Statistical genetics of aging-related genomic and phenotypic change

点击翻译按钮获取中文摘要

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.