Methods for Epidemiology Studies

描述：遗传流行病学的方法 在基因和环境因素独立的假设下，我们发展了半参数最大似然估计(SPMLE)，用于基因-环境相互作用的病例对照研究。传统的Logistic回归分析在这种情况下效率不高。我们利用轮廓似然技术得到SPMLE，并研究它的渐近理论。将结果推广到遗传因素和环境因素在一定条件下相互独立的情形。将该方法应用于卵巢病例对照数据，以研究BRCA1/2突变与口服避孕药使用之间的相互作用。我们将这种方法扩展到单倍型与疾病相关的病例对照研究中，允许丢失基因数据和单倍型阶段，再次假设基因和环境因素独立。我们还利用这一假设来增加以家庭为基础的病例对照研究的能力，以检测基因-环境相互作用、主要遗传效应和联合效应。使用这些方法还可以比较各种基于族的设计并提出设计建议。 我们开发了病例对照家系数据的分析方法，对先证者(病例或对照)进行基因分型，并为一级亲属提供发病时间信息。给出了相对风险、累积风险和残差家族聚集度的估计方法。研究表明，病例对照家系设计对用于容纳剩余家族相关性的Copula模型的错误指定具有稳健性，但仅具有病例先证者的样本对这种模型错误指定不具有稳健性。 在队列研究和嵌套式病例对照研究中，我们开发了单倍型与疾病相关性的分数测试。我们还为这类研究开发了远离零假设的估计程序。 我们开发了结合基于家庭的病例对照研究和零星病例对照研究的数据的方法。这些方法被用来证明黑素皮质素-1受体变异体携带者的黑色素瘤风险增加，在家庭和普通人群中都是如此。 我们开发了检测遗传预期的方法，即后代人发病年龄比前代人更早的趋势。这些方法被用来研究记录的发病年龄数据中的淋巴增殖状况。这些方法允许协变量调整、交错进入、审查和亲属之间的相关性，表明当根据NHL发病率的长期变化进行调整时，非霍奇金淋巴瘤(NHL)遗传预期的明显证据消失了。 我们发表了一种方法来计算基于家庭的关联测试所需的样本量，该方法基于基因分型的父母以及基因分型的受影响和未受影响的子女，基于对父母的基因分型和子女的疾病状况的评分测试。 我们开发了一种重抽样方法来控制多个测试程序的家族误差水平，以检测病例对照数据中的遗传关联。综合试验结合了基于单核苷酸多态性(SNP)和基于单倍型的程序，无论遗传疾病倾向是由SNP还是单倍型决定的，都具有很好的能力。还开发了控制错误发现率的相关两阶段程序。 R中的一个名为BayesMendel的计算机程序被开发并公开使用，以允许研究人员根据家族病史计算成为突变携带者的概率。我们还完成了家族史数据中的错误对估计突变携带者概率质量的影响的研究。

Description: Methods for Genetic Epidemiology We developed semiparametric maximum likelihood estimates (SPMLE) for case-control studies of gene-environment interactions under the assumption of independence of gene and environmental factors. Traditional logistic regression analysis is not efficient in this setting. We use a profile-likelihood technique to obtain SPMLE and study its asymptotic theory. The results are extended to deal with situations where genetic and environmental factors are independent conditional on some other factors. The method is applied to ovarian case-control data to investigate the interplay of BRCA1/2 mutations and oral contraceptive use. We extended this approach for case-control studies of haplotype associations with disease by allowing for missing genotype data and missing haplotype phase, again assuming independence of genotype and environmental factors. We also exploited this assumption to increase the power of family-based case-control studies to detect gene-environment interactions, main geneeffects, and joint effects. These methods also allow one to compare various family-based designs and make design recommendations. We developed methods of analysis for case-control family data in which probands (cases or controls) are genotyped and time to disease onset information is available for first-degree relatives. Methods to estimate relative risks, cumulative risks and residual familial aggregation are given. The work indicates that case-control family design is robust to misspecification of copula models used to accommodate residual familial correlation, but that samples with case probands only are not robust to such model misspecification. We developed score tests for associations of haplotypes with disease in cohort studies and in nested case-control studies. We also developed estimation procedures away from the null hypothesis for such studies. We developed methods to combine data from family based case-control studies and from sporadic case-control studies. These methods were used to demonstrate increased melanoma risk in carriers of variants of the melanocortin-1-receptor, both in families and in the general population. We developed methods to detect genetic anticipation, the tendency of later generations to have earlier ages at disease onset than preceding generations. These methods were used to study lymphoproliferative conditions in record linkage age-at-onset data. The methods, which allowed for covariate adjustments, staggered entry, censoring and correlations among relatives, showed that apparent evidence of genetic anticipation for non-Hodgkin lymphoma (NHL) disappeared when adjusted for secular changes in NHL incidence. We published methods to compute sample sizes required for family-based association tests based on genotyped parents and genotyped affected and unaffected offspring, based on a score test that conditions on parental genotypes and the disease status of offspring. We developed a resampling approach to control the family-wise error level of multiple testing procedures to detect genetic associations in case-control data. An omnibus test combines single nucleotide polymorphism (SNP)-based and haplotype-based procedures and has good power whether the genetic disease tendency is conferred by SNPs or haplotypes. A related two-stage procedure is also developed that controls the false discovery rate. A computer program in R called BayesMendel was developed and made publicly available to allow researches to compute the probability of being a mutation carrier based on family history. We also completed research of the effects of mistakes in the family history data on the quality of the estimated mutation carrier probabilities.