人类基因组分析中的缺失偏倚效应研究和拷贝数变异的突变估计
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摘要
技术的革命将遗传学的研究引入了组学的时代,通过芯片技术的运用产生了大量的遗传数据。为了深入地对数据进行挖掘,其他学科,如统计学、信息学等,与遗传学的结合越来越紧密。本文就五年来本人攻读博士学位期间的工作进行了总结,期望通过对两部分工作的介绍,展示统计学运用于解决遗传学问题的实例。
其一,我们对高通量单核苷酸多态分型平台的缺失偏倚现象及其对后续分析的影响进行了探究。高通量、低成本的分型平台的出现,使全基因组关联分析成为了可能。但是研究者往往将关注的目标及于如何提高分型的准确率,而忽视了另一质量问题——缺失数据的存在。为了研究缺失现象对全基因组关联分析的影响,我们对四个主流分型平台(TaqMan(?) SNP分型平台、GenomeLabTM SNPstream分型平台、BeadLab (Illumina)分型平台和Human Mapping 500K (Affymetrix)芯片)的缺失数据进行了重测序分型,实验证实了缺失偏倚现象在多个平台中均普遍存在。进而,我们从理论上分析了缺失偏倚对后续分析的影响,如等位基因／基因型频率的估计、哈迪——温伯格平衡检验和不同疾病模型下关联分析统计功效的影响等。研究显示,缺失偏倚往往导致关联分析统计功效的下降,而且这种下降通常要比单纯的样本缺失造成的影响严重。我们还分别比较了缺失偏倚、分型错误对频率估计、关联分析的影响。通过分析获知,大多数情况下因为分型质量问题造成的分析偏差可以通过提高分型响应度,即使会牺牲一定的分型准确率来尽可能避免、减小。这一发现提示我们过去通常对处于分型边界的读点进行不判读的做法需要被修正。如果是为了降低分析偏差,在全基因组关联分析中,分型响应度和错误率的筛选标准要互相配合。我们建议修改现行的质量控制标准,可以适当增加响应度的阈值而降低对分型准确率的要求。
其二,我们提出了近似估计拷贝数变异突变率的统计新方法。人类基因组中存在着拷贝数变异,而且这种变异和孟德尔遗传疾病、复杂疾病以及进化中的基因组可塑性相关。为了更好的理解拷贝数变异相关性状的成因,研究拷贝数变异的生成机制、估计它的突变率是十分重要的。多项用于揭示拷贝数变异成因的研究已经开展起来；但是从基因组水平对拷贝数变异突变率进行实验估计还是一个不现实的问题,它需要大量的样本量和精确的分型技术。本研究提出了一种可以运用群体基因型数据对拷贝数变异突变率进行近似估计的方法。这一估计可以通过基因组中不同拷贝数变异的比较,找寻到拷贝数变异的突变热点。运用该方法我们分析了来自HapMap计划的三个群体、4,330个拷贝数变异位点,发现大多数的拷贝数变异突变率大致在10-5/代水平,这与分子实验观察到的零星突变率估计相一致。值得一提的是,有132(3.0%)个拷贝数变异的突变率可达10-3/代水平,被认为是突变热点。进一步的分析发现,基因组结构和重排机制的不同可能造成了人类基因组中拷贝数变异热点的存在。
在不久的将来,由二代测序技术产生的海量数据将不断地涌现出来,许多悬而未决的遗传问题有望获得解决。对这些数据的挖掘工作离不开统计学、信息学等的运用,让我们做好准备迎接生命科学发展的这一黄金时代的到来。
The technological revolution makes genetics into a new era called'-omics'. A large number of genetic data have been produced through the application of the microarray technology. In order to carry out in-depth data mining, methods from other fields, e.g. statistics and informatics, have been applied into the study of genetics. My Ph.D. work was summarized by two parts in this thesis, which illustrated how statistics is applied into the genetic researches.
