Computational modeling of genetic variations by multi-omics integration todecipher personal genome

利用多组学集成对遗传变异进行计算建模以破译个人基因组 一个人的基因组通常包含数百万个基因变异。通过评估了解这些变体 它们对人的表型的功能影响，目前在人类遗传学和精确度方面引起了极大的兴趣 医药。尽管全基因组关联研究(GWAS)或数量性状基因座(QTL)研究 成功识别出与性状或分子表型相关的变异，其中大部分是非编码的 区域，并受到连锁不平衡的阻碍，使得随机变异的识别和解释 很难。此外，这些发现中的大多数都是常见的变体，然而，罕见的和个体特有的变体 在个人基因组中还没有得到充分的探索。了解这些变异不仅可以解释缺失的遗传性 也提高了精准医疗的水平。最近，全基因组的出现和流行 测序(WGS)和配对的多组学功能分析提供了前所未有的机会来识别 罕见的和特定于个人的休闲变体。然而，大多数WGS研究的样本量都不大 与全球气候变化网络相比，这使得WGS的分析特别具有挑战性。尽管如此，统计和 用于分析WGS的计算方法还不够发达。考虑到这些挑战和我独特的多- 学科培训，我的研究项目的总体目标是开发一种新型的机器学习课程， 非编码的识别、优先排序和解释的统计和系统生物学方法 通过整合GWAS、WGS和多组学功能分析来获得变异，这将为精确医学提供支持 通过识别用于疾病预防、诊断和治疗的个性化生物标记物。具体地说，在接下来的 在五年的时间里，我的实验室将(I)开发一种新的迁移学习方法来提高非编码的预测 使用多维组学特征的随意变体(II)开发多组学集成综合扫描测试 为了改进从全基因组测序数据中识别罕见的随机变异体(III)，开发一种 对个人基因组中非编码变异的影响进行评分的综合计算框架(IV) 利用全基因组遗传变异改进表型预测的新型多性状方法。 在……里面 同时，在印第安纳大学精密健康倡议的支持下，我们将把这些方法应用于 印第安纳州阿尔茨海默病中心和印第安纳州多发性骨髓瘤生物库对小说的不同研究 科学发现。我们将与合作的遗传学家和临床科学家密切合作，以解释 发现。重要的是，我们将与实验实验室合作来验证这些发现。与我们之前的 工作中，我们将继续将所有开发的方法转化为可访问的开源软件工具 对生物医学研究界很有用。

Computational modeling of genetic variations by multi-omics integration to decipher personal genome A person’s genome typically contains millions of genetic variants. Understanding these variants by assessing their functional impact on a person’s phenotype, is currently of great interest in human genetics and precision medicine. Though Genome-Wide Association Studies (GWAS) or Quantitative Trait Locus (QTL) studies have successfully identified variants associated with traits or molecular phenotypes, most of them are in noncoding regions and hampered by linkage disequilibrium, making the identification and interpretation of casual variants difficult. Moreover, most of these discoveries are common variants, however, rare and individual-specific variants in personal genome are underexplored. Understanding these variants will not only explain the missing heritability from GWAS but also improve the precision medicine. Recently, the advent and popularity of whole genome sequencing (WGS) and paired multi-omics functional assays provide an unprecedented opportunity to identify rare and individual-specific casual variants. However, the sample sizes of most WGS studies are modest compared to GWAS, making the WGS analysis particularly challenging. Nevertheless, statistical and computational methods for analyzing WGS are underdeveloped. Given these challenges and my unique multi- disciplinary training, the overall goals of my research program are to develop a novel class of machine learning, statistical and system biology approaches for the identification, prioritization and interpretation of noncoding variants by integrating GWAS, WGS and multi-omics functional assays, which will empower precision medicine by identifying individualized biomarkers for disease prevention, diagnosis and treatment. Specifically, in the next five years, my lab will (i) develop a novel transfer learning approach to improve the prediction of noncoding casual variants using multi-dimensional omics features (ii) develop a multi-omics integrated omnibus scan test to improve the identification of rare casual variants from whole-genome sequencing data (iii) develop an integrative computational framework for scoring impact of noncoding variants in personal genome (iv) develop a novel class of multi-trait methods to improve phenotype prediction using whole-genome genetic variations. In the meantime, supported by Indiana University Precision Health Initiative, we will apply the methodologies to different studies from Indiana Alzheimer’s Disease Center and Indiana Multiple Myeloma Biobank for novel scientific findings. We will work close with collaborated geneticists and clinician-scientists to interpret the discoveries. Importantly, we will work with experimental labs to validate the findings. In line with our previous work, we will continue to make all developed methods into open-source software tools that are accessible and useful to the biomedical research community.