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（例如，鉴定线粒体DNA（mtDNA）变体和从全基因组序列有效地估计mtDNA拷贝数）。对于算法的实验测试，我们正在利用BLSA（巴尔的摩老龄化纵向研究，见年度报告AG000775）、InCHIANTI（见年度报告AG001050）和SardiNIA（见年度报告AG000675）项目的特殊优势，以帮助组装三个队列中的线粒体序列数据和多表型数据。 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参与者和1，000名InChianti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65，000个深度测序个体的白色细胞（TOPMed程序，NHLBI），用于拷贝数的GWAS。我们还计划将我们的程序应用于英国生物银行项目的50万个全基因组序列。 扩展我们对mtDNA拷贝数（mtDNA Acn）分析的其他关注，我们一直在研究巴尔的摩老龄化纵向研究（BLSA）参与者的mtDNA Acn和人格之间的关联。我们使用修订的NEO人格量表（NEO-PI-R）评估五大人格特质和方面，并从全基因组DNA序列中有效地估计mtDNA。我们的初步分析表明，mtDNAcn是显着相关的特定领域的人格清单。我们还进行了中介分析，以表明mtDNA介导人格和死亡风险之间的关联。据我们所知，这是第一项研究表明mtDNA和人格之间存在可复制的联系。研究结果支持了我们的假设，即mtDNA是生物过程的生物标志物，可以解释人格与死亡率之间的部分关联。 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人的有效衰老率。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出广泛的生化和生理特征的SardiNIA和InCHIANTI的纵向研究老化。从我们的框架推断的生理年龄与实足年龄高度相关（R2> 0.8）。然后，我们定义了一个生理老化率（PAR）为每个主题，一个连续的特性测量的比例的主题预测的生理年龄，他/她的实足年龄。我们能够表明PAR是存活率的重要预测因子，表明衰老率对死亡率的影响，并表明减缓衰老率可能具有强烈的有益效果。PAR与基于DNA甲基化的表观遗传衰老评分相关，这证实了这两个评分虽然是从完全不同水平的生物学数据估计的，但都捕捉到了共同的衰老过程。此外，PAR是可遗传的，这使我们对PAR进行了全基因组关联研究，确定了两个影响衰老速度的重要遗传位点，其中之一涉及端粒酶活性。我们的研究结果支持PAR作为潜在的全身衰老机制的代理，我们的方法可用于评估针对衰老相关过程和疾病的治疗效果。

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 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 BLSA (Baltimore Longitudinal Study of Aging, see Annual Report AG000775), InCHIANTI (see Annual Report AG001050), and SardiNIA (see Annual Report AG000675) projects to help in the assembly of mitochondrial sequence data and multiple phenotypic data in the three cohorts. 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 and 1,000 InCHIANTI 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 of mitoCalc 100-fold (fastMitoCalc). The new program is being applied to white cells of 65,000 deeply sequenced individuals (TOPMed program, NHLBI), for GWAS on copy number. We are also planning to apply our programs to the 500,000 whole-genome sequences from the UK Biobank project. Expanding our other focus on the mtDNA copy number (mtDNAcn) analysis, we have been examining the association between mtDNAcn and personality in participants of the Baltimore Longitudinal Study of Aging (BLSA). We assess the big five personality traits and facets using the Revised NEO Personality Inventory (NEO-PI-R) and estimate mtDNAcn efficiently from whole-genome DNA sequences. Our preliminary analyses show that mtDNAcn is significantly associated with specific domains of the personality inventory. We have also performed mediation analysis to show that mtDNAcn mediates the association between personality and mortality risk. To our knowledge, this is the first study to show a replicable association between mtDNAcn and personality. The results support our hypothesis that mtDNAcn is a biomarker of the biological process that explains part of the association between personality and mortality. In another study, we have created a program that uses machine learning methods to measure effective rates of aging for 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 traits from the SardiNIA and InCHIANTI longitudinal studies of aging. Physiological age inferred from our framework is highly correlated with chronological age (R2>0.8). We then define a physiological aging rate (PAR) for each subject, a continuous trait measured as the ratio of the subjects predicted physiological age to his/her chronological age. We are able to show that PAR is a significant predictor of survival, indicating the effect of aging rate on mortality and suggesting that slowing down aging rate may have strong beneficial effects. PAR is correlated with DNA methylation-based epigenetic aging scores, confirming that both scores, although estimated from completely different levels of biological data, capture a common aging process. Furthermore, PAR is appreciably heritable, which leads us to a genome-wide association study of PAR that identifies two significant genetic loci influencing the rate of aging, one of which is involved in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism and our method can be used to evaluate the efficacy of treatments that target aging-related processes and disease.