Bootstrap-based testing of rare sequence variants using family data

描述（由申请人提供）：我们现在有大量的测试，使用无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的关联。然而，只有其中最简单的方法被推广到家庭数据。然而，使用相关病例的病例对照检验比仅基于不相关病例的检验更有效，特别是对于罕见变异。功率增益反映了受影响的亲属对罕见的因果变异的富集。提高功效至关重要，因为大多数破坏性变异在人群中发生的频率非常低，需要大量样本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是复杂的，并且需要简单，灵活的方法来推断它们的零分布，在受试者基因型之间存在相关性。 我们提出了一种新的方法，将所有病例对照关联检验扩展到所有受试者，无论他们的系谱关系如何。该新方法以一种新的方式使用Efron的引导，包括将受试者相关的基因型数据去相关以允许引导恢复，然后将引导数据重新相关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bootstrp（QB）方法，该方法使用家族数据来识别疾病与基因型复杂组合的关联。这一目标包括：i）评估用于家族数据的QB检验的1型误差和功效，并与：a）应用于无关受试者的相同检验;和B）用于家族数据的封闭形式的基于高斯的检验（当可用时）进行比较; ii）将QB方法扩展到包含群体结构和隐性相关性的数据，对于所述群体结构和隐性相关性，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方法用于癌症家族数据，以评估其对包含已知致癌变体的功能遗传单元的性能。这包括在乳腺癌家族登记处（BCFR）的受影响和未受影响的受试者中检测BRCA 1和BRCA 2与乳腺癌的相关性，以及在国际前列腺癌遗传学联盟（ICPCG）中检测HOXB 13与前列腺癌的相关性。 Aim 3将开发免费软件来实现QB方法用于现有的多位点病例对照关联测试。该软件将包括处理某些受试者在某些标记物处缺失的基因型数据的方法。该软件将允许拥有相关和不相关受试者数据的用户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，拟议的QB方法将通过分析那些最有可能携带致病疾病变异的基因，同时建立在bootstrap的已知优势上，为我们的下一代序列数据工具提供重要补充。这些优点包括易用性、鲁棒性和适用于各种应用的多功能性。随着计算资源现在例行可用，所提出的方法可以快速，容易地实现。 叙述：对许多人的基因组进行测序现在是具有成本效益的，它可能有助于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共同导致这种疾病，解开新的线索将需要评估来自多个疾病病例家庭的患病个体的基因组。我们提出了一种简单的方法，将任何新的测试应用于这些家庭，这应该会增加他们的效力。

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.