Bootstrap-based testing of rare sequence variants using family data

使用家族数据对罕见序列变异进行基于引导的测试

• 批准号: 8838745
• 负责人: Alice Whittemore
• 金额: $ 32.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8838745
• 关键词: Affect; BRCA1 gene; BRCA2 gene; Biological; Cancer Family; Chronic Disease; Complex; Computer software; Confidence Intervals; Data; Data Analyses; Disease; Evaluation; Family; Frequencies; Genes; Genetic; Genome; Genomic Segment; Genotype; Health; Hereditary Disease; Human; Individual; International Consortium on Prostate Cancer Genetics; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Methods; Odds Ratio; Participant; Performance; Population; Procedures; Rare Diseases; Relative (related person); Sample Size; Sequence Analysis; Structure; Testing; Variant; base; breast cancer family registry; case control; computing resources; cost effective; flexibility; genetic variant; genome sequencing; interest; malignant breast neoplasm; next generation sequencing; novel; rare variant; simulation; statistics; tool; user friendly software

DESCRIPTION (provided by applicant): We now have a large arsenal of tests for association between disease and rare variants in genomic regions using the genotypes of unrelated individuals. However only the simplest of them have been extended to family data. Yet case-control tests using related cases are more powerful than tests based only on unrelated cases, particularly for rare variants. The power gain reflects enrichment of affected relatives for rare causal variants. Increased power is critical because most damaging variants occur at very low frequencies in human populations, and huge sample sizes and external biological information will be needed to detect associations with disease. Biologically-based contrasts between the multi-locus genotypes of cases and controls are likely to be complex, and simple, flexible methods are needed to infer their null distributions in the presence of correlation among subjects¿ genotypes. We propose a new way to extend all case-control association tests to all subjects, regardless of their genealogical relationship. The new method, which uses the bootstrap of Efron in a novel way, involves ¿de-correlating¿ subjects¿ correlated genotype data to allow bootstrap resampling, and then ¿re-correlating¿ the bootstrapped data to infer the null distribution of the test statistic. Aim 1 will use simulations to validate the new Quasi-bootstrp (QB) method for using family data to identify associations of disease with complex combinations of genotypes. This aim includes: i) assessing the type-1 error and power of QB tests for family data in comparison to: a) the same tests applied to unrelated subjects; and b) closed-form Gaussian-based tests for family data when available; ii) extending the QB method to data containing population structure and cryptic relatedness, for which the correlation matrix between pairs of subjects must be estimated; iii) dealing with missing genotype data. Aim 2 will apply the QB method to cancer family data to evaluate its performance on functional genetic units containing known carcinogenic variants. This includes testing for BRCA1 and BRCA2 association with breast cancer in affected and unaffected subjects from families in the Breast Cancer Family Registry (BCFR) and testing for HOXB13 association with prostate cancer in the International Consortium on Prostate Cancer Genetics (ICPCG). Aim 3 will develop freely-available software to implement the QB method for existing multi-locus case-control association tests. This software will include methods for handling missing genotype data for some subjects at some markers. The software will allow users with data from related and unrelated subjects to evaluate associations with disease using any of the existing tests currently available only for unrelated subjects. If validated, the proposed QB method would provide a major addition to our tools for next-generation sequence data by analyzing those most likely to carry causal disease variants, while building on the known strengths of the bootstrap. These include ease of use, robustness, and versatility for a large variety of applications. With the computing resources now routinely available, the proposed method can be implemented quickly and easily. Narrative: Sequencing the genomes are many people is now cost-effective, and it may help us finds the groups of genes that cause chronic diseases such as cancer. However evidence now suggests that many very rare variants may act in concert to cause such disease, and unraveling the new clues will require evaluating the genomes of diseased individuals from families with multiple cases of the disease. We propose a simple way of applying any of the new tests to such families, which should increase their efficacy.

描述（由适用提供）：我们现在使用无关个体的基因型进行了大量的疾病和罕见变体之间关联的测试。但是，只有最简单的一个已扩展到家庭数据。然而，使用相关病例的病例对照测试比仅基于无关案例的测试更强大，尤其是对于稀有变体。功率增益反映了受影响的亲戚的稀有随意变体的增强。增加的功率至关重要，因为大多数破坏性变体在人类种群中发生在非常低​​的频率下，并且需要大量的样本量和外部生物学信息来检测与疾病的关联。基于生物学的病例和对照基因型之间的基于生物学上的对比很可能是复杂的，并且需要简单，灵活的方法来推断受试者基因型之间存在相关性的无效分布。我们提出了一种将所有病例对照关联测试扩展到所有受试者的新方法，无论其家谱关系如何。这种新方法以新颖的方式使用EFRON的引导程序，涉及``删除可爱的受试者关联的基因型数据来允许bootstrap重新采样了，然后重新授予bootstrate的数据以推断测试统计数据的零分布。 AIM 1将使用模拟来验证新的准启动方法（QB）方法，用于使用家庭数据来识别疾病与基因型复杂组合的关联。该目的包括：i）评估家庭数据的QB测试的类型1误差和功率与以下相比：a）适用于无关受试者的相同测试； b）可用的家庭数据基于封闭形式的高斯测试； ii）将QB方法扩展到包含种群结构和加密相关性的数据，必须估算对受试者对之间的相关矩阵； iii）处理缺失的基因型数据。 AIM 2将应用 QB方法来评估其在包含已知致癌变体的功能遗传单元上的性能。这包括在乳腺癌家族登记处（BCFR）中受影响和未受影响的受试者中对BRCA1和BRCA2与乳腺癌的关联以及HOXB13与前列腺癌国际联盟（ICPCG）国际联盟中的HOXB13关联的测试。 AIM 3将开发自由利用的软件，以实现QB方法，以用于现有的多洛克斯病例对照协会测试。该软件将包括用于处理某些标记的某些受试者缺少基因型数据的方法。该软件将允许使用来自相关和无关受试者的数据的用户使用当前仅适用于无关主题可用的任何现有测试来评估与疾病的关联。如果经过验证，则提出的QB方法将通过分析最有可能携带催化疾病变体的人来为我们的下一代序列数据提供重要的补充，同时建立在自举的已知优势上。这些包括易用性，鲁棒性和用于多种应用的多功能性。借助现在常规可用的计算资源，可以快速，轻松地实现所提出的方法。叙述：测序基因组是许多人现在具有成本效益，它可能会帮助我们找到引起慢性疾病（例如癌症）的基因组。然而，现在的证据表明，许多非常罕见的变体可能会协同起作用这种疾病，并且揭开新的簇将需要评估来自患有多种疾病病例的家庭的失职个体的基因组。我们提出了一种将任何新测试应用于此类家庭的简单方法，这应该提高其有效性。