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: 8681401
• 负责人: Alice Whittemore
• 金额: $ 31.6万
• 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 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8681401
• 关键词: Affect; BRCA1 gene; BRCA2 gene; Biological; Cancer Family; Chronic Disease; Complex; Computer software; Confidence Intervals; Data; Data Analyses; Disease; Disease Association; Evaluation; Family; Frequencies; Genes; Genetic; Genome; Genomics; Genotype; 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; public health relevance; 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（由申请人提供）：我们现在拥有大量使用不相关个体的基因型来检测疾病与基因组区域中罕见变异之间关联的测试。然而，只有其中最简单的方法被扩展到家庭数据。然而，使用相关病例的病例对照测试比仅基于不相关病例的测试更强大，特别是对于罕见的变异。权力增益反映了受影响亲属对罕见因果变异的富集。增加功效至关重要，因为大多数破坏性变异在人群中发生的频率非常低，并且需要大量样本量和外部生物信息来检测与疾病的关联。病例和对照的多位点基因型之间基于生物学的对比可能很复杂，在受试者基因型之间存在相关性的情况下，需要简单、灵活的方法来推断其零分布。 我们提出了一种新方法，将所有病例对照关联测试扩展到所有受试者，无论其谱系关系如何。新方法以一种新颖的方式使用 Efron 的引导程序，涉及对受试者相关基因型数据进行“去相关”以允许引导程序重采样，然后对引导数据进行“重新关联”以推断检验统计量的零分布。 目标 1 将使用模拟来验证新的准引导 (QB) 方法，该方法使用家族数据来识别疾病与复杂基因型组合的关联。这一目标包括： i) 评估家庭数据的 QB 测试的 1 类错误和功效，并与以下各项进行比较： a) 适用于不相关受试者的相同测试； b) 对家庭数据进行基于高斯的封闭式检验（如果有）； ii) 将 QB 方法扩展到包含群体结构和隐秘相关性的数据，为此必须估计受试者对之间的相关矩阵； iii) 处理缺失的基因型数据。 目标 2 将应用 QB 方法对癌症家族数据进行评估，以评估其对包含已知致癌变异的功能遗传单元的性能。这包括在乳腺癌家族登记处 (BCFR) 中受影响和未受影响的家庭中测试 BRCA1 和 BRCA2 与乳腺癌的关联性，以及在国际前列腺癌遗传学联盟 (ICPCG) 中测试 HOXB13 与前列腺癌的关联性。 Aim 3 将开发免费软件，以实施现有多位点病例对照关联测试的 QB 方法。该软件将包括处理某些受试者在某些标记处缺失的基因型数据的方法。该软件将允许用户使用来自相关和不相关受试者的数据，使用目前仅适用于不相关受试者的任何现有测试来评估与疾病的关联。 如果得到验证，所提出的 QB 方法将通过分析那些最有可能携带致病变异的序列数据，同时建立在引导程序的已知优势的基础上，为我们的下一代序列数据工具提供重要补充。这些包括易用性、稳健性和适用于各种应用的多功能性。利用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：对很多人的基因组进行测序现在具有成本效益，它可以帮助我们找到导致癌症等慢性疾病的基因组。然而，现在的证据表明，许多非常罕见的变异可能协同作用，导致这种疾病，而解开新的线索将需要评估来自多例该疾病的家庭中患病个体的基因组。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，这应该会提高其功效。