Ⅰ. We investigated missing call bias in high-throughput genotyping and its effects on further analyses. The advent of high-throughput and cost-effective genotyping platforms made genome-wide association (GWA) studies a reality. While the primary focus has been invested upon the improvement of reducing genotyping error, the problems associated with missing calls are largely overlooked. To probe into the effect of missing calls on GWAs, we demonstrated experimentally the prevalence and severity of the problem of missing call bias (MCB) in four genotyping technologies (TaqMan, SNPstream, Illumina Beadlab and Affymetrix Human Mapping 500K SNP array). Subsequently, we showed theoretically that MCB leads to biased conclusions in the subsequent analyses, including estimation of allele/genotype frequencies, the measurement of HWE and association tests under various modes of inheritance relationships. We showed that MCB usually leads to power loss in association tests, and such power change is greater than what could be achieved by equivalent reduction of sample size unbiasedly. We also compared the bias in allele frequency estimation and in association tests introduced by MCB with those by genotyping errors. Our results illustrated that in most cases, the bias can be greatly reduced by increasing the call-rate at the cost of genotyping error rate. The commonly used 'no-call'procedure for the observations of borderline quality should be modified. If the objective is to minimize the bias, the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. We suggested that the ongoing QC cut-off for call-rate should be increased, while the cut-off for genotyping error rate can be reduced properly.
Ⅱ. We proposed a novel statistical method to approximately estimate the mutation rate of copy number variants (CNVs). CNVs in the human genome were found to be contributing to both Mendelian and complex traits as well as genomic plasticity in evolution. The investigation of mutational mechanisms of CNVs and estimating their mutation rates are critical to understanding the etiology of the CNV-associated traits. Much progress has been made to unravel the mechanisms for CNV formation; however, the evaluation of their mutation rates at genome level poses an insurmountable practical challenge which requires large sample size and accurate typing. In this study, we showed that an approximate estimation of the mutation rates at CNVs could be achieved using population genotyping data. This estimation is sufficient to allow a comparison of mutation rates between CNVs across the genome for the purpose of identifying mutational hotspots. In the analysis of 4,330 CNVs from HapMap populations, we showed that the mutation rates of most CNVs are approximately at the order of 10-5 per generation, which is consistent with the observations in molecular assays. Notably, the mutation rates of 132 (3.0%) CNVs are at the order of 10-3 per generation, therefore, identified as hotspots. Further analysis revealed that the differences in genome architecture and rearrangement mechanism likely incited CNV hotspots in the human genome.
In the near future, masses of data produced by next generation sequencing, will be emerge out. It is likely to unravel many unknowns about genetics. The analysis of such amounts of data relies on the assistance of statistics and informatics. Let us prepare for the advent of this golden time of biology.
其一,我们对高通量单核苷酸多态分型平台的缺失偏倚现象及其对后续分析的影响进行了探究。高通量、低成本的分型平台的出现,使全基因组关联分析成为了可能。但是研究者往往将关注的目标及于如何提高分型的准确率,而忽视了另一质量问题——缺失数据的存在。为了研究缺失现象对全基因组关联分析的影响,我们对四个主流分型平台(TaqMan(?) SNP分型平台、GenomeLabTM SNPstream分型平台、BeadLab (Illumina)分型平台和Human Mapping 500K (Affymetrix)芯片)的缺失数据进行了重测序分型,实验证实了缺失偏倚现象在多个平台中均普遍存在。进而,我们从理论上分析了缺失偏倚对后续分析的影响,如等位基因／基因型频率的估计、哈迪——温伯格平衡检验和不同疾病模型下关联分析统计功效的影响等。研究显示,缺失偏倚往往导致关联分析统计功效的下降,而且这种下降通常要比单纯的样本缺失造成的影响严重。我们还分别比较了缺失偏倚、分型错误对频率估计、关联分析的影响。通过分析获知,大多数情况下因为分型质量问题造成的分析偏差可以通过提高分型响应度,即使会牺牲一定的分型准确率来尽可能避免、减小。这一发现提示我们过去通常对处于分型边界的读点进行不判读的做法需要被修正。如果是为了降低分析偏差,在全基因组关联分析中,分型响应度和错误率的筛选标准要互相配合。我们建议修改现行的质量控制标准,可以适当增加响应度的阈值而降低对分型准确率的要求。
其二,我们提出了近似估计拷贝数变异突变率的统计新方法。人类基因组中存在着拷贝数变异,而且这种变异和孟德尔遗传疾病、复杂疾病以及进化中的基因组可塑性相关。为了更好的理解拷贝数变异相关性状的成因,研究拷贝数变异的生成机制、估计它的突变率是十分重要的。多项用于揭示拷贝数变异成因的研究已经开展起来；但是从基因组水平对拷贝数变异突变率进行实验估计还是一个不现实的问题,它需要大量的样本量和精确的分型技术。本研究提出了一种可以运用群体基因型数据对拷贝数变异突变率进行近似估计的方法。这一估计可以通过基因组中不同拷贝数变异的比较,找寻到拷贝数变异的突变热点。运用该方法我们分析了来自HapMap计划的三个群体、4,330个拷贝数变异位点,发现大多数的拷贝数变异突变率大致在10-5/代水平,这与分子实验观察到的零星突变率估计相一致。值得一提的是,有132(3.0%)个拷贝数变异的突变率可达10-3/代水平,被认为是突变热点。进一步的分析发现,基因组结构和重排机制的不同可能造成了人类基因组中拷贝数变异热点的存在。
