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

通过多组学整合遗传变异的计算模型来破译个人基因组

基本信息

批准号：
10625423
负责人：
Li Chen
金额：
$ 35.73万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10625423
关键词：
Alzheimer&apos s Disease Biological Assay Biological Markers Biomedical Research Collaborations Communities Computer Models Computing Methodologies Data Development Diagnosis Dimensions Genetic Medicine Genetic Variation Genome Goals Heritability Human Genetics Indiana Individual Linkage Disequilibrium Machine Learning Methodology Methods Multiple Myeloma Persons Phenotype Precision Health Quantitative Trait Loci Research Role Sample Size Scanning Scientist Software Tools Statistical Methods Systems Biology Testing Training Universities Untranslated RNA Variant Work biobank computer framework disorder prevention empowerment genetic variant genome sequencing genome wide association study improved interest laboratory experiment molecular phenotype multidisciplinary multiple omics novel open source precision medicine programs trait transfer learning whole genome

项目摘要

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.

通过多词的整合对遗传变异的计算建模，以破译个人基因组一个人的基因组通常包含数百万个遗传变异。通过评估来了解这些变体它们对一个人的表型的功能影响，目前对人类遗传学和精度有极大的兴趣药品。尽管全基因组关联研究（GWAS）或定量性状基因座（QTL）研究已有成功鉴定出与性状或分子表型相关的变体，其中大多数是在非编码中区域并受到连锁dissequilbirium的阻碍，使随意变体的识别和解释难的。此外，这些发现中的大多数都是常见变体，但是，稀有和特定的变体在个人基因组中，没有被忽略的。了解这些变体不仅可以解释缺失的遗传力来自GWAS，但也可以改善精度医学。最近，整个基因组的冒险和流行测序（WGS）和配对的多词功能测定提供了前所未有的机会来识别稀有和特定的休闲变体。但是，大多数WGS研究的样本量很小与GWAS相比，使WGS分析特别挑战。然而，统计和用于分析WGS的计算方法欠发达。鉴于这些挑战和我独特的多纪律培训，我的研究计划的总体目标是开发一种新颖的机器学习，统计和系统生物学方法用于识别，优先级和解释非编码的方法通过整合GWAS，WGS和多态功能测定法，这将赋予精度医学通过确定用于预防疾病，诊断和治疗的个性化生物标志物。具体来说，在下一个五年，我的实验室（i）开发了一种新颖的转移学习方法来改善非编码的预测使用多维的OMICS功能（II）开发多摩管集成综合扫描测试的休闲变体为了改善全基因组测序数据（III）的罕见休闲变体的识别非编码变体在个人基因组（IV）中的评分影响的综合计算框架发展使用全基因组遗传变异改善表型预测的新型多特征方法。在同时，在印第安纳大学精确健康计划的支持下，我们将把这些方法应用于来自印第安纳州阿尔茨海默氏病中心和印第安纳州多发性骨髓瘤生物库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以验证发现。与我们以前的工作，我们将继续将所有开发的方法用于可访问的开源软件工具，并且对生物医学研究界有用。