Leveraging biobank-scale whole-genome sequencing for polygenic risk prediction

利用生物库规模的全基因组测序进行多基因风险预测

基本信息

批准号：
10716534
负责人：
Po-Ru Loh
金额：
$ 44.75万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-18 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10716534
关键词：
Algorithms Alleles Base Pairing Blood Blood Cells Cardiovascular Diseases Chromosome abnormality Clonal Expansion Collaborations Complex Computer software Computing Methodologies DNA DNA Sequence Data Data Set Disease European ancestry Frequencies Future Gene Frequency Genes Genetic Genetic Diseases Genetic Models Genetic Polymorphism Genetic Risk Genetic Variation Genome Genomics Genotype Haplotypes Hematologic Neoplasms Heritability Individual Inherited Letters Link Mediating Memory Methodology Methods Modeling Mutation Mutation Detection Performance Point Mutation Population Publications Research Resolution Resources Risk SNP array Sampling Single Nucleotide Polymorphism Somatic Mutation Source Statistical Algorithm Statistical Methods Structure Therapeutic Training Variant age related analytical method biobank cardiovascular risk factor causal variant cohort cost disorder risk empowerment experience genetic risk factor genetic variant genome sequencing genome-wide genome-wide analysis high risk human disease improved insertion/deletion mutation polygenic risk score precision medicine rare variant risk prediction risk variant trait whole genome

项目摘要

Project Summary/Abstract Whole-genome sequencing of population biobank cohorts holds great promise for enabling accurate prediction of genetically-mediated risk for heritable human diseases and traits. Such information has the potential to be a powerful resource for precision medicine, informing preventative and therapeutic decisions. To more fully realize this potential, new statistical methods are needed to incorporate all genetic variants – including structural variants, blood-derived somatic mutations, and rare SNPs and indels – into genetic risk models. These classes of genetic variation, which are known to include many variants with large effects on disease risk, can be detected in high-coverage whole-genome sequencing data now being generated at biobank scale. However, such variants have not been accessible from previous genetic data sets (which have relied on SNP- array genotyping and imputation). Consequently, existing methods for polygenic prediction have typically considered only common inherited SNPs and indels. We propose to develop a suite of statistical methods to enable these additional classes of genetic variants to be incorporated into models of genetic risk, thereby improving predictive power. For variant types that are currently difficult to ascertain even from whole-genome sequencing data – including somatic mutations and some types of structural variants – we will develop new genotyping algorithms that improve statistical inference by harnessing information across large sequenced cohorts. We will efficiently integrate information across all variant types into genetic risk models using fast Bayesian regression methods. We will apply these approaches to train genetic risk models for common diseases using data from very large biobank sequencing projects. This project will have three specific aims. First, we will develop and apply methods for incorporating structural variants into polygenic scores. Many structural variants are known to confer substantial disease risk but are at imperfectly modeled by existing polygenic scores, such that directly including such variants will increase prediction accuracy and cross-ancestry transferability. Second, we will develop and apply methods for incorporating somatic mutations detectable in blood-derived DNA into genetic risk models. Such acquired mutations, often indicative of clonal expansions of blood cells, provide an orthogonal source of risk compared to the inherited variants considered by standard polygenic scores. Third, we will develop and apply efficient computational methods for training polygenic score models on biobank-scale sequencing data. These methods will allow model-fitting to be performed on individual-level genetic data, optimizing prediction accuracy. We anticipate that these efforts will significantly improve performance of genetic risk models trained on current and future population-scale whole-genome sequencing data sets.

项目概要/摘要人口生物库队列的全基因组测序为实现准确预测带来了巨大希望此类信息有可能成为人类遗传疾病和性状的遗传风险。精准医学的强大资源，为预防和治疗决策提供信息。认识到这一潜力，需要新的统计方法来纳入所有遗传变异——包括结构变异、血液来源的体细胞突变以及罕见的 SNP 和插入缺失 – 纳入遗传风险模型。已知这些类别的遗传变异包括许多对疾病有重大影响的变异风险，可以在生物样本库规模生成的高覆盖率全基因组测序数据中检测到。然而，这些变异还无法从以前的遗传数据集（依赖于 SNP- 阵列基因分型和插补）检查，现有的多基因预测方法通常具有仅考虑常见的遗传 SNP 和插入缺失。我们建议开发一套统计方法，使这些额外类别的遗传变异能够被纳入遗传风险模型中，从而提高对变异类型的预测能力。目前即使从全基因组测序数据也很难确定——包括体细胞突变和某些类型的结构变异——我们将开发新的基因分型算法来改进统计推断通过利用大型测序群体的信息，我们将有效地整合所有信息。我们将使用快速贝叶斯回归方法将变异类型纳入遗传风险模型中。使用来自大型生物库测序项目的数据训练常见疾病的遗传风险模型。该项目将有三个具体目标，首先，我们将开发和应用合并结构的方法。已知许多结构变异会带来重大疾病风险，但目前仍存在这种风险。现有的多基因评分不完美地建模，因此直接包含此类变异会增加其次，我们将开发和应用预测准确性和跨祖先可迁移性。将血液来源 DNA 中可检测到的体细胞突变纳入遗传风险模型。突变通常表明血细胞的克隆扩增，提供了比较风险的正交来源第三，我们将开发和应用有效的多基因评分。用于在生物样本库规模测序数据上训练多基因评分模型的计算方法。将允许对个体水平的遗传数据进行模型拟合，从而优化预测准确性。预计这些努力将显着提高在当前和未来训练的遗传风险模型的性能未来人口规模的全基因组测序数据集。