Improving genotype accuracy and haplotypic analysis for genome-wide studies

提高全基因组研究的基因型准确性和单倍型分析

• 批准号: 7632327
• 负责人: BRIAN LEE BROWNING
• 金额: $ 14.42万
• 依托单位: AUCKLAND UNISERVICES LIMITED
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-07 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7632327
• 关键词: Accounting; Address; Alleles; Biological Assay; Cardiovascular Diseases; Computer software; Computing Methodologies; Data; Data Set; Diabetes Mellitus; Diagnosis; Disease; Genes; Genetic; Genetic Markers; Genotype; Haplotypes; Imagery; Individual; Inherited; Lead; Methods; Morphologic artifacts; Oligonucleotide Microarrays; Parents; Phase; Prevention; Research; Single Parent; Structure; Uncertainty; United States; Walking; density; disorder risk; genetic association; genetic variant; genome wide association study; graphical user interface; human disease; improved; novel; offspring; public health relevance; tool; trait

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) are an effective tool for indentifying common genetic variants that contribute to disease and heritable traits. These studies use high-density oligoneculeotide arrays to assay hundreds of thousands of diallelic genetic markers in each individual. However, genome-wide association studies can also produce hundred of spurious disease-gene associations caused by genotyping error. This research will develop statistical and computational methods that use inter-marker correlation to substantially improve genotype accuracy. All existing methods for calling genotypes for large-scale data ignore the correlation between genetic markers. This correlation is highly informative, but exploiting inter-marker correlation is computationally difficult because it requires inference of the marker alleles inherited from a single parent (the haplotype phase). Recently, we have developed a novel method of haplotype phase inference for large-scale data sets of unrelated individuals that is orders of magnitude faster and more accurate than competing methods. The next step will be to improve haplotype phase inference and genotype calling by performing both tasks simultaneously. This will enable genotype uncertainty to be taken into account when inferring haplotype phase and inter-marker correlation to be taken into account when calling genotypes. Our methods will improve genotype accuracy, improve haplotype phase inference accuracy, decrease false positive associations due to genotyping error, and increase power to detect true genetic associations. We will extend these novel methods to call genotypes and phase haplotypes for parent-offspring trios where the additional relatedness information will lead to even larger gains in accuracy. The improved genotype accuracy and phased haplotypes from our methods will contribute to improved understanding of the genetic contribution to human disease. Our research will also address one of the main impediments to haplotypic analysis: the difficulty in interpreting analysis results. We will develop interactive methods for visualizing haplotype structure and haplotype-trait associations. These new data exploration methods will greatly simplify the task of identifying sequences of genetic variants that are associated with a trait. PUBLIC HEALTH RELEVANCE: Heritable genetic variants contribute to many common diseases, such as cardiovascular disease and diabetes. This research will develop new methods and tools that improve the accuracy of genetic data and that improve our ability to identify genetic variants that increase risk of disease. These methods and tools will contribute to the prevention, diagnosis, and treatment of heritable diseases in the United States and throughout the world.

描述（由申请人提供）：全基因组关联研究（GWAS）是识别导致疾病和遗传性状的常见遗传变异的有效工具。这些研究使用高密度寡聚核苷酸阵列来分析每个个体中数十万个双等位基因遗传标记。然而，全基因组关联研究也可能产生数百个由基因分型错误引起的虚假疾病-基因关联。这项研究将开发统计和计算方法，使用标记间的相关性，大大提高基因型的准确性。所有现有的方法调用基因型的大规模数据忽略了遗传标记之间的相关性。这种相关性是高度信息化的，但是利用标记间相关性在计算上是困难的，因为它需要推断从单亲遗传的标记等位基因（单倍型阶段）。最近，我们已经开发出一种新的单倍型相位推断的大规模数据集无关的个人，是数量级更快，更准确的竞争方法。下一步将是通过同时执行这两项任务来改进单倍型相位推断和基因型识别。这将使基因型的不确定性被考虑时，推断单倍型阶段和标记间的相关性被考虑时，调用基因型。我们的方法将提高基因型的准确性，提高单倍型相位推断的准确性，减少假阳性协会由于基因分型错误，并增加功率检测真正的遗传协会。我们将扩展这些新的方法来调用基因型和相位单倍型的父母-后代三人组，其中额外的相关性信息将导致更大的收益的准确性。我们的方法提高了基因型准确性和定相单倍型，这将有助于更好地了解遗传对人类疾病的影响。我们的研究还将解决单倍型分析的主要障碍之一：解释分析结果的困难。我们将开发交互式方法来可视化单倍型结构和单倍型性状关联。这些新的数据探索方法将大大简化识别与性状相关的遗传变异序列的任务。 公共卫生相关性：遗传性遗传变异导致许多常见疾病，如心血管疾病和糖尿病。这项研究将开发新的方法和工具，提高遗传数据的准确性，并提高我们识别增加疾病风险的遗传变异的能力。这些方法和工具将有助于美国和全世界遗传性疾病的预防、诊断和治疗。