Statistical genetics of aging-related genomic and phenotypic change

为了帮助分析和理解受许多基因和环境因素影响的与衰老相关的"复杂"性状，我们遵循了开发用于全基因组基因分型和高通量测序研究分析的统计算法的道路。我们提出的新计算工具提供了分析其他类型数据的方法，从全基因组序列中有效地鉴定线粒体DNA（mtDNA）变体和估计mtDNA拷贝数。对于算法的实验测试，我们正在利用SardiNIA项目的特殊优势（见年度报告AG000675），以帮助在创始人撒丁岛人口中组装线粒体序列数据和多个表型数据。与此同时，我们正在与NIA和其他合作者进行全基因组关联研究（GWAS）和流行病学分析，以研究撒丁岛队列中的一系列年龄相关性状。例如，在过去的一年里，我们参与了与虚弱相关的特征（步行速度，握力和骨密度）以及听力作为年龄和性别函数的流行病学分析。 为了对大规模群体数据进行分析以研究mtDNA变异和拷贝数，我们开发了两个计算程序，为基于全基因组测序研究的mtDNA动力学分析提供了通用解决方案。一个程序（mitoCaller）专门用于识别mtDNA变异;另一个程序（mitoCalc）直接从基因组序列推断细胞中mtDNA的拷贝数。将这些程序应用于2，000名SardiNIA参与者的白细胞序列，我们已经表明异质性（一个位点上具有一个以上等位基因的mtDNA变体）随着年龄的增长而增加，并且拷贝数相对高度遗传，并且与代谢特征相关，特别是中央脂肪水平。在最近的工作中，我们将mitoCalc的速度提高了100倍（fastMitoCalc）。新程序正在应用于50，000个深度测序个体的白色细胞（TOPMed程序，NHLBI），用于拷贝数的GWAS。我们还开始分析mtDNA拷贝数或变异对性状和疾病的可能影响。 在另一项研究中，我们创建了一个程序，使用机器学习方法来测量个体衰老的生理速率。我们评估在何种程度上可以确定一个人的生理年龄作为一个综合评分推断从广泛的生化和生理数据。在SardiNIA人群研究中收集数据。我们使用机器学习策略对6,000名撒丁岛参与者的数据进行分析，这些参与者的年龄从12岁到81岁不等。最佳预测模型是从降维、分类和回归算法的多种组合中确定的。预测年龄和实际年龄之间的相关性非常强（R> 0.9），并且在同一个人的连续访问中表现出相对稳定性（R> 0.5）。然后，我们为每个参与者定义了有效衰老率（ERA），这是一种衡量个人预测年龄与实际年龄之比的特征。在SardiNIA队列中，ERA的推断值显示遗传遗传率为40%，这一发现支持了个体具有特征性衰老率的推断。这足以启动全基因组关联研究，确定影响衰老速度的遗传变异。 在一项正在进行的合作努力中，我们研究了DNA的一种特殊结构特征，G-四链体（G4）结构作为干扰线粒体复制机制的潜在DNA路障。在两个大的人群队列中，对与预测的G4形成结构相关的mtDNA变异的计算分析正在评估G4 mtDNA作为遗传变异和疾病风险的一个因素。与此同时，生化研究正在重建系统中进行，以评估线粒体复制体的进展，通过难以复制的序列，其特征在于G4结构。对可能的mtDNA复制阻断的研究补充了对独特的SardiNIA和InChianti人群的分析，以确定影响年龄相关性状的G4相关突变。

To help to analyze and understand aging-related "complex" traits that are affected by many genes and environmental factors, we have followed the path of developing statistical algorithms for the analyses of genome-wide genotyping and high-throughput sequencing studies. Our proposed new computational tools provide means to analyze additional types of data e.g., to identify mitochondrial DNA (mtDNA) variants and to estimate mtDNA copy number efficiently from whole-genome sequences. For experimental tests of the algorithms, we are capitalizing on the special advantages of the SardiNIA project (see Annual Report AG000675) to help in the assembly of mitochondrial sequence data and multiple phenotypic data in the founder Sardinian population. At the same time, we are carrying out genome-wide association studies (GWAS) and epidemiological analyses with NIA and other collaborators for a series of age-related traits in the Sardinian cohort. In the past year, for example, this has involved us in epidemiological analyses for frailty-related traits (walking speed, grip strength and bone density) and hearing capacity as a function of age and sex. In order to conduct analyses on large-scale consortium data to study mtDNA variation and copy number, we have developed two computational programs, providing a general solution for the analysis of mtDNA dynamics based on whole-genome sequencing studies. One program (mitoCaller) is designed specifically to identify mtDNA variants; the other (mitoCalc) infers mtDNA copy number in a cell directly from genome sequences. Applying the programs to leukocyte sequences of 2,000 SardiNIA participants, we have shown that heteroplasmies (mtDNA variants with more than one allele at a site) increase with age, and that copy number is relatively highly heritable and is correlated with metabolic traits, particularly central fat levels. In more recent work, we have increased the speed ofmitoCalc 100-fold (fastMitoCalc). The new program is being applied to white cells of 50,000 deep sequenced individuals (TOPMed program, NHLBI), for GWAS on copy number. We have also initiated analyses of possible effects of mtDNA copy number or variants on traits and diseases. In another study, we have created a program that uses machine learning methods to measure physiological rates of aging of individuals. We assess the extent to which an individual's physiological age could be determined as a composite score inferred from a broad range of biochemical and physiological data. Data were collected in the SardiNIA population study. We use machine learning strategies on data for 6,000 Sardinian participants, who ranged in age from 12 to 81. The best predictive models are determined from multiple combinations of dimensionality reduction, classification, and regression algorithms. They reach very strong correlations (R > 0.9) between predicted and actual ages, and show relative stability in successive visits of the same individuals (R>0.5). We then define an Effective Rate of Aging (ERA) for each participant, a trait measured as the ratio of an individual's predicted age to his/her chronological age. The inference that individuals have a characteristic rate of aging is supported by findings that in the SardiNIA cohort, the inferred values of ERA shows genetic heritability of 40%. This has been sufficient to initiate genome-wide association studies that identify genetic variants influencing the rate of aging. In an ongoing collaborative effort, we study a special structural feature of DNA, G-quadruplex (G4) structures as potential DNA roadblocks that perturb mitochondrial replication machinery. Computational analysis of mtDNA variants associated with predicted G4-forming structures in two large population cohorts are assessing G4 mtDNA as a factor contributing to genetic variability and disease risk. At the same time, biochemical studies are being performed in a reconstituted system to assess mitochondrial replisome progression through difficult-to-replicate sequences characterized by G4 structure. The study of possible mtDNA replication blockades complements analysis of the unique SardiNIA and InChianti populations to identify G4-associated mutations that affect age-related traits.