New methods and enhanced software for predicting functional SNPs

 描述(申请人提供)：基因组学研究的重点正迅速从积累遗传变异数据转向等位基因变异的功能解释。测序研究正在成为遗传学所有领域的标准方法，对预测突变功能影响的计算方法产生了前所未有的需求。我们不断开发和维护PolyPhen-2，这是一种预测错义突变功能影响的计算方法。PolyPhen-2根据比较序列分析和蛋白质结构分析进行预测。这种方法正被广泛应用于遗传学的各个领域。尽管拥有庞大的用户基础，我们也在继续努力提高预测精度，但该方法仍有很大的改进空间，迫切需要提高预测精度。我们最近对有害等位基因的群体遗传学研究指出，有害变异的分析和预测具有基本的复杂性。对这些复杂性、新类型的训练和验证数据以及算法方法的更好的理解使我们能够大幅改进计算方法和软件。我们还将通过解决以前未得到充分满足的需求来扩大该方法的实用性。基因发现研究在基因和变异水平上都对已识别的变异进行了优先排序。PolyPhen-2和其他预测方法目前解决的问题是，给定的变体是否可能影响基因功能。同样重要的考虑因素是，一个基因是否 这种变异是一种病态基因，以及该基因或结构域中的大多数错义变化是否可能对功能产生影响。深入的人口测序数据和已知疾病变异的目录可以与进化和结构分析结合使用，以确定基因的优先顺序。许多大规模测序项目正在从外显子基因组测序过渡到全基因组测序。这为非编码变异的分析打开了一个新的视角。非编码变异已被证明在多基因复杂表型的遗传学中起着关键作用。然而，以孟德尔方式分离的表型的大效应非编码变体的重要性尚不清楚，仍在争论中。通过单独支持孟德尔病病例的全基因组测序，这些病例与已知的基因座相关联，但缺乏蛋白质编码变体，我们将以无偏见的方式选择非编码孟德尔突变，分析潜在的潜在生物学，并将开发计算预报器。这种方法从根本上不同于现有的非编码变异分析工作，这些工作预测序列多样性的保守或减少，而不是直接确定致病效应。在具体目标1中，我们将在预测突变的功能影响的计算方法方面做出实质性改进，并将这些改进纳入PolyPhen软件。在具体目标2中，我们将根据人口和疾病遗传学数据开发基于基因的评分，并将其与基于变量的预测相结合。在具体目标3中，我们将把预测扩展到非编码变体。

 DESCRIPTION (provided by applicant): The focus of genomics research is rapidly shifting from the accumulation of genetic variation data to the functional interpretation of allelic variant. Sequencing studies are becoming the standard approach in all areas of genetics, generating an unprecedented demand for computational methods to predict the functional effect of mutations. We continuously develop and maintain PolyPhen-2, a computational method for predicting the functional effect of missense mutations. PolyPhen-2 makes predictions based on comparative sequence analysis and analysis of protein structure. This method is being widely applied in diverse areas of genetics. In spite of the large user base and our continuing efforts to increase prediction accuracy, there is an ample room for improvement and a great need to improve accuracy of the method. Our recent studies on population genetics of deleterious alleles point to fundamental complexities in the analysis and prediction of deleterious variation. The improved understanding of these complexities, new types of training and validation data and algorithmic approaches position us to substantially improve the computational method and the software. We will also expand the utility of the method by addressing previously underserved needs. Gene discovery studies prioritize identified variants both at the gene and the variant level. The question currently addressed by PolyPhen-2 and other prediction methods is whether a given variant is likely to affect gene function. Equally important considerations are whether a gene that harbors this variant is a morbid gene and whether most missense changes in this gene or a domain are likely to have a functional impact. Deep population sequencing data together with catalogs of known disease variants can be used in concert with evolutionary and structural analyses to prioritize genes. Many large-scale sequencing projects are transitioning from exomes to whole genome sequencing. This opens a perspective for the analysis of non-coding variation. Non-coding variation has been shown to play a key role in genetics of polygenic complex phenotypes. However, the importance of large effect non-coding variants for phenotypes that segregate in the Mendelian fashion is unclear and still under debate. Through separately supported whole genome sequencing of cases of Mendelian diseases linked to known loci but lacking protein-coding variants we will select non-coding Mendelian mutations in an unbiased fashion, analyze the potential underlying biology and will develop a computational predictor. This approach is fundamentally different from existing efforts on the analysis of non-coding variation that predict conservation or a reduction in sequence diversity rather than directly ascertain the pathogenic effect. In Specific Aim 1 we will make substantial improvements in computational methods for predicting the functional effect of mutations and incorporate these improvements into the PolyPhen software. In Specific Aim 2 we will develop gene-based scores based on population and disease genetics data and integrate them with the variant-based predictions. In Specific Aim 3 we will extend the prediction to non-coding variation.