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，我们建议开发三种统计算法用于全基因组基因分型和高通量测序研究的分析。我们提出的新统计方法提供了分析其他类型数据的方法，例如，线粒体DNA（mtDNA）变体，或用于全基因组关联研究（GWAS）的X染色体上的变体。为了测试这些算法，我们利用了SardiNIA项目的特殊功能（见年度报告AG 000675），该项目收集了超过300个数量性状的纵向数据以及撒丁岛种群创始人的全基因组遗传数据。 为了分析线粒体DNA变异及其对衰老相关性状的可能影响，针对核DNA开发的基因型调用和分析程序是不够的，因为每个细胞有100- 10，000个线粒体DNA拷贝，这些拷贝可以在任何位点发生变化（异质性），因此可以在不同拷贝的任何位置上具有4个碱基中的每一个。我们已经开发出一种算法，该算法的目标是识别mtDNA中的变异;它结合了每个序列读取中每个碱基的测序错误率，并且可以灵活地允许所有个体中变异位点处的不同等位基因分数。我们的程序是进一步适应的圆形线粒体基因组，从线性染色体的主要区别，假设大多数映射算法。我们正在评估来自2,000名SardiNIA项目参与者的全基因组测序的白细胞mtDNA序列中的同质性和异质性。迄今为止的结果提供有关mtDNA单倍型群和遗传的同源和异质性在撒丁岛的信息。正如预期的那样，母亲和她们的孩子基本上共享所有的同质性，但异质性的比例较小。总体异质性随着年龄的增长而增加，但到目前为止估计的斜率很小，在20岁至80岁之间平均增加1个异质性，次要等位基因分数阈值为4%。我们还根据观察到的mtDNA和常染色体DNA之间的序列覆盖率对mtDNA拷贝数进行了基于序列的估计。我们发现，mtDNA拷贝数平均为110个拷贝/白细胞，是54%的遗传，这意味着大量的mtDNA水平的遗传调节。随着年龄的增长，拷贝数也会适度但显著地减少，女性的平均拷贝数明显多于男性。线粒体DNA拷贝数与腰围和腰臀比显著相关，但与体重指数无关，表明与中心脂肪分布相关。我们目前正在进行mtDNA拷贝数的GWAS，旨在识别调节mtDNA水平的遗传变异。 为了准确分析GWAS中的X连锁遗传变异，我们建议在相应地进行GWAS之前使用RNA-seq数据来帮助识别X失活基因和逃避X失活的基因。在初步工作中，我们使用了来自密歇根大学进行的遗传研究的80个皮肤样本的mRNA-Seq数据。通过将我们的预测结果与Carrel和Willard，Nature（2005）的结果进行比较，我们能够以相对较高的准确度预测基因失活状态（在他们的工作中，基因的失活状态是通过实验确定的），并将“逃逸”基因的预测从Carrel和Willard估计的15%提高了5倍，Nature（2005）提供了X连锁基因的不完整列表，占75%。 为了研究和改善表型的预测，这是个性化医疗的主要目标，在正在进行的工作中，我们正在实施线性混合模型，以评估具有越来越全面的遗传数据的特定表型的预测准确性（例如，来自HapMap估算的基因型和基于测序的遗传数据），以及人口统计学数据（例如，家庭结构）和其他相关的表型性状。

To help to analyze and understand aging-related "complex" traits that are affected by many genes and environmental factors, we propose to develop three statistical algorithms for the analyses of genome-wide genotyping and high-throughput sequencing studies. Our proposed new statistical methods provide means to analyze additional types of data e.g., mitochondrial DNA (mtDNA) variants from sequencing, or variants on the X chromosome for genome-wide association studies (GWAS). To test these algorithms, we take advantage of the special features of the SardiNIA project (see Annual Report AG000675), which has collected longitudinal data for >300 quantitative traits together with the whole-genome genetic data in the founder Sardinia population. To analyze mitochondrial DNA variation and its possible effects on aging-related traits, the genotype-calling and analytic programs developed for nuclear DNA are not adequate, because each cell has 100-10,000 mtDNA copies that can vary at any site (heteroplasmy), and can therefore have each of the 4 bases at any position in various copies. We have developed an algorithm that is targeted to identify variants in mtDNA; it incorporates the sequencing error rate of each base in each sequence read and is flexible to allow for different allele fractions at a variant site across all individuals. Our procedure is further adapted to the circular mitochondrial genome, a key difference from the linear chromosomes assumed by most mapping algorithms. We are assessing homoplasmies and heteroplasmies in mtDNA sequences of leukocytes from whole-genome sequencing of 2,000 SardiNIA Project participants. The results to date provide information about mtDNA haplogroups and the inheritance of homo- and heteroplasmies in Sardinia. As expected, mothers and their children share essentially all homoplasmies but a lesser proportion of heteroplasmies. The overall heteroplasmy increases with age, but the slope is small in the estimates thus far, yielding an average increase of 1 heteroplasmy between ages 20 and 80 with the minor allele fraction threshold at 4%. We have also made a sequence-based estimate of mtDNA copy number based on the observed ratio of sequence coverage between mtDNA and autosomal DNA. We find that mtDNA copy number averages 110 copies/leukocyte and is 54% heritable, implying substantial genetic regulation of the level of mtDNA. Copy numbers also decrease modestly but significantly with age, and females on average have significantly more copies than males. The mtDNA copy numbers are significantly associated with waist circumference and waist-hip ratio, but not with body mass index, indicating an association with central fat distribution. We are currently performing GWAS of mtDNA copy number, aiming to identify genetic variants that regulate mtDNA levels. To accurately analyze X-linked genetic variants in GWAS, we propose to use RNA-seq data to help identify X-inactivated genes and genes escaping X-inactivation before performing GWAS accordingly. In preliminary work, we have used mRNA-Seq data from 80 skin samples available from a genetic study conducted at University of Michigan. We are able to predict gene inactivation status with relatively high accuracy by comparing our predictions to results from Carrel and Willard, Nature (2005) as a gold standard (in their work, inactivation status of a gene was determined experimentally), and improve the prediction of "escaping" genes by 5-fold from 15%, estimated by Carrel and Willard, Nature (2005) with an incomplete list of X-linked genes, to 75%. To investigate and improve the prediction of phenotypes, which is a major goal in personalized medicine, in ongoing work, we are implementing linear mixed models to evaluate the prediction accuracy of a certain phenotype with increasingly more comprehensive genetic data (e.g., from HapMap imputed genotypes and sequencing-based genetic data), together with demographic data (e.g., family structure) and other related phenotypic traits.