Bootstrap-based testing of rare sequence variants using family data

描述(由申请人提供)：我们现在有大量使用无关个体的基因类型来检测疾病和基因组区域罕见变异之间的相关性的测试。然而，它们中只有最简单的扩展到了家庭数据。然而，使用相关病例的病例对照试验比只基于不相关病例的试验更有效，特别是对罕见的变种。能量的增加反映了受影响的亲属对罕见的因果变异的丰富。增加能量是至关重要的，因为大多数破坏性的变异在人类群体中发生的频率非常低，而且需要巨大的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别之间的生物学对比可能是复杂的，需要简单、灵活的方法在受试者之间存在相关性的情况下推断它们的零分布。我们提出了一种新的方法，将所有病例对照关联测试扩展到所有受试者，而不考虑他们的家系关系。新方法以一种新颖的方式使用Efron的Bootstrap，包括将相关的基因数据去相关以允许Bootstrap重采样，然后重新关联Bootstrap数据以推断检验统计量的零分布。AIM 1将使用模拟来验证新的准自举(QB)方法，该方法使用家庭数据来确定疾病与复杂的基因类型组合的关联。这一目的包括：i)评估QB测试对家庭数据的第一类错误和威力，比较：a)适用于无关受试者的相同测试；以及b)对可用的家庭数据进行闭合形式的基于高斯的测试；ii)将QB方法扩展到包含群体结构和隐蔽相关性的数据，对于这些数据，必须估计受试者对之间的相关矩阵；iii)处理丢失的基因数据。AIM 2将应用 QB方法获得癌症家系数据，以评估其在含有已知致癌变异的功能遗传单位上的表现。这包括在乳腺癌家族登记(BCFR)中对受影响和未受影响的受试者进行BRCA1和BRCA2与乳腺癌的相关性测试，以及在国际前列腺癌遗传学联合会(ICPCG)中对HOXB13与前列腺癌的相关性进行测试。AIM 3将开发免费可用的软件，为现有的多位点病例对照关联试验实施QB方法。该软件将包括在某些标记上处理某些受试者缺失的基因数据的方法。该软件将允许拥有相关和非相关受试者数据的用户使用目前仅适用于无关受试者的任何现有测试来评估与疾病的相关性。如果得到验证，建议的QB方法将通过分析那些最有可能携带因果疾病变异的基因，同时建立在引导的已知优势之上，为我们的下一代序列数据工具提供重大补充。这些特性包括针对各种应用程序的易用性、健壮性和多功能性。在现有常规计算资源的情况下，所提出的方法可以快速、方便地实现。叙述：对基因组进行测序现在很多人都是划算的，它可能会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共同导致了这种疾病，而要解开新的线索，需要评估来自有多个病例的家庭的患病个体的基因组。我们提出了一种简单的方法，将任何一种新的测试应用于这些家庭，这应该会增加他们的效率。

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.