Disequilibrium Mapping of Complex Genetic Diseases

描述（由申请人提供）：拟议的研究将开发新的统计分析方法和计算机软件，用于解释人群中遗传变异的模式。每个人的基因组（除了同卵双胞胎）是独特的，遗传变异解释了个体之间观察到的许多特征的变化，包括行为，疾病易感性，身体外观等的差异。一种常见的变异形式是由点突变引起的DNA序列中的单碱基对变化引起的;这涉及DNA复制过程中错配碱基的意外插入。点突变的发生率非常低，但在数千代中从父母传递给后代的遗传物质中积累。由点突变产生的DNA变异称为单核苷酸多态性（SNP），是个体变异的重要遗传组成部分。高通量分子分析技术可以产生分布在个体基因组上的数百万个SNP的“基因型”。基因型是指遗传自母亲和父亲的染色体上的SNP的组合。单个染色体上的SNP模式主要由重组决定-减数分裂期间染色体之间的DNA片段交换（传递到卵子或精子）。拟议的研究将开发计算工具，以推断染色体重组率的精细尺度。复杂的遗传疾病（如克罗恩病）相对常见，被认为是环境和遗传的综合影响所致。以家庭为基础的疾病传播研究，在定位引起简单遗传疾病的基因方面效果很好（例如，囊性纤维化），对定位影响复杂疾病的基因的能力较低。拟议研究的另一个目标将是提供统计工具，以定位人类基因组中增加个体对复杂遗传疾病易感性的遗传变异，使用来自受疾病影响病例和未受影响对照的SNP数据群体样本。公共卫生相关性：这项拟议中的研究将提供新的统计方法和计算机软件，用于解释整个基因组中人类群体遗传变异的数据。具体而言，拟议的研究将有助于开发人类基因组局部重组率的精细地图;重组在确定人类群体中身体特征（和遗传疾病）的发生频率方面发挥着重要作用。此外，拟议的研究将提供新的方法来定位人类基因组中的遗传变化（突变），这些变化增加了个体对复杂遗传疾病（如II型糖尿病）的易感性。

DESCRIPTION (provided by applicant): The proposed research will develop new statistical analysis methods and computer software for interpreting patterns of genetic variation in human populations. The genome of every human (apart from identical twins) is unique and genetic variation explains much of the variation of features observed among individuals including differences in behavior, disease susceptibility, physical appearance, etc. New technologies are providing vast amounts of information concerning subtle DNA variations among individuals in human populations. A common form of variation arises from single base-pair changes in the DNA sequence caused by point mutation; this involves an accidental insertion of a mismatched base during DNA replication. Point mutations occur at a very low rate but accumulate in the genetic material transmitted from parents to offspring over thousands of generations. DNA variants produced by point mutation are termed single-nucleotide polymorphisms (SNPs) and are an important genetic component of individual variation. High-throughput molecular analysis technologies can produce "genotypes" for millions of SNPs spread over the genome of an individual. The genotype refers to the combination of SNPs on chromosomes inherited from the mother and father. Patterns of SNPs on individual chromosomes are determined mainly by recombination -- exchanges of DNA segments between chromosomes during meiosis (transmission to eggs or sperm). The proposed research will develop computational tools to infer rates of chromosomal recombination on a fine scale. Complex genetic diseases (Crohn disease, for example) are relatively common and are thought to result from the combined effects of environment and genetics. Family-based disease transmission studies, which have worked well to locate genes causing simple genetic diseases (e.g., cystic fibrosis), have low power to locate genes influencing complex diseases. Another goal of the proposed research will be to provide statistical tools to locate genetic variants in the human genome that increase individual susceptibility to complex genetic disease, using as data population samples of SNPs from disease-affected cases and unaffected controls. PUBLIC HEALTH RELEVANCE: The proposed research will provide new statistical methods and computer software for interpreting data on human population genetic variation across the genome. Specifically, the proposed research will facilitate the development of a fine-scale map of local recombination rates over the human genome; recombination plays an important role in determining frequencies of occurrence of physical traits (and genetic diseases) in human populations. As well, the proposed research will provide new methods for locating genetic changes (mutations) in the human genome that increase individual susceptibility to complex genetic disease such as type II diabetes.