Improving genotype accuracy and haplotypic analysis for genome-wide studies

描述（由申请人提供）：全基因组关联研究（GWAS）是识别导致疾病和可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肽阵列来测定每个个体的数十万双等位基因标记。然而，全基因组关联研究也可能产生数百种由基因分型错误引起的虚假疾病-基因关联。这项研究将发展统计和计算方法，利用标记间的相关性，大大提高基因型的准确性。所有现有的调用大规模数据基因型的方法都忽略了遗传标记之间的相关性。这种相关性具有很高的信息量，但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，我们开发了一种新的单倍型相位推断方法，用于不相关个体的大规模数据集，比竞争对手的方法更快，更准确。下一步将是通过同时执行这两项任务来改进单倍型阶段推断和基因型调用。这将使在推断单倍型阶段时考虑到基因型的不确定性，在调用基因型时考虑到标记间相关性。我们的方法将提高基因型准确性，提高单倍型阶段推断准确性，减少因基因分型错误而导致的假阳性关联，并提高检测真实遗传关联的能力。我们将把这些新方法扩展到亲子三胞胎的基因型和阶段单倍型，其中额外的相关性信息将导致准确性的更大提高。从我们的方法中提高的基因型准确性和分阶段单倍型将有助于提高对人类疾病遗传贡献的理解。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。我们将开发用于可视化单倍型结构和单倍型-性状关联的交互式方法。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。

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.