基于单倍型的关联分析方法
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摘要
人类基因组计划的完成,不论从数量上还是从质量上,都极大地丰富了人类遗传的数据资源,但也容易使人迷失在这浩如烟海的信息中。统计学,作为一种强有力的数据分析工具,越来越受到人们的重视并在遗传流行病的研究中发挥着不可替代的作用。
关联分析主要通过研究遗传标记物与可观测的性状之间的统计相关性,来寻找和定位致病基因,并为我们更好的地理解疾病遗传基础发挥了重要的作用。单倍型,作为一种常见的数据类型,被人们认为含有更多的连锁不平衡(LD)信息,而且与其他方法相比,基于单倍型的关联分析在识别疾病关联上有更大的功效,尤其是病例—对照研究中稀有疾病的情况。但是,对这些单倍型进行建模,其中的稀有单倍型会带来很多的统计问题——大量的参数会使功效减少、效率降低。为了克服这些问题,单倍型聚类是个不错的解决方式。本文着重介绍了在基于单倍型的关联分析中,如何有效地利用位点本身以及位点间的信息来提高检验的功效,其中包括一个参数方法和一个非参数方法。
本文首先介绍了基于单倍型聚类来进行关联分析的方法,称之为APEG,通过使用EG距离应用AP算法对单倍型进行有效合理的聚类。新提出的针对单倍型这一特殊数据类型的相似性度量EG距离,能够利用不同位点上以及位点之间的结构信息。通过模拟和真实数据的研究发现,APEG方法要比现存的其他方法在探测单倍型与疾病之间是否相关联方面拥有更大的功效,而且在基因定位上,也能够得到比较精确的估计。然后,我们介绍了基于U—统计量的非参数方法U-EGS,其优点是渐进正态性,而且不需要对样本总体的分布进行假设。U-EGS中引入的新的核函数EGS,是EG距离的一种推广,同样也能利用位点的信息。随后的模拟研究也证实了,在不同的参数下,对不同的疾病模型,使用能够融入位点信息的核函数EGS的U—统计量要比没有利用位点信息的U—统计量在统计功效上拥有更大的优势。
The completion of the Human Genome Projection, both on quantity and qual-ity, has enriched the data resource of human genetic, which makes people easily lost in the oceans of information. Statistics, as a powerful data analysis tool, has been focused on by more researchers, and it also has played an irreplaceable role in genetic epidemiology.
Association analysis, with the aim of investigating genetic variations, is de-signed to detect genetic associations with observable traits, which has played an increasing part in understanding the genetic basis of diseases. Haplotypes, as a common data style, are generally considered to possess more linkage disequilib-rium (LD) information, and haplotype-based association studies are believed to provide high resolution and potentially greater power for identifying genetic disease associations, compared to the other approaches, especially for the rare diseases in case-control studies. However, when modeling these haplotypes, they are subjected to statistical problems caused by rare haplotypes. Abundant parameters limits the power and decreases the efficiency. Fortunately, haplotype clustering offers an ap-pealing solution. This dissertation aims to propose new statistical methods, which combine the structure information of the loci in order to improve the power in the haplotype-based association studies.
In this dissertation, we first present APEG for haplotype clustering in haplotype-based association studies, which adopts "affinity propagation" clustering algorithm with EG distance. The new befitting similarity EG distance, designed specially for haplotypes, can incorporate haplotype structure information, which is believed to enhance the power and provide high resolution for identifying associations between genetic variants and disease. Our simulation studies show that the proposed ap-proach offers merits in detecting disease-marker associations in comparison with other methods. We also illustrate an application of our method to a real data set, which shows quite accurate estimates during fine mapping. Then, we develop a non-parametric method based on U-statistics called U-EGS, which has an asymptotic normally distribution and without assumption to the distribution of the samples. The following simulations also shows that the U-statistics with EGS, which could incorporate locus information, gains greater power than the U-statistics without locus information, under different parameters and different disease models.
关联分析主要通过研究遗传标记物与可观测的性状之间的统计相关性,来寻找和定位致病基因,并为我们更好的地理解疾病遗传基础发挥了重要的作用。单倍型,作为一种常见的数据类型,被人们认为含有更多的连锁不平衡(LD)信息,而且与其他方法相比,基于单倍型的关联分析在识别疾病关联上有更大的功效,尤其是病例—对照研究中稀有疾病的情况。但是,对这些单倍型进行建模,其中的稀有单倍型会带来很多的统计问题——大量的参数会使功效减少、效率降低。为了克服这些问题,单倍型聚类是个不错的解决方式。本文着重介绍了在基于单倍型的关联分析中,如何有效地利用位点本身以及位点间的信息来提高检验的功效,其中包括一个参数方法和一个非参数方法。
本文首先介绍了基于单倍型聚类来进行关联分析的方法,称之为APEG,通过使用EG距离应用AP算法对单倍型进行有效合理的聚类。新提出的针对单倍型这一特殊数据类型的相似性度量EG距离,能够利用不同位点上以及位点之间的结构信息。通过模拟和真实数据的研究发现,APEG方法要比现存的其他方法在探测单倍型与疾病之间是否相关联方面拥有更大的功效,而且在基因定位上,也能够得到比较精确的估计。然后,我们介绍了基于U—统计量的非参数方法U-EGS,其优点是渐进正态性,而且不需要对样本总体的分布进行假设。U-EGS中引入的新的核函数EGS,是EG距离的一种推广,同样也能利用位点的信息。随后的模拟研究也证实了,在不同的参数下,对不同的疾病模型,使用能够融入位点信息的核函数EGS的U—统计量要比没有利用位点信息的U—统计量在统计功效上拥有更大的优势。
The completion of the Human Genome Projection, both on quantity and qual-ity, has enriched the data resource of human genetic, which makes people easily lost in the oceans of information. Statistics, as a powerful data analysis tool, has been focused on by more researchers, and it also has played an irreplaceable role in genetic epidemiology.
Association analysis, with the aim of investigating genetic variations, is de-signed to detect genetic associations with observable traits, which has played an increasing part in understanding the genetic basis of diseases. Haplotypes, as a common data style, are generally considered to possess more linkage disequilib-rium (LD) information, and haplotype-based association studies are believed to provide high resolution and potentially greater power for identifying genetic disease associations, compared to the other approaches, especially for the rare diseases in case-control studies. However, when modeling these haplotypes, they are subjected to statistical problems caused by rare haplotypes. Abundant parameters limits the power and decreases the efficiency. Fortunately, haplotype clustering offers an ap-pealing solution. This dissertation aims to propose new statistical methods, which combine the structure information of the loci in order to improve the power in the haplotype-based association studies.
In this dissertation, we first present APEG for haplotype clustering in haplotype-based association studies, which adopts "affinity propagation" clustering algorithm with EG distance. The new befitting similarity EG distance, designed specially for haplotypes, can incorporate haplotype structure information, which is believed to enhance the power and provide high resolution for identifying associations between genetic variants and disease. Our simulation studies show that the proposed ap-proach offers merits in detecting disease-marker associations in comparison with other methods. We also illustrate an application of our method to a real data set, which shows quite accurate estimates during fine mapping. Then, we develop a non-parametric method based on U-statistics called U-EGS, which has an asymptotic normally distribution and without assumption to the distribution of the samples. The following simulations also shows that the U-statistics with EGS, which could incorporate locus information, gains greater power than the U-statistics without locus information, under different parameters and different disease models.
引文
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