性。家谱不是被视为参数，而是被视为潜在的随机变量，并在给定遗传数据的情况下从其后验分布中采样。然而，目前可用的MCMC采样器依赖于对大量序列分解的近似。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，我们将只考虑在现在之前的100代人之前的部分谱系。关于低频因果变异的基因组位置的信息很可能从更早的时间获得。 在疾病基因图谱中，准确的表型是至关重要的。对于大脑障碍，三维成像测量提供了对认知能力的客观评估，这些评估被认为比问卷得分更接近遗传影响。每幅图像通常有数百万个测量值，但关于疾病变化的信息被认为只存在于一个子集中。这项研究的第二个和更长期的重点是与我们的脑成像合作者密切合作，开发对临床有意义的个体之间特征相似性的测量方法，这些测量可以整合到拟议的基因图谱方法中。

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.