在不久的将来,由二代测序技术产生的海量数据将不断地涌现出来,许多悬而未决的遗传问题有望获得解决。对这些数据的挖掘工作离不开统计学、信息学等的运用,让我们做好准备迎接生命科学发展的这一黄金时代的到来。
The technological revolution makes genetics into a new era called'-omics'. A large number of genetic data have been produced through the application of the microarray technology. In order to carry out in-depth data mining, methods from other fields, e.g. statistics and informatics, have been applied into the study of genetics. My Ph.D. work was summarized by two parts in this thesis, which illustrated how statistics is applied into the genetic researches.
Ⅰ. We investigated missing call bias in high-throughput genotyping and its effects on further analyses. The advent of high-throughput and cost-effective genotyping platforms made genome-wide association (GWA) studies a reality. While the primary focus has been invested upon the improvement of reducing genotyping error, the problems associated with missing calls are largely overlooked. To probe into the effect of missing calls on GWAs, we demonstrated experimentally the prevalence and severity of the problem of missing call bias (MCB) in four genotyping technologies (TaqMan, SNPstream, Illumina Beadlab and Affymetrix Human Mapping 500K SNP array). Subsequently, we showed theoretically that MCB leads to biased conclusions in the subsequent analyses, including estimation of allele/genotype frequencies, the measurement of HWE and association tests under various modes of inheritance relationships. We showed that MCB usually leads to power loss in association tests, and such power change is greater than what could be achieved by equivalent reduction of sample size unbiasedly. We also compared the bias in allele frequency estimation and in association tests introduced by MCB with those by genotyping errors. Our results illustrated that in most cases, the bias can be greatly reduced by increasing the call-rate at the cost of genotyping error rate. The commonly used 'no-call'procedure for the observations of borderline quality should be modified. If the objective is to minimize the bias, the cut-off for call-rate and that for genotyping error rate should be properly coupled in GWA. We suggested that the ongoing QC cut-off for call-rate should be increased, while the cut-off for genotyping error rate can be reduced properly.
Ⅱ. We proposed a novel statistical method to approximately estimate the mutation rate of copy number variants (CNVs). CNVs in the human genome were found to be contributing to both Mendelian and complex traits as well as genomic plasticity in evolution. The investigation of mutational mechanisms of CNVs and estimating their mutation rates are critical to understanding the etiology of the CNV-associated traits. Much progress has been made to unravel the mechanisms for CNV formation; however, the evaluation of their mutation rates at genome level poses an insurmountable practical challenge which requires large sample size and accurate typing. In this study, we showed that an approximate estimation of the mutation rates at CNVs could be achieved using population genotyping data. This estimation is sufficient to allow a comparison of mutation rates between CNVs across the genome for the purpose of identifying mutational hotspots. In the analysis of 4,330 CNVs from HapMap populations, we showed that the mutation rates of most CNVs are approximately at the order of 10-5 per generation, which is consistent with the observations in molecular assays. Notably, the mutation rates of 132 (3.0%) CNVs are at the order of 10-3 per generation, therefore, identified as hotspots. Further analysis revealed that the differences in genome architecture and rearrangement mechanism likely incited CNV hotspots in the human genome.
In the near future, masses of data produced by next generation sequencing, will be emerge out. It is likely to unravel many unknowns about genetics. The analysis of such amounts of data relies on the assistance of statistics and informatics. Let us prepare for the advent of this golden time of biology.
引文
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