Computational tools to analyze SNP data from patients with mental illness

描述（由申请人提供）：拟议研究的广泛、长期目标是开发一种软件产品，可用于促进遗传变化的分析，以阐明自闭症谱系障碍、双相情感障碍和精神分裂症等疾病的染色体异常。最近的技术进步允许在单核苷酸多态性（SNP）阵列上分析患者的DNA样本，从每个样本中生成多达数百万个数据点。必须对这些数据进行分析，以确定染色体异常（例如DNA突变、半合子或纯合子缺失或易位），这些异常会导致这些疾病的风险。数据分析的两种主要方法包括拷贝数估计（基于样本与SNP阵列杂交的强度）和基因型分析（揭示杂合性和纯合性）。诸如Partek Genomics Suite（GS）的软件存在以执行数据分析和可视化。该提案的目标是通过将个体的遗传相关性信息纳入Partek GS的数据分析库，为高密度SNP数据的分析增加另一个维度。具体目标如下。(1)将SNPtrio纳入新的Partek GS模块。该程序分析来自父亲、母亲和孩子组成的三人组的基因型和拷贝数数据，并产生单亲遗传的图形和表格描述（例如，单亲等二体性，其中染色体或染色体片段的两个拷贝遗传自一个父母;已知这种机制会导致各种精神发育迟滞和其他综合征）。(2)将SNPduo整合到PartekGS中;该程序对SNP数据集进行成对分析，允许描述个体之间的相关性（通过身份状态测量）。这对于各种目的都很有用，包括识别离群值，重复样本，非亲子关系，以及确认谱系成员的遗传相关性。(3)整合一套分析工具，测量由一、二或三代组成的谱系中的减数分裂重组。这些工具可能有助于排除关联研究中的基因座或表征发生缺失或重复的机制。目标（1）至（3）中所述的软件工具将组装成Partek GS的新原型版本。在目标（4）中，该原型将用于分析来自700个家庭的2，883个个体中测量的500，000个SNP，这些家庭具有两个或更多个患有自闭症的个体。该分析将展示Partek GS原型的功能，展示将新工具用于遗传分析以发现可能在自闭症中起作用的染色体异常的有用性。 公共卫生相关性：新的可用技术允许测量患有疾病（如自闭症和精神分裂症）的个体相对于未受影响的个体（对照组）的DNA序列样本之间的数百万个变异。这项拟议的研究旨在创建软件分析工具，以促进发现疾病中的染色体异常。这可能导致对这些疾病的治疗，满足大量的公共卫生需求。

DESCRIPTION (provided by applicant): The broad, long-term objective of the proposed research is to develop a software product that can be used to facilitate the analysis of genetic changes in order to elucidate chromosomal abnormalities that underlie diseases such as autism spectrum disorder, bipolar disorder, and schizophrenia. Recent technological advances allow samples of DNA from patients to be analyzed on single nucleotide polymorphism (SNP) arrays, generating up to millions of data points from each sample. These data must be analyzed to identify chromosomal abnormalities (e.g. DNA mutations, hemizygous or homozygous deletions, or translocations) that confer risk for these diseases. Two main approaches to data analysis include copy number estimates (based on the intensity of hybridization of samples to SNP arrays) and genotype analysis (revealing heterozygosity and homozygosity). Software such as Partek Genomics Suite (GS) exists to perform data analysis and visualization. A goal of this proposal is to add another dimension to the analysis of high density SNP data by incorporating information about the genetic relatedness of individuals into the data analysis repertoire of Partek GS. The specific aims are as follows. (1) Incorporate SNPtrio into a new Partek GS module. This program analyzes genotype and copy number data from trios consisting of father, mother, and child and produces graphical and tabular descriptions of uniparental inheritance (e.g. uniparental isodisomy in which two copies of a chromosome or chromosomal segment are inherited from one parent; such a mechanism is known to cause a variety of mental retardation and other syndromes). (2) Incorporate SNPduo into PartekGS; this program performs pairwise analyses of SNP data sets, allowing the description of relatedness between individuals (by identity-by-state measurements). This is useful for a variety of purposes including identifying outliers, replicate samples, non-paternity, and confirming the genetic relatedness of members of a pedigree. (3) Incorporate a set of analytic tools that measure meiotic recombination in pedigrees consisting of one, two, or three generations. Such tools may be useful to exclude loci in association studies or to characterize mechanisms by which deletions or duplications occur. The software tools described in aims (1) to (3) will be assembled into a new prototype version of Partek GS. In aim (4), this prototype will be used to analyze a set of 500,000 SNPs measured in 2,883 individuals from 700 families having two or more individuals affected with autism. This analysis will demonstrate the functionality of the Partek GS prototype, demonstrating the usefulness of incorporating new tools for genetic analysis to discover chromosomal abnormalities that may have roles in autism. PUBLIC HEALTH RELEVANCE: Newly available technologies allow the measurement of millions of variations in DNA sequence between samples from individuals with diseases (such as autism and schizophrenia) relative to unaffected individuals (controls). The proposed research is designed to create software analysis tools that will facilitate the discovery of chromosomal abnormalities in diseases. This may lead to treatments for these disorders, serving a large public health need.